A Four−Gene-Based Risk Score With High Prognostic Value in Gastric Cancer

BackgroundGastric adenocarcinoma is an important contributor to cancer mortality and morbidity. This study aimed to explore the prognostic value of mutation patterns in gastric adenocarcinoma.Materials and MethodsWe extracted somatic mutation data for 437 gastric adenocarcinoma samples from The Cancer Genome Atlas (TCGA) Stomach Adenocarcinoma (STAD) cohort. Kaplan–Meier survival in the R package maftools was used to analyze associations between mutations and survival. Multivariate Cox proportional model was used to establish risk formula. A four-gene-based risk score was developed to predict the overall survival of patients with gastric adenocarcinoma. We used the Tianjin cohort dataset with survival information to further evaluate the clinical value of this mutation signature.ResultsForty-five survival-related mutated genes were identified and verified, most of which were co-occurring in their mutation pattern and co-occurring with MLH3 and polymerase ϵ (POLE) mutations. Gastric adenocarcinoma samples with the 45 mutated genes had a significantly higher mutation count. Four-gene [UTRN, MUC16, coiled-coil domain-containing protein 178 (CCDC178), and HYDIN] mutation status was used to build a prognostic risk score that could be translated into the clinical setting. The association between the four-gene-based signature and overall survival remained statistically significant after controlling for age, sex, TNM stage, and POLE mutation status in the multivariate model [hazard ratio (HR), 1.88; 95% CI, 1.33–2.7; p < 0.001]. The prognostic significance of the four-gene-based risk score identified in TCGA cohort was validated in the Tianjin cohort.ConclusionA four-mutated gene risk formula was developed that correlated with the overall survival of patients with gastric adenocarcinoma using a multivariable Cox regression model. In two independent genomic datasets from TCGA and Tianjin cohorts, low risk scores were associated with higher tumor mutation loads and improved outcome in patients with gastric adenocarcinoma. This finding may have implications for prognostic prediction and therapeutic guidance for gastric adenocarcinoma.

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Identification and Validation of an Individualized EMT-Related Prognostic Risk Score Formula in Gastric Adenocarcinoma Patients

BioMed Research International ◽

10.1155/2020/7082408 ◽

2020 ◽

Vol 2020 ◽

pp. 1-15 ◽

Cited By ~ 3

Author(s):

Deyu Zhang ◽

Siran Zhou ◽

Bingrong Liu

Keyword(s):

Gastric Adenocarcinoma ◽

Risk Score ◽

Prognostic Value ◽

Cox Regression ◽

Epithelial Mesenchymal Transition ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Gastrointestinal Malignancy ◽

Mesenchymal Transition ◽

Cancer Genome Atlas

Background. The epithelial-mesenchymal transition (EMT) is a pivotal process for fibrotic disease, embryonic development, and wound healing. Moreover, some evidence has proven that the disorder of EMT also plays an important role in carcinogenesis, especially invasion and metastasis of various tumors (Ritchie et al., 2015). Additionally, gastric adenocarcinoma (GAC) is a common gastrointestinal malignancy which is the fourth most commonly diagnosed tumor. Our study is aimed at identifying the prognostic value of EMT-related genes in gastric adenocarcinoma. Methods. Firstly, high-throughput and clinical data were downloaded from The Cancer Genome Atlas (TCGA) database. 99 differentially expressed EMT-related genes (ERGs) were obtained in these gastric adenocarcinoma data. Secondly, GO and KEGG enrichment analyses show that EMT may promote gastric carcinogenesis. Next, 10 ERGs associated with prognosis of gastric adenocarcinoma patients are screened out by univariate Cox regression, and 6 pivotal prognostic ERGs (MMP8, MMP11, TFDP3, MYB, F2, and CNTN1) are identified through multivariate Cox regression. These 6 genes are confirmed with significant prognostic value in gastric adenocarcinoma through overall survival (OS) analysis. Finally, a risk score formula is constructed and tested in another gastric adenocarcinoma cohort from GEO. Results. 99 differentially expressed EMT-related genes (ERGs) and their enriched pathways are identified. 10 ERGs are strongly related to the prognosis of GAC patients. A risk score formula of 6 prognosis-related ERGs used to predict the prognosis of gastric adenocarcinoma patients is identified and tested (risk score=0.448115∗expression value of MMP8+0.378892∗expression value of MMP11−0.3226∗expression value of MYB+1.322812∗expression value of TFDP3+0.325063∗expression value of F2+0.334197∗expression value of CNTN1). Conclusion. This study provides a potential prognostic signature for predicting prognosis of gastric adenocarcinoma patients and molecular insights of EMT in gastric adenocarcinoma, and the formula focusing on the prognosis of gastric adenocarcinoma can be effective.

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Clinicopathological signature and prognostic value of RNA:m5C methyltransferases in gliomas

10.21203/rs.2.22601/v1 ◽

2020 ◽

Author(s):

Peng Wang ◽

Kai Huang ◽

Miaojing Wu ◽

Qing Hu ◽

Chuming Tao ◽

...

Keyword(s):

Overall Survival ◽

Risk Score ◽

Prognostic Value ◽

Cox Regression ◽

Roc Curves ◽

Clinicopathological Features ◽

Gene Set Enrichment Analysis ◽

Consensus Clustering ◽

Score Model ◽

Genome Atlas

Abstract Background: Glioma is the most common primary intracranial tumor, accounting for the vast majority of intracranial malignant tumors. Aberrant expression of RNA:5-methylcytosine(m5C) methyltransferases has recently been the focus of research relating to the occurrence and progression of tumors. However, the prognostic value of RNA:m5C methyltransferases in glioma remains unclear. This study investigated RNA: m5C methyltransferase expression and defined its clinicopathological signature and prognostic value in gliomas. Methods: We systematically studied the RNA-sequence data of RNA:m5C methyltransferases underlying gliomas in the Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) datasets and identified different subtypes using Consensus clustering analysis. Gene Ontology (GO) and Gene Set Enrichment analysis (GSEA) was used to annotate the function of these genes. Univariate Cox regression and the least absolute shrinkage and selection operator (LASSO) Cox regression algorithm analyses were performed to construct the risk score model. Kaplan-Meier method and Receiver operating characteristic (ROC) curves were used to assess the overall survival of glioma patients. Additionally, Cox proportional regression model analysis was developed to address the connections between the risk scores and clinical factors. Results: Consensus clustering of RNA:m5C methyltransferases identified three clusters of gliomas with different prognostic and clinicopathological features. Meanwhile, Functional annotations demonstrated that RNA:m5C methyltransferases were significantly associated with the malignant progression of gliomas. Thereafter, five RNA:m5C methyltransferase genes were screened to construct a risk score model which can be used to predict not only overall survival but also clinicopathological features in gliomas. ROC curves revealed the significant prognostic ability of this signature. In addition, Multivariate Cox regression analyses indicated that the risk score was an independent prognostic factor for glioma outcome. Conclusion: We demonstrated the role of RNA:m5C methyltransferases in the initiation and progression of glioma. We have expanded on the understanding of the molecular mechanism involved, and provided a unique approach to predictive biomarkers and targeted therapy.

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The Landscape of Glycosyltransferase Gene Signature for Overall Survival Prediction in Hepatocellular Carcinoma

10.21203/rs.3.rs-114747/v1 ◽

2020 ◽

Author(s):

Qiang Cai ◽

Shizhe Yu ◽

Jian Zhao ◽

Duo Ma ◽

Long Jiang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

Risk Score ◽

Prognostic Value ◽

Cox Regression ◽

Risk Groups ◽

Gene Signature ◽

Low Risk ◽

Regression Analyses ◽

Risk Patients

Abstract Background: Hepatocellular carcinoma (HCC) is heterogeneous disease occurring in the background of chronic liver diseases. The role of glycosyltransferase (GT) genes have recently been the focus of research associating with the development of tumors. However, the prognostic value of GT genes in HCC remains not elucidated. This study aimed to demonstrate the GT genes related to the prognosis of HCC through bioinformatics analysis.Methods: The GT genes signatures were identified from the training set of The Cancer Genome Atlas (TCGA) dataset using univariate and the least absolute shrinkage and selection operator (LASSO) Cox regression analyses. Then, we analyzed the prognostic value of GT genes signatures related to the overall survival (OS) of HCC patients. A prognostic model was constructed, and the risk score of each patient was calculated as formula, which divided HCC patients into high- and low-risk groups. Kaplan-Meier (K-M) and Receiver operating characteristic (ROC) curves were used to assess the OS of HCC patients. The prognostic value of GT genes signatures was further investigated in the validation set of TCGA database. Univariate and multivariate Cox regression analyses were performed to demonstrate the independent factors on OS. Finally, we utilized the gene set enrichment analysis (GSEA) to annotate the function of these genes between the two risk categories. Results: In this study, we identified and validated 4 GT genes as the prognostic signatures. The K-M analysis showed that the survival rate of the high-risk patients was significantly lower than that of the low-risk patients. The risk score calculated with 4 gene signatures could predict OS for 3-, 5-, and 7-year in patients with HCC, revealing the prognostic ability of these gene signature. In addition, Multivariate Cox regression analyses indicated that the risk score was an independent prognostic factor for HCC. Functional analysis further revealed that immune-related pathways were enriched, and immune status in HCC were different between the two risk groups.Conclusion: In conclusion, a novel GT genes signature can be used for prognostic prediction in HCC. Thus, targeting GT genes may be a therapeutic alternative for HCC.

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A Novel Risk-Score Model With Eight MiRNA Signatures for Overall Survival of Patients With Lung Adenocarcinoma

Frontiers in Genetics ◽

10.3389/fgene.2021.741112 ◽

2021 ◽

Vol 12 ◽

Author(s):

Jun Wu ◽

Yuqing Lou ◽

Yi-Min Ma ◽

Jun Xu ◽

Tieliu Shi

Keyword(s):

Overall Survival ◽

Lung Adenocarcinoma ◽

Risk Score ◽

Early Stage ◽

Differential Expression Analysis ◽

The Cancer Genome Atlas ◽

Clinical Staging ◽

Risk Scores ◽

Regulatory Relationship ◽

Score Model

Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer with heterogeneous outcomes and diverse therapeutic responses. To classify patients into different groups and facilitate the suitable therapeutic strategy, we first selected eight microRNA (miRNA) signatures in The Cancer Genome Atlas (TCGA)-LUAD cohort based on multi-strategy combination, including differential expression analysis, regulatory relationship, univariate survival analysis, importance clustering, and multivariate combinations analysis. Using the eight miRNA signatures, we further built novel risk scores based on the predefined cutoff and beta coefficients and divided the patients into high-risk and low-risk groups with significantly different overall survival time (p-value < 2 e−16). The risk-score model was confirmed with an independent dataset (p-value = 4.71 e−4). We also observed that the risk scores of early-stage patients were significantly lower than those of late-stage patients. Moreover, our model can also provide new insights into the current clinical staging system and can be regarded as an alternative system for patient stratification. This model unified the variable value as the beta coefficient facilitating the integration of biomarkers obtained from different omics data.

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Combination of Immune-Related Genomic Alterations Reveals Immune Characterization and Prediction of Different Prognostic Risks in Ovarian Cancer

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.653357 ◽

2021 ◽

Vol 9 ◽

Author(s):

Xibo Zhao ◽

Shanshan Cong ◽

Qiuyan Guo ◽

Yan Cheng ◽

Tian Liang ◽

...

Keyword(s):

Ovarian Cancer ◽

Overall Survival ◽

Risk Score ◽

Immune Cell ◽

Cox Regression ◽

Case Fatality ◽

The Cancer Genome Atlas ◽

Genomic Alterations ◽

Predictive Values ◽

Score Model

With the highest case-fatality rate among women, the molecular pathological alterations of ovarian cancer (OV) are complex, depending on the diversity of genomic alterations. Increasing evidence supports that immune infiltration in tumors is associated with prognosis. Therefore, we aim to assess infiltration in OV using multiple methods to capture genomic signatures regulating immune events to identify reliable predictions of different outcomes. A dataset of 309 ovarian serous cystadenocarcinoma patients with overall survival >90 days from The Cancer Genome Atlas (TCGA) was analyzed. Multiple estimations and clustering methods identified and verified two immune clusters with component differences. Functional analyses pointed out immune-related alterations underlying internal genomic variables potentially. After extracting immune genes from a public database, the LASSO Cox regression model with 10-fold cross-validation was used for selecting genes associated with overall survival rate significantly, and a risk score model was then constructed. Kaplan–Meier survival and Cox regression analyses among cohorts were performed systematically to evaluate prognostic efficiency among the risk score model and other clinical pathological parameters, establishing a predictive ability independently. Furthermore, this risk score model was compared among identified signatures in previous studies and applied to two external cohorts, showing better prediction performance and generalization ability, and also validated as robust in association with immune cell infiltration in bulk tissues. Besides, a transcription factor regulation network suggested upper regulatory mechanisms in OV. Our immune risk score model may provide gyneco-oncologists with predictive values for the prognosis and treatment management of patients with OV.

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Ovarian Cancer Risk Scores Based on Immune-Related Pseudogenes to Predict Overall Survival and Guide Immunotherapy and Chemotherapy

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2021/1586312 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Jie Zhao ◽

Rixiang Zhao ◽

Xiaocen Wei ◽

Xiaojing Jiang ◽

Fan Su

Keyword(s):

Ovarian Cancer ◽

Overall Survival ◽

High Risk ◽

Cox Regression ◽

High Risk Group ◽

The Cancer Genome Atlas ◽

Risk Scores ◽

Ovarian Cancer Risk ◽

Cox Regression Analysis ◽

The Impact

Background. Ovarian cancer (OC) is the top of the aggressive malignancies in females with a poor survival rate. However, the roles of immune-related pseudogenes (irPseus) in the immune infiltration of OC and the impact on overall survival (OS) have not been adequately studied. Therefore, this study aims to identify a novel model constructed by irPseus to predict OS in OC and to determine its significance in immunotherapy and chemotherapy. Methods. In this study, with the use of The Cancer Genome Atlas (TCGA) combined with Genotype-Tissue Expression (GTEx), 55 differentially expressed irPseus (DEirPseus) were identified. Then, we constructed 10 irPseus pairs with the help of univariate, Lasso, and multivariate Cox regression analysis. The prognostic performance of the model was determined and measured by the Kaplan–Meier curve, a time-dependent receiver operating characteristic (ROC) curve. Results. After dividing OC subjects into high- and low-risk subgroups via the cut-off point, it was revealed that subjects in the high-risk group had a shorter OS. The multivariate Cox regression performed between the model and multiple clinicopathological variables revealed that the model could effectively and independently predict the prognosis of OC. The prognostic model characterized infiltration by various kinds of immune cells and demonstrated the immunotherapy response of subjects with cytotoxic lymphocyte antigen 4 (CTLA4), anti-programmed death-1 (PD-1), and anti-PD-ligand 1 (PD-L1) therapy. A high risk score was related to a higher inhibitory concentration (IC50) for etoposide ( P = 0.0099 ) and mitomycin C ( P = 0.0013 ). Conclusion. It was the first study to identify a novel signature developed by DEirPseus pairs and verify the role in predicting OS, immune infiltrates, immunotherapy, and chemosensitivity. The irPseus are vital factors predicting the prognosis of OC and could act as a novel potential treatment target.

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Identification Sixteen Metabolic Genes as Potential Biomarkers for Colon Adenocarcinoma

10.21203/rs.3.rs-116131/v1 ◽

2020 ◽

Author(s):

Binbin Cui ◽

Fuqiang Zhao ◽

Yanlong Liu ◽

Xinyue Gu ◽

Bomiao Zhang ◽

...

Keyword(s):

Gene Expression ◽

Overall Survival ◽

Risk Score ◽

Cox Regression ◽

Colon Adenocarcinoma ◽

Curve Analysis ◽

Gene Set Enrichment Analysis ◽

Risk Scores ◽

Metabolic Genes ◽

Cox Analysis

Abstract Purpose Colon adenocarcinoma (COAD) is the most common primary malignant tumor of the digestive tract. It is still important to find important markers that affect the prognosis of COAD. This research aims to identify some key prognosis-related metabolic genes (PRMG) and establish a clinical prognosis model for COAD patients. Method We used The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) to obtain gene expression profiles of COAD, and then identified differentially expressed prognostic-related metabolic genes through R language and Perl software, Through univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) Cox analysis to obtain target genes, established metabolic genes prognostic models and risk scores. Through COX regression analysis, independent risk factors affecting the prognosis of COAD were analyzed, and Receiver Operating Characteristic (ROC) curve analysis of independent prognostic factors was performed and a nomogram for predicting overall survival was constructed. Perform the consistency index (C-index) test and decision curve analysis (DCA) on the nomogram, and use Gene Set Enrichment Analysis (GSEA) to identify the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway of model genes. Result We selected PRMG based on the expression of metabolic genes, and used LASSO Cox regression to construct 16 metabolic gene (SEPHS1, P4HA1, ENPP2, PTGDS, GPX3, CP, ASPA, POLR3A, PKM, POLR2D , XDH, EPHX2, ADH1B, HMGCL, GPD1L and MAOA) models. The risk score generated from our model can well predict the survival prognosis of COAD. A nomogram based on the clinicopathological characteristics and risk scores of COAD can personally predict the overall survival rate of COAD patients. Conclusion We comprehensively identified metabolic genes related to the prognosis of COAD. The risk score based on the expression of 16 metabolic genes can effectively predict the prognosis of patients with COAD.

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Development of a Prognostic Signature Based on Hypoxia-related Genes for Thyroid Cancer

10.21203/rs.3.rs-438962/v1 ◽

2021 ◽

Author(s):

HongYang Zhang ◽

Sijia Li ◽

Wei Li

Keyword(s):

Overall Survival ◽

Thyroid Cancer ◽

Prognostic Value ◽

Cox Regression ◽

Enrichment Analysis ◽

Differentially Expressed ◽

Risk Scores ◽

Prognostic Signature ◽

Prognosis Model ◽

Cox Analysis

Abstract Background. We aimed to establish a model to predict the prognosis of patients with thyroid cancer based on differentially expressed hypoxia-related genes.Methods. By comparing the genes in TCGA database and hypoxiaDB database, we obtained differentially expressed genes (DEGs) related to hypoxia in thyroid cancer. Gene function enrichment analysis was performed, and a protein-protein interaction network was constructed using the STRING database. Univariate Cox regression were used to screen hypoxia-related genes with prognostic value. Subsequently, multivariate Cox analysis was used to determine prognostic markers based on thyroid cancer, a prognosis model based on these genes was established. The Kaplan-Meier analysis, Receiver operating characteristic (ROC) analysis and The Harrell’s concordance indexes in the training set and the validation set were used to evaluate the performance of the model. Finally, we conducted univariate analyses of the prognostic value of clinical data (including risk scores) of thyroid cancer patients.Results. 326 hypoxia-related thyroid cancer genes were found. Functional enrichment analysis demonstrated they were mainly involved in regulating biological functions. 23 genes have been proved to be associated with the prognosis of thyroid cancer with univariate Cox regression, among them, 11 marker genes were used to construct a new prognosis model by multivariate Cox analysis. Accordingly, the system of risk scores was constructed, patients with high-risk scores (P <0.005) had shorter overall survival than those with low-risk scores. The ROC curve indicated good performance of the eleven-gene signature at predicting overall survival. The Harrell’s concordance indexes in the internally validated for the 11-gene prognostic signature was 0.881. Moreover, univariate analysis showed that the risk score and age were significantly associated with patient overall survival. The model we created was significantly associated with patient overall survival.Conclusions. The model we established had excellent performance in the prognosis of thyroid cancer.

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An 8-lncRNA signature predicts the survival of triple-negative breast cancer patients without germline BRCA1/2 mutation

Medical Research ◽

10.6913/mrhk.202109_3(3).0003 ◽

2021 ◽

Vol 3 (3) ◽

pp. 15-32

Author(s):

Minling LIU ◽

Wei DAI ◽

Mengyuan ZHU ◽

Xueying LI ◽

Min WEI ◽

...

Keyword(s):

Breast Cancer ◽

Overall Survival ◽

Triple Negative Breast Cancer ◽

Triple Negative ◽

Cox Regression ◽

The Cancer Genome Atlas ◽

Risk Scores ◽

Training Cohort ◽

Cox Regression Analysis ◽

Platinum Based Chemotherapy

Purpose: TNBC with germline BRCA1/2 mutation (gBRCAm) have higher sensitivity to DNA damaging agents including platinum-based chemotherapy and PARP inhibitors. But the treatment of TNBC without gBRCAm remains challenging. This study aimed to develop a long non-coding RNA (lncRNA) signature of TNBC patients without gBRCAm to improve risk stratification and optimize individualized treatment. Methods: 98 TNBC patients without gBRCAm were acquired from The Cancer Genome Atlas database. The univariable Cox regression analysis and LASSO Cox regression model were applied to establish an lncRNA signature in the training cohort. Then Kaplan–Meier survival curve and time-dependent ROC curve were used to validate the prognostic ability of the signature. The qPCR assay was performed to confirm the expressions and clinicopathological correlations of two potential lncRNAs HAGLROS and TONSL-AS1 in 30 paired clinical triple-negative breast cancer samples without gBRCAm. Results: We developed an 8-lncRNA signature in the training cohort including HAGLROS, AL139002.1, AL391244.2, AP000696.1, AL391056.1, AL513304.1, TONSL-AS1 and AL031008.1. Patients with higher risk scores showed significantly worse overall survival compared to those with lower risk scores (P=0.00018 and P =0.0068 respectively). 30 paired specimens of TNBC without gBRCAm in our center showed that two potential lncRNAs HAGLROS and TONSL-AS1 were found frequently overexpressed, and significantly associated with tumor grade and invasion. Conclusion: We constructed a novel 8-lncRNA signature which significantly associated with the overall survival of TNBC patients without gBRCAm. Among those 8 lncRNAs, HAGLROS and TONSL-AS1 may be potential therapeutic targets which function needed further exploration.

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A Comprehensive Prognostic and Immune Analysis of SLC41A3 in Pan-Cancer

Frontiers in Oncology ◽

10.3389/fonc.2020.586414 ◽

2021 ◽

Vol 10 ◽

Author(s):

Jun Liu ◽

Shanqiang Zhang ◽

Wenjie Dai ◽

Chongwei Xie ◽

Ji-Cheng Li

Keyword(s):

Overall Survival ◽

Immune Cells ◽

Cox Regression ◽

Expression Profiles ◽

Prognostic Significance ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Cancer Prognosis ◽

Cox Regression Analysis ◽

Pan Cancer

SLC41A3, as a member of the 41st family of solute carriers, participates in the transport of magnesium. The role of SLC41A3 in cancer prognosis and immune regulation has rarely been reported. This study was designed to analyze the expression status and prognostic significance of SLC41A3 in pan-cancers. The mRNA expression profiles of SLC41A3 were obtained from The Cancer Genome Atlas (TCGA), the Genotype-Tissue Expression (GTEx), the Broad Institute Cancer Cell Line Encyclopedia (CCLE), and the International Cancer Genome Consortium (ICGC). The Cox regression and Kaplan-Meier analyses were used to evaluate the prognostic value of SLC41A3 in pan-cancer. Furthermore, the correlation between SLC41A3 expression and immune cells infiltration, immune checkpoint, mismatch repair (MMR), DNA methyltransferase (DNMT), tumor mutation burden (TMB), and microsatellite instability (MSI) were calculated using data form TCGA database. The results showed that the expression of SLC41A3 was down-regulated in kidney renal clear cell carcinoma (KIRC), and was associated with poor overall survival and tumor-specific mortality. Whereas, the expression of SLC41A3 was up-regulated in liver hepatocellular carcinoma (LIHC), and the results of Cox regression analysis revealed that SLC41A3 was an independent factor for LIHC prognosis. Meanwhile, a nomogram including SLC41A3 and stage was built and exhibited good predictive power for the overall survival of LIHC patients. Additionally, correlation analysis suggested a significant correlation between SLC41A3 and TMB, MSI, MMR, DNMT, and immune cells infiltration in various cancers. The overall survival and disease-specific survival analysis revealed that the combined SLC41A3 expression and immune cell score, TMB, and MSI were significantly associated with clinical outcomes in ACC, LIHC, and UVM patients. Therefore, we proposed that SLC41A3 may serve as a potential prognostic biomarker for cancer.

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