A Pan-Cancer Analysis of SRD5A1, a Potential New Carcinogenic Indicator Related to Immune Infiltration and Prognosis of UCEC

doi:10.21203/rs.3.rs-1951445/v1

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Research Article

A Pan-Cancer Analysis of SRD5A1, a Potential New Carcinogenic Indicator Related to Immune Infiltration and Prognosis of UCEC

https://doi.org/10.21203/rs.3.rs-1951445/v1

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Background:

Steroid 5-Alpha-reductase type I (SRD5A1) converts testosterone to dihydrotestosterone and regulates sex hormone levels, which facilitates tumor incidence or progression. However, the molecular mechanism behind SRD5A1's role in pan-cancer remains unknown.

Methods:

RNA-seq data from TCGA and the Genotype-Tissue Expression (GTEx) database were used to examine SRD5A1 expression. String, HPA, GEPIA2, TIMER2, and cBioportal database were used to explore the protein and immune cell infiltration information of SRD5A1. The R package “ClusterProfiler” was used to conduct KEGG and GO enrichment analyses, and CancerSEA was used to investigate the functional heterogeneity of cancer cells.

Results:

SRD5A1 expression was differentially and higher predicted worse survival status in most tumor samples. Increased expression of SRD5A1 was detrimental to the clinical prognoses of cancer patients, especially UCEC. SRD5A1 expression was closely correlated with T cell infiltration and immune checkpoints. There were significant correlations between SRD5A1 expression and tumor mutation burden (TMB) or microsatellite instability (MSI)in several cancers. High SRD5A1 levels were associated with the infiltration of myeloid-derived suppressor cells (MDSCs) and Th2 subsets of CD4+ T cells in most cancers. Enrichment analyses revealed that SRD5A1 participated in Transcription Androgen Receptor nuclear signaling and Metabolism. Finally, we validated pan-cancer SRD5A1 expression, and its impacts on immune infiltrate in UCEC.

Conclusion:

Our results suggest that SRD5A1 may contribute to the immune infiltration in the tumor microenvironment. SRD5A1 might synergize with other immune checkpoints serve as a carcinogenic indicator related to prognosis in pan-cancer, especially UCEC, and shed new light on therapeutics of cancers for clinicians.

Steroid 5-Alpha-reductase type I (SRD5A1)

Pan-Cancer analysis

The Cancer Genome Atlas (TCGA)

Uterine Corpus Endometrial Carcinoma（UCEC）

immune infiltration

prognosis

tumor microenvironment (TME)

Cancer is a major public health concern and the leading cause of death worldwide, with incidence and mortality rates rising at an alarming rate[1]. Even though cancer treatment has vastly improved in recent decades and now offers cures for many formerly fatal cases, a huge number of individuals still encounter therapeutic failure and succumb to cancer [2]. As a result, there is a pressing need to understand the molecular mechanisms underlying cancer pathogenesis and to discover effective biomarkers for cancer detection, diagnosis, and treatment[3]. Since large-scale genomics projects such as The Cancer Genome Atlas (TCGA) database and the NCBI Gene Expression Omnibus (GEO) public repository give matched molecular and clinical data of diverse malignancies, it is possible to systematically investigate the impact of single gene expression on survival. Scientists are currently very interested in the use of cancer biomarkers, which drives them to look for new prognostic biomarkers and therapeutic targets.

The 5α-R type I is an enzyme encoded by the SRD5A1 gene in humans[4]. Steroid 5α-reductase (SRD5A) converts testosterone (T) to dihydrotestosterone (DHT) in an irreversible manner[5]. DHT is a more potent derivative of T that binds to the androgen receptor (AR) 10 times better than T[5]. The DHT–AR complex then acts selectively on AR response elements, promoting the transcription of downstream target genes such as CD24, Wnt/β-catenin signaling pathway, and vascular endothelial growth factor (VEGF), all of which aid tumor proliferation, metastasis, and angiogenesis[6]. SRD5A has three isoforms: 5-R type I, 5-R type II, and 5-R type III, each of which is encoded by a different gene (SRD5A1, SRD5A2, and SRD5A3)[7] [8]. In humans, the protein isozyme encoded by the SRD5A1 gene was found expressed in non-reproductive tissues like the esophagus, liver, skin, and other tissues[9], but SRD5A2 was the most prevalent enzyme in reproductive organs[7]. SRD5A1 is highly expressed in prostate cancer (PC)[5, 10, 11], liver cancer [12], non-small cell lung cancer[13], breast cancer[14, 15], and colorectal cancer(CRC) [16]in previous studies. In colorectal cancer, SRD5A1 stimulates cell survival and migration via the nuclear factor-κb/vascular endothelial growth factor signaling pathway[16].SRD5A1 is significantly expressed in metastatic and castration-resistant prostate cancer, and its expression is linked to prostate cancer prognosis, suggesting that it could be an important target for prostate cancer therapy[17]. As an integral aspect of steroid metabolism, SRD5A1 converts testosterone to dihydrotestosterone and regulates sex hormone levels, which facilitates tumor incidence or progression. However, the molecular mechanism behind SRD5A1's role in pan-cancer remains unknown.

In the current research, we systematically characterized the prevalence and prognostic value of SRD5A1 expression in pan-cancer. To study the functions of SRD5A1 in prognosis and the immune response, we combined data from several databases, including The Cancer Genome Atlas (TCGA), Kaplan-Meier Plotter, TIMER2, GEPIA2, cBioPortal, STRING, Human Protein Atlas (HPA), and CancerSEA. Across numerous cancer types, we looked for possible links between SRD5A1 expression and tumor mutational burden (TMB), microsatellite instability (MSI), immune infiltration levels, and various immune-related genes. We also used the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis(GSEA) to investigate SRD5A1's biological function and pathways. The high expression of SRD5A1 was found to be associated with a poor prognosis in numerous cancer, especially in UCEC. Remarkably, SRD5A1 upregulation was associated with the increased infiltration of immune cells, including CD4+ T cells, CD8+ T cells, and myeloid-derived suppressor cells (MDSCs). According to pan-cancer analysis, the expression of SRD5A1 exhibited strong correlations with immune checkpoint genes. Our pan-cancer analysis provides a comprehensive understanding of SRD5A1's roles in oncogenesis in various malignancies, as well as options for promoting coordinated activities in the context of immunotherapy. These discoveries may have important implications in guiding both fundamental research and clinical practice.

Data Acquisition and Processing

As a landmark cancer genomics project, TCGA molecularly characterized more than 20,000 primary cancer and corresponding normal tissues across 33 cancer types[18]. In our analysis, TCGA transcriptome RNA-seq data and clinical information were downloaded using the UCSC Xena platform[19]. Transcripts per million (TPM) and fragments per kilobase million (FPKM) were used for quantification and comparison.

Analysis of SRD5A1 mRNA Expression Profiles

The mRNA expression profiles of SRD5A1 in major tissues and organs of the human body were explored in the Human Protein Atlas (HPA), as well as the single-cell transcriptomics analysis[20]. Differential mRNA expression analysis of normal and tumor samples in the “Gene_DE” module of TIMER2.0 database[21] .“Expression on Box Plots” module was used to depict box plots of expression differences between tumors and matched normal samples of the GTEx database, with the thresholds set as a P-value cutoff of 0.01 and log₂FC cutoff of 1, and “Match TCGA normal and GTEx data” was set. The log₂(TPM + 1) data was applied for the log scale. pathological stage analysis of SRD5A1were performed in the “Single Gene Analysis” module of GEPIA [22].

Survival Analysis

We downloaded the unified and standardized pan-cancer dataset from the UCSC (https://xenabrowser.net/) database: TCGA TARGET GTEx (PANCAN, N=19131, G=60499), and further we extracted ENSG00000145545 (SRD5A1) from it ) gene expression data in each sample, we further screened the sample sources: Primary Blood-Derived Cancer - Peripheral Blood (TCGA-LAML), Primary Tumor, Metastatic of TCGA-SKCM, Primary Blood-Derived Cancer - Bone Marrow, Primary Samples from Solid Tumor, Recurrent Blood-Derived Cancer - Bone Marrow, and we also draw from a TCGA prognosis study previously published in Cell [An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics, DOI: 10.1016/ j.cell.2018.02.052] obtained a high-quality TCGA prognostic data set, obtained TARGET follow-up data from UCSC's Cancer Browser (https://xenabrowser.net/datapages/) as a supplement and the follow-up time was shorter than For the 30-day sample, we further performed log2(x+0.001) transformation on each expression value. Finally, we also eliminated cancer types with less than 10 samples in a single cancer type.

We used the cox function of the R package survival (version 3.2-7) to build a Cox regression[23] to analyze the relationship between SRD5A1 expression and cancer prognosis, including overall survival (OS) and disease-specific survival (DSS). We calculated the log-rank P-value and hazard ratio (HR) with 95% confidence intervals (95% CI) via the “survival” package and utilized the “forest plot” package to visualize the survival analysis.

The Kaplan-Meier plotter, which is a web database aiming to evaluate the effect of 54,000 genes on survival in tumor types[24], was used to determine SRD5A1 expression-associated OS outcomes of patients. Additionally, the GEPIA2 database was also utilized to determine the correlation between SRD5A1 mRNA expression and OS and disease-free survival (DFS) of cancers[25].

Genomic Alterations and Mutation Profiles

Based on the cBioPortal (RRID:SCR_014555), SRD5A1 mutation frequency and general mutation count in cancer patients were calculated to analyze the genomic alterations of SRD5A1 in various TCGA cancer types[26]. The genome alterations of SRD5A1 included copy number amplification, deep or shallow deletion, missense mutation with uncertain significance, and mRNA upregulation. RNA-sequencing expression (level 3) profiles and corresponding clinical information for SRD5A1 were downloaded from the TCGA dataset(https://portal.gdc.com). Tumor mutation burden (TMB) is derived from the article The Immune Landscape of Cancer published by Vesteinn Thorsson et al. in 2018; Microsatellite instability (MSI) is derived from the Landscape of Microsatellite Instability Across 39 Cancer Types article published by Russell Bonneville et al. in 2017. All the analysis methods and R package were implemented by R version 4.0.3. If not stated otherwise, two-group data was performed by the Wilcox test. P values less than 0.05 were considered statistically significant (*P < 0.05).

Immune Infiltration Analysis

The ESTIMATE algorithm[27], which is a method that infers the fraction of immune and stromal cells in tumor samples via analysis of gene expression signatures, was applied to evaluate the immune cell infiltration levels (ImmuneScore) and the abundance of stromal components (StromalScore) for each TCGA sample in the RStudio software with the “estimate” package. (version 1.0.13, https://bioinformatics.mdanderson.org/public-software/estimate/, DOI: 10.1038/ncomms3612).We downloaded the unified and standardized pan-cancer dataset from the UCSC (https://xenabrowser.net/) database: TCGA TARGET GTEx (PANCAN, N=19131, G=60499), and further we extracted ENSG00000145545 (SRD5A1) from it ) genes and 60 two types of immune checkpoint pathway genes (Inhibitory (24), Stimulatory (36), derived from the document The Immune Landscape of Cancer, DOI: 10.1016/j.immuni.2018.03.023) marker genes in each sample In the expression data, we further screened the sample sources: Primary Solid Tumor, Primary Tumor, Primary Blood-Derived Cancer - Bone Marrow, Primary Blood-Derived Cancer - Peripheral Blood samples, we also filtered all normal samples, and further The log2(x+0.001) transformation was performed on each expression value, and next we calculated the Pearson correlation between ENSG00000145545 (SRD5A1) and the marker genes of the five immune pathways. Higher ImmuneScore or StromalScore indicated a larger proportion of immune or stromal components in the tumor microenvironment (TME). The generated heatmap suggested statistical significance and provided the purity-adjusted partial Spearman's rho value, which avoided the effect of outliers. Besides, the "Immune-Gene" tool of the TIMER2.0 database was applied to explore the association between SRD5A1 level and immune cell infiltration in all TCGA cancers. Immune cells including myeloid-derived suppressor cells (MDSCs), Th1 and Th2 subsets of CD4+ T cells were selected. The TIDE and XCELL algorithms were applied to estimate the immune infiltration and the results were depicted as a heatmap and scatter plots. “Gene_Corr” module of TIMER2.0 database was utilized to explore the correlations between SRD5A1 expression with MDSC /CD4+ Th2 cell (E) infiltration level, the plot with which correlation coefficient > 0.45 were presented.

Enrichment Analysis

The protein-protein interaction (PPI) network was established by applying the Search Tool for the Retrieval of Interacting Genes with the following input parameters: “evidence”, and 0.400 confidence level (34). The protein interaction file from the STRING database (RRID:SCR_005223) was imported into the Cytoscape software (RRID:SCR_003032,version 3.10.2) for PPI network construction, visualization, and analysis[28]. Besides, we adjusted the parameter “minimum required interaction score” to conformity = 0.400 and set the parameter "max number of interactors to show" as "no more than 10 interactors", to get access to experimentally determined SRD5A1-binding proteins.

GeneMANIA (http://www.genemania.org) is a flexible user-friendly web interface for generating hypotheses about gene function, analyzing gene lists, and prioritizing genes for functional assays[29].

We also downloaded the Simple Nucleotide Variation dataset at level 4 of all TCGA samples processed by MuTect2 software (DOI: 10.1038/nature08822) from Genomic Data Commons Data Portal (GDC Data Portal, RRID:SCR_014514) [30], and we integrated the samples' Mutation data, protein domain information was obtained from the R package map tools (version 2.2.10).

The "Similar Genes Detection" function of the GEPIA2 database was utilized to acquire the first 100 SRD5A1-correlated genes based on the TCGA and GTEx datasets. “Correlation Analysis” module of GEPIA2 was applied to compute pair-wise gene expression correlations between SRD5A1 and selected genes, using the Pearson correlation method. We also used the “Gene_Corr" function of the TIMER2.0 database to acquire the heatmap data of corresponding genes, containing the partial correlation coefficient (cor) and P-value calculated by the purity-adjusted Spearman’s rank correlation test. These two sets of genes were combined to perform Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis by STRING and were plotted by http://www.bioinformatics.com.cn, a free online platform for data analysis and visualization.

The resulting enriched pathways were visualized using the “ggplot2” R package. Besides, we conducted the Gene Ontology (GO) analysis to access the molecular functions (MF) via the “clusterProfiler” R package.

CancerSEA is a dedicated database providing a cancer single-cell functional state atlas, involving 14 functional states of 41,900 cancer single cells from 25 cancer types [31]. In the present research, the CancerSEA database was applied for the functional analysis of SRD5A1.

The CAMOIP website (https://www.camoip.net.) is free for all users, and no login is required to access all functions. CAMOIP was used to explore the pathways related to SRD5A1 in LUSC. Gene expression enrichment analysis was carried out between datasets with low or high SRD5A1 mRNA expression. All the pictures in CAMOIP are based on R software (v.4.0).

Data analysis of patients with immunotherapy

To study the association between SRD5A1 mutation and immune characteristics, multiple cancer data obtained from TCGA were analyzed using TISIDB[32] (http://cis.hku.hk/TISIDB), a database integrated multiple types of data resources in onco-immunology. RNA-sequencing expression (level 3) profiles and corresponding clinical information for xx were downloaded from the TCGA dataset (https://portal.gdc.com).To assess the reliable results of immune score evaluation, we used immuneeconv, It is an R software package that integrates six latest algorithms, including TIMER, xCell, MCP-counter, CIBERSORT, EPIC, and quanTIseq. SIGLEC15, IDO1, CD274, HAVCR2, PDCD1, CTLA4, LAG3, and PDCD1LG2 are the transcripts associated with the immune checkpoint. Extracting the expression of 8 genes, observing the expression value of the immune-checkpoint-related genes. All the analysis methods and R package were implemented by R version 4.0.3. If not stated otherwise, two-group data was performed by the Wilcox test. P values less than 0.05 were considered statistically significant (*P < 0.05).

Evaluation of Prognostic in UCEC

The TCGA dataset was used to retrieve RNA-sequencing expression (level 3) profiles and clinical information for UCEC. The difference in survival between these groups was compared using the log-rank test. The prediction accuracy of SRD5A1 mRNA was compared using the timeROC(v 4.0) analysis. Log-rank tests and univariate cox proportional hazards regression were used to generate p-values and hazard ratios (HR) with 95 percent confidence intervals (CI) for Kaplan-Meier curves. R (foundation for statistical computing 2020) version 4.0.3 was used to implement all of the analysis methodologies and R packages (ggrisk; Survival, survminer; timeROC). p-value <0.05 was considered statistically significant. Here we use the “limma” R package to determine differentially expressed genes (DEGs) between TCGA UCEC samples and normal controls, and use the p.adjust function to calculate the significant FDR of each gene. For Gene Set Enrichment Analysis(GSEA,RRID:SCR_003199), we obtained the GSEA software (version 3.0) from the GSEA (DOI: 10.1073/pnas.0506580102, http://software.broadinstitute.org/gsea/index.jsp) website, according to SRD5A1 expression, the samples were divided into two groups and C7:all immunologic signature gene sets were downloaded from the Molecular Signatures Database (DOI: 10.1093/bioinformatics/btr260, http://www.gsea-msigdb.org/gsea/downloads.jsp) .gmt subsets to evaluate related pathways and molecular mechanisms, grouped based on gene expression profiles and phenotypes, set the minimum gene set to 5 and the maximum gene set to 5000, resampling one thousand times, P-value of < 0.05 and an FDR of < 0.25 were considered statistically significant. Immune cell scores are calculated CIBERSORT and COX regression model in UCEC were analyzed on the CAMOIP website.*P < 0.05; **P<0.01; ***P<0.001; ****P<0.0001.All the pictures in CAMOIP are based on R software (v.4.0).

SRD5A1 Expression Profiles in Human Normal Tissues and Cancers

The design of this study as significant in Figure 1. To determine the expression profiles of SRD5A1 in human normal tissues, we investigated the mRNA expression patterns of SRD5A1 in various non-tumor tissues and single-cell types based on publicly available genome-wide expression data. As presented in Figure 2A, among all detected tissues and cell types, the highest PHF19 expression was observed in the liver, followed by the skin and breast, based on the Consensus dataset. RNA tissue specificity was enhanced in the liver. Concerning RNA blood cell-type specificity, interestingly, the PHF19 mRNA expression was enriched in basophil, when analyzed in the HPA datasets (Figure 2B). To further evaluate the expression status of SRD5A1 spanning various cancer types, we analyzed the TCGA RNA sequencing data by applying the TIMER2.0 approach, As showed in Figure 2C, SRD5A1 expression was significantly elevated in multiple cancer types, including Bladder Urothelial Carcinoma (BLCA, p=7.9e-8), Breast invasive carcinoma (BRCA, p=3.5e-52), Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC, p=8.5e-5), Cholangiocarcinoma (CHOL, p=3.8e-3), Colon adenocarcinoma (COAD, p=2.4e- 89), Esophageal carcinoma (ESCA, p=8.2e-15), GBM (Glioblastoma multiforme ,p=1.5e-21), Head and Neck squamous cell carcinoma (HNSC, p=1.1e-3), HNSC-HPV+, Kidney renal papillary cell carcinoma (KIRP, p=4.7e-7), Liver hepatocellular carcinoma (LIHC, p=1.2e-11), Lung adenocarcinoma (LUAD, p=2.2e-118), Lung squamous cell carcinoma (LUSC, p=1.6e-137), Pheochromocytoma and Paraganglioma (PCPG, p=3.8e-3), Prostate adenocarcinoma (PRAD, p=4.5e-28), Rectum adenocarcinoma (READ, p=1.4e-5), Stomach adenocarcinoma (STAD, p=2.8e-62), Thyroid carcinoma (THCA, p=2.8e-25) and Uterine Corpus Endometrial Carcinoma (UCEC, p=2.8e-3) than in their respective normal samples. By integrating data from the GTEx database as normal controls, we further performed a differential-expression analysis of SRD5A1 between tumor and normal samples. We observed significant upregulation in 30 tumors such as Glioma (GBMLGG, p=5.7 e-54), Brain Lower Grade Glioma (LGG, p=2.7e-43), Stomach and Esophageal carcinoma (STES, p=1.6e-38), Pan-kidney cohort (KIPAN (KICH+KIRC+KIRP), p=9.6e-4), Colon adenocarcinoma/Rectum adenocarcinoma Esophageal carcinoma (COADREAD, p=1.5e-102)), KIRC (Kidney renal clear cell carcinoma , p=4.7e-7), High-Risk Wilms Tumor (WT, p =4.6e-15), Ovarian serous cystadenocarcinoma (OV, p=2.7e-27), Pancreatic adenocarcinoma (PAAD, p=1.1e-48), Testicular Germ Cell Tumors (TGCT, p=1.2e-19), Uterine Carcinosarcoma (UCS, p=1.1e-12), Acute Lymphoblastic Leukemia (ALL, p=5.7e-8), Acute Myeloid Leukemia (LAML, p=1.2e -42), Adrenocortical carcinoma (ACC, p=2.1e-6), Kidney Chromophobe (KICH, p=7.3e-11).And significant down-regulation in 3 tumors such as LIHC, Skin Cutaneous Melanoma (SKCM, p=5.3 e-42), and CHOL (Figure 2D). Besides, we further evaluated the correlation of SRD5A1 expression with cancer pathological stages, including ACC, BLCA, BRCA, CHOL, COAD, ESCA, HNSC, KICH, KIRC, KIRP, LIHC, LUAD, LUSC, MESO, PAAD, READ, SKCM, STAD, TGCT, THCA, UVM (Uveal Melanoma) (Figure 2E). We observed significant differences among 3 tumors such as GBMLGG(Stage I=143,II=139,III=150,IV=58)(p=0.01)、SARC(Stage I=50,II=54,III=64,IV=24)(p=0.04)、THCA(Stage I=98,II=119,III=94,IV=50)(p=0.02).The results determined a positive relationship between SRD5A1 level and advanced tumor stages.

Multifaceted Prognostic Analysis of SRD5A1 in Cancers

To investigate the clinical significance of SRD5A1 in cancer patients, we downloaded the TCGA mRNA sequencing and clinical information of 44 cancer types from the UCSC Xena platform and calculated the correlations of SRD5A1 expression with overall survival (OS), disease-specific survival (DSS), Disease-free interval (DFS) and Progression-free interval(PFS) of patients using the univariate Cox survival analysis. As shown in Figure 3A, the forest plots suggested that elevated SRD5A1 expression was significantly associated with worse OS in 6 tumor types (TCGA-GBMLGG (N=619, p=0.04, HR=1.22 (1.01, 1.48)), TCGA-SARC (N=254, p=0.03, HR=1.25 (1.02, 1.53) )), TCGA-GBM(N=144,p=3.1e-3,HR=1.50(1.15,1.96)), TCGA-THYM(N=117,p=0.03,HR=1.83(1.05,3.17)), TCGA-MESO (N=84, p=0.01, HR=1.48 (1.09, 2.01)), TCGA-LAML (N=209, p=1.4e-3, HR=1.31 (1.11, 1.54))) showed that high expression of SRD5A1 were strongly associated with poor patient outcomes, in 2 tumor types (TCGA-KIRC (N=515, p=0.04, HR=0.83 (0.69, 0.99)), TARGET-NB (N=151, p=0.02, HR=0.68 (0.49, 0.95) )) showed that medium and low expression have poor OS. While 6 tumor types ( TCGA-BRCA(N=1025,p=0.03,HR=1.21(1.02,1.43))、TCGA-SARC(N=248,p=0.03,HR=1.28(1.02,1.60))、TCGA-KIRP(N=272,p=0.03,HR=1.66(1.06,2.59))、TCGA-PRAD(N=490,p=0.02,HR=5.58(1.41,22.06))、TCGA-GBM(N=131,p=3.6e-3,HR=1.54(1.15,2.06))、TCGA-MESO(N=64,p=0.01,HR=1.72(1.14,2.61) were significantly associated with worse DSS(Figure 3B). High expression in 1 tumor type (TCGA-READ (N=29, p=0.01, HR=10.95 (1.36, 87.83))) was associated with poor DFS, and low expression in 2 tumor types (TCGA-LGG (N=126, p=0.03, HR=0.48 (0.25, 0.94)), TCGA-GBMLGG (N=127, p=0.04, HR=0.50 (0.26, 0.96))) were significantly associated with worse DFS(Figure 3C).Finally observed in 3 tumor types (TCGA-GBM (N=143, p=0.02, HR=1.43 (1.06, 1.93)), TCGA-THYM (N=117, p=0.04, HR=1.41 (1.02, 1.95)), high expression in TCGA-BLCA (N=397, p=0.03, HR=1.15 (1.02, 1.31))) poor prognosis with worse PFS (Figure 3D).

Kaplan-Meier (KM) survival curves comparing SRD5A1 high and low expressing patients were also constructed to further evaluate the prognostic potential of SRD5A1 via the Kaplan-Meier plotter database. The results revealed that high SRD5A1 mRNA expression predicted worse OS in ACC, BRCA, CESC, COAD, GBM, LUAD, MESO, PAAD, SARC, THCA, THYM, and UCEC, nevertheless, patients with lower SRD5A1 mRNA expression showed remarkably improved OS in KIRP, and STAD(all log-rank P values < 0.05) (Figure 4A). However, high methylation of SRD5A1 predicted worse OS in ACC, CHOL, LUAD, and THCA, and lower methylation of SRD5A1 showed remarkably improved OS in KIRP, LGG, LIHC, SKCM, and THYM(all log-rank P values < 0.05) (Figure 4B).

We next compared the survival contribution of SRD5A1 in multiple cancer types, estimated using the Mantel-Cox test through the GEPIA2 database, and the survival maps accompanied with OS curves in Figure 5A and DFS curves in Figure 5B are presented. High transcriptional levels of SRD5A1 were linked to unfavorable prognosis in OS of BRAC, GMB, and PAAD, while low transcriptional levels of SRD5A1 were linked to unfavorable prognosis in OS of KIRC, and DFS analysis data showed that elevated SRD5A1 level was related to unfavorable prognosis for GMB and SARC (all log-rank P values < 0.05). Overall, the above data indicated that SRD5A1 expression was significantly correlated with patient prognosis in various cancers, especially in UCEC, and the relevance of SRD5A1 to clinical outcomes may shed new light on the underlying pathogenesis of different tumors.

Mutation Landscape of SRD5A1 in Cancers

We inspected the genomic alterations and mutation profiles of SRD5A1 in the TCGA cancer cohorts by employing the cBioPortal database. As presented in Figure 6A, the highest alteration frequency of SRD5A1 appeared in LUSC patients with “amplification” mutation" as the predominant type, while the type of “deep deletion” copy number alteration (CNA) was primary in UCS. We also analyzed the general mutation count of SRD5A1 in 10953 patients / 10967 samples from TCGA datasets (Figure 6B). Besides, for SRD5A1, the most common mutation in cancers was missense mutation, and not less than 2.0% of patients with SKCM and 0.7% GBM had observed mutations (Figure 6C).To investigate whether these distinct characteristics of SRD5A1 mutation could translate into cancer prognosis, we compared the OS (P = 3.684e-4), DFS (P = 4.471e-3), disease-specific survival (P = 1.70 e-3), and PFS (P = 6.214e-4) between patients with SRD5A1 mutant cancer and patients with SRD5A1 nonmutant cancer(Figure 6D).Spearman correlation analysis of TMB and MSI with SRD5A1 gene expression. The abscissa represents the correlation coefficient between genes and TMB and MSI, the ordinate represents different tumors. The size of the dots represents the size of the correlation coefficient, and different colors represent the significance of the p-value. The bluer the color, the smaller the p-value (Figure 6E).

SRD5A1 Expression Correlates To Tumor Immune Infiltration

Tumor-infiltrating immune cells, as principal compositions of the TME, are frequently involved in tumor behaviors including cancer initiation, progression, or metastasis, and are deemed as independent predictors of sentinel lymph node status and cancer prognosis. Given that SRD5A1 expression correlates with TMB and MSI which affect response to cancer immunotherapy, we next explored the correlations between SRD5A1 expression level and the abundance of immune cell infiltrates. By adopting the ESTIMATE method, we first computed the immune and stromal scores of cancer tissues. As Figure 7A indicated, the top three correlations between SRD5A1 expression with the immune and stromal scores in STES, SKCM-M, and LAML (all data P-values < 0.001). Since immune checkpoint-associated genes participate in the immunosuppressive mechanism that allows tumor cells to escape anti-tumor immunity, we next investigated the correlations between SRD5A1 expression and immune checkpoint-related genes, SRD5A1 expression is associated with 60 genes of two types of immune checkpoint pathways, 24 are inhibitory, of which 36 are stimulatory (Figure 7B). The heatmap exhibited that SRD5A1 expression was positively and statistically significantly correlated with the immune infiltration of myeloid-derived suppressor cells (MDSCs) in 18 cancer types, and negatively relevant to that in 4 cancer types. Intriguingly, SRD5A1 expression was also positively relevant to the infiltration abundance of CD4+ Th1 cells, CD4+ Th2 cells in 4 cancer types and 18 cancer types, respectively, and negatively relevant to that in 9 cancer types and 4 cancer types, respectively (Figure 7C). in Figure 7D, using the TIDE algorithm (with the correlation coefficient > 0.5). The results indicated that SRD5A1 expression was obviously positively correlated with the infiltration abundance of MDSCs in MESO (Cor = 0.510, P = 2.46E-06), UCEC (Cor =0.487, P = 1.54E-17). As shown in Figure 7E, SRD5A1 expression was also significantly associated with the infiltration levels of CD4+ Th2 cells in BLAC (Cor = 0.488, P = 4.26E-21), BRAC (Cor = 0.480, P = 1.04E-55), MESO (Cor = 0.473, P = 9.86E-05),with all correlation coefficients ＞0.45.The profiles illustrated that SRD5A1, to a certain extent, was engaged in the immune infiltration-related pathways and served a critical role in the immuno-oncological interactions.

Enrichment Analysis of SRD5A1-Related Partners

To further decipher the underlying molecular mechanisms by which SRD5A1 contributes to carcinogenesis, we next investigated the available experimentally confirmed SRD5A1-binding proteins and SRD5A1 expression-associated genes for pathway enrichment analyses. In total, 52 SRD5A1-interacted proteins were retrieved from the STRING database by experimental evidence, and the PPI network of proteins with a confidence level > 0.400 was presented in Figure 8A. Besides, we discovered functionally similar genes in GeneMANIA, the GeneMANIA results also revealed the functions of the differentially expressed SRD5A1 and their associated molecules (such as HSD17B3, and SRD5A2) were primarily related to Physical Interactions (Figure 8B). We next acquired the top 100 genes associated with SRD5A1 expression based on TCGA and GTEx datasets by utilizing the GEPIA2 database. As seen in Figure 8C, the SRD5A1 expression was significantly positively correlated with the expression of LAD1(Ladinin 1, R=0.52), PKP3(Plakophilin 3, R=0.51), PVRL1(Poliovirus receptor-related protein 1, R=0.50), and PERP (P53 apoptosis effector related to PMP22, R=0.50). The correlation heatmap showed that SRD5A1 was positively related to the above genes in the majority of TCGA cancers (Figure 8D). Further, these two datasets were combined to perform KEGG pathway and GO molecular function (MF) enrichment analyses. As presented in Figure 8E, several pathways including “Steroid hormone biosynthesis”, "Ovarian steroidogenesis”, “Metabolic pathways”, “Cortisol synthesis and secretion”, and “Cushing syndrome” were revealed as the most significantly enriched KEGG pathways. Next, we characterized what kinds of proteins were enriched in SRD5A1. For this purpose, we collected the Gene Ontology annotations of all the human proteins and used them as the background (Figure 8F). The Cellular response to Steroid dehydrogenase activity (0034754) and Steroid metabolic process (0008202) were the two main and related categories of biological process terms. The largest category of molecular function terms was Steroid dehydrogenase activity (0016229) and Oxidoreductase activity (0016491), while Endoplasmic reticulum membrane (0005789) was the largest category of molecular function terms. We also performed a single-cell analysis by using the CancerSEA database and determined that SRD5A1 stimulated a multitude of carcinogenic processes, including the promotion of angiogenesis and stemness in different cancer cell types (Figure 8G).

In light of the significant positive association between SRD5A1 expression and the number of immune neoantigens, TMB levels, and MSI in LUSC, we investigated the potential effect of SRD5A1 on LUSC progression, then GSEA analysis was used to explore the signaling pathways between low expression and high expression group of SRD5A1 in LUSC to identify the relevant pathways and underlying mechanisms, and the top one terms of KEGG(Non−homologous end−joining), Reactome(Type I hemidesmosome assembly), GO−BP(hemidesmosome assembly), GO−CC(juxtaparanode region of the axon) and GO−MF(arachidonic acid epoxygenase activity )analysis and transcription factor targets were exhibited(Figure 8H).

Data analysis of patients with immunotherapy

Next, we investigated the correlations between SRD5A1 mutation and various immune signatures, including 28 TIL types, 24 immune inhibitors, 45 immunostimulators, 21 major histocompatibility complex molecules, 40 chemokines, and 18 chemokine receptors, in 30 tumors in the TCGA cohort (Figure 9). Most immune-related genes were increased in SRD5A1 mutant samples compared to nonmutant samples, and many of them were statistically significant. These findings revealed that the immune system was more active in SRD5A1 mutant cancer, which may be classified as a "hot" tumor immunologically. Moreover, our data provided strong evidence that cancer epigenetic driver mutations could shape tumor immune phenotype.

SRD5A1 is prognostic, high expression is unfavorable in UCEC

The distribution of risk scores, survival statuses, and signature gene expression patterns for UCEC patients in training were visualized in Figure 10A. We first calculated and plotted the prognostic Kaplan-Meier survival curves (Figure 10B), the results showed that a high level of SRD5A1 expression was related to a poor prognosis in UCEC. Additionally, we also ran a time-dependent ROC curve study to confirm the risk-score model's predictive classification efficiency in UCEC, and the area under the curve (AUC) values for 1-, 3-, and 5-year overall survival are shown in Figure 10C. DEGs analysis shows up-regulated 262 genes and down-regulated 800 genes(Figure 10D)and the top 4 GSEA as shown in Figure 10E. Furthermore, a total of 180 samples were included in which mutations were detected, of which the plotted samples included a total of 59 (32.8%) (Figure 10F). Immune cell infiltration landscapes between high SRD5A1expression and low SRD5A1 expression groups in UCEC analyzed by CIBERSORT shown activated memory CD4+ T cells, T follicular helper cells, regulatory T cells (Tregs), resting natural killer (NK) cells, M1 macrophages, resting dendritic cells and activated dendritic cells significantly different(Figure 10G). In addition, as demonstrated in Figure 10H, the risk score was capable of independently predicting the prognosis of UCEC after controlling for SRD5A1 expression and age in both univariate COX regression model and multivariate COX regression model analyses. These findings indicated that the eleven-gene prognostic signature was effective in predicting the prognosis of UCEC patients and that it may be used to enhance the gold standard for clinical diagnosis.

Immunohistochemical staining of SRD5A1 in cancers

To ensure positive confirmation of the pathophysiological roles of SRD5A1, we applied experimental validation to investigate its clinicopathological characteristics. We performed immunohistochemically (IHC) analyses in 78 cancer samples across BLAC, CESC, COAD, LUAD, LUSC, PAAD, SKCM, and STAD(Figure 11A–H). Adjacent or distant noncancerous tissues from the surgical margin were used as the control tissues. We found that the SRD5A1 protein expressions were significantly higher in tumor tissues in comparison with the control tissues, Most cancers displayed moderate to strong cytoplasmic immunoreactivity with a granular pattern. Lymphomas and testicular cancers along with most gliomas and the results were quantitated in Figure 11I, which indicated the extensive carcinogenic effects of SRD5A1.

The SRD5A1 enzyme is involved in bile acid biosynthesis, and androgen and estrogen metabolism. In prostate cancer, a previous study assessed the genotype distribution of 22 single nucleotide polymorphisms in SRD5A1 and SRD5A2 in a cohort of Spanish prostate cancer patients and found it is a potential factor implicated in inhibitor treatment[33]. Other studies have also shown that high nuclear SRD5A1 expression is associated with higher prostate cancer stage[8]. Single nucleotide polymorphisms in the genes SRD5A1/2 are associated with prostate cancer risk[34], while loss of SRD5A1 expression is associated with metastatic prostate cancer[35]. Furthermore, throughout the progression of prostate cancer, SRD5A1 immunostaining increases whereas SRD5A2 immunostaining decreases, and SRD5A1 expression increases in recurrent and metastatic tumors[36].In a model cell line of endometrial cancer, SRD5A1 plays a key role in progesterone metabolism[37]. Studies have also shown that SRD5A1 is associated with intratumoral dihydrotestosterone concentration and is an independent prognostic factor in UCEC[38].In breast cancer, the expression of SRD5A1 and SRD5A2 is elevated in tumor tissue compared to normal breast tissue[14]. Studies have also shown that intratumoral 5α-dihydrotestosterone concentrations are mainly determined by SRD5A1 and aromatase in breast cancer tissues[39]. Postmenopausal breast cancer risk associated with combination therapy may be affected by variants in the SRD5A1 gene[40]. Prognostically, increases in 5alphaR activity and glucocorticoid secretion rates over time are associated with the development of metabolic disease and may represent targets for therapeutic intervention[41]. Several studies have explored several SRD5A1 and SRD5A2 variants as independent predictors of biochemical recurrence after radical prostatectomy and have shown positive associations[42].

We investigated the pan-cancer expression profiles of SRD5A1, as well as the relationship between SRD5A1 aberrant expression and patient prognosis in various malignancies, in this work. SRD5A1 expression was highly up-regulated in a variety of malignancies as compared to noncancerous tissues, implying that SRD5A1 has wide oncogenic properties in cancers and potential prospects in cancer research. SRD5A1 is related to intratumoral dihydrotestosterone concentration and is an independent prognostic factor in UCEC, according to findings from a prior study. This present result is consistent with findings of a previous study in 2005 that differential alterations in SRD5A1 levels were associated with the development and progression of prostate cancer[36] .In ACC, BRCA, CESC, COAD, GBM, LUAD, MESO, PAAD, SARC, THCA, THYM, and UCEC, COX regression analysis revealed that raised high SRD5A1 mRNA expression predicted shorter OS; however, we also evaluated high methylation of SRD5A1 predicted worse OS in ACC, CHOL, LUAD, and THCA. The survival contribution of SRD5A1 was then compared in multiple cancer types, utilizing the Mantel-Cox test and the GEPIA2 database, as well as survival maps with OS and DFS curves. In the OS of BRAC, GMB, and PAAD, elevated SRD5A1 levels were linked to adverse prognosis, and DFS analysis data revealed that elevated SRD5A1 levels were linked to unfavorable prognosis for GMB and SARC. Notably, SRD5A1 expression was found to be substantially linked with patient prognosis in various malignancies, particularly in UCEC, according to the findings.

In conclusion, the findings revealed the complex roles of SRD5A1 aberrant expression in cancer development and prognosis, as well as the critical signaling pathways linked to the super pathway of steroid hormone biosynthesis and Metabolism, GO annotations related to this gene include electron transfer activity and 3-oxo-5-alpha-steroid 4-dehydrogenase activity. We also discovered that SRD5A1 plays a key function in controlling immune cell infiltration in tumors and that it may have therapeutic benefits in cancer treatment.

Conflict of Interest

The authors declare no conflict of interest.

Funding

This research was supported by The Startup Project for High-level Talents of Fujian Medical University (XRCZX2020016), Fujian Provincial Key Laboratory of Drug Target Discovery and Structure-Function Research open topic (FKLDSR-202104).

Author Contributions

LHX and JBX conceived and designed the study, performed data analysis, interpretation, and wrote the manuscript. CW and CHZ collected and assembled data. YHZ and LHX performed data analysis. YX and FH conceived and designed the study, wrote and gave final approval of the manuscript. All authors read and approved the manuscript.

Ethics approval and consent to participate

As the data used in this study are publicly available, no ethical approval was required.

Consent for publication

Not applicable.

Conflict of Interest

The authors declare no conflict of interest.

Acknowledgements

Not applicable.

Data Availability Statement

Publicly available datasets were analyzed in this study. All relevant data can be found here: UCSC Xena (http://xena.ucsc.edu/), TCGA dataset (https://portal.gdc.com) , Human Protein Atlas (https://www.proteinatlas.org), TIMER2.0 (http://timer.cistrome.org/), GEPIA2 (http://gepia2.cancer-pku.cn), Kaplan-Meier plotter (https://kmplot.com/analysis/), cBioPortal (http://www.cbioportal.org/), STRING (https://www.string-db.org/), GeneMANIA (http://www.genemania.org), CancerSEA (http://biocc.hrbmu.edu.cn/CancerSEA/), TISIDB (http://cis.hku.hk/TISIDB), and CAMOIP (https://www.camoip.net) .

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A Pan-Cancer Analysis of SRD5A1, a Potential New Carcinogenic Indicator Related to Immune Infiltration and Prognosis of UCEC

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