Identification of mRNA Prognostic Markers for TGCT by Integration of Co-Expression and CeRNA Network

Testicular germ cell tumor (TGCT) is the most common malignant tumor in young men and is associated with poor prognosis. We assessed the RNA expression profiles of 13 TGCT tissues and 4 adjacent normal tissues by transcriptome sequencing to identify novel prognostic biomarkers. We detected several differentially expressed mRNAs in TGCT that were functionally annotated by GO and KEGG enrichment analyses to tumorigenesis-related processes such as immunity and chemotherapeutic resistance. An mRNA-lncRNA-miRNA regulatory network was constructed using RNA-Seq data and public databases, and integrated with TCGA database to develop a prediction model for metastasis and recurrence. Finally, GRK4, PCYT2 and RGSL1 were identified as predictive markers of survival and therapeutic response. In conclusion, we found several potential predictors for TGCT prognosis and immunotherapeutic response by ceRNA network analysis.

Introduction

Testicular germ cell tumors (TGCTs) are prevalent among males between the ages of 20 and 35 years, and the incidence rates are increasing globally. Approximately 90-95% of testicular cancers are TGCTs that are derived from the germ cell lineage (1). Histologically, TGCTs are classified into the seminomas (SEM) and non-seminomas (non-SEM), of which the latter can be sub-divided into teratomas, yolk sac tumors, choriocarcinomas, embryonal carcinomas (ECs) and mixed tumors (2). Non-SEM has a high risk of metastasis and recurrence, and the ECs in particular that originate from the malignant testicular stem cells are the most aggressive TGCT subtype due to vascular invasion (3–5). However, the mechanisms underlying the poor prognosis of TGCTs are poorly understood.

Although TGCT is generally sensitive to cisplatin-based chemotherapy, approximately 20% of the patients are unresponsive to the treatment and have significantly worse prognoses or a high risk of recurrence, along with complications such as peripheral neuropathy, hormonal disturbances, sexual dysfunction, infertility, cognitive impairment and psychosocial effects (6–9). Despite the familial nature of TGCTs, the driver genes for tumorigenesis or metastasis have not been identified so far that may be potentially targeted for therapy (10). Currently, most TGCT-related deaths can be attributed to metastasis and cisplatin resistance (11). Therefore, it is crucial to identify novel biomarkers of TGCTs to improve early diagnosis and relapse-free survival (RFS).

Recent studies have shown that in addition to mRNAs, microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) are involved in the pathogenesis of TGCTs (12, 13). Competing endogenous RNA (ceRNA) networks play a vital role in the regulation of oncogenic pathways (14). In this study, we identified multiple dysregulated RNAs in TGCTs relative to adjacent normal tissues by transcriptome sequencing. The differentially expressed mRNAs (DEmRNAs) were associated with immune cell infiltration during TGCTs progression. We constructed a ceRNA network for TGCTs based on these dysregulated mRNAs and lncRNAs, and a model for predicting RFS and immunotherapeutic responses by integrating the ceRNAs network and clinical data from public databases. These findings provide new insights into the molecular basis of TGCTs pathogenesis.

Materials and Methods

Clinical Specimens

Thirteen TGCT tissue samples (11 from SEM and 2 from non-SEM patients with predominantly malignant embryonal carcinoma) and 4 para-carcinoma tissues were obtained from the Department of Urology, the Affiliated Cancer Hospital of Xiangya School of Medicine of Central South University (Table 1). Fresh tissues were collected and snap frozen in liquid nitrogen. The study was approved by the Ethics Committee, and informed consent was obtained from each patient.

TABLE 1

Table 1 Clinical case information of 17 testicular tissues in our sequencing.

RNA-Sequencing

Total RNA was extracted from tissue samples using Trizol reagent (Thermo, USA), and 1µL per sample was used to determine the concentration using a NanoDrop 1000 spectrophotometer. After removing rRNAs (Ribo-Zero Gold rRNA Removal Kit, MRZG12324, Illumina), the remaining mRNAs and ncRNAs were purified using Agencourt RNA Clean XP Beads. The enriched mRNA and ncRNA fragments were cut into shorter sequences using a fragmentation buffer and reverse-transcribed using the Transcriptor First-strand cDNA synthesis Kit (Roche, USA). The cDNA fragments were purified to repair the ends add poly (A) and ligate the Illumina sequencing joints (QiaQuick PCR extraction kit). The second-strand cDNA was digested with uracil-N-glucanase, and amplified by PCR. The Sensitivity DNA assay Kit (Agilent Technologies) was used to analyze the quality of the library. Finally, ABI StepOnePlus Real-Time PCR System (Life Technologies) was used to quantify and pool the sequences, and the DNA sequenced on the Illumina HiSeqTM 4000 platform (Gene Denovo, Guangzhou, China).

Bioinformatics Analysis

The differentially expressed mRNAs between the two groups were screened using the edgeR software with FDR (false discovery rate) < 0.01 and log2FC (fold change) >1 as the thresholds for significant differences. The DEmRNAs were functionally annotated by gene ontology (GO) using the R software ClusterProfile package, and KEGG pathways using the Metascape software (http://metascape.org/gp/index.html#/main/step1) (15).

Construction of the lncRNA-miRNA-mRNA Network

The DElncRNAs and DEmRNAs between the non-tumor and SEM, non-tumor and non-SEM, and non-tumor and non-SEM+SEM groups were identified using the edgeR software as described above. The common DElncRNAs and DEmRNAs among the three comparison groups were screened, and those with Spearman correlation coefficient r2>0.9999 and p<0.001 were selected for network construction. The target miRNAs of the lncRNA-mRNA interaction pairs were predicted using the R software miRNAtap package, TargetScan (16) and Miranda (17). The miRNAs obtained from all three programs were used to construct the lncRNA-miRNA-mRNA network, which was visualized using Cytoscape_v3.8.0.

Public Database Data Mining

The DEmRNAs were validated using the GEPIA2 database (http://gepia2.cancer-pku.cn/#index) (18). The RFS of the different groups (total cases 134) were plotted using the Kaplan-Meier Plotter (http://kmplot.com/analysis/index.php?p=background), and compared in terms of P values and hazard ratios (HR) with 95% confidence intervals (CI) by the Log-rank test and univariate Cox proportional hazard regression (19).

Risk Factor Analysis

RNA-sequencing data (level 3) of 134 tumors and corresponding clinical information were obtained from The Cancer Genome Atlas (TCGA) dataset (https://www.cancer.gov/). The predictive accuracy and risk score of genes were compared by TimeRoc analysis. The minimum absolute shrinkage and selection operator (Lasso) regression algorithm was used for feature selection with 10-fold cross-validation using the R software package GLMnet. All statistical analyses were performed using R software (R Foundation for Statistical Computing, 2020) version V4.0.3. P <0.05 was considered statistically significant.

Gene Set Variation Analysis

The genomic gene set analysis in TGCT was used the Gene Set Cancer Analysis (GSCA) (http://bioinfo.life.hust.edu.cn/GSCA/#/), which is an integrated database for genomic and immunogenomic gene set cancer analysis (20). Immunogenomic analysis was performed by ImmuCellAI (Immune Cell Abundance Identifier) algorithm with 24 immunes cells (http://bioinfo.life.hust.edu.cn/ImmuCellAI#!/) (21). P <0.05 was considered statistically significant.

Results

Identification of Differentially Expressed mRNAs in TGCT

A total of 11,469 mRNAs were differentially expressed between the non-tumor and non-SEM samples, of which 3,894 were up-regulated and 7,575 were down-regulated in the latter (Figures 1A, D). Furthermore, there were 21,506 DEmRNAs in SEM relative to the non-tumor samples, of which 5974 were up-regulated and 15,632 were down-regulated (Figures 1B, D). Finally, the SEM and non-SEM tumors exhibited 20,197 DEmRNAs, including 5,468 up-regulated and 14,729 down-regulated mRNAs compared to the non-tumor samples (Figures 1C, D). The expression levels of the randomly selected ADAM19, DNMT3L, BRCC3, ZMIZ1, BRAP, DCAF5, FBF1 and OPTN were verified using the GEPIA2 database (Figure 1E).

FIGURE 1

Figure 1 Identification of DEmRNAs across different groups. (A-C). Volcano plots showing the DEmRNAs between (A) non-tumor vs non-SEM, (B) non-tumor vs SEM, and (C) non-tumor vs SEM and non-SEM with |Log2FC|>1 and FDR <0.01. (D) Histogram showing the DEmRNAs between groups. (E) The validation of randomly selected DEmRNAs in the GEPIA database with |Log2FC|>1 and p <0.01 as the cut-off values. *Difference was statistically significant.

Expression Pattern of Top 100 mRNAs in TGCT

To determine the contribution of DEmRNAs to TGCT, we analyzed the top 100 mRNAs with high expression levels in the non-tumor (vs SEM + non-SEM), SEM (vs non-tumor + non-SEM), and non-SEM (vs non-tumor + SEM) samples (Figure 2A). The highly expressed genes in the non-tumor samples were enriched in the GO terms such as “structural constituent of ribosome”, “structural molecule activity” and “nucleoside-triphosphatase activity” (Figure 2B), and the “Tight junction”, “Gap junction” and “Apoptosis” pathways (Figure 2E). In the non-SEM samples, the significantly enriched GO terms were “molecular function”, “MHC protein complex binding” and “translation factor activity, RNA binding” (Figure 2C), whereas the main KEGG pathways were enriched in immune-related signaling pathways, such as “B cell receptor signaling pathway”, “Antigen processing and presentation” and “intestinal immune network for IgA production” (Figure 2F). The highly expressed genes in SEM showed significant enrichment in “MHC protein complex binding” “structural constituent of ribosome” and “molecular function” (Figure 2D), and the immune-related signaling pathways, such as “Antigen processing and presentation”, “intestinal immune network for IgA production” and “Th17 cell differentiation” (Figure 2G). Furthermore, the top 100 highly expressed mRNAs in all groups were associated with multiple transcription factors, including SMARCA5 and SMARCC1, which were overexpressed in all three groups (Figure 2H).

FIGURE 2

Figure 2 Intergroup-specific expression and functional enrichment analysis of mRNAs. (A) The heatmap of top 100 highly expressed mRNAs in the non-tumor, SEM and non-SEM samples. (B–G). The top 20 GO terms and KEGG pathways in non-tumor (B, E), SEM (C, F), and non-SEM (D, G) groups. (H) Bubble chart of putative transcription factors of the top 100 mRNAs in three groups.

Enrichment Analysis of Common DEmRNAs

As shown in Figure 3A, there were many down-regulated mRNAs in SEM and non-SEM samples compared to the controls (Figure 3A). Specifically, 3047 mRNAs were up-regulated and 7,883 mRNAs were downregulated in the SEM and total TGCT (SEM + non-SEM) samples relative to the non-tumor controls (Figures 3B, C). The non-SEM and combined TGCT samples showed upregulation of 196 mRNAs and downregulation of 88 mRNAs compared to the non-tumor group. Four upregulated and 18 downregulated mRNAs were common to both SEM and non-SEM groups compared to the non-tumor control (Figures 3B, C). A pairwise comparison of all groups revealed that 2,091 upregulated mRNAs and 6,270 downregulated mRNAs were common. In addition, 1,603 and 1,191 mRNAs were respectively upregulated and downregulated only in the non-SEM group relative to the non-tumor group, whereas 732 upregulated and 1,461 downregulated mRNAs were specific to the non-tumor-vs-SEM comparison, and 134 upregulated and 488 downregulated mRNAs were only detected when comparing non-tumor samples to all TGCT samples (Figures 3B, C). GO analysis revealed that the DEmRNAs common to all three groups were enriched in “cell motility”, “movement of cell or subcellular component” and “cell cycle” (Figure 3D), and the top 20 pathways were “cell adhesion molecules”, “adherence junction” and “Tight junction” (Figure 3E). Furthermore, the signaling pathways significantly associated with the top 100 common DEmRNAs included “apoptosis signaling pathway”, “positive regulation of cellular component movement”, “leukocyte activation involved in immune” and “cytokine signaling in immune system”, and were interconnected (Figure 3F). The protein-protein interaction (PPI) network of the top 100 common DEmRNAs revealed 3,098 connections (Figure 3G).

FIGURE 3

Figure 3 Enrichment analysis of common DEmRNAs. (A) Heat map showing hierarchical clustering of common DEmRNAs in the non-tumor, SEM and non-SEM groups. (B, C) Upset plots showing the distribution of (B) upregulated and (C) downregulated mRNAs for the indicated pairs. (D, E) Bar graph showing the top 20 enriched GO and KEGG pathways associated with DEmRNAs common to all three pairs. (F) The interactive network of the enriched pathways. Different colors represent different biological processes. (G) The protein-protein interaction network (PPI) of the top 100 common DEmRNAs. Color intensity is indicative of the number of connections.

Construction and Analysis of the lncRNA-miRNA-mRNA Network

A total of 2,924 DElncRNAs and 4,842 DEmRNAs were common to the non-tumor vs SEM, non-tumor vs non-SEM+SEM, and non-tumor vs non-SEM comparisons (Figures 4A, B). The correlated mRNA-lncRNA pairs were screened with R2 > 0.9999 and p < 0.001 as the criteria. Nine lncRNAs were identified, including SPATA42, AL450226.1, TCONS_00053325, DPYD-AS2, ELW67898.1, DIAPH1-AS1, Z97205.2, ISPD-AS1 and TCONS_00022110. The nine mRNAs were FAM71E2, HEPACAM2, RGSL1, DPYD-AS2, ZPBP2, DYNLRB2, PCYT2, BANF2, and GRK4. The regulatory miRNAs of the candidate mRNAs and lncRNAs were predicted by the R software miRNAtap package, TargetScan (16), and Miranda (17). We identified 8 lncRNA–miRNA–mRNA regulatory networks that included 9 lncRNAs nodes, 9 mRNAs nodes, and 82 miRNAs nodes with 166 possible interactions (Figure 4C). The DEmRNAs common to non-tumor vs non-SEM and non-tumor vs SEM pairs were associated with “movement of cell or subcellular component”, “reproduction” and “cell motility” both in the groups of (Figures 4D, E), whereas “male gamete generation”, “cell differentiation” and “reproduction progress” were the functions enriched in the DEmRNAs between non-tumor vs non-SEM + SEM (Figure 4F). The top 20 enriched pathways among the DEmRNAs between non-tumor vs non-SEM groups were “focal adhesion” and “cell adhesion molecules” (Figure 4G), that for non-tumor vs SEM were “biosynthesis of secondary metabolism”, “steroid hormone biosynthesis” and “cell adhesion molecules” (Figure 4H), and “cell adhesion molecules” and “metabolic pathway” were enriched for non-tumor vs non-SEM + SEM (Figure 4I).

FIGURE 4

Figure 4 The lncRNA-miRNA-mRNA ceRNA network in TGCT. (A, B) Wayne diagrams of DElncRNAs (A) and DEmRNAs (B) in three comparison groups, with |log2FC|>2, FDR<0.01 as the thresholds. (C) The lncRNA–miRNA–mRNA network consisting of 9 lncRNAs (blue squares), 82 miRNAs (yellow triangles) and 9 mRNAs (rose-red circles) with 166 possible interactions. (D–I) Enrichment analysis of the DEmRNAs in (D, G) non-tumor vs non-SEM, (E, H) non-tumor vs SEM, and (F, I) non-tumor vs SEM + non-SEM. The top 20 enriched GO terms and pathways are shown.

Evaluation of Clinical Outcomes

To explore the clinical value of the nine mRNAs (see above) in TGCTs, we obtained the raw RNA-Seq data (Level 3) of 134 TGCTs and the corresponding clinical information from TCGA dataset (https://www.cancer.gov/) (Supplementary Table 1). A polygenic risk score (PRS) was then calculated based on the correlation between the expression levels of the nine mRNAs and prognosis using the LASSO cox regression analysis (Figures 5A, B). Accordingly, the patients were divided into the high-risk and low-risk groups (Figure 5C, top). Overexpression of GRK4, PCYT2 and RGSL1 was associated with increased mortality rates (Figure 5C, middle) and a higher risk score (Figure 5C, bottom). The prognostic index was calculated as follows:

$$\text{Risk} \text{score}=(1.841)*\text{RGSL}1+(0.1582)*\text{PCYT}2+(0.3161)*\mathrm{GRK}4$$

FIGURE 5

Figure 5 Development of the prognostic index based on ceRNA-related genes. (A) The coefficient of the selected feature is shown by the lambda parameter, with the value of the independent variable lambda on the horizontal axis and the coefficient of the independent variable on the vertical axis. (B) The relationship between partial likelihood deviation and log (λ) was plotted using the Lasso Cox regression model. (C) Risk score curve showing survival of patients and expression profiles of the three prognostic genes in low- and high-risk groups. (D) Kaplan-Meier curve showing the association between patient survival and the three-gene signature. Median survival duration (years) corresponds to a 50% survival rate. The ROC curve and AUC of the risk model at different times. (E–G) Survival of patients stratified based on GRK4, PCYT2, and RGSL1 expression.

KM survival analysis also confirmed that the high expression level of this gene set portended poor prognosis (log-rank p=0.0404, HR=2.077, Figure 5D). Furthermore, the area under the time-dependent ROC curve of the gene set was greater than 0.5, indicating good predictive power (Figure 5D). To further establish the prognostic value of GRK4, PCYT2 and RGSL1, we analyzed the correlation between their expression levels with RFS using Kaplan-Meier Plotter. PCTY2 (P=0.049, Figure 5F) expression level showed a significant negative correlation with RFS, while that of GRK4 (P=0.12, Figure 5E) and RGSL1 (P=0.064) were negatively correlated with RFS without statistical significance (Figures 5F, G).

GSCA of GRK4, PCYT2, and RGSL1

The potential therapeutic relevance of GRK4, PCYT2 and RGSL1 was evaluated by GSCA (http://bioinfo.life.hust.edu.cn/GSCA/#/) (20) and Spearman correlation analysis. As shown in Figure 6A, the expression levels of this gene set in TGCT showed a significant negative correlation with DNA damage, androgen receptor (AR), estrogen receptor (ER) and other pathways, and positive correlation with RASMAPK, TSCmTOR and other pathways (Figure 6A). Furthermore, the gene set was also negatively correlated with the infiltration of immune cells such as Cytotoxic, TFH, B cell, CD4_T and CD8_T, and positively with Monocyte, Th2 and Macrophage (Figure 6B, Supplementary Tables 2, 3).

FIGURE 6

Figure 6 The correlation of GRK4, PCYT2 and RGSL1 with cancer-related pathways, immune cell infiltration and GDSC. (A) Association between GSVA score and cancer-related pathways in TGCT. (B) Correlation between immune cell infiltrates and GSVA enrichment score in the selected cancers. (C) Correlation between gene expression and GDSC drug sensitivity. *P-value < 0.05; ^#FDR < 0.05.

Finally, we analyzed the correlation between drug sensitivity of cancer cell lines in GDSC (Genomics of Drug Sensitivity in Cancer), and the GRK4, PCYT2 and RGSL1 expression levels (22). GRK4 was correlated with the target drug 17-AAG and increased sensitivity to RDEA119, PD-0325901, CX5461, Trametinib and Selumetinib. PCYT2 showed a significant positive correlation with sensitivity to targeted drugs such as CX-5461, PI-103 (PI3K and mTOR inhibitors) and MPS-1-IN-1 (Figure 6C).

Discussion

Although previous studies have identified the hereditary nature of TGCT with an estimated 37–49% familial cases (23, 24), the only moderate penetrance gene is GHEK2 whose pathogenic variants have been associated with the risk of TGCT (25). The testis expresses the largest number of genes of any mammalian organ (26), especially high numbers of genes predominantly expressed (27). In our study, it also had numerous DEmRNAs in TGCTs. We identified several dysregulated mRNAs in TGCT tissues, which are likely involved in tumor development and progression. The upregulated mRNAs were functionally annotated to immunomodulation, cell migration and invasion, indicating a correlation between infiltration of immune cells and TGCT metastasis. The dynamics of the immune microenvironment in the scoring model based on stroma and immune cell infiltration are essential factors predicting cancer prognosis and chemotherapy response (28). Tumor-infiltrating immune cells are the primary immune signatures closely related to the clinical outcomes of immunotherapies (29). Therefore, we explored the influence of immune cells on the efficacy of immunotherapy and the clinical progression of patients with TGCT. GRK4, PCYT2 and RGSL1 were negatively correlated with the infiltration of Cytotoxic, TFH, B cell, CD4_T and CD8_T populations, and positively with that of Monocyte, Th2 and Macrophage. Recently, a study also reported that advanced TGCT is associated with a decrease in T cells and NK cells, and increased infiltration of Tregs, neutrophils, mast cells and macrophages (30). Furthermore, chimeric antigen receptor T (CAR-T) cells also showed anti-tumor activity against metastatic EC xenografts in a mouse model (31). Our results showed that upregulated mRNAs in TGCTs are likely involved in immunomodulation and immune cell infiltration, and thus promising therapeutic targets.

Numerous studies have shown that lncRNAs can compete with other RNAs to bind miRNAs, and function as ceRNAs to regulate the expression of target mRNAs (32). CeRNAs are repeatedly dysregulated in cancer, and involved in tumor initiation and progression (14). We constructed a lncRNA-miRNA-mRNA ceRNA regulatory network with the TGCT DEmRNAs and DElncRNAs and identified HEPACAM1 (TCONS_00185248) as one of the hub genes. As other studies have shown, hepatocyte cell adhesion molecules 1 and 2 (HEPACAM1 and 2) are members of the immunoglobulin family, and inhibit cell cycle progression in breast cancer via p53, p21 and p27 signaling (33). In addition, the nine mRNAs of the ceRNA regulatory networks included GRK4, PCYT2 and RGSL1, which showed prognostic relevance and was associated with cancer-related pathways such as DNA Damage, AR, ER, RASMAPK, and TSC-mTOR. A meta-analysis also showed that a relative reduction in androgens compared to the general population increases the risk of TGCT (34). Other studies have reported that the TSC1/2-mTOR pathway regulates the function of diverse immune cells, along with cell growth and metabolism (35–37). In our study, the expression level of the gene set was correlated with the infiltration of various immune cells and therefore may regulate the immune responses to TGCT via the TSC1/2-mTOR pathway. Furthermore, GRK4 and PCYT2 were correlated with the sensitivity to multiple chemotherapeutic drugs, and PCYT2 showed a significant negative correlation with RFS in TGCT patients. Thus, GRK4, PCYT2 and RGSL1 are reliable prognostic markers and potential therapeutic targets for TGCTs.

We constructed a ceRNA network for TGCT based on the DEmRNAs and DElncRNAs for the first time, and identified markers involved in TGCT prognosis and chemotherapy resistance, which can help select the optimum clinical regimen. However, there are some limitations in our study that ought to be considered. First, our conclusions are only based on bioinformatics analysis and other methods, and further experimental verification is needed. Second, our sequencing data lacks the expression information of miRNAs, since the sample is not enough to detect the level of miRNAs. Third, patients were enrolled from only one hospital, the possible influence of regional and ethnic factors cannot be eliminated. In addition, the sample size in this study was relatively small. Finally, the involvement of GRK4, PCYT2 and RGSL1 in cancer-related pathways and drug susceptibility needs further experimental validation. In summary, our findings provide new insights into the pathogenesis of TGCT and identify GRK4, PCYT2 and RGSL1 as key prognostic markers and therapeutic targets.

Conclusion

We identified several dysregulated mRNAs in TGCT that are related to immunomodulation, cell migration and invasion. In addition, GRK4, PCYT2 and RGSL1A are potential TGCT-specific biomarkers that can predict RFS, tumor immunity and chemotherapeutic resistance.

Data Availability Statement

Publicly available datasets were analyzed in this study. This data can be found here: https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga.

Ethics Statement

The studies involving human participants were reviewed and approved by Hunan Cancer Hospital, Department of Urology, The Affiliated Cancer Hospital of Xiangya School of Medicine of Central South University. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

HB, LT, and LF conceived and planned the experiments. FZ, DZ, and ZL collected clinical samples. HB, FZ, and ZL analyzed and interpreted the data. FZ and HB drafted the manuscript. HB, JF, and LT proofed the manuscript. LF revised and confirmed the final manuscript. All authors contributed to the article and approved the submitted version.

Funding

This research was funded by the National Key Research and Development Program of China (Nos. 2016YFC1000600), Changsha Municipal Natural Science Foundation (Nos. kq2014033), Hunan Provincial Grant for Innovative Province Construction (Nos. 2019SK4012), and the Fundamental Research Funds for the Central Universities of Central South University (Nos. 160171016).

Conflict of Interest

Authors DZ, LX, HB and LF were employed by the company China International Trust and Investment Corporation (CITIC).

The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fendo.2021.743155/full#supplementary-material

References

1. Singh R, Fazal Z, Freemantle S, Spinella M. Between a Rock and a Hard Place: An Epigenetic-Centric View of Testicular Germ Cell Tumors. Cancers (2021) 13:1506. doi: 10.3390/cancers13071506

PubMed Abstract | CrossRef Full Text | Google Scholar

2. Williamson SR, Delahunt B, Magi-Galluzzi C, Algaba F, Egevad L, Ulbright TM, et al. The World Health Organization 2016 Classification of Testicular Germ Cell Tumours: A Review and Update From the International Society of Urological Pathology Testis Consultation Panel. Histopathology (2017) 70:335–46. doi: 10.1111/his.13102

PubMed Abstract | CrossRef Full Text | Google Scholar

3. Moul JW, McCarthy WF, Fernandez EB, Sesterhenn IA. Percentage of Embryonal Carcinoma and of Vascular Invasion Predicts Pathological Stage in Clinical Stage I Nonseminomatous Testicular Cancer. Cancer Res (1994) 54:362–4.

PubMed Abstract | Google Scholar

4. Ondrus D, Matoska J, Belan V, Kausitz J, Goncalves F, Hornak M. Prognostic Factors in Clinical Stage I Nonseminomatous Germ Cell Testicular Tumors: Rationale for Different Risk-Adapted Treatment. Eur Urol (1998) 33:562–6. doi: 10.1159/000019656

PubMed Abstract | CrossRef Full Text | Google Scholar

5. Pont J, Holtl W, Kosak D, Machacek E, Kienzer H, Julcher H, et al. Risk-Adapted Treatment Choice in Stage I Nonseminomatous Testicular Germ Cell Cancer by Regarding Vascular Invasion in the Primary Tumor: A Prospective Trial. J Clin Oncol: Off J Am Soc Clin Oncol (1990) 8:16–20. doi: 10.1200/jco.1990.8.1.16

CrossRef Full Text | Google Scholar

6. Caggiano C, Cavallo F, Giannattasio T, Cappelletti G, Rossi P, Grimaldi P, et al. Testicular Germ Cell Tumors Acquire Cisplatin Resistance by Rebalancing the Usage of DNA Repair Pathways. Cancers (2021) 13:787. doi: 10.3390/cancers13040787

PubMed Abstract | CrossRef Full Text | Google Scholar

7. Chovanec M, Abu Zaid M, Hanna N, El-Kouri N, Einhorn LH, Albany C. Long-Term Toxicity of Cisplatin in Germ-Cell Tumor Survivors. Ann Oncol: Off J Eur Soc Med Oncol (2017) 28:2670–9. doi: 10.1093/annonc/mdx360

CrossRef Full Text | Google Scholar

8. Chovanec M, Lauritsen J, Bandak M, Oing C, Kier GG, Kreiberg M, et al. Late Adverse Effects and Quality of Life in Survivors of Testicular Germ Cell Tumour. Nat Rev Urol (2021) 18:227–45. doi: 10.1038/s41585-021-00440-w

PubMed Abstract | CrossRef Full Text | Google Scholar

9. Fung C, Dinh P Jr., Ardeshir-Rouhani-Fard S, Schaffer K, Fossa SD, Travis LB. Toxicities Associated With Cisplatin-Based Chemotherapy and Radiotherapy in Long-Term Testicular Cancer Survivors. Adv Urol (2018) 2018:8671832. doi: 10.1155/2018/8671832

PubMed Abstract | CrossRef Full Text | Google Scholar

10. Lobo J, Gillis A, Jerónimo C, Henrique R, Looijenga L. Human Germ Cell Tumors Are Developmental Cancers: Impact of Epigenetics on Pathobiology and Clinic. Int J Mol Sci (2019) 20:258. doi: 10.3390/ijms20020258

CrossRef Full Text | Google Scholar

11. Lobo J, Constâncio V, Leite-Silva P, Guimarães R, Cantante M, Braga I, et al. Differential Methylation EPIC Analysis Discloses Cisplatin-Resistance Related Hypermethylation and Tumor-Specific Heterogeneity Within Matched Primary and Metastatic Testicular Germ Cell Tumor Patient Tissue Samples. Clin Epigenet (2021) 13:70. doi: 10.1186/s13148-021-01048-y

CrossRef Full Text | Google Scholar

12. Das MK, Furu K, Evensen HF, Haugen OP, Haugen TB. Knockdown of SPRY4 and SPRY4-IT1 Inhibits Cell Growth and Phosphorylation of Akt in Human Testicular Germ Cell Tumours. Sci Rep (2018) 8:2462. doi: 10.1038/s41598-018-20846-8

PubMed Abstract | CrossRef Full Text | Google Scholar

13. Morup N, Rajpert-De Meyts E, Juul A, Daugaard G, Almstrup K. Evaluation of Circulating miRNA Biomarkers of Testicular Germ Cell Tumors During Therapy and Follow-Up-A Copenhagen Experience. Cancers (2020) 12:759. doi: 10.3390/cancers12030759

CrossRef Full Text | Google Scholar

14. Xu D, Wang L, Pang S, Cao M, Wang W, Yu X, et al. The Functional Characterization of Epigenetically Related lncRNAs Involved in Dysregulated CeRNA-CeRNA Networks Across Eight Cancer Types. Front Cell Dev Biol (2021) 9:649755. doi: 10.3389/fcell.2021.649755

PubMed Abstract | CrossRef Full Text | Google Scholar

15. Zhou Y, Zhou B, Pache L, Chang M, Khodabakhshi AH, Tanaseichuk O, et al. Metascape Provides a Biologist-Oriented Resource for the Analysis of Systems-Level Datasets. Nat Commun (2019) 10:1523. doi: 10.1038/s41467-019-09234-6

PubMed Abstract | CrossRef Full Text | Google Scholar

16. Agarwal V, Bell GW, Nam J-W, Bartel DP. Predicting Effective microRNA Target Sites in Mammalian mRNAs. Elife (2015) 4:e05005. doi: 10.7554/eLife.05005

CrossRef Full Text | Google Scholar

17. Pasquinelli AE. MicroRNAs and Their Targets: Recognition, Regulation and an Emerging Reciprocal Relationship. Nat Rev Genet (2012) 13:271–82. doi: 10.1038/nrg3162

PubMed Abstract | CrossRef Full Text | Google Scholar

18. Tang Z, Kang B, Li C, Chen T, Zhang Z. GEPIA2: An Enhanced Web Server for Large-Scale Expression Profiling and Interactive Analysis. Nucleic Acids Res (2019) 47:W556–60. doi: 10.1093/nar/gkz430

PubMed Abstract | CrossRef Full Text | Google Scholar

19. Nagy Á., Munkácsy G, Győrffy B. Pancancer Survival Analysis of Cancer Hallmark Genes. Sci Rep (2021) 11:6047. doi: 10.1038/s41598-021-84787-5

PubMed Abstract | CrossRef Full Text | Google Scholar

20. Liu C, Hu F, Xia M, Han L, Zhang Q, Guo A. GSCALite: A Web Server for Gene Set Cancer Analysis. Bioinf (Oxford England) (2018) 34:3771–2. doi: 10.1093/bioinformatics/bty411

CrossRef Full Text | Google Scholar

21. Miao YR, Zhang Q, Lei Q, Luo M, Xie GY, Wang HX, et al. ImmuCellAI: A Unique Method for Comprehensive T-Cell Subsets Abundance Prediction and Its Application in Cancer Immunotherapy. Adv Sci (2020) 7:1902880. doi: 10.1002/advs.201902880

CrossRef Full Text | Google Scholar

22. Yang WJ, Soares J, Greninger P, Edelman EJ, Lightfoot H, Forbes S, et al. Genomics of Drug Sensitivity in Cancer (GDSC): A Resource for Therapeutic Biomarker Discovery in Cancer Cells. Nucleic Acids Res (2013) 41:D955–61. doi: 10.1093/nar/gks1111

PubMed Abstract | CrossRef Full Text | Google Scholar

23. Litchfield K, Thomsen H, Mitchell JS, Sundquist J, Houlston RS, Hemminki K, et al. Quantifying the Heritability of Testicular Germ Cell Tumour Using Both Population-Based and Genomic Approaches. Sci Rep (2015) 5:13889. doi: 10.1038/srep13889

PubMed Abstract | CrossRef Full Text | Google Scholar

24. Mucci LA, Hjelmborg JB, Harris JR. Familial Risk and Heritability of Cancer Among Twins in Nordic Countries (Vol 315, Pg 68, 2016). JAMA-Journal Am Med Assoc (2016) 315:822–2. doi: 10.1001/jama.2016.0197

CrossRef Full Text | Google Scholar

25. AlDubayan SH, Pyle LC, Gamulin M, Kulis T, Moore ND, Taylor-Weiner A, et al. Association of Inherited Pathogenic Variants in Checkpoint Kinase 2 (CHEK2) With Susceptibility to Testicular Germ Cell Tumors. JAMA Oncol (2019) 5:514–22. doi: 10.1001/jamaoncol.2018.6477

PubMed Abstract | CrossRef Full Text | Google Scholar

26. Xia B, Yan Y, Baron M, Wagner F, Barkley D, Chiodin M, et al. Widespread Transcriptional Scanning in the Testis Modulates Gene Evolution Rates. Cell (2020) 180:248–+. doi: 10.1016/j.cell.2019.12.015

PubMed Abstract | CrossRef Full Text | Google Scholar

27. de Goede OM, Nachun DC, Ferraro NM, Gloudemans MJ, Rao AS, Smail C, et al. Population-Scale Tissue Transcriptomics Maps Long Non-Coding RNAs to Complex Disease. Cell (2021) 184:2633–+ e19. doi: 10.1016/j.cell.2021.03.050

PubMed Abstract | CrossRef Full Text | Google Scholar

28. Luo Q, He F, Cao J. A Stromal and Immune Cell Infiltration-Based Score Model Predicts Prognosis and Chemotherapy Effect in Colorectal Cancer. Int Immunopharmacol (2021) 99:107940. doi: 10.1016/j.intimp.2021.107940

PubMed Abstract | CrossRef Full Text | Google Scholar

29. Pandya PH, Murray ME, Pollok KE, Renbarger JL. The Immune System in Cancer Pathogenesis: Potential Therapeutic Approaches. J Immunol Res (2016) 2016:4273943. doi: 10.1155/2016/4273943

PubMed Abstract | CrossRef Full Text | Google Scholar

30. Siska PJ, Johnpulle RAN, Zhou A, Bordeaux J, Kim JY, Dabbas B, et al. Deep Exploration of the Immune Infiltrate and Outcome Prediction in Testicular Cancer by Quantitative Multiplexed Immunohistochemistry and Gene Expression Profiling. Oncoimmunol (2017) 6:e1305535. doi: 10.1080/2162402x.2017.1305535

CrossRef Full Text | Google Scholar

31. Richie JP. Re: CD30-Redirected Chimeric Antigen Receptor T Cells Target CD30(+) and CD30(-) Embryonal Carcinoma via Antigen-Dependent and Fas/FasL Interactions. J Urol (2020) 203:883–4. doi: 10.1097/ju.0000000000000780

PubMed Abstract | CrossRef Full Text | Google Scholar

32. Qi Y, Ma Y, Peng Z, Wang L, Li L, Tang Y, et al. Long Noncoding RNA PENG Upregulates PDZK1 Expression by Sponging miR-15b to Suppress Clear Cell Renal Cell Carcinoma Cell Proliferation. Oncogene (2020) 39:4404–20. doi: 10.1038/s41388-020-1297-1

PubMed Abstract | CrossRef Full Text | Google Scholar

33. Klopfleisch R, Klose P, da Costa A, Brunnberg L, Gruber AD. HEPACAM1 and 2 Are Differentially Regulated in Canine Mammary Adenomas and Carcinomas and its Lymph Node Metastases. BMC Vet Res (2010) 6:15. doi: 10.1186/1746-6148-6-15

PubMed Abstract | CrossRef Full Text | Google Scholar

34. Henderson BE, Ross RK, Pike MC, Casagrande JT. Endogenous Hormones as a Major Factor in Human Cancer. Cancer Res (1982) 42:3232–9.

PubMed Abstract | Google Scholar

35. Byles V, Covarrubias AJ, Ben-Sahra I, Lamming DW, Sabatini DM, Manning BD, et al. The TSC-mTOR Pathway Regulates Macrophage Polarization. Nat Commun (2013) 4:2834. doi: 10.1038/ncomms3834

PubMed Abstract | CrossRef Full Text | Google Scholar

36. Fingar DC, Richardson CJ, Tee AR, Cheatham L, Tsou C, Blenis J. mTOR Controls Cell Cycle Progression Through Its Cell Growth Effectors S6K1 and 4E-BP1/eukaryotic Translation Initiation Factor 4E. Mol Cell Biol (2004) 24:200–16. doi: 10.1128/mcb.24.1.200-216.2004

PubMed Abstract | CrossRef Full Text | Google Scholar

37. Weichhart T, Costantino G, Poglitsch M, Rosner M, Zeyda M, Stuhlmeier KM, et al. The TSC-mTOR Signaling Pathway Regulates the Innate Inflammatory Response. Immunity (2008) 29:565–77. doi: 10.1016/j.immuni.2008.08.012

PubMed Abstract | CrossRef Full Text | Google Scholar