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Exosomal transcript cargo and functional correlation with HNSCC patients’ survival

BMC Cancer volume 24, Article number: 1144 (2024) Cite this article

Abstract

Background

HPV status in a subset of HNSCC is linked with distinct treatment outcomes. Present investigation aims to elucidate the distinct clinicopathological features of HPV-positive and HPV-negative HNSCC and investigate their association with the HNSCC patient survival.

Materials and methods

The total RNA of exosomes from HPV-positive (93VU147T) and HPV-negative (OCT-1) HNSCC cells was isolated, and the transcripts were estimated using Illumina HiSeq X. The expression of altered transcripts and their clinical relevance were further analyzed using publicly available cancer transcriptome data from The Cancer Genome Atlas (TCGA).

Results

Transcriptomic analyses identified 3785 differentially exported transcripts (DETs) in HPV-positive exosomes compared to HPV-negative exosomes. DETs that regulate the protein machinery, cellular redox potential, and various neurological disorder-related pathways were over-represented in HPV-positive exosomes. TCGA database revealed the clinical relevance of altered transcripts. Among commonly exported abundant transcripts, SGK1 and MAD1L1 showed high expression, which has been correlated with poor survival in HNSCC patients. In the top 20 DETs of HPV-negative exosomes, high expression of FADS3, SGK3, and TESK2 correlated with poor survival of the HNSCC patients in the TCGA database.

Conclusion

Overall, our study demonstrates that HPV-positive and HPV-negative cells’ exosomes carried differential transcripts cargo that may be related to pathways associated with neurological disorders. Additionally, the altered transcripts identified have clinical relevance, correlating with patient survival in HNSCC, thereby highlighting their potential as biomarkers and as therapeutic targets.

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Introduction

Head and Neck Squamous Cell Carcinoma (HNSCC) are a heterogeneous group of cancer majorly affecting the lip and oral cavity. According to the GLOBOCAN 2022 database, it is most frequently encountered cancer in India [1]. The last 50 years have witnessed a significant shift in the incidence and prevalence of SCC of different constituent subsites of the head and neck (H&N) region. These changes have been linked to changes in exposure to various known carcinogens and risk factors. Traditionally, HNSCC has been linked to tobacco and alcohol abuse. However, cancer registry data and several cohort studies have documented the association of human papillomavirus (HPV) infection within the tumor tissues of H&N region [2, 3]. Over the last 3 decades, reports addressing HPV status and clinical outcome of HNSCC showed that HPV-associated HNSCC have different clinico-pathological characteristics in comparison to HPV-negative HNSCC [4]. HPV-positive tumors have a high proportion of well-differentiated squamous cell carcinoma and they respond better to chemotherapy. Moreover, they have a better prognosis [5]. A series of studies have highlighted molecular signatures that distinguish HPV-positive from HPV-negative HNSCC. The differential gene expression leads to distinct tumor types in the tumor with their characteristic tumor microenvironment (TME). One of the major contributors to the TME is tumor-cell released or derived exosomes (TDEs) which help in the intercellular communication within the TME and strongly condition it for tumor promotion.

Exosomes are the smallest type of extracellular vesicles having a size range from 30–100 nm and they are secreted by all types of cells into an aqueous extracellular microenvironment [6]. Originally, they were identified as cellular trashcans and export of damaged cell components [7]. However, recent studies specifically highlight their signaling impact on neighboring cells [8]. Exosomes originate from the inward budding of endosomes to form multivesicular endosomes (MVEs). These MVEs upon fusing with the cell surface membrane, release exosomes into the extracellular medium [9]. This endosomal pathway of exosome biogenesis is also exploited by the viruses to alter the content of EVs assisting viral persistence and pathogenesis [10, 11].

The presence of different types of exosomal cargoes such as DNA, RNA, miRNA, lncRNA, and proteins have been reported in exosomes of different cancer including HNSCC [12]. Accumulating evidence highlights the presence of mRNA in the exosomal cargo of normal [13, 14], transformed [15] and, cancer cells [16,17,18]. Molecular and functional profiling of TDEs in HPV-positive and HPV-negative HNSCC showed the presence of immunomodulatory molecules. HPV-positive exosomes uniquely carried HPV E6/E7, p16, and survivin, thereby indicating heterogeneity in the cargo of TDEs of HPV-positive exosomes [19]. Moreover, the HPV-positive exosomes were capable of sustaining dendritic cell functions. These observations indicated TDEs role in promoting anti-tumor response and hence better outcomes in HPV-positive HNSCC patients. Apart from this, our previous studies have indicated that HPV-positive cervical cancer (CaCx) exosomes promote pro-angiogenic response in endothelial cells via upregulation of Hh-GLI signaling and modulate downstream angiogenesis-related target genes [20, 21]. These CaCx exosomes showed mRNA as a distinct cargo of TDEs. A comprehensive NGS-based transcriptomic analysis of these CaCx exosomes showed the enrichment of pro-tumorigenic HPVE6*I transcripts in the exosomal RNA cargo [22]. It has been reported that silencing of HPV E6/E7 oncoproteins profoundly affects both the composition as well as the number of exosomes secreted by HPV-positive CaCx [23]. Together, these studies opened new avenues for using exosomes and their exported cargoes for clinical use.

In view of the facts, the exosomal cargo draws specific attention for deeper analysis particularly in the context of HPV-status and in HNSCC where HPV serves as a biomarker of better prognosis. A comparative evaluation of exosomal cargo of HPV-positive and negative cancers at the transcriptome level will provide a unique opportunity to understand this phenomenon. Therefore, in the present work, we aim to use multi-omics approaches to dissect different RNAs of tumor exosome components using high throughput transcriptomic technologies and validate the leads using publicly available HNSCC transcriptomic database.

Materials

Cell lines

HPV-negative HNSCC cell line, OCT1 was a kind gift from Dr. Anant Narayan Bhatt, Institute of Nuclear Medicine & Allied Sciences- Defence Research and Development Organization (INMAS-DRDO), Delhi, India. HPV-positive HNSCC cell lines UDSCC2 and 93VU147T were kind gifts from Dr. Kathrin Scheckenbach, University of Dusseldorf, Germany, and Dr. Steenbergen, Vrije Universiteit, Netherlands, respectively.

Cancer transcriptomic data

In this study, transcriptome analysis was performed on the cancer transcriptome data, publicly made available by The Cancer Genome Atlas (TCGA). A total of 558 datasets of primary HNSCC tumor (n = 514) and normal head and neck tissues (n = 44) were analyzed using the online tool UALCAN (http://ualcan.path.uab.edu) hosted by the University of ALabama at Birmingham CANcer [24]. The categorization of samples was based on HPV readcounts. HPV status was assessed at the Broad Institute by DNA sequencing and PathSeq algorithm [25]. Specifically, a total of 80 datasets showed the presence of HPV transcripts in HNSCC tissues, whereas 434 were confirmed negative for HPV transcripts. HPV status was not available for 6 samples, hence, they were excluded from the HPV-related analysis.

Primers

Primers used for polymerase chain reaction (PCR), their amplicon size, and the annealing temperatures are listed in Table ST1. HPLC-purified primers were used for genomic PCR and were custom-synthesized and procured from Microsynth (Switzerland) or Eurofins Scientific (Luxembourg).

Antibodies

Various antibodies used for characterization of HNSCC exosomes in HNSCC cells via immunoblotting are listed in Table ST2. Antibodies were either procured from Santa Cruz Biotechnology Inc. (California, USA), or Abcam (Cambridge, UK).

Other important research material

Key reagents, media, and commercial kits used in the present study are listed in Table ST3. All other chemicals, unless specified, were of analytical or molecular biology grade and were procured from Sigma-Aldrich (Missouri, USA).

HPV consensus and HPV16 type-specific genomic PCR

HPV positivity was available cell lines confirmed by PCR-based method using PGMY09/11 consensus primers [26] and HPV16 type-specific primers for E6 and E7 regions as described earlier [22]. Total genomic DNA (50 ng/10 µl reaction) was used for PCR amplification of HPV16 E6 and E7 on BioMetra TAdvanced Thermal Cycler (Analytik Jena, Jena, Germany) in a 10 µl reaction system. The PCR reaction was programmed as follows: 95 °C for 2 min, 35–40 cycles including denaturation at 95 °C for 30 s, annealing that varied in the range of 55–56 °C for 40 s, polymerization at 72 °C for 10 min followed by final extension of 10 min at 72 °C. The list of primers used in the study is listed in Table ST1. The NPCR products were analyzed using 2% agarose gel stained with EtBr (0.1 μg/mL) and visualized on the Amersham gel documentation system (Cytiva). UDSCC2 and 93VU147T showed positivity for HPV16 whereas, OCT1 was negative for HPV16 primers (Supplementary Figure SF1). The international standard for HPV16 DNA from WHO was used as a positive control, whereas, nuclease-free water was taken as a negative control.

Isolation of exosomes from HNSCC cell culture conditioned medium

For the preparation of exosome containing cell-free medium, Exosomes were isolated using commercially-available Total Exosome Isolation kit (ThermoFisher Scientific) according to the manufacturer’s protocol. Briefly, the filtered cell culture conditioned medium derived from HNSCC cells (1 × 106) cultured in a 100 mm plate in 10% exosome-depleted FBS for 2–3 days was used for exosome isolation. Conditioned medium was centrifuged at 5000 rpm for 30 min to pellet down cellular debris and filtered through a 0.22 µm syringe filter. Next, the total exosome isolation kit reagent was added to the filtered conditioned medium and the mixture was kept at 4˚C overnight.

Preparation of Exo-depleted FBS

For isolation of exosomes, the HNSCC cells were cultured with 10% exosome-depleted FBS. To get exosome-depleted FBS, normal.

FBS was ultra-centrifuged at 1,20,000 × g for 16 h as shown in Supplementary Figure SF2. The mixture was centrifuged at 10,000 × g for 60 min at 4˚C and supernatant was removed. The pellets containing the exosomes were resuspended in 1 X PBS, and the protein concentrations were determined using the bicinchoninic acid (BCA) assay as per the manufacturer’s protocol and used for downstream analysis or stored at -80 °C until further use.

Colorimetric acetylcholine esterase activity assay for exosome quantification

The exosome pellet was briefly washed with PBS and resuspended in a minimal volume of 1 X PBS. Exosome quantification was performed using the EXOCET Exosome Quantitation Kit from System Biosciences (#EXOCET96A-1). Briefly, exosomes were lysed using the EXOCET gentle lysis solution to preserve the enzymatic activity of the exosomal AChE enzyme. 50 μl of the exosome suspension after lysis was added in a 96-well plate containing 50 μl of EXOCET Reaction Buffer A:B in a ratio of (100:1) of the colorimetric estimation kit. The samples were incubated at 37 °C for at least 10–20 min at room temperature. Absorbance was taken at 405 nm using a microplate reader (BioTek Synergy HTX Multimode Reader, California, USA). The data from different dilutions of known reference standards was used to generate a standard curve as described by the manufacturer (Supplementary Figure SF3). Accordingly, the values of the unknown sample were converted to determine the number of exosomes isolated and used for subsequent experiments.

Transmission electron microscopy (TEM)

TEM analysis of exosome samples was performed according to previously published reports [27, 28] with minor modifications. The exosomes isolated from HNSCC cell lines were resuspended in 20 µl of 1 X PBS and were adsorbed onto formvar-coated copper grids for 10 min at room temperature for proper attachment. The grids were stained with PTA (Phosphotungstic Acid) solution for 30 s and air dried for overnight. Grids were placed on stubs and then imaging was done by using a JEOL 2100F transmission electron microscope (JEOL Ltd., Tokyo, Japan) operated at 200 kV.

Isolation of exosomal proteins, and immunoblotting for exosome-specific markers

Total exosome proteins were isolated using RIPA Lysis Buffer as described earlier [20]. The concentration of protein samples was determined by the BCA spectrophotometric method. Proteins were stored in small aliquots at -80 °C refrigerator (Haier, Qingdao, China) till further use. Proteins (50 μg/lane) were resolved in 10–12% polyacrylamide gel using 2X Laemmli buffer (100 mM Tris–HCL pH 8.0, 20 mM EDTA pH 8.0, 4% SDS, 20% glycerol, 10% β-mercaptoethanol, 0.02% bromophenol blue) and transferred to PVDF membranes (0.45 µm; Millipore) by wet transfer method at 25 V for 2 h. Membranes were blocked with 5% (w/v) bovine serum albumin in tris-buffered saline supplemented with Tween 20 (0.1%) (TBST) for 2 h, and incubated with the pre-standardized dilution of primary antibodies in TBST overnight at 4 °C. The list of antibodies and their standardized dilutions are given in the Membranes were washed with tris-buffer saline (TBS: 150 mM NaCl, 50 mM Tris–HCl, pH 7.6) and were incubated with horseradish peroxidase (HRP-conjugated) secondary antibodies diluted in 5% BSA in TBS-Tween (0.1%) for 60 min at room temperature. Immuno-active bands were detected on a ChemiDoc-XRS (Bio-Rad) imaging system or Amersham Imager 600 after 2–5 min treatment of the blot with an enhanced chemiluminescent substrate luminol detection kit.

RNA isolation, NGS library preparation, library validation and sequencing

The processes of RNA isolation, NGS library preparation, and high-throughput sequencing were contracted to Clevergene Biocorp. Pvt Ltd. (Bengaluru, Karnataka, India). Total exosomal RNA was extracted using the TRIzol reagent following the manufacturer’s protocol. Isolated RNA concentrations were measured using the Qubit (ThermoFisher Scientific), and RNA integrity was assessed with an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, California). For each library, 1 ng of total RNA per sample was utilized, and prepared using the NEBNext Ultra II RNA Library Prep Kit for Illumina. Library quality was evaluated using the DNA 5000 ScreenTape on an Agilent 4150 Tape Station system. A mixture of 1 μL of library and 10 μL of DNA sample buffer was vortexed for a minute and centrifuged before loading into the Agilent 4150 Tape Station instrument (Agilent Technologies). The total cDNA was ligated with P7 and P5 adapter sets, and the PCR products were gel-purified and verified for quality using a Bioanalyzer. Paired-end sequencing of these libraries was conducted on an Illumina HiSeq X platform, ensuring a minimum of 25–30 million reads per sample. All the analysis steps and software used for transcriptomic data analyses are detailed in Supplementary Figure SF4.

Sequence data QC

The quality of the NGS data was assessed using FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) and MultiQC [29]. Quality checks included evaluations of base call quality distribution, the percentage of bases above Q20 and Q30, %GC content, and sequencing adapter contamination. Raw sequence reads was then processed with fastp to eliminate adapter sequences and low-quality bases [30].

Alignment and expression analysis

The reads passed the QC were mapped with indexed Human reference genome (GRCh38.p7) and HPV16 reference genome using the STAR v2 aligner [31]. Uniquely mapped reads were utilized for transcript assembly with StringTie, using its default settings [32]. Transcripts from all samples were then merged into a single GTF file using the StringTie merge function. This merged GTF file was compared and annotated against the reference GFF using gffcompare [33].

Differential transcript abundance analysis

Differential analysis was conducted using DESeq2 [34]. The read counts were normalized using variance-stabilizing transformation, and differential enrichment analysis was performed. In the comparison of HPV-negative to HPV-positive samples, OCT1 served as the reference group, and 93VU147T was used as the test group. Transcripts with an absolute log2 fold change of ≥ 1.5 and a p value of ≤ 0.05 were considered significant. The distribution of differentially exported transcripts (DET) across the samples was visualized using volcano plots. Transcripts showing significant differential expression for the human reference genome (GRCh38.p7) were subsequently used for Gene Ontology (GO) and KEGG pathway enrichment analyses.

GO and pathway analysis

Cluster Profiler R Bioconductor package was used to carry out enrichment analysis for Biological Process (BP), Molecular Function (MF), Cellular Component (CC), and KEGG Pathways [35]. GO and pathway terms with adjusted p value ≤ 0.05 were considered significant. The GO enrichment results were visualized using the GOplot package in R [36]. GO plot package calculated the z score using the following formula:

z score = (up-down)/\(\sqrt{count}\)

where “up” is the number of up-regulated genes in a GO term, and “down” is the number of down-regulated genes. The z-score indicates the expression profile of genes within a GO term. Pathway visualizations were created using the Pathview package to examine the differential expression levels of genes within the pathways [37].

Analysis of the top 20 most abundant and DET HNSCC exosomal transcripts in HNSCC patients—expression analysis

The expression of different transcripts in HNSCC tissues was examined in the UALCAN database (http://ualcan.path.uab.edu/analysis.html), as described earlier [24]. UALCAN is a comprehensive, user-friendly, interactive web resource for analyzing cancer OMICS data. We used the UALCAN database (http://ualcan.path.uab.edu/analysis.html) to analyze the expression of the top 20 most abundant and DET HNSCC exosomal transcripts in HNSCC and normal tissues in the TCGA database based on HPV readcounts’ perspective. Further, the statistical p values were noted.

Kaplan–meier analysis

Kaplan–Meier analysis of the top 20 most abundant and DET HNSCC exosomal transcripts was performed using the UALCAN database, which allows analysis of survival annotated with high-throughput transcriptome data. The evaluation considered the variable ‘days_to_death’ for dead patients and ‘days_to_last_follow_up’ for those still alive. The primary tumor samples were categorized into two groups: ‘High expression’ comprising samples with gene expression values equal to or exceeding the 75th percentile, and ‘Low/Medium expression’ consisting of samples with gene expression values below the 75th percentile [24].

Results

Quantification and bio-physical characterization of HNSCC exosomes

To estimate the abundance of exosomes isolated from the HNSCC cell-conditioned medium, we performed a colorimetric acetylcholine esterase activity assay. Acetylcholine esterase (AchE) is known to be present on exosome surfaces and is often used for the estimation of exosomal content in a sample. All three HNSCC cell lines were confirmed for their HPV status prior to their exosome isolation (Supplementary Figure SF1). Both, HPV-negative (OCT1) and HPV16-positive (93VU147T, UDSCC2) released a quantifiable number of exosomes. As per the estimates provided by the kit manufacturer, a protein concentration of 1 μg roughly corresponds to 5 × 106 exosomes. OCT1 showed a higher number of exosome release in comparison to UDSCC2 and 93VU147T (Fig. 1A). Next, we studied HNSCC cells exosomes for the presence of various conventional exosome-specific markers by immunoblotting. In the absence of suitable internal control, the equal amount of exosomal proteins (50 μg/lane) were loaded. Proteins isolated from HNSCC exosomes showed the presence of exosomal marker proteins, TSG101 and HSP70. (Fig. 1B). Further, TEM was performed for morphometric and size estimation of HNSCC exosomes as described in the methods. Phosphotungstic acid-stained exosomes from all HNSCC cell lines onto formvar coated copper grids showed characteristic circular/spherical shaped morphology and showed diameter size ranging from 17 to 40 nm. There was no noticeable difference in the diameter of the exosome visualized with respect to the exosomes’ HPV-status (Fig. 1C).

Fig. 1
figure 1

Quantification and bio-physical characterization of HPV-positive and HPV-negative HNSCC exosomes. A Exosome-specific acetylcholine esterase activity in HPV-positive and HPV-negative HNSCC exosomes. Image shows color produced during enzymatic reaction of a representative experiment. Concentration of exosomes represented as 1 μg/μl (~ 5 × 10.6 exosomes/μl) derived from HPV-positive and HPV-negative HNSCC exosomes isolated from 10 ml condition media and dispensed in 50 μl PBS. Data is representative of three independent experiments plotted as the mean concentration of exosomes (μg/μl) ± SD (error bars); *p value ≤ 0.0001 compared with HPV-negative exosomes. B Immunoblotting for exosome marker proteins. Representative immunoblots of TSG101 and HSP70. Exosomal proteins 50 µg/lane isolated from HNSCC cells separated on 10% SDS-PAGE were transferred on PVDF membrane and probed for HSP70 and TSG101 antibodies. C Morphometric and particle size analysis of HNSCC cells’ exosomes using Transmission Electron Microscopy. Representative transmission electron photomicrographs of HSCCC cells exosomes OCT1, 93VU147T, and UDSCC2. Marking on the images indicate respective diameter sizes of visualized exosomes. Scale bar—100 nm. Inset shows zoomed image of single exosomes with size estimates (diameter)

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Quality and integrity assessment of RNA isolated from exosomes

To assess exosomal mRNA cargo, total RNA isolated from HPV-negative (OCT1) and HPV-positive (93VU147T) HNSCC cells were enriched for poly-A containing transcripts. Reverse transcription of the RNA enriched for poly-A transcripts resulted in a quantifiable double-stranded (ds) cDNA. Both preparations showed quantifiable presence of RNA in HNSCC exosomes. However, the RNA content was very low (OCT1—2.4 ng/μl and 93VU147T—4.5 ng/μl). ds cDNA library corresponding to the exosomal mRNA showed detectable amplification and successful library preparation in OCT1 and 93VU147T exosomes. The library appeared as a prominent smear form having fragment size from 197–984 bp for OCT1 and 192–971 bp for 93VU147T using DNA5000 ScreenTape (Fig. 2A, B). Qubit™ concentration of ds cDNA before sequencing for OCT1 was 1.9 ng/μl and for 93VU147T was 2.7 ng/μl.

Fig. 2
figure 2

Exosomal cDNA library preparation from enriched exosomal RNA. A cDNA Library Tapestation profile indicating the total mRNA start–end regions for each exosomal RNA preparation. B Total ds cDNA electropherogram of exosomal RNA on Agilent 2100 Bioanalyzer wherein Y-axis of electropherogram represents the arbitrary fluorescence unit (FU) intensity and X-axis represents size of transcripts (bp)

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Sequence data QC and transcript alignment

The sequence data was checked for base call quality distribution, % bases above Q20, Q30, %GC, and sequencing adapter contamination. Both the samples passed the QC threshold (Q20 > 95%) (Table 1). Raw sequences following removal of adapter sequence and low-quality reads were approximately 87.7% for both types of exosomes that aligned with the reference human genome (GRch38.p7) (Table 2).

Table 1 Summary of raw sequence data and quality
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Table 2 Read alignment statistics
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Overall abundance analysis of transcripts of HPV-negative and HPV-positive exosomes

Out of 606,022 reference transcripts for which the assembled HNSCC exosomal transcripts were analyzed for any relation, 310,987 in OCT1 and 342,202 in 93VU147T transcripts were present in respective exosomes with a minimum of 1 mapped read for each transcript type (Table 3).

Table 3 Number of exported transcripts in each sample (transcripts with at least 1 mapped read)
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The expression similarity between OCT1 and 93VU147T exosomes by Principal Components Analysis showed that both the samples clustered into two separate groups (Fig. 3A). In both exosome types, transcripts were comparable as they showed similar distribution of annotated and protein-coding transcripts (Fig. 3B, C).

Fig. 3
figure 3

Analysis of overall distribution of transcripts derived from HPV-positive and HPV-negative HNSCC exosomes. A Principal Component Analysis (PCA) of exosomal transcripts in HPV-positive vs. HPV-negative HNSCC cells. B Analysis of overall abundance and distribution of transcripts and their biotypes in HPV-positive and HPV-negative HNSCC exosomes. Percentage of total annotated transcripts. C. Distribution of RNA variants in different HNSCC cell types

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Abundance analysis of top 20 transcripts of HPV-negative and HPV-positive exosomes

Analysis of top 20 transcripts in OCT1 and 93VU147T collectively showed dominant levels of ZNF280D as the most abundant transcript (OCT1—0.90, 93VU147T-0.43) (Tables 4 and  5).

Table 4 Overall abundance and distribution of top 20 transcripts in HPV-negative (OCT1) HNSCC exosomes
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Table 5 Overall abundance and distribution of top 20 transcripts in HPV-positive (93VU147T) HNSCC exosomes
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All other transcripts were relatively at low proportion (OCT1—≤ 0.23% and 93VU147T—≤ 0.18%). Nevertheless, among the top 20, 9 transcripts were commonly represented in both exosomes (Fig. 4A, B).

Fig. 4
figure 4

Overall Top 20 exported transcripts in HNSCC cells’ exosomes. A Venn diagram of overall top 20 transcripts in HPV-positive exosomes and HPV-negative exosomes. B Heat map showing the expression of the top 20 exosomal transcripts. To enhance the visibility, magnified images of figure components have also been added in Supplementary File 1

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Differential abundance analysis of exosomal transcripts from HPV-negative and HPV-positive HNSCC cells

Hierarchical clustering of the exosome’s transcripts showed a total of 3,785 transcripts as differentially-exported in HPV-positive exosomes as shown in Fig. 5A. Out of differentially-exported transcripts (DET), approximately 75% (2830) transcripts were high and 25% (955) were low in HPV-positive exosomes (Supplementary File 2). These transcripts were predominantly coding but a small proportion (approximately 5%) of transcripts were non-coding in DET (Fig. 5B).

Fig. 5
figure 5

Differentially exported transcripts (DETs) profile of HNSCC cells’ exosomes. A Volcano plot showing overall clustering of differentially exported exosomal transcripts (DETs) in HPV-positive vs. HPV-negative HNSCC exosomes. Blue color indicates log2 fold change ≥ 1.5 and p value ≤ 0.05. Red dots indicate absolute log2 fold change ≥ 1.5 and adjusted p value ≤ 0.05. Gray lines indicate the marginal lines separating DETs from non-DETs, with the horizontal lines denoting the p value threshold (p ≤ 0.05) and vertical lines the fold change cut off (≥ 1.5 or < 0.67). B Pie chart showing percentage of annotated DETs biotypes. To enhance the visibility, magnified images of figure components have also been added in Supplementary File 1

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Functional enrichment analysis of exosomal transcripts

GO enrichment analysis using GO bubble plot revealed the presence of 140 enriched GO categories that crossed adjusted p value ≤ 0.05 for DET (Fig. 6A). The DET belonged to 73 GO categories of biological pathways, 40 of molecular functions, and 27 categories of cellular components (Fig. 6B). Among top 20 significantly-enriched GO terms, in biological pathways category maximum DET associated with purine metabolic processes followed by viral transcription and translation initiation; structural constituent of ribosomes and cytoskeleton in molecular function category (Fig. 6C). In cellular components category, maximum transcripts were involved in cell-substrate junction and nuclear speck formation.

Fig. 6
figure 6

Functional clustering of the differentially-exported transcripts profile of exosomes of HNSCC cell lines as per GO terms. A GO clustering of DETs in HPV-positive vs. HPV-negative exosomes using Bubble plot of the enriched GO terms. GO terms both with a p value < 0.05 were selected. The size of the bubble is proportionate to the number of genes involved in the GO term. Bubble color code: red – Biological Process (BP); blue – Molecular Function (MF); green – Cellular Component (CC). B Overall percentage of enriched GO terms involved in different biological pathways, molecular function, and cellular components. C Analysis of top 20 enriched biological pathways, molecular functions, and cellular components in exosomes of HNSCC cell lines. To enhance the visibility, magnified images of figure components have also been added in Supplementary File 1

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KEGG Pathway analysis of DET

The top 10 enriched KEGG pathways associated with DET are shown in Fig. 7A. KEGG pathway analyses revealed that DET was involved in key pathways like proteasome [23], and ribosome [38]. Another set of important pathways that overrepresented were various neurological disorders, such as Parkinson’s disease [20], Alzheimer’s disease [30], amyotrophic lateral sclerosis [39] and Huntington’s disease [24] (Fig. 7B-D). In addition, transcripts of nearly 15 annotated genes played a crucial function in the Non-Alcoholic fatty liver disease pathway and 21 in Prion disease that were enriched in HPV-positive exosomes (Fig. 7E).

Fig. 7
figure 7

KEGG Pathway analysis of differentially-exported transcripts. Top 10 enriched KEGG pathways of differentially-exported transcripts in HPV-positive and HPV-negative HNSCC exosomes (A), Pathways involved in Proteasome and Ribosome (B), Parkinson disease and Alzheimer disease (C), Amyotrophic lateral sclerosis and Huntington disease (D), Non-alcoholic fatty liver disease and Prion disease (E). Number in brackets indicates total number of DET annotated in given pathway in functional enrichment analysis. To enhance the visibility, magnified images of figure components have also been added in Supplementary File 1

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Identification of HPV transcripts in exosomal cargo

Further, we examined, the exosomal compartment for export of HPV transcripts using all mapped and uniquely mapped reads on HPV16 reference genome using Integrative Genomics Viewer (IGV). The data showed presence of very low abundance reads sparingly scattered all over the reference genome of HPV16 and lacked enrichment of specific region (Fig. 8).

Fig. 8
figure 8

Enrichment of HPV-related reads in exosomal transcripts. Integrated Genome Viewer snapshots showing mapping of all mapped reads and uniquely mapped reads detected in 93VU147T and OCT1 exosomes to HPV16 reference genome

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Level of the top 20 most abundant exported-transcripts in HNSCC tissues

To check the clinical relevance of the most abundant transcripts in the HNSCC exosomes, we examined HNSCC transcriptomic data set available at TCGA for altered transcripts level in tumor tissues and their correlation with survival in corresponding HNSCC patients. Out of 9 common exosomal transcripts, 6 transcripts displayed significantly altered expression. However, the expression of only three transcripts, SLCO5A1, SGK1 and MAD1L1 were high in primary tumors. Whereas the rest showed reduced expression in comparison to the normal tissues (Fig. 9A). Among 11 exclusive transcripts of OCT1 (Fig. 4A), 5 transcripts, MYRIP, C16orf87, IL17D, EVC and EIF5A2 were significantly altered in primary tumors. The expression of transcripts C16orf87, EVC & EIF5A2 showed a high level in HNSCC tissues; whereas the expression of MYRIP and IL17D transcripts declined (Fig. 9B).

Fig. 9
figure 9

Expression of overall top 20 exported transcripts present in HPV-positive and HPV-negative HNSCC cells’ exosomes in TCGA HNSCC cohort. Box-whisker plots representation of common transcripts having significant expression, present in both (A), in OCT1 (B) and in 93VU147T (C). Blue and orange bars represent total transcripts count in normal and primary tumor samples respectively. The data were grouped as normal (n = 44) and primary tumor (n = 520). Welch’s T-test was applied to estimate the significance of differences in expression levels between normal and primary tumors with *p value < 0.05. (*Normal v/s Primary tumor). p value in red boxes show genes with positive correlation as shown in exosomal DETs. These are original pictures captured from UALCAN database. For clarity and readability and visibility of components please see Supplementary File 1

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Among 11 exclusive transcripts of HPV-positive exosomes (Fig. 4A), 8 showed altered expression in primary tumors in comparison to the normal tissues (Table 5 ). All these transcripts, PFN1, FADS3, FTH1, BRSK1, GPBP1L1, ATP1A1, GRIN2D, and HSPA8 showed a high level of expression as observed in transcripts exported in HPV-positive exosomes (Fig. 9C).

Survival of HNSCC patients based on the expression level of most-abundant exosomal transcripts

Survival analysis in HNSCC cohort of TCGA showed high level of ZNF280D, SGK1, and MAD1L1 common transcripts were associated with poor survival in HNSCC patients (Fig. 10A). Also, C16orf87 high level was correlated with poor survival of the patients in OCT1 (Fig. 10B). On the contrary, in case of PFN1 transcripts detected in 93VU147T exosomes, its high expression was correlated with better survival of the HNSCC patients. For the rest of the transcripts FADS3, FTH1, and GPBP1L1 represented in HPV-positive exosomes, their low expression was correlated with better survival of the patients (Fig. 10C).

Fig. 10
figure 10

Kaplan–Meier survival analysis of overall top 20 exported transcripts present in HPV-positive and HPV-negative HNSCC cells’ exosomes. Survival-ship curve of transcripts having significant expression represented common in both top 20 exosomal types (A), in HPV-negative (B) and HPV-positive exosomes (C). The primary tumor samples were categorized into two groups: ‘High expression’ comprising samples with gene expression values equal to or exceeding the 75th percentile, and ‘Low/Medium expression’ consisting of samples with gene expression values below the 75th percentile. Welch’s T-test was applied to estimate the significance of differences in expression levels between normal and primary tumors with p value < 0.05. These are original pictures captured from UALCAN database. For clarity and readability and visibility of components please see Supplementary File 1

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Level of top 20 DET in HNSCC tissues

Among the top 20 DET of OCT1, 9 showed significant expression in HNSCC tissue transcriptome datasets. Out of these, 4 transcripts (MID1, SGK3, TESK2, and ATP5B showed high expression in HPV-positive samples compared to HPV-negative samples and followed the same trend as observed in transcripts exported in HPV-positive HNSCC exosomes (Fig. 11A). On the other hand, IKZF2 showed low expression specifically in HPV-negative HNSCC. Among the transcripts that corresponded to top 20 DET of 93VU147T, 13 showed altered expression. Out of these, 7 transcripts namely ATP5B, EEF2, RPS14, GPBP1L1, SLC25A3, ATP1A1 and UBB showed high expression in HPV-positive tumor tissues (Fig. 11B).

Fig. 11
figure 11

Expression of top 20 DETs present in HPV-positive and HPV-negative HNSCC cells’ exosomes in TCGA HNSCC cohort. Box-whisker plots representation of DETs present in HPV-positive (A) and HPV-negative (B) exosomes. Blue orange and yellow bars represent total transcripts count in Normal, HPV-positive, and HPV-negative cohorts, respectively. The data were grouped based on HPV status [negative (n = 434) and positive (n = 80)], Welch’s T-test was applied to estimate the significance of differences in expression levels between normal and tumor subgroups based on HPV status with # pvalue < 0.05. (.#HPV-positive v/s HPV-negative). p value in red boxes show genes with positive correlation as shown in exosomal DETs. These are original pictures captured from UALCAN database. For clarity and readability and visibility of components please see Supplementary File 1

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Survival of HNSCC patients based on transcripts corresponding to the top 20 DET

Survival analysis of HNSCC patients stratified based on the level of transcripts corresponding to DET in OCT1 exosomes showed an association of high level of 3 transcripts (FADS3, SGK3, and TESK2) that were related with poor survival of the HNSCC patients (Fig. 12A). In contrast, high level of IKZF2 and ACTB transcript were associated with better survival. On the other hand, HNSCC patients with high level of PFN1, ALDOA, ACTB, FTH1, FADS3, and GPBP1L1 represented in 93VU147T exosomes DET, unexpectedly showed poor survival (Fig. 12B).

Fig. 12
figure 12

Kaplan–Meier survival analysis of top 20 DETs present in HPV-positive and HPV-negative HNSCC cells’ exosomes. Survival ship curve of differentially- exported transcripts present in HPV-positive (A) and HPV-negative (B) exosomes. The primary tumor samples were categorized into two groups: ‘High expression’ comprising samples with gene expression values equal to or exceeding the 75th percentile, and ‘Low/Medium expression’ consisting of samples with gene expression values below the 75th percentile. Welch’s T-test was applied to estimate the significance of differences in expression levels between HPV-positive and HPV-negative cohorts with p value < 0.05. These are original pictures captured from UALCAN database. For clarity and readability and visibility of components please see Supplementary File 1

Full size image

Discussion

While exploring the importance of exosomes in promoting carcinogenesis in HNSCC through modulation of TME, the present study started with the quantification and characterization of exosomes from HPV-negative (OCT1) and HPV-positive (93VU147T cells). Our data showed that HPV-negative cells released a higher number of exosomes compared to HPV-positive cells. Transmission electron microscopy (TEM) revealed a narrow range of isolated exosomes diameter, whereas immunoblotting confirmed the expression of exosomal markers (TSG101 and HSP70). Interestingly, our data showed that these exosomal preparations possessed a small but distinct population of poly-A containing mRNA that resulted in successful mRNA library preparation. Transcriptomic analysis of HPV-positive and HPV-negative HNSCC exosomes showed distinct profiles, with the ZNF280D transcript being dominantly exported in both OCT1 and 93VU147T. A total of 3,780 differentially-exported transcripts were identified, with 2,830 showing high level and 955 low level in HPV-positive exosomes when compared to their HPV-negative counterparts. GO analyses revealed enrichment of 140 GO categories. TCGA data analysis indicated high expression of SGK1 and MAD1L1 in primary tumors which were common in both HPV-positive and HPV-negative exosomes, with low expression correlating to better HNSCC patient survival. Seven Differentially exported transcripts (DET) EEF2, ATP5B, ATP1A1, RPS14, UBB, GPBP1L1, and SLC25A3 exhibited higher expression in HPV-positive samples and were linked to patient survival. Notably, the HPV16-positive exosomes also contained HPV transcripts albeit at low level.

In the present study, we characterized exosomes isolated from a panel of HPV-positive and HPV-negative HNSCC cell lines using a commercial kit. A colorimetric acetylcholine esterase (AcHE) enzyme activity assay is a standard method to quantify the isolated exosomes [40, 41]. AcHE enzyme is abundantly present on the surface of exosomes and often used for the estimation of exosome load [42]. We found a higher number of exosomes release in HPV-negative cell (OCT1). A similar result was observed in another study, where silencing of HPV E6/E7 oncoproteins led to increased exosome release from HeLa cells [23]. In the morphometric analysis by TEM, spherical/circular morphology of isolated exosomes was observed with diameters ranging from 17-40 nm. Generally, exosomes have spherical structure [43], however, cup shaped bilayer morphology has also been reported [44]. Further, the expression of exosome specific markers was observed for the molecular characterization of isolated exosomes. Exosomes showed high expression of TSG101 and HSP70. HSP70, a heat shock protein, and TSG101, a tumor susceptibility gene protein is widely used exosomal markers for exosome characterization [45]. Overall, our exosomal preparations showed uniformity in size and displayed features characteristics of this class of microvesicles.

The present study showed the presence of RNA in HNSCC exosomes. Quantitative assessment of the RNA yield from exosomes demonstrated an approximately twofold difference in exosomal RNA yield among HPV-positive and HPV-negative cells. Although exosomal release is a regulated process controlled by GPCR’s [46], the reason for the differential content of packaged exosomal RNA is not known and may primarily depend on cell type, metabolic state of the parent cell and its microenvironment [39].

Further investigation, showed that exosomal RNA contained poly A-tailed transcripts that successfully reverse transcribed and were PCR amplifiable. Likewise, some reports showed PCR-amplifiable mRNA exportation via exosomes in glioblastoma [16], colorectal [17], breast [18] and prostate cancer [47]. In our previous study, we also showed that cervical cancer exosomes carried poly-A tail transcripts [22]. The exported trasncripts ranged from 100–1000 bp in our present study, which coincided with our previous report where CaCx exosomes also carried mRNA fragments having the same range. Though we have shown transcripts as long as 1000 bp in the exosomes, we could not locate if these transcripts were translationally competent. However, others have shown that exosomal transcripts may have the competency to translate in the recepient cell [48].

The QC analysis of our NGS data revealed that both samples successfully met the QC threshold (Q20 > 95%). We examined the sequence data for the distribution of base call quality, the percentage of bases above Q20 and Q30, %GC, and the presence of sequencing adapter contamination. Additionally, existing reports recommend evaluating other parameters such as RNA integrity, raw read data, the percentage of alignment of raw reads with the reference genome, and the percentage of the total reference genome covered by sequenced transcripts during QC [49]. It has been emphasized that incomplete QC of RNA-seq can lead to misinterpretation of results [50]. According to our QC report, our exosomal samples fulfilled the suggested parameters and are suitable for further downstream analysis.

In the overall abundant transcripts found in HNSCC exosomes, 72% were protein-coding types. These protein-coding transcripts are considered to be the major contributors to the molecular events occurring in the recipient cells [51]. In another study, it was shown that lung cancer derived exosomes are found to be enriched with 95% protein coding transcripts [52]. Our NGS analyses revealed the presence of 9 common transcripts in the top 20 within HPV-negative and HPV-positive exosomes. Among these 9 transcripts, ZNF280D was the most abundant transcript in both HPV-positive and HPV-negative HNSCC exosomes. ZNF280D belongs to the Zinc finger protein family which has been implicated in various disease conditions [38]. Notably, recent research has shed light on the role of ZNF in different cancers, including colorectal [53], cervical [54], gastric [55], and breast cancer [56]. While numerous studies have enumerated the involvement of ZNFs in various pathological conditions including cancer, it is noteworthy that only a single study has investigated the role of ZNF280D. It was reported to be found in enamel matrix derivative (EMD) that modulate adhesion of gingival fibroblast [57]. However, its transportation via exosomes and its relevance in reconditioning of TME has not been elucidated till date.

DESeq2 analysis revealed that both the exosomes had striking differences in the exported mRNA cargo. This suggests that HPV infection positively regulates the selective cargo packaging of exosomes. It may be presumed that upregulated export of transcripts in exosomes may be influenced by HPV infection mediated-upregulated expression of these transcripts inside the parent cell. Previously, HPV infection was shown to be involved in modulation of gene expression at the global level in 3D organotypic raft cultures of cervical tissues [58]. Exploring the functions performed by differentially-exported transcripts in HPV-positive exosomes will help the scientific community better understand the pathophysiology of HPV-associated HNSCC and may contribute to novel therapeutic development.

The DET belonged to 73 GO categories of biological pathways, 40 molecular functions, and 27 categories of cellular components. Among biological pathways, transcripts involved in purine metabolic pathway were maximally represented which may contribute to the excess of the purine metabolites in exosomes isolated from HNSCC tissues [59]. Further, genes responsible for purine metabolism were shown to be upregulated in HNSCC patient samples compared to healthy control which may have an impact on the packaging of these transcripts in HNSCC cell-derived exosomes [59]. Reports suggest the involvement of purine metabolic signaling in modulation of immune landscape of TME [60]. Cell-substrate junctions and focal adhesions are maximally contributing GO categories of cellular components. A weakly metastatic SW480 colorectal cancer cell line exosomes showed unique abundance of proteins enriched to these categories in comparison to absence in SW620, a highly metastatic colorectal cell line [61]. Further, the upregulation of genes involved in cell-substrate junctions and focal adhesion GO categories in exosomes was linked with poor prognosis of HNSCC patients [62].

Functional gene analysis using KEGG unexpectedly revealed enriched transcripts that played key roles in various biological and molecular functions associated with neurological disorders, such as Parkinson’s disease, Alzheimer’s disease, amyotrophic lateral sclerosis, Huntington’s disease, non-alcoholic fatty liver disease, and prion disease. Similarly, numerous studies have indicated the role of exosomes in the progression of neurodegenerative disorders such as Alzheimer’s [63], Parkinson’s [64, 65], prion disease [66], amyotrophic lateral sclerosis [67], and Huntington’s disease [68], by delivering proteins or molecules linked to the pathology of these diseases. Therefore, the exosomal cargo of HPV-positive HNSCC cells play a role in regulating neurological disorders.

Apart from host cell derived transcripts, HPV-derived RNA may also contribute to the exosomal cargo from HPV-positive HNSCC. Though, the presence of non-coding RNAs derived from HPV is not well documented, a few sporadic studies have reported sense and antisense long control region (LCR) associated RNAs (pRNAs) and ncRNA in HPV18-positive and HPV16-positive cervical cancer cell lines respectively that regulated expression of E6 and E7 oncogenes [69, 70]. When we mapped the reads from exosomal transcripts on the HPV16 genome, it failed to show enrichment of any HPV-specific transcripts or ncRNAs. A few of the transcripts mapped onto the HPV16-genome, however the spread was all over the genome. A similar observation was made in exosomes of cervical cancer cells [22]. This indicates that HPV derived RNA may not be present or may be represented at very low level.

Further, the TCGA database analysis revealed clinical relevance of altered transcripts in HNSCC exosomes. Among commonly exported abundant transcripts, SGK1 and MAD1L1 showed high expression, correlated with poor survival in HNSCC patients. Similarly, high expression of SGK1 correlated with high tumor grade and worse clinical outcomes in non-small cell lung cancer [71]. In contrast, low expression of SGK1 was correlated with high tumor grade and increased recurrence rate in the case of prostate cancer [72]. Similarly, the high expression of MAD1L1 is correlated with poor survival and drug resistance in breast cancer [73]. Existing report suggests presence of mutation in MAD1L1 in the case of colon and lung cancer [74], however, its clinical relevance has not been elucidated yet.

During the validation of the clinical relevance of the top 20 DET of HPV-negative exosomes by the TCGA database, high expression of FADS3, SGK3, and TESK2 correlated with poor survival of the HNSCC patients. On the other hand, in the top 20 DET of HPV-positive exosomes, low level of FADS3, FTH1, and GPBP1L1 correlated with better survival of the patients. Recently, the overexpression of FADS3 has been correlated with poor survival and HNSCC pathogenesis, high lymph node metastasis, advanced tumor stage and lympho-vascular invasion [75]. Similarly, higher expression of FTH1 has also been correlated with poor prognosis and lymph node metastasis in case of HNSCC [76] as well as in renal carcinoma [77]. The expression of GPBP1L1 has also been correlated with survival of glioma patients [78]. DETs detected in HNSCC exosomes had clinical relevance and can be used as biomarkers.

Overall, our study revealed that exosomes from HPV-positive and HPV-negative HNSCC cells contain distinct sets of transcripts that could influence protein synthesis and degradation. Additionally, these exosomes may carry a unique class of transcripts linked to pathways associated with neurological disorders. Analysis of TCGA data further suggests that these altered transcripts could serve as biomarkers for predicting patient survival.

Availability of data and materials

The data sets used and or/analyzed during the current study are available from the corresponding author upon reasonable request.

References

  1. Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74(3):229–63.

    Article  PubMed  Google Scholar 

  2. Syrjanen K, Syrjanen S, Lamberg M, Pyrhonen S, Nuutinen J. Morphological and immunohistochemical evidence suggesting human papillomavirus (HPV) involvement in oral squamous cell carcinogenesis. Int J Oral Surg. 1983;12(6):418–24.

    Article  CAS  PubMed  Google Scholar 

  3. Syrjanen S, Happonen RP, Virolainen E, Siivonen L, Syrjanen K. Detection of human papillomavirus (HPV) structural antigens and DNA types in inverted papillomas and squamous cell carcinomas of the nasal cavities and paranasal sinuses. Acta Otolaryngol. 1987;104(3–4):334–41.

    Article  CAS  PubMed  Google Scholar 

  4. Aggarwal N, Yadav J, Thakur K, Bibban R, Chhokar A, Tripathi T, et al. Human Papillomavirus Infection in Head and Neck Squamous Cell Carcinomas: Transcriptional Triggers and Changed Disease Patterns. Front Cell Infect Microbiol. 2020;10: 537650.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Wu Q, Wang M, Liu Y, Wang X, Li Y, Hu X, et al. HPV Positive Status Is a Favorable Prognostic Factor in Non-Nasopharyngeal Head and Neck Squamous Cell Carcinoma Patients: A Retrospective Study From the Surveillance, Epidemiology, and End Results Database. Front Oncol. 2021;11: 688615.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Li I, Nabet BY. Exosomes in the tumor microenvironment as mediators of cancer therapy resistance. Mol Cancer. 2019;18(1):32.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Johnstone RM. The Jeanne Manery-Fisher Memorial Lecture 1991. Maturation of reticulocytes: formation of exosomes as a mechanism for shedding membrane proteins. Biochem Cell Biol. Biochem Cell Biol. 1992;70(3–4):179–90.

    Article  CAS  PubMed  Google Scholar 

  8. H Rashed M, Bayraktar E, K Helal G, Abd-Ellah MF, Amero P, Chavez-Reyes A, et al. Exosomes: From Garbage Bins to Promising Therapeutic Targets. Int J Mol Sci. 2017;18(3):538.

  9. Guenat D, Hermetet F, Pretet JL, Mougin C. Exosomes and Other Extracellular Vesicles in HPV Transmission and Carcinogenesis. Viruses. 2017;9(8):211.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Turner DL, Korneev DV, Purdy JG, de Marco A, Mathias RA. The host exosome pathway underpins biogenesis of the human cytomegalovirus virion. Elife. 2020;9:e58288.

  11. Alenquer M, Amorim MJ. Exosome Biogenesis, Regulation, and Function in Viral Infection. Viruses. 2015;7(9):5066–83.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Wang Y, Zhang M, Zhou F. Biological functions and clinical applications of exosomal long non-coding RNAs in cancer. J Cell Mol Med. 2020;24(20):11656–66.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Ratajczak J, Wysoczynski M, Hayek F, Janowska-Wieczorek A, Ratajczak MZ. Membrane-derived microvesicles: important and underappreciated mediators of cell-to-cell communication. Leukemia. 2006;20(9):1487–95.

    Article  CAS  PubMed  Google Scholar 

  14. Deregibus MC, Cantaluppi V, Calogero R, Lo Iacono M, Tetta C, Biancone L, et al. Endothelial progenitor cell derived microvesicles activate an angiogenic program in endothelial cells by a horizontal transfer of mRNA. Blood. 2007;110(7):2440–8.

    Article  CAS  PubMed  Google Scholar 

  15. Bernard MA, Zhao H, Yue SC, Anandaiah A, Koziel H, Tachado SD. Novel HIV-1 miRNAs stimulate TNFalpha release in human macrophages via TLR8 signaling pathway. PLoS ONE. 2014;9(9): e106006.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Skog J, Wurdinger T, van Rijn S, Meijer DH, Gainche L, Sena-Esteves M, et al. Glioblastoma microvesicles transport RNA and proteins that promote tumour growth and provide diagnostic biomarkers. Nat Cell Biol. 2008;10(12):1470–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Hong BS, Cho JH, Kim H, Choi EJ, Rho S, Kim J, et al. Colorectal cancer cell-derived microvesicles are enriched in cell cycle-related mRNAs that promote proliferation of endothelial cells. BMC Genomics. 2009;10:556.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Jenjaroenpun P, Kremenska Y, Nair VM, Kremenskoy M, Joseph B, Kurochkin IV. Characterization of RNA in exosomes secreted by human breast cancer cell lines using next-generation sequencing. PeerJ. 2013;1: e201.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Ludwig S, Sharma P, Theodoraki MN, Pietrowska M, Yerneni SS, Lang S, et al. Molecular and Functional Profiles of Exosomes From HPV(+) and HPV(-) Head and Neck Cancer Cell Lines. Front Oncol. 2018;8:445.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Bhat A, Sharma A, Bharti AC. Upstream Hedgehog signaling components are exported in exosomes of cervical cancer cell lines. Nanomedicine. 2018;13(17):2127–38.

    Article  CAS  PubMed  Google Scholar 

  21. Bhat A, Yadav J, Thakur K, Aggarwal N, Tripathi T, Chhokar A, et al. Exosomes from cervical cancer cells facilitate pro-angiogenic endothelial reconditioning through transfer of Hedgehog-GLI signaling components. Cancer Cell Int. 2021;21(1):319.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Bhat A, Yadav J, Thakur K, Aggarwal N, Chhokar A, Tripathi T, et al. Transcriptome analysis of cervical cancer exosomes and detection of HPVE6*I transcripts in exosomal RNA. BMC Cancer. 2022;22(1):164.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Honegger A, Leitz J, Bulkescher J, Hoppe-Seyler K, Hoppe-Seyler F. Silencing of human papillomavirus (HPV) E6/E7 oncogene expression affects both the contents and the amounts of extracellular microvesicles released from HPV-positive cancer cells. Int J Cancer. 2013;133(7):1631–42.

    Article  CAS  PubMed  Google Scholar 

  24. Chandrashekar DS, Karthikeyan SK, Korla PK, Patel H, Shovon AR, Athar M, et al. UALCAN: An update to the integrated cancer data analysis platform. Neoplasia. 2022;25:18–27.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Campbell JD, Yau C, Bowlby R, Liu Y, Brennan K, Fan H, et al. Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas. Cell Rep. 2018;23(1):194-212 e6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Coutlee F, Gravitt P, Kornegay J, Hankins C, Richardson H, Lapointe N, et al. Use of PGMY primers in L1 consensus PCR improves detection of human papillomavirus DNA in genital samples. J Clin Microbiol. 2002;40(3):902–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Wu Y, Deng W, Klinke DJ 2nd. Exosomes: improved methods to characterize their morphology, RNA content, and surface protein biomarkers. Analyst. 2015;140(19):6631–42.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Thery C, Amigorena S, Raposo G, Clayton A. Isolation and characterization of exosomes from cell culture supernatants and biological fluids. Current protocols in cell biology. 2006;Chapter 3:Unit 3 22.

  29. Ewels P, Magnusson M, Lundin S, Kaller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics. 2016;32(19):3047–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Chen S, Zhou Y, Chen Y, Gu J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. 2018;34(17):i884–90.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29(1):15–21.

    Article  CAS  PubMed  Google Scholar 

  32. Pertea M, Pertea GM, Antonescu CM, Chang TC, Mendell JT, Salzberg SL. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat Biotechnol. 2015;33(3):290–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Pertea G, Pertea M. GFF Utilities: GffRead and GffCompare. F1000Res. 2020;9:ISCB Comm J-304.

  34. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012;16(5):284–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Walter W, Sanchez-Cabo F, Ricote M. GOplot: an R package for visually combining expression data with functional analysis. Bioinformatics. 2015;31(17):2912–4.

    Article  CAS  PubMed  Google Scholar 

  37. Luo W, Brouwer C. Pathview: an R/Bioconductor package for pathway-based data integration and visualization. Bioinformatics. 2013;29(14):1830–1.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Agnihotri R, Gaur S. The Role of Zinc Finger Proteins in Various Oral Conditions. ScientificWorldJournal. 2022;2022:4612054.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Kowal J, Tkach M, Thery C. Biogenesis and secretion of exosomes. Curr Opin Cell Biol. 2014;29:116–25.

    Article  CAS  PubMed  Google Scholar 

  40. Savina A, Vidal M, Colombo MI. The exosome pathway in K562 cells is regulated by Rab11. J Cell Sci. 2002;115(Pt 12):2505–15.

    Article  CAS  PubMed  Google Scholar 

  41. Essandoh K, Yang L, Wang X, Huang W, Qin D, Hao J, et al. Blockade of exosome generation with GW4869 dampens the sepsis-induced inflammation and cardiac dysfunction. Biochem Biophys Acta. 2015;1852(11):2362–71.

    CAS  PubMed  Google Scholar 

  42. Perez-Gonzalez R, Gauthier SA, Kumar A, Levy E. The exosome secretory pathway transports amyloid precursor protein carboxyl-terminal fragments from the cell into the brain extracellular space. J Biol Chem. 2012;287(51):43108–15.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Chernyshev VS, Rachamadugu R, Tseng YH, Belnap DM, Jia Y, Branch KJ, et al. Size and shape characterization of hydrated and desiccated exosomes. Anal Bioanal Chem. 2015;407(12):3285–301.

    Article  CAS  PubMed  Google Scholar 

  44. Hofmann L, Medyany V, Ezic J, Lotfi R, Niesler B, Roth R, et al. Cargo and Functional Profile of Saliva-Derived Exosomes Reveal Biomarkers Specific for Head and Neck Cancer. Front Med (Lausanne). 2022;9: 904295.

    Article  PubMed  Google Scholar 

  45. Canning P, Alwan A, Khalil F, Zhang Y, Opara EC. Perspectives and Challenges on the Potential Use of Exosomes in Bioartificial Pancreas Engineering. Ann Biomed Eng. 2022;50(10):1177–86.

    Article  PubMed  Google Scholar 

  46. Wiley RD, Gummuluru S. Immature dendritic cell-derived exosomes can mediate HIV-1 trans infection. Proc Natl Acad Sci U S A. 2006;103(3):738–43.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Grunvogel O, Colasanti O, Lee JY, Kloss V, Belouzard S, Reustle A, et al. Secretion of Hepatitis C Virus Replication Intermediates Reduces Activation of Toll Like Receptor 3 in Hepatocytes. Gastroenterology. 2018;154(8):2237–2251.e16.

  48. Valadi H, Ekstrom K, Bossios A, Sjostrand M, Lee JJ, Lotvall JO. Exosome-mediated transfer of mRNAs and microRNAs is a novel mechanism of genetic exchange between cells. Nat Cell Biol. 2007;9(6):654–9.

    Article  CAS  PubMed  Google Scholar 

  49. Sheng Q, Vickers K, Zhao S, Wang J, Samuels DC, Koues O, et al. Multi-perspective quality control of Illumina RNA sequencing data analysis. Brief Funct Genomics. 2017;16(4):194–204.

    CAS  PubMed  Google Scholar 

  50. Guo Y, Zhao S, Sheng Q, Ye F, Li J, Lehmann B, et al. Multi-perspective quality control of Illumina exome sequencing data using QC3. Genomics. 2014;103(5–6):323–8.

    Article  CAS  PubMed  Google Scholar 

  51. Ogawa Y, Tsujimoto M, Yanoshita R. Next-Generation Sequencing of Protein-Coding and Long Non-protein-Coding RNAs in Two Types of Exosomes Derived from Human Whole Saliva. Biol Pharm Bull. 2016;39(9):1496–507.

    Article  CAS  PubMed  Google Scholar 

  52. Luo HT, Zheng YY, Tang J, Shao LJ, Mao YH, Yang W, et al. Dissecting the multi-omics atlas of the exosomes released by human lung adenocarcinoma stem-like cells. NPJ Genom Med. 2021;6(1):48.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Yang L, Hamilton SR, Sood A, Kuwai T, Ellis L, Sanguino A, et al. The previously undescribed ZKSCAN3 (ZNF306) is a novel “driver” of colorectal cancer progression. Cancer Res. 2008;68(11):4321–30.

    Article  CAS  PubMed  Google Scholar 

  54. Li N, He Y, Mi P, Hu Y. ZNF582 methylation as a potential biomarker to predict cervical intraepithelial neoplasia type III/worse: A meta-analysis of related studies in Chinese population. Medicine (Baltimore). 2019;98(6): e14297.

    Article  CAS  PubMed  Google Scholar 

  55. Li Y, Zhao Q, Fan LQ, Wang LL, Tan BB, Leng YL, et al. Zinc finger protein 139 expression in gastric cancer and its clinical significance. World J Gastroenterol. 2014;20(48):18346–53.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Harper J, Yan L, Loureiro RM, Wu I, Fang J, D’Amore PA, et al. Repression of vascular endothelial growth factor expression by the zinc finger transcription factor ZNF24. Cancer Res. 2007;67(18):8736–41.

    Article  CAS  PubMed  Google Scholar 

  57. Machado Jr DZ. Mass Spectrometry-Based Proteomics Analysis of Bioactive Proteins in EMD that Modulate Adhesion of Gingival Fibroblast to Improve Bio-Integration of Dental Implants. 2019. https://ir.lib.uwo.ca/etd/6064/.

  58. Kang SD, Chatterjee S, Alam S, Salzberg AC, Milici J, van der Burg SH, et al. Effect of Productive Human Papillomavirus 16 Infection on Global Gene Expression in Cervical Epithelium. J Virol. 2018;92(20):10–1128.

  59. Ludwig N, Gillespie DG, Reichert TE, Jackson EK, Whiteside TL. Purine Metabolites in Tumor-Derived Exosomes May Facilitate Immune Escape of Head and Neck Squamous Cell Carcinoma. Cancers (Basel). 2020;12(6):1602.

  60. Azambuja JH, Ludwig N, Braganhol E, Whiteside TL. Inhibition of the Adenosinergic Pathway in Cancer Rejuvenates Innate and Adaptive Immunity. Int J Mol Sci. 2019;20(22):5698.

  61. Liu X, Li N, Zhang C, Wu X, Zhang S, Dong G, et al. Identification of metastasis-associated exoDEPs in colorectal cancer using label-free proteomics. Transl Oncol. 2022;19: 101389.

    Article  PubMed  PubMed Central  Google Scholar 

  62. You Y, Du Z, Xu G, Tian Z, Xiao M, Wang Y. Identification of Exosome-Related Genes Associated with Prognosis and Immune Infiltration Features in Head-Neck Squamous Cell Carcinoma. Biomolecules. 2023;13(6):958.

  63. Saman S, Kim W, Raya M, Visnick Y, Miro S, Saman S, et al. Exosome-associated tau is secreted in tauopathy models and is selectively phosphorylated in cerebrospinal fluid in early Alzheimer disease. J Biol Chem. 2012;287(6):3842–9.

    Article  CAS  PubMed  Google Scholar 

  64. Jiang C, Hopfner F, Katsikoudi A, Hein R, Catli C, Evetts S, et al. Serum neuronal exosomes predict and differentiate Parkinson’s disease from atypical parkinsonism. J Neurol Neurosurg Psychiatry. 2020;91(7):720–9.

    Article  PubMed  Google Scholar 

  65. Niu M, Li Y, Li G, Zhou L, Luo N, Yao M, et al. A longitudinal study on alpha-synuclein in plasma neuronal exosomes as a biomarker for Parkinson’s disease development and progression. Eur J Neurol. 2020;27(6):967–74.

    Article  CAS  PubMed  Google Scholar 

  66. Vella LJ, Sharples RA, Lawson VA, Masters CL, Cappai R, Hill AF. Packaging of prions into exosomes is associated with a novel pathway of PrP processing. J Pathol. 2007;211(5):582–90.

    Article  CAS  PubMed  Google Scholar 

  67. Chen PC, Wu D, Hu CJ, Chen HY, Hsieh YC, Huang CC. Exosomal TAR DNA-binding protein-43 and neurofilaments in plasma of amyotrophic lateral sclerosis patients: A longitudinal follow-up study. J Neurol Sci. 2020;418: 117070.

    Article  CAS  PubMed  Google Scholar 

  68. Ananbeh H, Vodicka P, Kupcova Skalnikova H. Emerging Roles of Exosomes in Huntington’s Disease. Int J Mol Sci. 2021;22(8):4085.

  69. Kassab MA, Mudassir M, Singh A, Muthuraman N, Bhagat M, Palanichamy JK, et al. Gene Silencing and Activation of Human Papillomavirus 18 Is Modulated by Sense Promoter Associated RNA in Bidirectionally Transcribed Long Control Region. PLoS One. 2015;10(6):e0128416.

    Article  PubMed  PubMed Central  Google Scholar 

  70. Kisseljova NP, Fedorova MD, Zaikina AE, Elkina NV, Goldobina EE, Elkin DS, et al. Identification regulatory noncoding RNAs of human papilloma virus type 16 (Papillomaviridae: Alphapapillomavirus: Human papillomavirus) in cervical tumors. Vopr Virusol. 2022;67(3):217–26.

    Article  CAS  PubMed  Google Scholar 

  71. Abbruzzese C, Mattarocci S, Pizzuti L, Mileo AM, Visca P, Antoniani B, et al. Determination of SGK1 mRNA in non-small cell lung cancer samples underlines high expression in squamous cell carcinomas. J Exp Clin Cancer Res. 2012;31(1):4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Szmulewitz RZ, Chung E, Al-Ahmadie H, Daniel S, Kocherginsky M, Razmaria A, et al. Serum/glucocorticoid-regulated kinase 1 expression in primary human prostate cancers. Prostate. 2012;72(2):157–64.

    Article  CAS  PubMed  Google Scholar 

  73. Sun Q, Zhang X, Liu T, Liu X, Geng J, He X, et al. Increased expression of mitotic arrest deficient-like 1 (MAD1L1) is associated with poor prognosis and insensitive to Taxol treatment in breast cancer. Breast Cancer Res Treat. 2013;140(2):323–30.

    Article  CAS  PubMed  Google Scholar 

  74. Tsukasaki K, Miller CW, Greenspun E, Eshaghian S, Kawabata H, Fujimoto T, et al. Mutations in the mitotic check point gene, MAD1L1, in human cancers. Oncogene. 2001;20(25):3301–5.

    Article  CAS  PubMed  Google Scholar 

  75. Su K, Wang Y, Gu H, Ma L, Xuan G. Overexpression of fatty acid desaturase 3 predicts poor prognosis in head and neck squamous cell carcinoma. Medicine (Baltimore). 2022;101(49): e32119.

    Article  CAS  PubMed  Google Scholar 

  76. Liao Q, Yang J, Lu Z, Jiang Q, Gong Y, Liu L, et al. FTH1 indicates poor prognosis and promotes metastasis in head and neck squamous cell carcinoma. PeerJ. 2023;11: e16493.

    Article  PubMed  PubMed Central  Google Scholar 

  77. Huang H, Qiu Y, Huang G, Zhou X, Zhou X, Luo W. Value of Ferritin Heavy Chain (FTH1) Expression in Diagnosis and Prognosis of Renal Cell Carcinoma. Med Sci Monit. 2019;25:3700–15.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Vieira FG, Bispo R, Lopes MB. Integration of Multi-Omics Data for the Classification of Glioma Types and Identification of Novel Biomarkers. Bioinform Biol Insights. 2024;18:11779322241249564.

    Article  PubMed  PubMed Central  Google Scholar 

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Funding

The study was supported by research grants from Indian Council of Medical Research (ICMR-ICRC) (No. 5/13/4/ACB/ICRC/2020/NCD-III) and ICMR AdHOC (2021–10573/GENOMIC/ADHOC-BMS), grant from CCRH (F.No. 1–46/2022–23/CCRH/Tech./DM-RCT-Telomerase/Part-I/1588), grant from CCRH F.No.17–30/2023–24/CCRH/Tech/Coll./DU Cervical Cancer Phase-II/498, grant from Institution of Eminence University of Delhi (Ref. No. /IoE/2023–24/12/FRP), to ACB is thankfully acknowledged. Study was partly supported by Senior Research Fellowship to JY (09/045(1629)/2019-EMR-I), NA (09/045(1622)/2018-EMR-I), and DJ (09/0045/(11635)/2021-EMR-1); Junior Research Fellowship to AC (09/0045(12901)/2022-EMR-1) by Council of Scientific and Industrial Research (CSIR). Senior Research Fellowship TT (764/(CSIR-UGC NET JUNE 2019) and AC [573(CSIR-UGC NET JUNE 2017)] by University Grants Commission (UGC). Senior Research Fellowship to UJ and CCK by Indian Council of Medical Research (ICMR-ICRC) (No. 5/13/4/ACB/ICRC/2020/NCD-III).

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Authors and Affiliations

  1. Department of Zoology, Molecular Oncology Laboratory, University of Delhi (North Campus), Delhi, 110007, India

    Joni Yadav, Apoorva Chaudhary, Tanya Tripathi, Divya Janjua, Udit Joshi, Nikita Aggarwal, Arun Chhokar, Chetkar Chandra Keshavam, Anna Senrung & Alok Chandra Bharti

  2. Department of Zoology, Deshbandhu College, University of Delhi, Delhi, India

    Arun Chhokar

  3. Department of Zoology, Daulat Ram College, University of Delhi, Delhi, India

    Anna Senrung

Authors
  1. Joni Yadav
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  2. Apoorva Chaudhary
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  3. Tanya Tripathi
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  4. Divya Janjua
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  5. Udit Joshi
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  7. Arun Chhokar
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  8. Chetkar Chandra Keshavam
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  9. Anna Senrung
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  10. Alok Chandra Bharti
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Contributions

Conceptualization: ACB; Data curation: JY, ACB; Formal analysis: JY, TT, AC, DJ, UJ, AC, NA, CCK, AS; Funding acquisition: ACB. Investigation: JY, TT, AC, ACB; Methodology: JY, TT, AC, DJ, UJ, AC, NA, CCK, AS; Project administration: ACB; Resources: ACB; Supervision: ACB; Validation: JY, TT, AC, ACB; Visualization: JY, TT, AC, DJ, UJ, AC, NA, CCK, AS, ACB; Writing original draft: JY, TT, ACB; Review and editing: JY, ACB.

Corresponding author

Correspondence to Alok Chandra Bharti.

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Yadav, J., Chaudhary, A., Tripathi, T. et al. Exosomal transcript cargo and functional correlation with HNSCC patients’ survival. BMC Cancer 24, 1144 (2024). https://doi.org/10.1186/s12885-024-12759-9

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Keywords

BMC Cancer

ISSN: 1471-2407

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