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GLIDR-mediated regulation of tumor malignancy and cisplatin resistance in non-small cell lung cancer via the miR-342-5p/PPARGC1A axis

BMC Cancer volume 24, Article number: 1126 (2024) Cite this article

Abstract

Background

Lung cancer, particularly non-small cell lung cancer (NSCLC), remains a significant cause of cancer-related mortality, with drug resistance posing a substantial obstacle to effective therapy. LncRNAs have emerged as pivotal regulators of NSCLC progression, suggesting potential targets for cancer diagnosis and treatment. Therefore, identifying new lncRNAs as therapeutic targets and comprehending their underlying regulatory mechanisms are crucial for treating NSCLC.

Materials and methods

RNA-sequencing data from 149 lung adenocarcinoma (LUAD) patients, including 130 responders and 19 nonresponders to primary treatment, were analyzed to identify the most effective lncRNAs. The effects and regulatory pathways of the selected lncRNAs on NSCLC and cisplatin resistance were investigated.

Results

Glioblastoma-downregulated RNA (GLIDR) was the most effective lncRNA in nonresponsive NSCLC patients undergoing primary treatment, and it was highly expressed in NSCLC patients and those with cisplatin-resistant NSCLC. Reducing GLIDR expression enhanced cisplatin sensitivity in resistant NSCLC and decreased the malignant characteristics of NSCLC. Moreover, bioinformatic analysis and luciferase assays revealed that microRNA-342-5p (miR-342-5p) directly targets GLIDR. MiR-342-5p overexpression inhibited NSCLC cell proliferation, migration, and invasion, whereas miR-342-5p inhibition promoted NSCLC malignancy, which was rescued by suppressing GLIDR. Peroxisome proliferator-activated receptor-gamma coactivator-1alpha (PPARGC1A) was identified as a downstream target of miR-342-5p. PPARGC1A inhibition increased cisplatin sensitivity in resistant NSCLC. Moreover, PPARGC1A inhibition suppresses NSCLC malignancy, whereas PPARGC1A overexpression promoted it. Furthermore, GLIDR overexpression was found to counteract the inhibitory effects of miR-342-5p on PPARGC1A, and increased PPARGC1A expression reversed the inhibition of NSCLC malignancies caused by decreased GLIDR.

Conclusions

GLIDR is a prognostic marker for cisplatin treatment in NSCLC and a therapeutic target in cisplatin-resistant NSCLC. GLIDR promotes NSCLC progression by sponging miR-342-5p to regulate PPARGC1A expression and regulates cisplatin resistance through the miR-342-5p/PPARGC1A axis, underscoring its potential as a therapeutic target in cisplatin-resistant NSCLC.

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Introduction

Lung cancer is the second most diagnosed cancer worldwide, causing more than 1.5 million deaths annually [1]. Non-small cell lung cancer (NSCLC) accounts for more than 80% of all lung cancer cases [2]. Unfortunately, the five-year overall survival of NSCLC patients is less than 15%, primarily due to late-stage diagnosis, limited therapeutic options, and the acquisition of treatment resistance [3, 4]. The intricate molecular mechanisms governing NSCLC initiation, progression, and resistance have impeded the development of effective therapies, necessitating the identification of new biomarkers for early diagnosis, prognosis, and treatment [5].

Accumulating evidence has demonstrated that long noncoding RNAs (lncRNAs) are aberrantly expressed in various cancer types and play important roles in tumorigenesis and cancer progression by affecting multiple aspects of cancer cell behavior [6, 7]. Notably, lncRNAs operate as competitive endogenous RNA (ceRNA) networks, regulating gene expression in a variety of biological processes. For example, the lncRNAs HULC and NR2F1-AS1 have been shown to modulate gene expression in pancreatic and gastric cancers, respectively, by acting as ceRNAs [8, 9]. Therapeutic targeting of lncRNAs offers promising potential for cancer treatment, including overcoming chemoresistance, as evidenced by studies in breast and liver cancers [10, 11]. Overall, identifying and understanding the functions and molecular mechanisms of lncRNAs have expanded our understanding of the regulatory mechanisms underlying gene expression, offering new opportunities for therapeutic intervention in NSCLC.

Glioblastoma downregulated RNA (GLIDR) is a recently discovered lncRNA that is upregulated in prostate cancer and glioma cells, promoting glioma progression through the miR-4677-3p/MAGI2 axis [12, 13]. In addition, GLIDR has been associated with drug resistance in glioblastoma [14]. Although GLIDR has been reported to accelerate the tumorigenesis of LUAD via the miR-1270/TCF12 axis, its role in NSCLC drug resistance has received limited attention [15]. Hence, identifying the molecular functions of GLIDR in NSCLC may hold significant promise for improving NSCLC treatment strategies.

In this study, we observed differential expression of GLIDR in NSCLC patients, a finding related to patient response to primary treatment. We aimed to comprehensively elucidate the functions and regulation of GLIDR in NSCLC and its role in cisplatin resistance. Through both in vitro and in vivo experiments, we validated the upregulation of GLIDR in various NSCLC cell lines and established the dependency of NSCLC malignancy and progression on GLIDR expression, as well as in the context of cisplatin resistance. Our results revealed a novel ceRNA mechanism by which GLIDR regulates NSCLC progression and cisplatin resistance. These results suggest that GLIDR may serve as a potential target for addressing drug resistance in NSCLC treatment.

Materials and methods

Cell culture

Human NSCLC cell line A549 (obtained in 2020), A549-DDP(obtained in 2022), and H1975 (obtained in 2022) were maintained in RPMI-1640 (Biological Industries, HAEMEK, Israel) supplemented with 10% fetal bovine serum (FBS), H460 (obtained in 2020) were grown in DMEM (Biological Industries) supplemented with 10% FBS, and Calu-3 (obtained in 2022) were cultured in MEM (including NEAA, Biological Industries)supplemented with 10% FBS. Human bronchial epithelial cell line (HBE) (obtained in 2020) was cultured in RPMI-1640 supplemented with 10% FBS. All cell lines were grown in a humidified incubator with 5% CO2 at 37°C. A549-DDP cell line was purchased from Pricella (Wuhan, China), all the other cell lines were purchased from American type culture collection (Manassas, USA). All the cells were tested every 4 months for mycoplasma contamination using the TransDetect® PCR Mycoplasma Detection Kit (TRANS, Beijing, China). The identities of all cell lines were confirmed via human short tandem repeat (STR) profiling cell authentication service.

LncRNA-seq and gene expression analysis

Total RNAs were extracted from HBE and A549 cells using the RNAprep Pure Plant Kit (Tiangen, Beijing, China), and RNA integrity was assessed using the RNA Nano 6000 Assay Kit of the Agilent Bioanalyzer 2100 system (Agilent Technologies, CA, USA). Libraries were prepared using the Hieff NGS Ultima Dual-mode mRNA Library Prep Kit for Illumina (Yeasen Biotechnology (Shanghai) Co., Ltd.). The library fragments were purified with an AMPure XP system (Beckman Coulter, Beverly, USA). Library quality was assessed on the Agilent Bioanalyzer 2100 system and sequenced on an Illumina NovaSeq platform to generate 150 bp paired-end reads. Raw data (raw reads) in fastq format were first processed through in-house Perl scripts, transformed into clean reads and mapped to the reference genome sequence using HISAT2 tools software. The StringTie Reference Annotation Based Transcript (RABT) assembly method was used to construct and identify both known and novel transcripts. DESeq2 was used to analyze the differential gene expression of each group. The resulting P values were adjusted using Benjamini and Hochberg’s approach. Genes with an adjusted P value < 0.01 and fold change ≥ 2 found by DESeq2 were assigned as differentially expressed. The raw read files can be accessed via GEO (GSE243455, Accessible link: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE243455).

Tissue microarray

The tissue microarray (HLugA180Su08, Ethics number: YB M-05-01) contained 97 NSCLC tissues and 81 paracancerous tissues for the detection of GLIDR expression by in situ hybridization (ISH), which were obtained from Outdo Biotech Co., Ltd. (Shanghai, China). All these patients underwent surgery between September 2004 and April 2009, and the patients were followed for survival until April 2011. This study was conducted with ethical standards in accordance with the World Medical Association Declaration of Helsinki. All patients gave informed consent to participate in this study, and the protocol of this study was approved by the Ethics Committee of Inner Mongolia University. Details of the clinical parameters of these patients are summarized in Supplemental Table 3. The tissue microarray [HlugA060PG02, Ethics number: YB M-05-02 (Shanghai Outdo Biotech Co., Ltd. Shanghai, China)] contained 29 NSCLC tissue samples and 30 human paracancerous tissue samples for immunohistochemical analysis to detect PPARGC1A. Details of each sample are listed in Supplemental Table 4. The stained slides were digitally captured and quantified independently using a stain intensity score (SIS) of 0, 0.5, 1, 2, and 3 to describe the intensities of PPARGC1A in each sample. The staining positive rate score (SPRS) ranged from 0 to 100%. All the microarray data provided in the current study is MIAME compliant [16].

Tissue cDNA samples

The cDNA of NSCLC tissues was provided by a commercial cDNA microarray, which was purchased from Shanghai Xinchao Biotech Co., Ltd. The lot number of the microarray was HLugA030PG01, which contained 15 cancer tissue samples and 15 normal adjacent tissue samples for expression analysis of GLIDR and PPARGC1A.The study involving human participants was approved by the Ethics Committee of Shanghai Outdo Biotech Company with NO. YB M-05-02.

Mouse model

BALB/c-nu mice (4–6 weeks old, 20 males, 20 females) were purchased from Charles River Laboratories (Beijing, China) and randomly grouped for experiments. To investigate the impact of GLIDR overexpression in NSCLC in vivo, 1 million cells overexpressed GLIDR were suspended in 100 µL of PBS and subcutaneously injected into the flank of BALB/c-nu mice to grow four weeks (n = 6 for each group, three males and three females for each group). To evaluate the effects of GLIDR inhibition in NSCLC in vivo, BALB/c-nu mice (n = 4 for each group, two males and two females for each group) were received 1 million cells suspended in 100 µL of PBS. Subsequently, 2 weeks later, synthetic anti-sense oligonucleotides or non-sense oligonucleotides were injected directly into the tumors of the mice twice a week for three weeks (5 nmol for each tumor). For the assessment of miR-342-5p function in NSCLC in vivo, 1 million cells overexpressing miR-342-5p were suspended in 100 µL of PBS and subcutaneously injected into the flank of BALB/c-nu mice to grow four weeks (n = 10 for each group, five males and five females for each group).

All tumor sizes were measured twice per week using digital calipers. All the mice were humanely euthanized using a CO2 system, followed by cervical dislocation to confirm death, and tumor specimens were collected and weighed.

qRT-PCR

Total RNA was isolated with TRIzol (Invitrogen, Massachusetts, USA) and subsequently purified using a total RNA kit (Promega, Wisconsin, USA). cDNA was synthesized using the GoScriptTM Reverse Transcription System (Promega) following the manufacturer’s protocol. qRT-PCR was performed using GoTaq® Real-Time PCR Systems (Promega) in a total volume of 20 µL on an Applied Biosystems 7500 machine. The expression levels were calculated using the 2-△△Ct method, and GAPDH was used as an endogenous control.

For miRNA detection, miRNAs were extracted using the MiRNeasy Mini Kit (Takara, Kusatsu, Japan) and synthesized using the miRNA First-strand Synthesis Kit (TaKaRa) following the manufacturer’s protocol. The expression levels were calculated using the 2−△△Ct method, and U6 was used as an endogenous control. All primer sequences are listed in Supplemental Table 1.

Subcellular fractionation assay

The subcellular fractionation assay was performed with a PARIS Kit (Invitrogen) according to the manufacturer’s protocol in LUAD cells. Subsequently, the GLIDR level in cytoplasmic and nuclear fractions was tested through qRT-PCR. GAPDH and U6 served as cytoplasmic and nuclear controls, respectively.

Western blotting

As described previously [17], cells were lysed with RIPA lysis buffer containing Roche’s Complete Proteinase Tablets (Sigma, Missouri, USA). Thirty micrograms of total protein were quantified and separated by 10% SDS-polyacrylamide gel electrophoresis and then transferred to polyvinylidene difluoride membranes. All membranes were blocked with 5% BSA in TBST and incubated overnight at 4°C with primary antibodies against PPARGC1A. Tubulin and GAPDH were used as loading controls. Blots were developed with Clarity Western ECL Substrate (Bio-Rad, California, USA). All antibodies are listed in Supplemental Table 2. The source files of Western blots are provided in Supplemental Data.

Construction of GLIDR and PPARGC1A overexpressing or knockdown cell lines

The primers of lncRNA GLIDR and PPARHC1A coding sequence (CDS) were designed in Primer Premier 5. The PCR products were purified and inserted into digested PMD-19T vector. Then the lncRNA GLIDR or PPARGC1A CDS were subcloned from PMD-19T into the pIRES2-ZsGreen1 overexpression vector.

The siRNA of lncRNA GLIDR and PPARHC1A mRNAs were synthesized by GenePharma (Shanghai, China) and were transiently transfected into the A549 and H460 via the LipofectamineTM2000 kit (Invitrogen).

All the plasmids were confirmed by sequencing. Western blots or qRT-PCR were performed to confirm the gene overexpression and inhibition. All primer sequences are listed in Supplemental Table 1.

Cell proliferation analysis

3,000 HBE, A549, or H460 cells were seeded in 96-well plates. MTS (Promega) were introduced into the culture media at 24 h, 48 h, 72 h, and 96 h post-seeding (Promega), then allowed to incubate for 4 h at 37°C before reading OD490 nm absorption on a multi-plate reader (Thermo Fisher, Massachusetts, USA). Cell proliferation was calculated via the normalization of cell viability values from MTS to the starting point (24 h).

For determination of IC50, A549 or A549-DDP cells were transiently transfected with GLIDR overexpression plasmids, PPARGC1A overexpression plasmids, siGLIDR or siPPARGC1A for 24 h. Subsequently, 4,000 transiently transfected A549 or A549-DDP cells were seeded in 96-well plates overnight. A four-fold serial dilution of the cisplatin ranging from 1000 µM to 0.94 µM was added to the 96-well plates for 48 h. MTS (Promega) was then performed into the culture media, followed by a 2 h incubation at 37°C before measuring the OD490 nm absorption. All plots were generated via Graphpad Prism v8.0.

Cell migration analysis

Scratch test was used to analyze the NSCLC migration. A549 or H460 cells were seeded into 6-well plates at 100% cell confluence, a scratch wound in the monolayer was created using a sterile 10 µl pipette tip. Then the media were refreshed with the serum-free media. Images were captured at 0 h, 24 h and 48 h using inverted microscope (Nikon, Tokyo, Japan).

Transwell cell culture plates were also used to analyze the cell migration. The upper chambers of the inserts were seeded with 1 × 104 A549 or H460 cells with 200 µL serum-free media and the low chamber was filled with 600 µL media containing 20% FBS as a chemoattractant. After 15–20 h of incubation in humidified incubator with 5% CO2, the non-migrated cells in the upper chamber were swabbed off and the plates were fixed, stained, and then observed under an inverted microscope. Cells in five microscopic field views of representative areas in each of the groups were counted and averaged for plots.

Cell invasion analysis

The inner chambers of the transwell plates were coated with 100 µL Matrigel gel (CORINING, New York, USA) and incubated at 37˚C for 6–10 h to produce an artificial basement membrane. The rest of the procedure was as described in migration assay above. Both the migration assay and the invasion assay were performed concurrently, and the former, aside being an assay on its own, was additionally used as control for the later.

Clonogenic assay of cell

500 cells were seeded in 6-well plates and incubated for 7 days in humidified incubator with 5% CO2. Then the plates were fixed with 95% ethanol, stained by 1% crystal violet (Sigma), taken photos and counted the colonies.

Cell cycle analysis by flow cytometry

The Cell Cycle Analysis Kit (Bioss, Massachusetts, USA) was used to detect the cell cycle following up the manufacture’s instruction. In brief, cells were washed twice with PBS, pelleted, and fixed with cold 95% ethyl alcohol overnight at 4°C. Then the fixed cells were washed with PBS and incubated with Rnase A and PI at 37 °C for 30 min in the dark. Cell cycle distribution was determined using flow cytometry and the analyzed by the ModFit.

Cell apoptosis assay

Cell apoptosis assay was perform using Annexin V-FITC/PI Cell Apoptosis Detection Kit (TRANS) according to the manufacture’s instruction. Briefly, the cells in each group were washed with PBS and resuspended with Annexin V Binding Buffer, then incubated with Annexin V-FITC and PI at room temperature for 15 min in the dark. The cells without extra incubation were used as the negative group. Finally, the cell samples were analyzed using flow cytometry and analyzed by FlowJo.

Luciferase activity analysis

The partial oligo sequences of lncRNA or mRNA 3’UTR which contained the wild or mutant binding site of miRNAs were synthesized in complementary DNA oligos. Annealed oligos were inserted into digested pmirGLO vector. All plasmids were confirmed by sequencing. The plasmids were cotransfected with miR-NC and miRNA mimic into H460 cells. Luciferase activity analysis was conducted on the Dual-Luciferase Reporter Assay System (Promega) according to the manufacturer’s protocol.

RNA immunoprecipitation (RIP) assay

Following the manufacturer’s instructions, RIP experiments were conducted using RIP kit (Merck, Shanghai, China) to lyse A549 and H460 cells and prepare immunoprecipitation magnetic beads to construct immunoprecipitation of RNA binding protein-RNA complex with anti-Argonaute2 (AGO2, Abcam, ab57113) or a negative control IgG antibody (Millipore). According to the instructions, immunoprecipitation RNA was purified, and the recovered RNA was quantitatively analyzed by RT-qPCR.

Immunohistochemical assay

Tumor samples from BALB/c-nu mice were fixed in 10% formalin and embedded in paraffin, and immunohistochemistry was performed using an HP IHC Detection Kit (Absin, Beijing, China) following the manufacturer’s protocol. In brief, tumor tissue slices were deparaffinized with xylene and rehydrated in gradient alcohol (anhydrous, 95%, and 75%). After washing with PBS, the endogenous peroxidase of the tissue slices was blocked by incubation with H2O2 and blocking solution, followed by incubation with primary antibodies against PPARGC1A for 20 min at room temperature and incubation with a Primary Antibody Amplifier. Then, the sections were incubated with secondary antibody conjugated with horseradish peroxidase (HRP) for 10 min. After the DAB coloring reaction, the images were captured by confocal microscopy (Nikon).

Survival analysis

Kaplan-Meier patient survival curves of GLIDR and ARRDC1-AS1 were generated using GDC TCGA Lung Adenocarcinoma (LUAD) data from UCSC Xena (http://xena.ucsc.edu/) [18]. Curves of miR-342-5p were generated using TCGA LUAD data from OncoLnc (http://www.oncolnc.org/) [19] and analyzed by GraphPad. PPARGC1A patient (stages 2–4) survival curves using TCGA LUAD data from Kaplan-Meier Plotter (https://kmplot.com/analysis/) [20]. If not specific, all the gene expression was above or below the median expression level.

Heatmap analysis

The patient data were downloaded from the TCGA Firehose Legacy database in cBioPortal (https://www.cbioportal.org/) [21, 22]. Patient samples were separated into 3 groups, complete remission (CR, response group, n = 130), progressive disease (PD, no response group, n = 7) and stable disease (SD, no response group, n = 12), in accordance with the primary therapy outcome success type. A heatmap was prepared from the top 30 differentially expressed lncRNAs and created by Heatmapper (http://www.heatmapper.ca/) [23] with the “spearman” clustering distance method and “average linkage” clustering methods.

Statistical analysis

For experiments without repeated measures, data were analyzed via two-way ANOVA when there were two independent variables; otherwise, a two-tailed Student’s t test was used. T tests and ANOVAs were performed via GraphPad, and all other analyses were performed via R v3.2.2. For the tissue microarray, we used the Mann-Whitney test. For all analyses, p < 0.05, two-sided was considered significant. All the in vitro experiments were independently repeated at least three times. The data from our work are expressed as the mean ± standard deviation (SD) of three independent experiments.

Results

LncRNA GLIDR is a therapeutic target in cisplatin resistance

The RNA-sequencing data from 149 patients with LUAD who received primary treatment after diagnosis were analyzed. Among them, the clinical symptoms were completely resolved in 130 patients who responded to treatment (CR), while 19 patients had no response to primary treatment; the clinical symptoms of 12 patients were stable (SD), and those of 7 patients were progressive (PD). The top thirty lncRNAs were more highly expressed (≥ 1.2-fold) in both the PD and SD groups (nonresponsive patient group) than in the CR group (Fig. 1A). We performed heatmap analysis to further evaluate those lncRNAs in LUAD patients and found that GLIDR was more relevant in patients with a treatment response (Fig. 1B). GLIDR expression was higher in non-responding LUAD patients than in responding patients (Fig. 1C). However, the P value was not statistically significant, which may be attributed to the two groups having vastly different sample sizes. These results suggest that GLIDR is crucial in drug resistance in clinical therapy. Cisplatin is a commonly used chemotherapeutic treatment for NSCLC, and many patients are treated early on, however drug resistance can commonly develop [24]. Accordingly, we investigated whether GLIDR regulates cisplatin resistance in NSCLC. We detected GLIDR expression in the cisplatin-resistant A549-DDP cell line. GLIDR was more abundant in the cisplatin-resistant cell line than in the cisplatin-sensitive cell line (Fig. 1D). In addition, decreased GLIDR expression increased the sensitivity of A549-DDP cells to cisplatin (Fig. 1E). GLIDR has been reported to regulate LUAD through the control of TCF12 expression [15], but TCF12 is not significantly different between cisplatin-resistant and cisplatin-sensitive cell lines (Figure S1), suggesting that GLIDR regulates cisplatin resistance in LUAD through a different mechanism.

We then investigated GLIDR expression in patient tissues using qPCR, which included 15 NSCLC patient tissue samples and 15 normal adjacent tissue samples, and observed significantly increased GLIDR expression in NSCLC (Fig. 1F). In situ hybridization was also performed to examine the expression of GLIDR in patient samples, GLIDR expression was higher in 97 NSCLC patient samples than in 81 normal tissue samples (Fig. 1G). Patients with elevated expression of GLIDR had shorter overall survival times (Fig. 1H). Furthermore, multivariate analysis of 97 NSCLC patient samples revealed that GLIDR was associated with NSCLC patient stage (Supplemental Table 5). We further used RNA sequencing to examine the expression of GLIDR in normal lung epithelial HBE cells and NSCLC A549 cells. GLIDR was strongly expressed in A549 cells compared with HBE cells (Fig. 1I). Next, the GLIDR level in A549 cells, as well as other types of NSCLC cell lines, was confirmed by qPCR and compared with that in HBE cells (Fig. 1J). These results imply that GLIDR is highly expressed in NSCLC patient samples and cell lines, suggesting that GLIDR has an important role in NSCLC tumor progression.

Fig. 1
figure 1

LncRNA GLIDR is highly expressed in cisplatin resistant NSCLC. A. RNA-sequencing analysis workflow. B. Heatmap of the top 30 lncRNAs differentially expressed between primary treatment-responsive and nonresponsive LUAD patients. Expression values for each lncRNA (row) were normalized across all samples (columns) by Z score. Distinct gene clusters identified by the Spearman rank correlation method. Blue: low expression, yellow: high expression. CR: complete remission, PD: progressive disease, SD: stable disease. C. The expression of GLIDR between primary treatment-responsive (n = 130) and nonresponsive (n = 19) LUAD patients. D. GLIDR expression comparison between cisplatin-resistant and cisplatin-sensitive NSCLC cell lines. E. IC50 of the cisplatin-resistant cell line upon reducing GLIDR expression. F. GLIDR expression in NSCLC tissues detected by qPCR in NSCLC tissues (n = 15) and normal tissues (n = 15). G. Representative images indicating the expression of GLIDR in 97 NSCLC tissues and 81 normal tissues detected with in situ hybridization. Right panel, ISH staining scores were calculated in NSCLC and normal tissues. H. Kaplan-Meier survival curve analysis for NSCLC patients in G. I. Volcano composition display of variance analysis in the normal lung epithelial cell line HBE and NSCLC cell line A549. |Log2FC| > 1, p < 0.01. J. GLIDR expression in different NSCLC cell lines compared to HBE cells. *p < 0.05, *** p < 0.001

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GLIDR is required for NSCLC malignancy

To fully elucidate the function of GLIDR in NSCLC, we knocked down GLIDR expression in A549 and H460 cells using siRNA and observed significantly lower cell proliferation than in the negative control group (Fig. 2A). Cell cycle analysis revealed that GLIDR knockdown led to a greater percentage of H460 cells in the G0-G1 phase and a lower percentage of H460 cells in the S phase than in the control, suggesting that inhibition of GLIDR resulted in cell cycle arrest in H460 cells (Fig. 2B). Flow cytometry analysis of apoptosis with FITC/PI double staining showed that H460 cells transfected with si-GLIDR had a greater apoptotic rate than cells transfected with si-NC (Fig. 2C). Moreover, the expression of the proapoptotic gene Bax increased, but the expression of the apoptotic inhibitor gene Bcl-2 decreased when GLIDR was inhibited in H460 cells (Figure S2). Wound healing and Transwell assays indicated that the migration and invasion abilities of A549 and H460 cells were markedly suppressed by GLIDR downregulation (Fig. 2D, E). Similarly, the number of colonies formed was significantly reduced following GLIDR inhibition in A549 and H460 cells (Fig. 2F). Furthermore, to evaluate GLIDR functions in tumor proliferation in vivo, H460 cells overexpressing GLIDR were generated and evaluated in BALB/c-nu mice. The xenograft tumors formed by H460 cells with high expression of GLIDR were heavier than those generated by control H460 cells (Fig. 2G). Conversely, inhibition of GLIDR with antisense oligonucleotides, which is considered a promising approach for clinical cancer treatment by negatively regulating lncRNAs [25], slowed tumor development (Fig. 2H, I). These data suggest that GLIDR is required for and promotes the progression of NSCLC cells both in vitro and in vivo.

Fig. 2
figure 2

Reducing lncRNA GLIDR inhibits the malignant phenotype of NSCLC. A. Proliferation of A549 and H460 cells after inhibition of GLIDR expression. B. Cell cycle assay of H460 cells. C. Apoptosis assay of H460 cells. D-E. Migration and invasion assays of A549 and H460 cells. F. Clonogenic assay on A549 and H460 cells. G. Total of 1 million H460 cells overexpressing GLIDR were injected into mice to monitor tumor progression. Representative image (upper panel) and quantification (lower right panel) of individual tumors harvested at the end time point. The expression of GLIDR was indicated by qPCR in H460 cells (lower left panel). H. Monitoring of tumor progression with low GLIDR expression. Representative image (left panel) and quantification (right panel) of individual tumors harvested at the end time point. The suppression of GLIDR in H460 cells using antisense oligonucleotides (ASOs) was confirmed by qPCR (middle panel). A total of 1 million H460 cells were injected into mice for 10 days, ASOs for GLIDR were injected into the tumors in mice, and tumors were collected after 3 weeks. I. Representative IHC images of KI67 in subcutaneous H460 tumors treated with antisense oligonucleotides for GLIDR. * p < 0.05, ** p < 0.01, *** p < 0.001

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MiR-342-5p targets GLIDR

We then investigated the mechanism by which GLIDR regulates NSCLC progression. The subcellular localization of lncRNAs is highly informative of their biological function. LncRNAs act as transcription regulators in the nucleus but regulate mRNA processing and modulate posttranscriptional mRNAs in the cytoplasm [26]. Therefore, we detected the cellular distribution of GLIDR in A549 and H460 cells using subcellular fractionation. Like GAPDH (a positive control for cytoplasmic localization), GLIDR mRNA was more highly expressed in the cytoplasm than in the nucleus, whereas U6 mRNA (a positive control for nuclear localization) was more highly expressed in the nucleus (Fig. 3A). This finding indicates a regulatory role for GLIDR in the cytoplasm, such as posttranscriptional regulation in NSCLC. ceRNA-mediated posttranscriptional regulatory mechanisms are common in cancers, and many lncRNAs act as ceRNAs and target miRNAs to regulate the expression of mRNAs that affect the occurrence and development of cancer [27]. To identify GLIDR-mediated ceRNA networks, we first screened GLIDR-binding miRNAs using DIANA-LncBase v2, and three candidate miRNAs (miR-342-5p, miR-524-5p, and miR-6886-5p) were highly predicted to bind to the GLIDR. To further determine whether GLIDR is a target gene of these miRNAs, we inserted the GLIDR-binding sequence for each miRNA, either wild-type (GLIDR-WT) or mutant (GLIDR-MT), into the luciferase reporter vector to perform a luciferase reporter assay in H460 cells (Fig. 3B). The luciferase activity of GLIDR-WT was markedly decreased following miR-342-5p overexpression using oligo mimics and was not affected by miR-6886-3p or miR-524-5p overexpression (Fig. 3C, S3A, B). Next, the negative correlation between GLIDR and miR-342-5p was predicted in LUAD tumor samples in TCGA (Fig. 3D). Furthermore, reducing GLIDR with siRNA increased the expression of miR-342-5p, whereas decreasing miR-342-5p expression with an inhibitor promoted GLIDR expression (Fig. 3E, F). These data suggest that GLIDR is targeted by miR-342-5p and that the expression of GLIDR and miR-342-5p is negatively correlated.

Fig. 3
figure 3

GLIDR is a target gene of miR-342-5p. A. GLIDR expression in the nucleus and cytoplasm, GAPDH as a positive control in the cytoplasm and U6 as a positive control in the nucleus. B. Sequence alignments of miR-342-5p and GLIDR were predicted by the DIANA-LncBase v2 database. C. Dual luciferase reporter assay of GLIDR and miR-342-5p. D. The Pearson’s correlation between GLIDR and miR-345-5p in LUAD tumor samples in TCGA, n = 70. E. The Expression of GLIDR and miR-342-5p in A549 and H460 cells after decreasing GLIDR expression with siRNA. F. The expression of miR-342-5p and GLIDR in A549 and H460 cells with lower miR-342-5p expression. * p < 0.05, ** p < 0.01, *** p < 0.001, and n.s. indicates nonsignificant

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MiR-342-5p inhibits NSCLC malignancies

We found that the level of miR-342-5p was lower in NSCLC cells than in HBE cells (Fig. 4A). Additionally, we predicted the function of miR-342-5p in the mirPath database, and the result showed that the mitotic cell cycle term was significantly enriched (Figure S4). To determine the role of miR-342-5p in NSCLC, we increased the expression of miR-342-5p in A549, H460 and normal HBE cells using oligo mimics (Fig. 4B, S5A). The results showed that miR-342-5p specifically inhibited the proliferation of NSCLC cell lines but not the normal lung epithelial cell line (Fig. 4C, S5B). The opposite results were observed in NSCLC cells after miR-342-5p inhibition (Figure S5C). Moreover, the migration and invasion of NSCLC cells were significantly inhibited after increasing miR-342-5p expression in A549 and H460 cells and were promoted when miR-342-5p expression was decreased (Fig. 4D, E, S5D, E). Colony formation assays showed that the number of colonies formed was visibly reduced following the upregulation of miR-342-5p in A549 and H460 cells (Fig. 4F). Moreover, to assess the function of miR-342-5p in vivo, we pre-overexpressed miR-342-5p in H460 cells, injected the cells into mice, evaluated the xenograft tumors four weeks after injection, and measured the tumor volumes. The weights of the xenograft tumors generated from H460 cells with high miR-342-5p expression were lower than those of the tumors formed from control H460 cells (Fig. 4G). To examine the relationship between miR-342-5p and survival in patients with NSCLC, we investigated TCGA LUAD patient data (590 patients) from OncoLnc. Patients with high miR-342-5p expression had a favorable prognosis compared with those with low expression (Fig. 4H). These data suggest that miR-342-5p inhibits NSCLC progression in vitro and in vivo.

Next, we assessed the correlation between miR-342-5p and GLIDR expression in the context of the regulation of the NSCLC phenotype. Rescue experiments were performed, and the results revealed that a decrease in GLIDR reversed the promoting effect of the miR-342-5p inhibitor on the proliferation, migration, and invasion of NSCLC cells (Fig. 4I, J). These findings indicate that miR-342-5p acts as a tumor suppressor in NSCLC and that its effect is mediated by GLIDR regulation.

Fig. 4
figure 4

An increase in miR-342-5p inhibits the malignant phenotype of NSCLC. (A) Expression of miR-342-5p in HBE, A549 and H460 cells. (B) Expression of miR-342-5p in A549 and H460 cells transfected with miR-342-5p mimics. (C) Proliferation of A549 and H460 cells when the expression of miR-342-5p was upregulated. D-E. Migration and invasion assays of A549 and H460 cells. F. Clonogenic assay on A549 and H460 cells. G. Representative image of individual tumors harvested at the end time point (left panel). One million H460 cells infected with miR-NC or miR-342-5p mimics were injected into mice to monitor tumor progression. Quantitation of tumor weight for each tumor (right panel), n = 6 for the miR-342-5p mimics group, n = 8 for the miR-NC group. H. Survival curve of patients with LUAD with miR-342-5p expression. I. Proliferation of A549 and H460 cells when the expression of miR-342-5p was inhibited or when GLIDR was overexpressed. Expression of miR-342-5p in each group was measured by qPCR (upper panel), proliferation was shown in the lower panel. J. Migration and invasion assays of A549 and H460 cells. * p < 0.05, ** p < 0.01, *** p < 0.001

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MiR-342-5p targets PPARGC1A in NSCLC

To further investigate the regulatory mechanisms of GLIDR-mediated ceRNA networks in NSCLC, we explored the downstream target genes of miR-342-5p. The target genes of miR-342-5p were predicted using TargetScan7, TargetMiner, miRDB, miRWALK, and TargetRank, and the overlapping candidate genes were further analyzed. We conducted a further overlap analysis of the candidate genes with the upregulated genes identified in the cisplatin-resistant cell lines from the GSE144520 dataset to determine the regulatory mechanisms of GLIDR in cisplatin resistance. Seven potential coding genes were enriched with a high probability, but only PPARGC1A was reported to be related to cisplatin resistance (Fig. 5A). We also confirmed that PPARGC1A was increased in cisplatin-resistant cells (Fig. 5B). In TCGA LUAD tumor samples, PPARGC1A and miR-342-5p expression was negatively correlated (Fig. 5C). In addition, miR-342-5p upregulation significantly reduced PPARGC1A mRNA levels in NSCLC cells (Fig. 5D), suggesting that PPARGC1A may be a target gene of miR-342-5p. Then, we performed luciferase reporter assays using binding sequences of PPARGC1A to miR-342-5p (WT or MT). Compared with that in the control group, the expression of PPARGC1A-WT, rather than PPARGC1A-MT, was significantly decreased following the increase in miR-342-5p using mimics, indicating that PPARGC1A is a direct target gene of miR-342-5p (Fig. 5E, F). Moreover, RIP assays revealed that GLIDR, miR-342-5p and PPARGC1A were significantly enriched in the RNA-induced silencing complex (RISC) relative to control IgG in both the A549 and H460 cell lines, indicating that GLIDR, miR-342-5p and PPARGC1A form a ceRNA regulatory network (Fig. 5G). In addition, PPARGC1A was significantly decreased in xenograft tumors with high miR-342-5p expression (Fig. 5H). These results demonstrate that PPARGC1A is a target gene of miR-342-5p and is regulated by GLIDR, suggesting that GLIDR regulates cisplatin resistance through the miR-342-5p/PPARGC1A axis.

In our sequencing data, PPARGC1A was also more highly expressed in the NSCLC A549 cell line than in the normal lung epithelial HBE cell line (Figure S6A). This finding was further confirmed at both the RNA and protein levels in NSCLC A549 and H460 cells (Figure S6B). High expression of PPARGC1A was observed in NSCLC cDNA samples obtained from NSCLC patients and normal patients (Figure S6C). Additionally, the IHC results showed that PPARGC1A expression was higher in clinical LUAD patient samples than in paracancerous tissue samples (Figure S6D). Higher PPARGC1A expression indicated a worse prognosis for LUAD patients at late stages (2–4) but was favorable for LUAD patients at an early stage (Figure S6E, F). These data suggest that PPARGC1A has an important function in NSCLC patients and could be a diagnostic marker.

To clarify the role of PPARGC1A in NSCLC, we analyzed the NSCLC phenotype following the regulation of PPARGC1A expression. The proliferation, migration, invasion, and colony formation of NSCLC cells were suppressed when PPARGC1A expression was inhibited by siRNA (Figure S6G-J). In contrast, increased expression of PPARGC1A promoted NSCLC proliferation, and Transwell assays indicated that the migration and invasion abilities of A549 and H460 cells were markedly increased by PPARGC1A upregulation but were reversed by miR-342-5p overexpression (Fig. 5I, G). Taken together, these results indicate that PPARGC1A facilitates the malignant phenotype of NSCLC and is regulated by miR-342-5p.

Fig. 5
figure 5

PPARGC1A is the target gene of miR-342-5p. A. Overlapping of miR-342-5p predicted target genes from different predicted tools and upregulated DE genes in cisplatin-resistant cell line RNA sequencing data. B. PPARGC1A expression in cisplatin-resistant and cisplatin-sensitive NSCLC cell lines. C. The Pearson’s correlation between PPARGC1A and miR-345-5p in LUAD tumor samples in TCGA, n = 95. D. Upregulation of miR-342-5p inhibited the expression of PPARGC1A in both A549 and H460 cells. E. Sequence alignments of miR-342-5p and PPARGC1A predicted by the TargetScanVert database. F. Dual luciferase reporter assay of PPARGC1A and miR-342-5p. G. RIP assay of GLIDR, miR-342-5p and PPARGC1A with Ago2 antibody or control IgG antibody in A549 (left panel) and H460 (right panel) cell lines. Relative gene enrichment detected by real-time RT-PCR. H. Representative IHC images of PPARGC1A in subcutaneous H460 tumors overexpressing miR-342-5p. I. Proliferation of A549 and H460 cells. J. Migration assay of A549 and H460 cells. ** p < 0.01, *** p < 0.001

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GLIDR affects NSCLC malignancies and cisplatin resistance by sponging mir-342-5p to regulate PPARGC1A expression

Finally, the correlation between GLIDR and PPARGC1A was predicted in NSCLC cell lines, and a positive correlation was revealed (Fig. 6A). We next conducted a rescue experiment to examine the interaction ceRNA network between PPARGC1A, GLIDR, and miR-342-5p in NSCLC cells. As shown in Fig. 6B, silencing GLIDR expression reduced the expression of PPARGC1A in NSCLC cells. GLIDR overexpression restored PPARGC1A expression inhibited by miR-342-5p upregulation in H460 cells (Fig. 6C). Moreover, the upregulation of PPARGC1A expression reversed the inhibitory effect of GLIDR on the proliferation, migration, and colony formation of NSCLC cells (Fig. 6D-G). These results suggest that GLIDR competes with PPARGC1A for binding to miR-342-5p to regulate NSCLC malignancy. Next, we detected whether GLIDR regulates cisplatin resistance by regulating PPARGC1A expression. The results revealed that downregulation of PPARGC1A expression led to increased cisplatin sensitivity in cisplatin-resistant cells, similar to the effect observed with downregulation of GLIDR in A549-DDP cells. However, despite the ability of GLIDR to increase PPARGC1A expression, this effect was insufficient to reestablish cisplatin resistance, while upregulating PPARGC1A expression in A549 cells did not confer resistance to cisplatin (Fig. 6H, I). Overall, we concluded that reducing GLIDR expression decreased cisplatin resistance by releasing miR-342-5p to suppress the expression of PPARGC1A in drug-resistant NSCLC. However, our findings indicate that increasing PPARGC1A expression does not confer drug resistance in cells, which implies that while PPARGC1A is a regulator of drug resistance, it is not an inducer of drug resistance in NSCLC.

Fig. 6
figure 6

Increased PPARGC1A rescues the function of GLIDR in the malignant phenotype of NSCLC. A. The Pearson’s correlation between GLIDR and PPARGC1A in NSCLC cell lines, n = 93. B. Expression of PPARGC1An in A549 and H460 cells after interfering with the expression of GLIDR or NC. C. PPARGC1A expression comparison at the protein level. The size of the PPARGC1A protein is 105 kDa, and the size of GAPDH is 36 kDa. D. Proliferation of H460 cells when GLIDR expression was downregulated with or without PPARGC1A. E-F. Migration assay of H460 cells. G. Clonogenic assay on H460 cells. H-I. IC50 for cisplatin-resistant cell line A549-DDP (G) and cisplatin-sensitive cell line A549 (H) after modulating the expression of PPARGC1A and GLIDR. ** p < 0.01

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Discussion

Although NSCLC treatment has advanced dramatically over the past decade and the survival rate of patients has improved, many patients die as a result of remaining resistant to clinical drug treatment. Elucidating the underlying mechanisms in resistant patients will encourage the development of NSCLC therapy [28]. Growing evidence reveals that lncRNAs expressed in a tissue-specific manner are associated with specific cancer types, are involved in regulating genes affecting the cell cycle, survival, and metastasis, and can be a therapeutic target for NSCLC treatment [29]. In this study, we observed that LUAD patients who exhibited poor responses to primary treatment in clinical trials had elevated levels of GLIDR expression. Furthermore, GLIDR was found to be upregulated in cisplatin-resistant NSCLC, and decreasing GLIDR expression rendered NSCLC cells more sensitive to cisplatin treatment. These findings suggest that GLIDR is a potential biomarker for predicting cisplatin treatment outcomes in clinical settings and that GLIDR represents a promising therapeutic target for overcoming cisplatin resistance.

Previous studies have demonstrated that GLIDR functions as an oncogene, driving glioma progression via the miR-4677-3p/MAGI2 axis and promoting a malignant phenotype in prostate cancer by targeting miR-128-3p [12, 13]. Consistent with these findings, our results suggest that GLIDR also functions as an oncogene to promote the aggressiveness of NSCLC via the miR-342-5p/PPARGC1A axis. In this study, we demonstrated that GLIDR was substantially expressed in NSCLC patients and cell lines, and that suppressing GLIDR expression significantly inhibited the malignant features of NSCLC. These findings suggest that targeting GLIDR could effectively reduce tumor proliferation, invasion, and metastasis, improving patient prognosis. Antisense oligonucleotides (ASOs) are recognized as novel and promising therapeutic agents with the potential to treat a wide range of diseases, and the FDA has approved several ASOs for clinical therapy. Such as, mipomersen for familial hypercholesterolemia treatment, eteplirsen for Duchenne muscular dystrophy treatment [30]. In this study, direct injection of GLIDR-targeting ASOs into NSCLC tumors led to notable inhibition of tumor growth, highlighting the potential of ASO therapy targeting GLIDR in treating NSCLC. Nevertheless, the targeted delivery of ASOs continues to present challenges [30], and exploring alternative delivery methods such as chemical modification or exosomal delivery may offer solutions to successfully implement clinically targeted GLIDR therapy for NSCLC.

GLIDR reportedly accelerate LUAD tumorigenesis via the miR-1270/TCF12 axis [15]. Therefore, we hypothesized that GLIDR may regulate cisplatin resistance by modulating TCF12 expression. However, unlike GLIDR, TCF12 expression does not differ between cisplatin-resistant and cisplatin-sensitive cell lines, which suggests that the regulation of GLIDR in cisplatin resistance is not mediated through the GLIDR/miR-1270/TCF12 axis. In this study, we found that GLIDR can be targeted by miR-342-5p. While miR-342-5p was increased in the serum exosomes of NSCLC patients with brain metastases and EGFR mutations [31, 32], our work revealed that miR-342-5p levels were reduced in NSCLC. Previous studies have shown that miR-342-5p inhibits HER2 signaling in breast cancer and interacts with the lncRNA circALG1 to regulate colorectal cancer progression [33, 34]. Consistent with these findings, our data revealed that miR-342-5p acts as a tumor suppressor in NSCLC and is favorable for LUAD patients.

In recent years, the ceRNA hypothesis has been increasingly reported in cancer and has been shown to play an important role in posttranscriptional regulation and tumorigenesis. Numerous lncRNAs have been reported to act as ceRNAs by sponging miRNAs to regulate gene expression, exerting oncogenic functions in lung cancer cells [35,36,37,38,39,40,41,42]. This study identified PPARGC1A, a master regulator of mitochondrial biogenesis and function, as a target gene in the GLIDR ceRNA network [43]. PPARGC1A has been reported to promote the progression of different malignant tumors through various mechanisms [44,45,46,47,48]. Our findings support this concept, as PPARGC1A expression is elevated in NSCLC clinical samples and cell lines. Furthermore, we showed that the overexpression of PPARGC1A aided in the progression of NSCLC, whereas the suppressing of PPARGC1A resulted in a decrease in the malignancy of NSCLC. Notably, survival analysis revealed that PPARGC1A was unfavorable for LUAD patients at stages 2–3, but stage 1 patients with high expression of PPARGC1A had longer survival, suggesting that PPARGC1A could be a prognostic marker in NSCLC.

PPARGC1A has been reported to be therapeutically exploitable for cisplatin resistance [49]. Consistently, our study revealed that PPARGC1A is increased in cisplatin-resistant NSCLC and that downregulation of PPARGC1A expression, similar to reducing GLIDR, also increased the sensitivity of cisplatin-resistant NSCLC to cisplatin. Conversely, increasing PPARGC1A expression alongside GLIDR upregulation did not reverse sensitivity to cisplatin in cisplatin-resistant NSCLC. The cause may be primarily due to PPARGC1A being a regulator of cisplatin resistance in NSCLC rather than an inducer of cisplatin resistance in NSCLC.

It is critical to emphasize the need to increase the sample size and cell types to improve the reliability and validity of the findings. Additionally, the current study verified the key role of GLIDR in cisplatin resistance in NSCLC solely by in vitro cellular tests; hence, future investigations should include a broader range of in vivo experiments to corroborate these findings. Furthermore, while we observed high expression of GLIDR and PPARGC1A in LUAD patient samples and investigated their correlation with cisplatin resistance in the LUAD cell line A549, the overall prevalence of the role of GLIDR and its cisplatin resistance regulatory mechanism in NSCLC has not been fully validated. To fully comprehend this complicated mechanism, it is necessary to collect several NSCLC sample types.

Conclusions

In conclusion, our study revealed a regulatory relationship between GLIDR, miR-342-5p, and PPARGC1A, in which GLIDR functions as a ceRNA for miR-342-5p to increase PPARGC1A expression in NSCLC, and GLIDR regulates cisplatin resistance through the miR-342-5p/PPARGC1A axis. These findings provide a new perspective for investigating NSCLC therapies and cisplatin resistance. Understanding the intricate regulatory network involving GLIDR, miR-342-5p, and PPARGC1A could open new avenues for targeted therapies and interventions in NSCLC management.

Data availability

The data that support the findings of this study are openly available in cBioPortal at [https://www.cbioportal.org/study/summary? id=luad_tcga] and [https://www.cbioportal.org/study/summary? id=lusc_tcga]. In addition, our sequencing data are available in the National Center for Biotechnology Information database with the number GSE243455.

Abbreviations

ceRNA:

competing endogenous RNA

GLIDR:

glioblastoma downregulated RNA

lncRNA:

long noncoding RNA

LUAD:

lung adenocarcinoma

NSCLC:

non-small cell lung cancer

PPARGC1A:

peroxisome proliferator-activated receptor-gamma coactivator-1alpha

References

  1. Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2021. CA Cancer J Clin. 2021;71(1):7–33.

    Article  PubMed  Google Scholar 

  2. Woodard GA, Jones KD, Jablons DM. Lung Cancer Staging and Prognosis. Cancer Treat Res. 2016;170:47–75.

    Article  PubMed  Google Scholar 

  3. Ye Z, Huang Y, Ke J, Zhu X, Leng S, Luo H. Breakthrough in targeted therapy for non-small cell lung cancer. Biomed Pharmacother. 2021;133:111079.

    Article  PubMed  CAS  Google Scholar 

  4. Chen Z, Chen X, Lu B, Gu Y, Chen Q, Lei T, Nie F, Gu J, Huang J, Wei C, et al. Up-regulated LINC01234 promotes non-small-cell lung cancer cell metastasis by activating VAV3 and repressing BTG2 expression. J Hematol Oncol. 2020;13(1):7.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Yarmishyn AA, Kurochkin IV. Long noncoding RNAs: a potential novel class of cancer biomarkers. Front Genet. 2015;6:145.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Barth DA, Prinz F, Teppan J, Jonas K, Klec C, Pichler M. Long-noncoding RNA (lncRNA) in the regulation of Hypoxia-Inducible factor (HIF) in Cancer. Noncoding RNA 2020, 6(3).

  7. Teppan J, Barth DA, Prinz F, Jonas K, Pichler M, Klec C. Involvement of long non-coding RNAs (lncRNAs) in Tumor Angiogenesis. Noncoding RNA 2020, 6(4).

  8. Xin X, Wu M, Meng Q, Wang C, Lu Y, Yang Y, Li X, Zheng Q, Pu H, Gui X, et al. Long noncoding RNA HULC accelerates liver cancer by inhibiting PTEN via autophagy cooperation to miR15a. Mol Cancer. 2018;17(1):94.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Liu T, Liu Y, Wei C, Yang Z, Chang W, Zhang X. LncRNA HULC promotes the progression of gastric cancer by regulating miR-9-5p/MYH9 axis. Biomed Pharmacother. 2020;121:109607.

    Article  PubMed  CAS  Google Scholar 

  10. Shin VY, Chen J, Cheuk IWY, Siu M-T, Ho C-W, Wang X, Jin H, Kwong A. Long non-coding RNA NEAT1 confers oncogenic role in triple-negative breast cancer through modulating chemoresistance and cancer stemness. Cell Death Dis 2019, 10(4).

  11. Huang H, Chen J, Ding CM, Jin X, Jia ZM, Peng J. LncRNA NR2F1-AS1 regulates hepatocellular carcinoma oxaliplatin resistance by targeting ABCC1 via miR‐363. J Cell Mol Med. 2018;22(6):3238–45.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. Yu Q, Li X, Feng T. GLIDR promotes the progression of glioma by regulating the miR-4677-3p/MAGI2 axis. Exp Cell Res. 2021;406(1):112726.

    Article  PubMed  CAS  Google Scholar 

  13. Zhang Y, Kong Z, Zhang Y, Huang W, Wu H, Wan X, Li Y. Increased expression of long non-coding RNA GLIDR in prostate cancer. Cancer Biomark. 2017;19(2):145–50.

    Article  PubMed  CAS  Google Scholar 

  14. Witusik-Perkowska M, Jaskólski DJ, Liberski PP, Szemraj J. If Artificial in Vitro Microenvironment can Influence Tumor Drug Resistance Network via Modulation of lncRNA expression?—Comparative analysis of Glioblastoma-Derived Cell Culture models and initial tumors in vivo. Cell Mol Neurobiol. 2020;42(4):1005–20.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Tai G, Fu H, Bai H, Liu H, Li L, Song T. Long non-coding RNA GLIDR accelerates the tumorigenesis of lung adenocarcinoma by miR-1270/TCF12 axis. Cell Cycle. 2021;20(17):1653–62.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  16. Burgarella S, Cattaneo D, Pinciroli F. M. M: MicroGen: a MIAME compliant web system for microarray experiment information and workflow management. BMC Bioinformatics 2005(1):6.

  17. Li X, Liu Y, Mu Q, Tian J, Yu H. MiR-290 family maintains developmental potential by targeting p21 in mouse preimplantation embryosdouble dagger. Biol Reprod. 2022;106(3):425–40.

    Article  PubMed  Google Scholar 

  18. Goldman MJ, Craft B, Hastie M, Repecka K, McDade F, Kamath A, Banerjee A, Luo Y, Rogers D, Brooks AN, et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol. 2020;38(6):675–8.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. J. A: OncoLnc: linking TCGA survival data to mRNAs, miRNAs, and lncRNAs. PeerJ Comput 2016, 11:e67.

  20. Gyorffy B, Surowiak P, Budczies J, Lanczky A. Online survival analysis software to assess the prognostic value of biomarkers using transcriptomic data in non-small-cell lung cancer. PLoS ONE. 2013;8(12):e82241.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, Jacobsen A, Byrne CJ, Heuer ML, Larsson E, et al. The cBio Cancer Genomics Portal: an Open platform for exploring Multidimensional Cancer Genomics Data. Cancer Discov. 2012;2(5):401–4.

    Article  PubMed  Google Scholar 

  22. Gao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, Sumer SO, Sun Y, Jacobsen A, Sinha R, Larsson E et al. Integrative Analysis of Complex Cancer Genomics and Clinical profiles using the cBioPortal. Sci Signal 2013, 6(269).

  23. Babicki S, Arndt D, Marcu A, Liang Y, Grant JR, Maciejewski A, Wishart DS. Heatmapper: web-enabled heat mapping for all. Nucleic Acids Res. 2016;44(W1):W147–53.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Kryczka J, Kryczka J, Czarnecka-Chrebelska KH, Brzeziańska-Lasota E. Molecular mechanisms of Chemoresistance Induced by Cisplatin in NSCLC Cancer Therapy. Int J Mol Sci 2021, 22(16).

  25. Winkle M, El-Daly SM, Fabbri M, Calin GA. Noncoding RNA therapeutics - challenges and potential solutions. Nat Rev Drug Discov. 2021;20(8):629–51.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Geisler S, Coller J. RNA in unexpected places: long non-coding RNA functions in diverse cellular contexts. Nat Rev Mol Cell Biol. 2013;14(11):699–712.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Wang Y, Mo Y, Peng M, Zhang S, Gong Z, Yan Q, Tang Y, He Y, Liao Q, Li X, et al. The influence of circular RNAs on autophagy and disease progression. Autophagy. 2022;18(2):240–53.

    Article  PubMed  CAS  Google Scholar 

  28. Thai AA, Solomon BJ, Sequist LV, Gainor JF, Heist RS. Lung cancer. Lancet. 2021;398(10299):535–54.

    Article  PubMed  Google Scholar 

  29. Goodall GJ, Wickramasinghe VO. RNA in cancer. Nat Rev Cancer. 2021;21(1):22–36.

    Article  PubMed  CAS  Google Scholar 

  30. Gagliardi M. AT. A: The challenges and strategies of antisense Oligonucleotide Drug Delivery. Biomedicines 2021 Apr, 9(4):433.

  31. Xu Q, Ye L, Huang L, Zhou L, Chen X, Ye M, Wu G, Zhan P, Lv T, Song Y. Serum exosomal miRNA might be a Novel Liquid Biopsy to identify Leptomeningeal Metastasis in Non-small Cell Lung Cancer. OncoTargets Therapy. 2021;14:2327–35.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Xia J, Luo M, Dai L, Wang L, Wang L, Zhu J. Serum exosomal microRNAs as predictive markers for EGFR mutations in non–small-cell lung cancer. J Clin Lab Anal 2021, 35(5).

  33. Lindholm EM, Leivonen S-K, Undlien E, Nebdal D, Git A, Caldas C, Børresen-Dale A-L, Kleivi K. Mir-342-5p as a potential Regulator of HER2 breast Cancer cell growth. MicroRNA. 2019;8(2):155–65.

    Article  PubMed  CAS  Google Scholar 

  34. Lin C, Ma M, Zhang Y, Li L, Long F, Xie C, Xiao H, Liu T, Tian B, Yang K et al. The N6-methyladenosine modification of circALG1 promotes the metastasis of colorectal cancer mediated by the miR-342-5p/PGF signalling pathway. Mol Cancer 2022, 21(1).

  35. Wang M, Mao C, Ouyang L, Liu Y, Lai W, Liu N, Shi Y, Chen L, Xiao D, Yu F, et al. Long noncoding RNA LINC00336 inhibits ferroptosis in lung cancer by functioning as a competing endogenous RNA. Cell Death Differ. 2019;26(11):2329–43.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Zhang H, Zhang H, Li X, Huang S, Guo Q, Geng D. LINC01089 functions as a ceRNA for mir-152-3p to inhibit non-small lung cancer progression through regulating PTEN. Cancer Cell Int. 2021;21(1):143.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Dong J, Li B, Lin D, Lu D, Liu C, Lu X, Tang X, Li L, Zhu D, Liu J, et al. LincRNA00494 suppresses non-small cell Lung Cancer Cell Proliferation by regulating SRCIN1 expression as a ceRNA. Front Oncol. 2020;10:79.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Li J, Wu X, Cao W, Zhao J. Long non-coding RNA NCK1-AS1 promotes the proliferation, migration and invasion of non-small cell lung cancer cells by acting as a ceRNA of miR-137. Am J Transl Res. 2020;12(10):6908–20.

    PubMed  PubMed Central  CAS  Google Scholar 

  39. Xie JJ, Guo QY, Jin JY, Jin D. SP1-mediated overexpression of lncRNA LINC01234 as a ceRNA facilitates non-small-cell lung cancer progression via regulating OTUB1. J Cell Physiol. 2019;234(12):22845–56.

    Article  PubMed  CAS  Google Scholar 

  40. Chen X, Wang Z, Tong F, Dong X, Wu G, Zhang R. lncRNA UCA1 promotes Gefitinib Resistance as a ceRNA to target FOSL2 by sponging miR-143 in non-small cell Lung Cancer. Mol Ther Nucleic Acids. 2020;19:643–53.

    Article  PubMed  CAS  Google Scholar 

  41. Yang J, Qiu Q, Qian X, Yi J, Jiao Y, Yu M, Li X, Li J, Mi C, Zhang J, et al. Long noncoding RNA LCAT1 functions as a ceRNA to regulate RAC1 function by sponging mir-4715-5p in lung cancer. Mol Cancer. 2019;18(1):171.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  42. Li Y, Gao L, Zhang C, Meng J. LncRNA SNHG3 promotes proliferation and metastasis of Non-small-cell Lung Cancer cells through miR-515-5p/SUMO2 Axis. Technol Cancer Res Treat. 2021;20:15330338211019376.

    PubMed  PubMed Central  CAS  Google Scholar 

  43. Chambers JM, Wingert RA. PGC-1alpha in Disease: recent renal insights into a Versatile Metabolic Regulator. Cells 2020, 9(10).

  44. Huang X, Pan L, Zuo Z, Li M, Zeng L, Li R, Ye Y, Zhang J, Wu G, Bai R, et al. LINC00842 inactivates transcription co-regulator PGC-1alpha to promote pancreatic cancer malignancy through metabolic remodelling. Nat Commun. 2021;12(1):3830.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  45. Andrzejewski S, Klimcakova E, Johnson RM, Tabaries S, Annis MG, McGuirk S, Northey JJ, Chenard V, Sriram U, Papadopoli DJ, et al. PGC-1alpha promotes breast Cancer metastasis and confers Bioenergetic flexibility against metabolic drugs. Cell Metab. 2017;26(5):778–e787775.

    Article  PubMed  CAS  Google Scholar 

  46. Li JD, Feng QC, Qi Y, Cui G, Zhao S. PPARGC1A is upregulated and facilitates lung cancer metastasis. Exp Cell Res. 2017;359(2):356–60.

    Article  PubMed  CAS  Google Scholar 

  47. Wang P, Guo X, Zong W, Li Y, Liu G, Lv Y, Zhu Y, He S. PGC-1alpha/SNAI1 axis regulates tumor growth and metastasis by targeting miR-128b in gastric cancer. J Cell Physiol. 2019;234(10):17232–41.

    Article  PubMed  CAS  Google Scholar 

  48. Dan L, Wang C, Ma P, Yu Q, Gu M, Dong L, Jiang W, Pan S, Xie C, Han J, et al. PGC1alpha promotes cholangiocarcinoma metastasis by upregulating PDHA1 and MPC1 expression to reverse the Warburg effect. Cell Death Dis. 2018;9(5):466.

    Article  Google Scholar 

  49. Cruz-Bermudez A, Laza-Briviesca R, Vicente-Blanco RJ, Garcia-Grande A, Coronado MJ, Laine-Menendez S, Palacios-Zambrano S, Moreno-Villa MR, Ruiz-Valdepenas AM, Lendinez C, et al. Cisplatin resistance involves a metabolic reprogramming through ROS and PGC-1alpha in NSCLC which can be overcome by OXPHOS inhibition. Free Radic Biol Med. 2019;135:167–81.

    Article  PubMed  CAS  Google Scholar 

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Acknowledgements

We thank Drs. Shang Su, Yawei Zhao and Xue Mei at the University of Toledo for their help in polishing the manuscript.

Funding

This study was supported by grants from the National Natural Science Foundation of China (31760333), the Key Technology Research Plan Project of Inner Mongolia Autonomous Region (2021GG0153) and a grant from the Science and Technology Major Project of Inner Mongolia Autonomous Region of China to the State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock (2019ZD031). All these study sponsors had no roles in the study design or the collection, analysis, and interpretation of data.

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Author notes

  1. Ruihua Liu and Jiemin Wang these authors contributed equally.

Authors and Affiliations

  1. State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Science, Inner Mongolia University, Hohhot, 010070, China

    Ruihua Liu, Jiemin Wang, Lichun Zhang, Shu Wang, Xiangnan Li, Yueshi Liu & Haiquan Yu

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Contributions

H.Y. conceived, directed, and supervised the project. H.Y., R.L., J.W. and Z. L. designed the study. R.L., J.W. performed the in vivo experiments. R.L., J.W., Z. L, W.S., X.L. and Y. L. performed the in vitro experiments and analyzed the data. R.L. and J.W. prepared the manuscript. R.L. wrote the manuscript. R.L., J.W., Z. L, W.S., X. L, Y. L. and H.Y. review the manuscript. All the authors have read and agreed to the submitted version of the manuscript.

Corresponding author

Correspondence to Haiquan Yu.

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Ethics approval

All studies adhered to procedures consistent with the National Research Council Guide for the Care and Use of Laboratory Animals and were approved by the Institutional Animal Care and Use Committee at Inner Mongolia University [SYXK (Inner Mongolia) 2020–0006]. All experiments were performed per ARRIVE guidelines and regulations. And all methods are reported per the ARRIVE guidelines for the reporting of animal experiments.

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Liu, R., Wang, J., Zhang, L. et al. GLIDR-mediated regulation of tumor malignancy and cisplatin resistance in non-small cell lung cancer via the miR-342-5p/PPARGC1A axis. BMC Cancer 24, 1126 (2024). https://doi.org/10.1186/s12885-024-12845-y

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BMC Cancer

ISSN: 1471-2407

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