Skip to main content
Download PDF

Simulating the restoration of normal gene expression from different thyroid cancer stages using deep learning

BMC Cancer volume 22, Article number: 612 (2022) Cite this article

Abstract

Background

Thyroid cancer (THCA) is the most common endocrine malignancy and incidence is increasing. There is an urgent need to better understand the molecular differences between THCA tumors at different pathologic stages so appropriate diagnostic, prognostic, and treatment strategies can be applied. Transcriptome State Perturbation Generator (TSPG) is a tool created to identify the changes in gene expression necessary to transform the transcriptional state of a source sample to mimic that of a target.

Methods

We used TSPG to perturb the bulk RNA expression data from various THCA tumor samples at progressive stages towards the transcriptional pattern of normal thyroid tissue. The perturbations produced were analyzed to determine if there are consistently up- or down-regulated genes or functions in certain stages of tumors.

Results

Some genes of particular interest were investigated further in previous research. SLC6A15 was found to be down-regulated in all stage 1–3 samples. This gene has previously been identified as a tumor suppressor. The up-regulation of PLA2G12B in all samples was notable because the protein encoded by this gene belongs to the PLA2 superfamily, which is involved in metabolism, a major function of the thyroid gland. REN was up-regulated in all stage 3 and 4 samples. The enzyme renin encoded by this gene, has a role in the renin-angiotensin system; this system regulates angiogenesis and may have a role in cancer development and progression. This is supported by the consistent up-regulation of REN only in later stage tumor samples. Functional enrichment analysis showed that olfactory receptor activities and similar terms were enriched for the up-regulated genes which supports previous research concluding that abundance and stimulation of olfactory receptors is linked to cancer.

Conclusions

TSPG can be a useful tool in exploring large gene expression datasets and extracting the meaningful differences between distinct classes of data. We identified genes that were characteristically perturbed in certain sample types, including only late-stage THCA tumors. Additionally, we provided evidence for potential transcriptional signatures of each stage of thyroid cancer. These are potentially relevant targets for future investigation into THCA tumorigenesis.

Peer Review reports

Background

Thyroid cancer is the most common endocrine malignancy, with an estimated 44,280 new cases resulting in over 2,000 deaths in 2021 [1, 2]. The incidence of thyroid cancer is increasing worldwide with an annual percent change around 6% in recent years [3]. This increase is at least partly due to increasing diagnostic capabilities through novel imaging techniques that can easily be used to detect potentially malignant thyroid nodules [4]. Early detection of thyroid cancer helps to reduce overall mortality, but over-diagnosis may cause individuals who would not have developed malignant cancer to undergo unnecessary treatments, subjecting them to procedural risks and financial burdens [5]. Thyroid cancer, however, can present in aggressive variants that grow rapidly, metastasize, and negatively impact normal functions like breathing and swallowing if left untreated [4]. Therefore, it is important to differentiate late-stage and more aggressive THCA tumors so patients can receive the appropriate treatment.

A common treatment for thyroid cancer is thyroidectomy: partial or complete removal of the thyroid gland [5]. The thyroid normally produces thyroid hormone which is essential for proper growth and regulation of metabolism [6]. Considering its importance for maintaining normal functions, the unnecessary loss of this gland should be avoided to optimize patients’ quality of life. Later stages of well-differentiated thyroid carcinomas, especially stage 4, are associated with higher risk of recurrence and more aggressive variants, making this a necessary treatment despite the ultimate burden [7].

Well-differentiated thyroid cancer, with papillary thyroid cancer as the most prevalent form, most often involves genetic alterations that constitutively activate the mitogen-activated protein kinase (MAPK) cascade, especially chromosomal rearrangement of RET and point mutation of BRAF or RAS [7, 8]. Thyroid cancer may also be triggered by an overactive phosphatidylinositol-3 kinase (PI3K/AKT) pathway due to activating mutations in RAS, PIK3CA, or AKT1, or inactivation of PTEN [9].

There is an urgent need to better understand the molecular differences between THCA tumors at different pathologic stages. Past research in breast cancer revealed evidence of distinct gene expression levels in different tumor grades [10]. Previous studies have predicted cancer stage using machine learning techniques with clinical and pathological datasets [11, 12]. In this study, we used a novel deep learning tool, which has been successfully applied to a previous cancer study, to observe abnormal RNA expression levels in individuals with various stages of THCA and find potential signatures for each stage [13]. Linking these signatures to accurate diagnostic, prognostic, and treatment strategies is of high importance.

The Transcriptome State Perturbation Generator (TSPG) is a tool created to leverage generative deep learning for the detection of changes in gene expression needed to transform a labeled source sample into the feature space of another, target sample type [13]. Using RNA-Seq feature data from labeled sample groups, TSPG first trains a deep learning model to classify samples based on their true class label. In this case, the model is trained to make a prediction about the pertinent label for a given transcriptomic expression vector. Given an unlabeled RNA-Seq vector, this model would learn to predict the true label. Then, an adversarial neural network is trained to subtly perturb those expression vectors so that the classification model will make an errant prediction, instead classifying the perturbed sample as an assigned target class. It does this by changing the transcriptomic profile of the provided sample to look like that of the target class. By examining the most significantly perturbed genes, one can identify differently transitioned genes. Since the deep learning model relies on feature integration at multiple layers, the gene expression patterns of an input gene list (e.g. all genes) are tweaked across the whole distribution. Reducing the gene set is certainly possible by using a limited input gene list for training. For example, TSPG has previously been tested on the Hallmark gene list subset from the Molecular Signatures Database (MSigDB) [13].

We demonstrate that TSPG can learn how to change the gene expression patterns of individual THCA tumors to reflect the expression profile of normal thyroid tissue. This method has previously been used to identify transcriptional aberrations for a specific patient diagnosed with papillary renal cell carcinoma [13]. We are interested in the precision medicine applications of this tool for thyroid cancer, so we have applied this technique to THCA tumors of different stages. While the expression levels are considered and perturbed separately for each individual tumor, the results were combined to identify patterns among stages of THCA tumors. We considered RNA expression data in this study while much of the existing similar research on THCA utilized medical imaging data or clinical attributes [14, 15]. Previous studies have used machine learning methods with transcriptome sequencing data in different cancer types to investigate things like tissue of origin and staging, but this study uniquely reports such results for thyroid cancer [16, 17].

In this study, we used TSPG to perturb the RNA expression data from individual thyroid tumor samples at various stages towards normal thyroid tissue gene expression. The perturbations produced were analyzed to determine if there are consistently up-regulated or down-regulated genes in the various stage progressions of THCA tumors. We have thus provided evidence for potential transcriptional signatures of each stage of thyroid cancer.

Methods

Preparation of TSPG input data. Normalized and batch corrected FPKM gene expression matrices (GEMs) were downloaded from an existing dataset for The Cancer Genome Atlas (TCGA) THCA tumor samples, TCGA normal thyroid tissue, and Genotype-Tissue Expression (GTEx) normal thyroid tissue [18]. One GEM was formed by merging the three GEMs and then it was log2 transformed and quantile normalized using GEMprep [19]. This GEM contained RNA-Seq expression levels for 19,239 genes in 51 TCGA solid tissue normal thyroid samples, 318 GTEx solid tissue normal thyroid samples, 244 TCGA stage 1 THCA solid tissue tumor samples, 47 TCGA stage 2 THCA solid tissue tumor samples, 95 TCGA stage 3 solid tissue tumor samples, and 46 TCGA stage 4 solid tissue tumor samples. Biospecimen and clinical data were downloaded from GDC data portal for all TCGA THCA samples (Table S1 and Table S2). The American Joint Committee on Cancer (AJCC) pathologic stage was matched to the corresponding subject identifier using VLOOKUP in Excel.

Perturbation of samples toward GTEx normal thyroid. Transcriptome State Perturbation Generator (TSPG) was utilized to perturb the RNA expression values of THCA tumor samples toward the RNA expression values of normal thyroid samples [20]. Ten of each sample type (stage 1 tumor, stage 2 tumor, stage 3 tumor, stage 4 tumor, TCGA normal thyroid, and GTEx normal thyroid) were randomly selected and removed from the training dataset. The list of samples with their pathologic stage and primary diagnosis is available in Table S3. Labels files were produced in the correct format to label each sample ID as tumor-s1, tumor-s2, tumor-s3, tumor-s4, normal-tcga, or normal-gtex based on the clinical data described above. The GEMs were formatted as required by converting to numpy arrays and transposing using GEMprep. GTEx normal thyroid tissue was used as the target class.

Analysis of sample perturbations made by TSPG. From the perturbations file produced by TSPG, significantly perturbed genes in each sample were defined as those having a perturbation value greater than 2 standard deviations above or below the mean of all perturbation values for that individual. The appropriate genes were extracted for each of the sixty perturbed individuals and saved in text files. Then, the consistently tumor-upregulated or tumor-downregulated genes in each sample type were determined by identifying genes that were significantly negatively or positively perturbed, respectively, in all 10 samples from that class. The average number of tumor-upregulated (negatively perturbed) and tumor-downregulated (positively perturbed) genes were calculated for each class using the corresponding ten perturbed samples. Additional calculations included the average number of tumor-upregulated and tumor-downregulated genes shared between two samples of the same type and, in each direction, the average proportion of perturbed genes shared to the total number of unique perturbed genes between two samples of the same type. Bar charts visualizing each of these were produced in Excel.

Functional Enrichment. Functional enrichment was performed using FUNC-E [21]. Tumor-upregulated and tumor-downregulated genes from each sample type were used as different modules. Query lists contained the significantly positively perturbed and significantly negatively perturbed genes, separately, in any sample from each sample type. The genomic background was all genes contained in the GEM. A terms list with Gene Ontology (GO), Interpro (IPR), and Kyoto Encyclopedia of Genes and Genomes (KEGG) vocabularies was generated [22,23,24]. The term mapping list was generated by downloading tab-separated value (TSV) files from ensemble biomart containing Gene name and one of GO term accession, KEGG Pathway and Enzyme ID, or Interpro ID. These TSVs were then merged into one file and filtered to contain only genes present in the genomic background and only terms present in the terms list. The p-value cutoff for enrichment used was 0.01, but the resulting enriched terms were filtered for a Bonferroni corrected p-value of less than 0.00001.

Results

The classes included in this paper are stage 1 THCA tumor, stage 2 THCA tumor, stage 3 THCA tumor, stage 4 THCA tumor, normal thyroid tissue originating from The Cancer Genome Atlas (TCGA), and normal thyroid tissue originating from the Genotype-Tissue Expression (GTEx) repository. We were able to unify the TCGA and GTEx samples into a unified matrix thanks to the work done by Wang et al. [18]. They have supplied a database of batch-corrected and re-normalized samples from both datasets, so the normal samples from TCGA and those from GTEx could potentially be studied as a single data source. However, we maintained TCGA normal and GTEx normal thyroid tissue as two separate groups in this analysis based on preliminary results suggesting some difference between them. An initial t-SNE plot created using the unified matrix showed TCGA normal samples forming a separate cluster from the GTEx normal samples. Normal thyroid tissue originating from GTEx was used as the target sample type for TSPG. The number of samples from each class are shown in Table 1.

Table 1 Samples included in the thyroid cancer gene expression matrix
Full size table

Using TSPG, we perturbed 10 samples of each type toward the target of GTEx normal thyroid tissue, including normal-to-normal perturbations to act as a baseline. This provided insight as to how THCA tumors would need to change to revert to normal thyroid tissue. TSPG outputs a matrix containing perturbation values applied to each gene in an individual sample (Table S4). A positive perturbation represents a gene that must increase its expression level to reach the average normal expression, so it is down-regulated in the tumor (tumor-downregulated). Conversely, a negative perturbation represents a tumor-upregulated gene. The terms tumor-downregulated and tumor-upregulated will be used to describe genes that were positively and negatively perturbed, respectively, by TSPG. This is meant to convey their expression level in the perturbed sample relative to the average normal thyroid expression, so even the normal-to-normal perturbations may be referred to as tumor-downregulated (positive) or tumor-upregulated (negative). During the initial training for the model used in this study, the classifier had a reported test accuracy of 0.781 and the generator had a reported perturbation accuracy of 1.000. After the training phase, there was a reported perturbation accuracy of 1.000 when the 60 randomly selected samples were perturbed.

The gene expression profiles of all THCA tumor samples and normal thyroid samples included in the study as well as the samples perturbed toward normal expression levels were visualized with a t-SNE plot (Fig. 1A). The normal thyroid tissue from both TCGA and GTEx clustered together with the perturbed samples and very few tumor samples. Most tumor samples segregated away from the normal samples. The largest difference is between the tumor and normal classes, however, there does appear to be some separation within the tumor class. The two tumor clusters appear to have similar distributions of pathologic stages.

Fig. 1
figure 1

Expression profiles of 19,239 genes in THCA tumor and normal thyroid samples. (A) The t-SNE plot shows RNA expression profiles of normal thyroid tissue from GTEx, normal thyroid tissue from TCGA, THCA tumor samples from TCGA of various stages, and all the perturbed samples with GTEx normal thyroid tissue as the target. (B) The representative heatmap shows the expression vectors of one Stage 3 THCA tumor sample (X), the perturbations applied (P), the perturbed sample (X + P), and average for the target class of normal thyroid tissue from GTEx (mu_T). Missing values in the training and perturbed sample GEMs were imputed with the minimum value of the training GEM

Full size image

Heatmaps were produced for each of the 60 perturbed samples. One representative heatmap for a stage 3 THCA tumor sample is shown in Fig. 1B. In order from left to right, Fig. 1B shows the expression levels of all 19,239 genes present in the GEM for the original tumor sample (X), the perturbations applied to each gene in that sample (P), the expression levels of the sample after perturbation (X + P), and the average expression levels in the target class of normal thyroid tissue from GTEx (mu_T). The red region of the perturbation box represents positive perturbations, when the expression level of a gene must be increased to reach the normal level, which indicates tumor-downregulated gene. The blue region represents negative perturbations, when the expression level of a gene must be decreased to resemble the normal level, which indicates a tumor-upregulated gene.

Figure 2 shows that, on average, there are more significantly tumor-upregulated genes than tumor-downregulated genes across all sample types. There are similar average numbers of significantly perturbed genes in both directions in all stages of THCA tumors and normal thyroid tissue from GTEx, but there were fewer significantly down-regulated genes in normal thyroid tissue from TCGA. The greatest average number of significantly up-regulated genes was identified in the TCGA normal samples.

Fig. 2
figure 2

Number of genes found by TSPG to be significantly perturbed in samples within each sample type. Significantly tumor-downregulated genes, as indicated by positive perturbations toward normal thyroid tissue from GTEx, are shown in red. Significantly tumor-upregulated genes, as indicated by negative perturbations toward normal thyroid tissue from GTEx, are shown in blue. Error bars represent standard error

Full size image

Table 2 displays the average perturbation values for genes that were significantly tumor-downregulated or tumor-upregulated in samples of the same type. All cancer stages have similar average perturbation values of tumor-downregulated genes, while both TCGA and GTEx normal thyroid samples have lower average values. There is a less drastic difference between the tumor and normal samples for the significantly tumor-upregulated genes, but the average perturbation values are still greater in all tumors than in normal thyroid tissue.

Table 2 Average perturbation values of significantly perturbed genes in each sample type
Full size table

Figure 3 displays how many genes are significantly perturbed in multiple samples from each type. The average number of tumor-upregulated genes shared between two samples of the same type is greater than that of the tumor-downregulated genes in all sample types. Normal thyroid tissue from both TCGA and GTEx have a lower average number of shared down-regulated genes than any of the tumor types. Normal thyroid tissue samples from GTEx have fewer shared up-regulated genes on average than normal thyroid samples from TCGA and all tumor types except stage 2. Stage 2 THCA tumor samples have fewer shared tumor-downregulated genes between samples than any other stage of THCA tumor samples.

Fig. 3
figure 3

Number of shared significantly perturbed genes within each sample type. Significantly tumor-downregulated genes, as indicated by positive perturbations toward normal thyroid tissue from GTEx, are shown in red. Significantly tumor-upregulated genes, as indicated by negative perturbations toward normal thyroid tissue from GTEx, are shown in blue. Error bars represent standard error

Full size image

Figure 4 shows the average proportion of number of shared perturbed genes out of the total number of unique genes between samples of the same type. The greatest average proportion of significantly perturbed genes shared between samples within a class is about 0.32 in the tumor-downregulated genes identified in stage 1 THCA tumors. The lowest value, from down-regulated genes in normal GTEx samples, is less than 0.10. In all stages of THCA tumors, the average proportion of shared tumor-downregulated genes between two samples of the same class tend to be similar or slightly greater than that of the tumor-upregulated genes. The average proportion of shared tumor-upregulated genes is greater than that of shared tumor-downregulated genes in all normal thyroid tissue from either TCGA or GTEx, but there is a larger difference in the samples from TCGA.

Fig. 4
figure 4

Proportion of shared significantly perturbed genes between samples, out of their unique significantly perturbed genes, within each sample type. Significantly tumor-downregulated genes, as indicated by positive perturbations toward normal thyroid tissue from GTEx, are shown in red. Significantly tumor-upregulated genes, as indicated by negative perturbations toward normal thyroid tissue from GTEx, are shown in blue. Error bars represent standard error

Full size image

The genes that were significantly tumor-upregulated or tumor-downregulated in all 10 samples of each type are seen in Table 3. There were no commonly perturbed genes in either direction among GTEx normal thyroid tissue samples, but all other sample types had at least one tumor-up-regulated and one tumor-downregulated gene common to all 10 samples. PLA2G12B and RP11-73M18.2 are up-regulated in all tumor samples of any stage. All stage 1, stage 2, and stage 3 tumor samples tested had down-regulation of SLC6A15. Later-stage tumors (stage 3 and stage 4) showed down-regulated REN levels compared to normal thyroid tissue.

Table 3 Commonly perturbed genes in each sample type
Full size table

We performed functional enrichment analysis on the tumor-upregulated and tumor-downregulated gene sets for each sample type to determine the collective functions of the genes in each set. Each gene set contained genes that were significantly perturbed, in the appropriate direction, in at least one individual of the given sample type. Functional enrichment results can be seen in Table S5 and Table S6. Olfactory receptor activity and related terms were enriched in tumor-upregulated gene sets of all sample types (Table S5). There were no significant enriched terms for the tumor-downregulated gene sets from the stage 1, stage 2, and stage 3 tumor classes. Translation and related terms were enriched in the tumor-downregulated gene sets of the classes that did have significant enrichment (Table S6).

Discussion

In this study, we used a TSPG simulation to identify tumor to normal gene expression state transitions and determine which genes exhibit aberrant expression in thyroid tumors relative to the normal thyroid gland. In essence, this is a precision medicine approach where we place an individual’s tumor (n = 1) into the context of other tumor samples. We previously used this approach to identify gene shifts in a single patient with Type II papillary renal cell carcinoma, but in that study we did not consider tumor stage [13]. Here, we examined 60 individuals consisting of 10 each of six different sample types, including stage 1–4 THCA tumors from TCGA and normal thyroid tissue from TCGA or GTEx. The results from the 10 samples in each class were pooled together to represent their sample type and used to investigate whether there were consistent genetic signatures for each stage of THCA. These randomly selected samples included tumors of multiple subtypes of THCA across all stages (Table S3). Future research might validate the results of this study using a limited set of samples of only one subtype of thyroid cancer to assess potential bias in pooling samples with varying primary diagnoses.

The t-SNE plot in Fig. 1A shows that the THCA tumor samples of all stages typically segregate from the normal thyroid samples. There are a few stage 1, stage 2, and stage 3 tumors that appear to cluster with the normal tissue, meaning those tumor samples have similar gene expression profiles to normal thyroid samples. These may represent tumors with very few or very small expression changes or could potentially be a sign of error in labeling or contamination with normal tissue during the RNA sequencing process. There is also evidence of two separate clusters of tumor samples containing approximately the same proportion of each stage. Further examination is needed to determine the significance and cause. Some possible causes for this discrepancy may be gender, age, and race of the individuals from whom the samples were obtained as well as the cancer subtype, or metastasis status of the samples.

The representative heatmap provided in Fig. 1B demonstrates the importance of the perturbations discussed in this study. The expression vector of the tumor sample is distinct from that of the average expression in normal thyroid tissue. However, the tumor with perturbations applied shows expression levels that are very similar to the average levels from normal thyroid samples. The t-SNE plot also supports the success of TSPG because all the samples that were perturbed with a target class of GTEx normal thyroid tissue cluster with the normal thyroid samples, indicating similar gene expression profiles (Fig. 1A).

Across all sample types, there was a greater average number of unique significantly up-regulated genes than unique significantly down-regulated genes in the original sample compared to expression in GTEx normal thyroid tissue (Fig. 2). Despite this difference, the proportion of shared perturbed genes to total unique perturbed genes between two samples of the same type was similar in the tumor-downregulated and tumor-upregulated directions across tumor samples of all stages (Fig. 4).

We expected many of the genes related to normal thyroid function would be consistently down-regulated in all tumor samples. However, this was not supported by the functional enrichment because three of the tumor classes had no significantly enriched terms for their tumor-downregulated gene sets (Table S6). Additionally, the stage 4 tumor-downregulated genes were mostly enriched for terms related to translation, none of which were unique to this gene set because they were also significant for the down-regulated genes in both normal sample types.

There were some genes that were consistently up-regulated or down-regulated in tumor samples of all or most stages, which suggests they play a role in tumorigenesis and may maintain a consistent abnormal expression level even as the tumor progresses (Table 3). Additionally, there were some unique genes found that were significantly up- or down-regulated in all tumor samples of only one stage. These are good candidate genes for future studies to investigate thyroid cancer progression to later stages.

Some genes were found to be consistently up- or down-regulated in certain sample types (Table 3). SLC6A15 was down-regulated in all individuals with stage 1, 2, or 3 THCA. Previous studies suggest this gene acts as a tumor suppressor [25]. PLA2G12B was up-regulated in all tumor samples. Previous studies have observed relationships between other genes in the phospholipases A2 (PLA2) superfamily with various cancers, like over-expression of PLA2G5 correlated with poor prognosis in patients with glioma tumors and differential expression of some PLA2 genes in normal colons and colon adenocarcinomas [26, 27]. Proteins belonging to the PLA2 superfamily are involved in metabolism, which means they may be an important indicator of normal thyroid function [28]. Therefore, disruption of PLA2 protein expression levels, as seen in all THCA tumor samples in this study, may be a potential signal of abnormal thyroid function and thyroid cancer development. REN was up-regulated in all later stage (stages 3 and 4) tumor samples. This gene codes for the enzyme renin, which is a component of the renin-angiotensin system [29]. Previous research indicates that the renin-angiotensin system has a role in multiple cancer types, likely due to its regulation of angiogenesis [30, 31]. Angiogenesis is an important process in cancer because increased blood flow to the tumor allows it to grow larger [32]. Since tumor size is part of the staging system, with larger tumors classified as later stages, it is possible that the up-regulation of REN, which induces angiogenesis, is a factor in the progression of cancer to a later stage.

There were no significantly perturbed genes that were found in all 10 GTEx normal-to-GTEx normal control samples tested (Table 3). This suggests that the perturbations applied to the GTEx normal thyroid samples, to make them appear as the target of GTEx normal thyroid tissue, were unique to individuals and represent the expected natural variations in expression among healthy individuals [33]. Whereas there were common positive and negative perturbations among the tumor samples classified as the same stage, which indicates common changes in expression that lead to the cancer phenotype. However, it should be noted that the normal thyroid samples from TCGA also had a relatively large number of common significantly up-regulated genes in all 10 samples. It is not clear whether this is a result of batch error which differentiates the TCGA and GTEx datasets, which was corrected using the methods described by Wang et al. [18]. One future experiment that may reveal more about this occurrence is to classify all normal thyroid tissue from either TCGA or GTEx together as “normal” to see if using the average expression of all normal samples as the target would eliminate the apparent difference between samples from TCGA and GTEx.

Table 2 shows that the significant perturbations applied to the normal thyroid samples, from either GTEx or TCGA, tend to be smaller than those applied to THCA tumor samples of any stage. This is a result of our method for determining the significantly perturbed genes for each sample which included those that were greater than 2 standard deviations away from the mean perturbation value within that sample. Since any of the genes in the normal samples required only small perturbations to match the average normal expression levels, Fig. 2 shows there were similar numbers of genes that were considered significantly perturbed in tumor and normal samples. There do not appear to be consistent differences in the number or value of perturbations between stages of THCA.

In this study, genes with a perturbation value of two standard deviations greater or less than the mean of all perturbations for a particular sample were deemed significantly perturbed. We were most interested in these genes that required the largest changes in expression to return to normal values because they would represent the genes that are most strongly up-regulated or down-regulated in tumors so likely have a role in tumorigenesis. However, past research has suggested that small changes in gene dosage can have a role in cancer development [34]. Future research utilizing TSPG to understand an individual’s cancer progression could consider genes with a perturbation value exceeding a threshold based on the gene’s average expression level in normal samples in order to include more subtle changes to gene expression.

Functional enrichment results for the significant tumor-downregulated and tumor-upregulated genes in each sample type revealed some similarities among the gene sets. All sample types showed enrichment for olfactory receptor activity and related terms in the tumor-upregulated gene set (Table S5). This result for the tumors aligns with previous research finding connections between abundance or stimulation of olfactory receptors and cancer [35, 36]. However, the enrichment of these functions in both normal-to-normal perturbed gene sets means these findings should be considered cautiously. We would not expect the genes perturbed in normal samples with a normal target to be enriched for relevant functions because those perturbations are expected to be random. The similar functional enrichment results for tumor-upregulated genes in all classes may indicate bias in the genes perturbed by TSPG or could potentially suggest the presence of unrecognized THCA tumor contaminating the TCGA or GTEx normal thyroid tissue. The lack of functional enrichment for down-regulated genes in tumors of stages 1, 2, and 3 was also surprising (Table S6). This is because we expected genes related to normal thyroid function to be down-regulated consistently among THCA tumor samples.

This research expands on the use of TSPG to determine how gene expression in individual tumor samples differs from that of the corresponding normal tissue. We analyzed 10 samples from each sample type perturbed toward a target of normal thyroid tissue (GTEx) to identify consistent changes in expression in different stages of THCA. This method could generate gene sets that could be used to classify tissue samples based on clinical attributes such as pathologic stage. Future experiments could explore the classification accuracy of these significantly perturbed gene sets using an existing public repository [37]. Another area to develop our understanding of differences between stages of cancer would be to look at differential expression of the identified candidate genes in samples from each stage. We also propose the use of these methods in other cancer types to determine if the differences between stages are unique to each cancer or if there are similarities.

Conclusions

In this study, RNA expression levels from samples of THCA tumors and normal thyroid tissue were obtained from the TCGA and GTEx repositories and perturbed using TSPG with a target class of GTEx normal thyroid. These perturbations were analyzed and revealed commonly up-regulated or down-regulated genes in all or certain stages of THCA tumors. SLC6A15 was found to be down-regulated in all stage 1–3 samples, and other studies have identified this gene as a tumor suppressor. The up-regulation of PLA2G12B in all samples was notable because the protein encoded by this gene belongs to the PLA2 superfamily, which is involved in metabolism, a major function of the thyroid gland. REN was up-regulated in all stage 3 and 4, or later stage, samples. The enzyme renin encoded by this gene, has a role in the renin-angiotensin system; this system regulates angiogenesis and may have a role in cancer development and progression. This is supported by the consistent up-regulation of REN only in later stage tumor samples. Functional enrichment results showed that olfactory receptor activities and similar terms were enriched for the up-regulated genes which supports previous research concluding that abundance and stimulation of olfactory receptors is linked to cancer. TSPG can be a useful tool in exploring large gene expression datasets and extracting the meaningful differences between distinct classes of data. We hope this research and future studies that stem from it will promote accurate diagnosis and appropriate treatment for THCA patients.

Availability of data and materials

Unified gene expression FPKM files were downloaded from https://doi.org/10.6084/m9.figshare.5330593. These processed data were originally obtained from The Cancer Genome Atlas project (https://portal.gdc.cancer.gov) and The Genotype-Tissue Expression (GTEx) project (https://gtexportal.org/home). All relevant data generated or analyzed during our study are included in this published article and its supplementary information files. If other data are required to interpret our findings, please contact the corresponding author at ffeltus@clemson.edu.

Abbreviations

THCA:

Thyroid cancer

TSPG:

Transcriptome state perturbation generator

GEM:

Gene expression matrix

TCGA:

The Cancer Genome Atlas

GTEx:

Genotype-Tissue Expression

AJCC:

American Joint Committee on Cancer

PLA2:

Phospholipases A2

MAPK:

Mitogen-activated protein kinase

MSigDB:

Molecular Signatures Database

TSV:

Tab-separated value

GO:

Gene Ontology

IPR:

Interpro

KEGG:

Kyoto Encyclopedia of Genes and Genomes

References

  1. Wiltshire JJ, Drake TM, Uttley L, Balasubramanian SP. Systematic Review of Trends in the Incidence Rates of Thyroid Cancer. Thyroid (New York, N.Y.). 2016;26(11):1541–52.

  2. Howlader N, Noone AM, Krapcho M, Miller D, Brest A, Yu M, Ruhl J, Tatalovich Z, Mariotto A, Lewis DR, Chen HS. SEER Cancer Statistics Review. 1975–2018;2021 ASI 4474–35. 2021.

  3. Kitahara CM, Sosa JA. The changing incidence of thyroid cancer. Nat Rev Endocrinol. 2016;12(11):646–53.

    Article  Google Scholar 

  4. Cabanillas ME. Dr, McFadden DG, MD, Durante C. MD Thyroid cancer The Lancet (British edition). 2016;388(10061):2783–95.

    CAS  Google Scholar 

  5. Li M, Maso LD, Vaccarella S. Global trends in thyroid cancer incidence and the impact of overdiagnosis. Lancet Diabetes Endocrinol. 2020;8(6):468–70.

    Article  Google Scholar 

  6. Mullur R, Liu Y, Brent GA. Thyroid Hormone Regulation of Metabolism. Physiol Rev. 2014;94(2):355–82.

    Article  CAS  Google Scholar 

  7. Abdullah MI, Junit SM, Ng KL, Jayapalan JJ, Karikalan B, Hashim OH. Papillary Thyroid Cancer: Genetic Alterations and Molecular Biomarker Investigations. Int J Med Sci. 2019;16(3):450–60.

    Article  CAS  Google Scholar 

  8. D’Cruz AK, Vaish R, Vaidya A, Nixon IJ, Williams MD, Vander Poorten V, et al. Molecular markers in well-differentiated thyroid cancer. Eur Arch Otorhinolaryngol. 2018;275(6):1375–84.

    Article  Google Scholar 

  9. Prete A, Borges de Souza P, Censi S, Muzza M, Nucci N, Sponziello M. Update on Fundamental Mechanisms of Thyroid Cancer. Frontiers in endocrinology (Lausanne) 2020;11:102.

  10. Ma X, Salunga R, Tuggle JT, Gaudet J, Enright E, McQuary P, et al. Gene Expression Profiles of Human Breast Cancer Progression. Proceedings of the National Academy of Sciences - PNAS. 2003;100(10):5974–9.

    Article  CAS  Google Scholar 

  11. Lai K, Chiang H, Chen W, Tsai F, Jeng L. Artificial Neural Network-Based Study Can Predict Gastric Cancer Staging. Hepatogastroenterology. 2008;55(86–87):1859–63.

    PubMed  Google Scholar 

  12. Tewari A, Narayan P. Novel staging tool for localized prostate cancer: A pilot study using genetic adaptive neural networks. J Urol. 1998;160(2):430–6.

    Article  CAS  Google Scholar 

  13. Targonski C, Bender MR, Shealy BT, Husain B, Paseman B, Smith MC, et al. Cellular State Transformations Using Deep Learning for Precision Medicine Applications. Patterns. 2020;1(6): 100087.

    Article  Google Scholar 

  14. Yang CQ, Gardiner L, Wang H, Hueman MT, Chen D. Creating Prognostic Systems for Well-Differentiated Thyroid Cancer Using Machine Learning. Frontiers in endocrinology (Lausanne). 2019;10:288.

    Article  CAS  Google Scholar 

  15. Li X, Zhang S, Zhang Q, Wei X, Pan Y, Zhao J, et al. Diagnosis of thyroid cancer using deep convolutional neural network models applied to sonographic images: a retrospective, multicohort, diagnostic study. Lancet Oncol. 2019;20(2):193–201.

    Article  Google Scholar 

  16. Wei IH, Shi Y, Jiang H, Kumar-Sinha C, Chinnaiyan AM. RNA-Seq Accurately Identifies Cancer Biomarker Signatures to Distinguish Tissue of Origin. Neoplasia. 2014;16(11):918–27.

    Article  CAS  Google Scholar 

  17. Singireddy S, Alkhateeb A, Rezaeian I, Rueda L, Cavallo-Medved D, Porter L. Identifying differentially expressed transcripts associated with prostate cancer progression using RNA-Seq and machine learning techniques. IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology. 2015;1-5.

  18. Wang Q, Armenia J, Zhang C, Penson AV, Reznik E, Zhang L, et al. Unifying cancer and normal RNA sequencing data from different sources. Scientific data. 2018;5(1): 180061.

    Article  CAS  Google Scholar 

  19. GEMprep. https://github.com/SystemsGenetics/GEMprep. Accessed 7 Sept 2020.

  20. TSPG. TSPG. https://github.com/ctargon/TSPG. Accessed 7 Sept 2020.

  21. FUNC-E. FUNC-E. https://github.com/SystemsGenetics/FUNC-E. Accessed 31 Aug 2021.

  22. Botstein D, Cherry JM, Ashburner M, Ball CA, Blake JA, Butler H, et al. Gene Ontology: tool for the unification of biology. Nat Genet. 2000;25(1):25–9.

    Article  Google Scholar 

  23. Mitchell AL, Attwood TK, Babbitt PC, Blum M, Bork P, Bridge A, et al. InterPro in 2019: improving coverage, classification and access to protein sequence annotations. Nucleic Acids Res. 2019;47(D1):D351–60.

    Article  CAS  Google Scholar 

  24. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28(1):27–30.

    Article  CAS  Google Scholar 

  25. Chen Y, Li H, Liang W, Guo Y, Peng M, Ke W, et al. SLC6A15 acts as a tumor suppressor to inhibit migration and invasion in human papillary thyroid cancer. J Cell Biochem. 2021;122(8):814–26.

    Article  CAS  Google Scholar 

  26. Wu C, Su J, Wang X, Wang J, Xiao K, Li Y, et al. Overexpression of the phospholipase A2 group V gene in glioma tumors is associated with poor patient prognosis. Cancer management and research. 2019;11:3139–52.

    Article  CAS  Google Scholar 

  27. Mounier CM, Wendum D, Greenspan E, Fléjou J, Rosenberg DW, Lambeau G. Distinct expression pattern of the full set of secreted phospholipases A2 in human colorectal adenocarcinomas: sPLA2-III as a biomarker candidate. Br J Cancer. 2008;98(3):587–95.

    Article  CAS  Google Scholar 

  28. Kuefner MS. Secretory Phospholipase A2s in Insulin Resistance and Metabolism. Frontiers in endocrinology (Lausanne). 2021;12: 732726.

    Article  Google Scholar 

  29. Fyhrquist F, Saijonmaa O. Renin-angiotensin system revisited. J Intern Med. 2008;264(3):224–36.

    Article  CAS  Google Scholar 

  30. Sobczuk P, Szczylik C, Porta C, Czarnecka AM. Renin angiotensin system deregulation as renal cancer risk factor. Oncol Lett. 2017;14(5):5059–68.

    PubMed  PubMed Central  Google Scholar 

  31. Herr D, Rodewald M, Fraser HM, Hack G, Konrad R, Kreienberg R, et al. Potential role of Renin–Angiotensin-system for tumor angiogenesis in receptor negative breast cancer. Gynecol Oncol. 2008;109(3):418–25.

    Article  CAS  Google Scholar 

  32. Hanahan D, Weinberg R. Hallmarks of Cancer: The Next Generation. Cell. 2011;144(5):646–74.

    Article  CAS  Google Scholar 

  33. Cheung VG, Conlin LK, Weber TM, Arcaro M, Jen K, Morley M, et al. Natural variation in human gene expression assessed in lymphoblastoid cells. Nat Genet. 2003;33(3):422–5.

    Article  CAS  Google Scholar 

  34. Alimonti A, Carracedo A, Nardella C, Sampieri K, Richardson AL, Brogi E, et al. Subtle variations in Pten dose determine cancer susceptibility. Nat Genet. 2010;42(5):454–8.

    Article  CAS  Google Scholar 

  35. Sanz G, Leray I, Dewaele A, Sobilo J, Lerondel S, Bouet S, et al. Promotion of Cancer Cell Invasiveness and Metastasis Emergence Caused by Olfactory Receptor Stimulation. PLoS ONE. 2014;9(1): e85110.

    Article  Google Scholar 

  36. Masjedi S, Zwiebel LJ, Giorgio TD. Olfactory receptor gene abundance in invasive breast carcinoma. Sci Rep. 2019;9(1):13736–812.

    Article  Google Scholar 

  37. Targonski CA, Shearer CA, Shealy BT, Smith MC, Feltus FA. Uncovering biomarker genes with enriched classification potential from Hallmark gene sets. Sci Rep. 2019;9(1):9747–810.

    Article  Google Scholar 

Download references

Acknowledgements

Most computation was performed on the Clemson University Palmetto cluster.

Funding

Not applicable.

Author information

Authors and Affiliations

  1. Department of Genetics & Biochemistry, Clemson University, Biosystems Research Complex, 302C, 19 105 Collings St,., SC, 29634, Clemson, USA

    Nicole M. Nelligan & F. Alex Feltus

  2. Biomedical Data Science and Informatics Program, Clemson, SC, 29634, USA

    M. Reed Bender & F. Alex Feltus

  3. Clemson Center for Human Genetics, Greenwood, SC, 29646, USA

    F. Alex Feltus

Authors
  1. Nicole M. Nelligan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. Reed Bender
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. F. Alex Feltus
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

NN and FAF designed the research; NN performed the core experiments and wrote the manuscript; MRB provided supportive experiments; NN, MRB, and FAF edited the manuscript. All authors approved the final manuscript.

Corresponding author

Correspondence to F. Alex Feltus.

Ethics declarations

Ethics approval and consent to participate

All clinical data was anonymized and from public repositories that adhere to the NIH institutional data use policy (https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga/history/policies) which is in accordance to guidelines of Declaration of Helsinki (https://www.wma.net/policies-post/wma-declaration-of-helsinki-ethical-principles-for-medical-research-involving-human-subjects/).

Consent for publication

Not applicable.

Competing interests

Not applicable.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nelligan, N.M., Bender, M.R. & Feltus, F.A. Simulating the restoration of normal gene expression from different thyroid cancer stages using deep learning. BMC Cancer 22, 612 (2022). https://doi.org/10.1186/s12885-022-09704-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12885-022-09704-z

Keywords

  • Thyroid cancer
  • Deep learning
  • Transcriptome
  • TSPG

BMC Cancer

ISSN: 1471-2407

Contact us