Glycolysis Changes the Microenvironment and Therapeutic Response Under the Driver of Gene Mutation in Esophageal Adenocarcinoma

Background: Esophageal cancer is one of the most leading and lethal malignancies. Glycolysis and the tumor microenvironment (TME) are responsible for cancer progressions. We aimed to study the relationships between glycolysis, TME, and therapeutic response in esophageal adenocarcinoma (EAC).Materials and Methods: We used the ESTIMATE algorithm to divide EAC patients into ESTIMATE high and ESTIMATE low groups based on the gene expression data downloaded from TCGA. Weighted gene co-expression network analysis (WGCNA) and Gene Set Enrichment Analysis (GSEA) were performed to identify different glycolytic genes in the TME between the two groups. The prognostic gene signature for overall survival (OS) was established through Cox regression analysis. Impacts of glycolytic genes on immune cells were assessed and validated. Next, we conducted the glycolytic gene mutation analysis and drug therapeutic response analysis between the two groups. Finally, the GEO database was employed to validate the impact of glycolysis on TME in patients with EAC.Results: A total of 78 EAC patients with gene expression profiles and clinical information were included for analysis. Functional enrichment results showed that the genes between ESTIMATE high and ESTIMATE low groups (N = 39, respectively) were strongly related with glycolytic and ATP/ADP metabolic pathways. Patients in the low-risk group had probabilities to survive longer than those in the high-risk group (p < 0.001). Glycolytic genes had significant impacts on the components of immune cells in TME, especially on the T-cells and dendritic cells. In the high-risk group, the most common mutant genes were TP53 and TTN, and the most frequent mutation type was missense mutation. Glycolysis significantly influenced drug sensitivity, and high tumor mutation burden (TMB) was associated with better immunotherapeutic response. GEO results confirmed that glycolysis had significant impacts on immune cell contents in TME.Conclusion: We performed a comprehensive study of glycolysis and TME and demonstrated that glycolysis could influence the microenvironment and drug therapeutic response in EAC. Evaluation of the glycolysis pattern could help identify the individualized therapeutic regime.

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Targeting UCHL1 Induces Cell Cycle Arrest in High-Risk Multiple Myeloma with t(4;14)

Pathology & Oncology Research ◽

10.3389/pore.2021.606567 ◽

2021 ◽

Vol 27 ◽

Author(s):

Parin Kamseng ◽

Teerapong Siriboonpiputtana ◽

Teeraya Puavilai ◽

Suporn Chuncharunee ◽

Karan Paisooksantivatana ◽

...

Keyword(s):

Cell Cycle ◽

Multiple Myeloma ◽

High Risk ◽

Molecular Mechanism ◽

Risk Group ◽

Enrichment Analysis ◽

High Risk Group ◽

Gene Set Enrichment Analysis ◽

Functional Enrichment ◽

Newly Diagnosed

Multiple myeloma (MM) patients considered to be at high cytogenetic risk commonly fail to respond to standard treatment. A thorough understanding of the molecular mechanism of MM development is, therefore, needed. We endeavored to explore the transcriptional signature among different subgroups of newly diagnosed MM using gene chip-based expression microarray. Bone marrow samples of 15 newly diagnosed Thai MM patients were included. The chromosomal translocation t(4;14) was the most frequently identified genetic alteration in the high-risk subgroup. Cluster analysis from expression profiling demonstrated that high-risk MM have a distinctly different expression pattern compared to standard-risk patients. The most significant differentially expressed gene was UCHL1. Functional enrichment analysis by Gene Set Enrichment Analysis, FUNRICH, and Gene Ontology Panther pathway revealed the gene sets involved in cell cycle control to be enriched in the t(4;14) high-risk group. Interestingly, among the well-established downstream targets of UCHL1, only CCND2 was significantly expressed in the t(4;14) high-risk group. Suppression of UCHL1 protein level by LDN-5744 inhibitor could arrest the cell cycle in G1 phase in cell lines. These findings shed light on the molecular mechanism of UCHL1 in t(4;14) high-risk MM and support the evidence that alteration of the UCHL1 pathway may play a role in the pathogenesis of high-risk MM.

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Development of a Novel Autophagy-Related Prognostic Signature and Nomogram for Hepatocellular Carcinoma

Frontiers in Oncology ◽

10.3389/fonc.2020.591356 ◽

2020 ◽

Vol 10 ◽

Author(s):

Qiongxuan Fang ◽

Hongsong Chen

Keyword(s):

Gene Expression ◽

Hepatocellular Carcinoma ◽

High Risk ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

Regression Analyses ◽

Gene Model

BackgroundHepatocellular carcinoma (HCC) is the seventh most common malignancy and the second most common cause of cancer-related deaths. Autophagy plays a crucial role in the development and progression of HCC.MethodsUnivariate and Lasso Cox regression analyses were performed to determine a gene model that was optimal for overall survival (OS) prediction. Patients in the GSE14520 and GSE54236 datasets of the Cancer Genome Atlas (TCGA) were divided into the high-risk and low-risk groups according to established ATG models. Univariate and multivariate Cox regression analyses were used to identify risk factors for OS for the purpose of constructing nomograms. Calibration and receiver operating characteristic (ROC) curves were used to evaluate model performance. Real-time PCR was used to validate the effects of the presence or absence of an autophagy inhibitor on gene expression in HepG2 and Huh7 cell lines.ResultsOS in the high-risk group was significantly shorter than that in the low-risk group. Gene set enrichment analysis (GSEA) indicated that the association between the low-risk group and autophagy- as well as immune-related pathways was significant. ULK2, PPP3CC, and NAFTC1 may play vital roles in preventing HCC progression. Furthermore, tumor environment analysis via ESTIMATION indicated that the low-risk group was associated with high immune and stromal scores. Based on EPIC prediction, CD8+ T and B cell fractions in the TCGA and GSE54236 datasets were significantly higher in the low-risk group than those in the high-risk group. Finally, based on the results of univariate and multivariate analyses three variables were selected for nomogram development. The calibration plots showed good agreement between nomogram prediction and actual observations. Inhibition of autophagy resulted in the overexpression of genes constituting the gene model in HepG2 and Huh7 cells.ConclusionsThe current study determined the role played by autophagy-related genes (ATGs) in the progression of HCC and constructed a novel nomogram that predicts OS in HCC patients, through a combined analysis of TCGA and gene expression omnibus (GEO) databases.

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Identification of a Nomogram from Ferroptosis-Related Long Noncoding RNAs Signature to Analyze Overall Survival in Patients with Bladder Cancer

Journal of Oncology ◽

10.1155/2021/8533464 ◽

2021 ◽

Vol 2021 ◽

pp. 1-18

Author(s):

Yuanshan Cui ◽

Zhongbao Zhou ◽

Yumeng Chai ◽

Xuanyan Che ◽

Yong Zhang

Keyword(s):

Overall Survival ◽

Bladder Cancer ◽

High Risk ◽

Risk Group ◽

Expression Profiles ◽

Noncoding Rnas ◽

High Risk Group ◽

Long Noncoding Rnas ◽

Gene Set Enrichment Analysis ◽

Functional Enrichment

Purpose. This study aimed to establish a nomogram to predict the overall survival (OS) of patients with bladder cancer (BC) by ferroptosis-related long noncoding RNAs (FRlncRNAs) signature. Methods. We obtained FRlncRNAs expression profiles and clinical data of patients with BC from the Cancer Genome Atlas database. The patients were divided into the training set, testing set, and overall set. Lasso regression and multivariate Cox regression were used to establish the FRlncRNAs signature, the prognosis of each group was compared by Kaplan–Meier (K-M) analysis, and the receiver operating characteristic (ROC) curve evaluated the accuracy of the model. The Gene Set Enrichment Analysis (GSEA) was used for the visualization of the functional enrichment for FRlncRNAs. The databases of GEPIA and K-M Plotter were used for subsequent functional analysis of major FRlncRNAs. Results. Thirteen prognostic FRlncRNAs (LINC00942, MAFG-DT, AL049840.3, AL136084.3, OCIAD1-AS1, AC062017.1, AC008074.2, AC018653.3, AL031775.1, USP30-AS1, LINC01767, AC132807.2, and AL354919.2) were identified to be significantly different, constituting an FRlncRNAs signature. Patients with BC were divided into low-risk group and high-risk group by this signature in the training, testing, and overall sets. K-M analysis showed that the prognosis of patients in the high-risk group was poor and the difference in the subgroup analyses was statistically significant. ROC analysis revealed that the predictive ability of the model was more accurate than traditional assessment methods. A risk score based on FRlncRNAs signature was an independent prognostic factor for the patients with BC (HR = 1.388, 95%CI = 1.228–1.568, P < 0.001 ). Combining the FRlncRNAs signature and clinicopathological factors, a predictive nomogram was constructed. The nomogram can accurately predict the overall survival of patients and had high clinical practicability. The GSEA analysis showed that the primary pathways were WNT, MAPK, and cell-matrix adhesion signaling pathways. The major FRlncRNAs (MAFG-DT) were associated with poor prognosis in the GEPIA and K-M Plotter database. Conclusion. Thirteen prognostic FRlncRNAs and their nomogram were accurate tools for predicting the OS of BC, which might be molecular biomarkers and therapeutic targets.

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A Hypoxia-related LncRNA Signature Predicts Survival of Primary Lower-grade glioma

10.21203/rs.3.rs-335140/v1 ◽

2021 ◽

Author(s):

Yanjia Hu ◽

Jing Zhang ◽

Jing Chen

Keyword(s):

High Risk ◽

Risk Score ◽

Cox Regression ◽

Risk Group ◽

Enrichment Analysis ◽

High Risk Group ◽

Gene Set Enrichment Analysis ◽

Lower Grade ◽

Prognostic Signature ◽

Gene Set Enrichment

Abstract Background Hypoxia-related long non-coding RNAs (lncRNAs) have been proven to play a role in multiple cancers and can serve as prognostic markers. Lower-grade gliomas (LGGs) are characterized by large heterogeneity. Methods This study aimed to construct a hypoxia-related lncRNA signature for predicting the prognosis of LGG patients. Transcriptome and clinical data of LGG patients were obtained from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). LGG cohort in TCGA was chosen as training set and LGG cohorts in CGGA served as validation sets. A prognostic signature consisting of fourteen hypoxia-related lncRNAs was constructed using univariate and LASSO Cox regression. A risk score formula involving the fourteen lncRNAs was developed to calculate the risk score and patients were classified into high- and low-risk groups based on cutoff. Kaplan-Meier survival analysis was used to compare the survival between two groups. Cox regression analysis was used to determine whether risk score was an independent prognostic factor. A nomogram was then constructed based on independent prognostic factors and assessed by C-index and calibration plot. Gene set enrichment analysis and immune cell infiltration analysis were performed to uncover further mechanisms of this lncRNA signature. Results LGG patients with high risk had poorer prognosis than those with low risk in both training and validation sets. Recipient operating characteristic curves showed good performance of the prognostic signature. Univariate and multivariate Cox regression confirmed that the established lncRNA signature was an independent prognostic factor. C-index and calibration plots showed good predictive performance of nomogram. Gene set enrichment analysis showed that genes in the high-risk group were enriched in apoptosis, cell adhesion, pathways in cancer, hypoxia etc. Immune cells were higher in high-risk group. Conclusion The present study showed the value of the 14-lncRNA signature in predicting survival of LGGs and these 14 lncRNAs could be further investigated to reveal more mechanisms involved in gliomas.

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Establishment of Immune-related Gene Pair Signature to Predict Lung Adenocarcinoma Prognosis

Cell Transplantation ◽

10.1177/0963689720977131 ◽

2020 ◽

Vol 29 ◽

pp. 096368972097713

Author(s):

Xueping Jiang ◽

Yanping Gao ◽

Nannan Zhang ◽

Cheng Yuan ◽

Yuan Luo ◽

...

Keyword(s):

High Risk ◽

Lung Adenocarcinoma ◽

Gene Pair ◽

Risk Group ◽

High Risk Group ◽

Gene Set Enrichment Analysis ◽

M2 Macrophage ◽

Related Gene ◽

Immune Related Gene ◽

Potential Biomarker

Tumor microenvironment (TME) has critical impacts on the pathogenesis of lung adenocarcinoma (LUAD). However, the molecular mechanism of TME effects on the prognosis of LUAD patients remains unclear. Our study aimed to establish an immune-related gene pair (IRGP) model for prognosis prediction and internal mechanism investigation. Based on 702 TME-related differentially expressed genes (DEGs) extracted from The Cancer Genome Atlas (TCGA) training cohort using the ESTIMATE algorithm, a 10-IRGP signature was established to predict LUAD patient prognosis. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses showed that DEGs were significantly associated with tumor immune response. In both TCGA training and Gene Expression Omnibus validation datasets, the risk score was an independent prognostic factor for LUAD patients using Lasso-Cox analysis, and patients in the high-risk group had poorer prognosis than those in the low-risk one. In the high-risk group, M2 macrophage and neutrophil infiltrations were higher, while the levels of T cell follicular helpers were significantly lower. The gene set enrichment analysis results showed that DNA repair signaling pathways were involved. In summary, we established an IRGP signature as a potential biomarker to predict the prognosis of LUAD patients.

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Development and validation of an individualized immune-related gene pairs prognostic signature in papillary renal cell carcinoma

10.21203/rs.3.rs-29603/v1 ◽

2020 ◽

Cited By ~ 1

Author(s):

Xianghong Zhou ◽

Shi Qiu ◽

Di Jin ◽

Kun Jin ◽

Xiaonan Zheng ◽

...

Keyword(s):

Gene Expression ◽

High Risk ◽

Risk Group ◽

High Risk Group ◽

Related Gene ◽

Prognostic Signature ◽

Gene Pairs ◽

Immune Related Gene ◽

Immune Related Genes

Abstract Background: Papillary renal carcinoma (PRCC) is one of the important subtypes of kidney cancer, with a high degree of heterogeneity. At present, there is still a lack of robust and accurate biomarkers for the diagnosis, prognosis and treatment selection of PRCC. Considering the important role of tumor immunity in PRCC, we aim to construct a signature based on immune-related gene pairs (IRGPs) to estimate the prognostic of patients with PRCC.Methods: We obtained gene expression profiling and clinical information of patients with PRCC from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), which were divided into discovery and validation cohorts, respectively. The immune-related genes in the samples were used to construct gene pairs, and the immune-related genes pairs (IRGPs) with robust impact for overall survival (OS) were screened out to construct the signature by univariate analysis, multivariate Cox analysis, and least absolute shrinkage and selection operator (Lasso) analysis. Then we verified the prognostic role of the signature, and assessed the relationship between this signature with tumor immune infiltration and functional pathways.Results: A total of 315 patients were included in our study, and divided to discovery (n=287) and validation (n=28) cohorts. Finally, we selected 14 IRGPs with a panel of 22 unique genes to construct the prognostic signature. According to the signature, we stratified patients into high-risk group and low-risk group. In both discovery and validation cohorts, the results of Kaplan-Meier analysis showed that there were significant differences in OS between the two groups (p<0.001). Combined with multiple clinical pathological factors, the results of multivariate analyses confirmed that this signature was an independent predictor of OS (HR, 3.548; 95%CI, 2.096−6.006; p<0.001). The results of immune infiltration analysis demonstrated that the abundance of multiple tumor-infiltration lymphocytes such as CD8+ T cells, Tregs, and T follicular cell helper were significantly higher in the high-risk group. Functional analysis showed that multiple immune-related signaling pathways were enriched in the high-risk group.Conclusions: We successfully established an individualized prognostic immune-related gene pairs signature, which can accurately and independently predict the OS of patients with PRCC.

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Identification of prognostic hypoxia-related genes signature on the tumor microenvironment in esophageal cancer

Mathematical Biosciences and Engineering ◽

10.3934/mbe.2021384 ◽

2021 ◽

Vol 18 (6) ◽

pp. 7743-7758

Author(s):

Linlin Tan ◽

◽

Dingzhuo Cheng ◽

Jianbo Wen ◽

Kefeng Huang ◽

...

Keyword(s):

Esophageal Cancer ◽

High Risk ◽

Immune Cells ◽

Immune Cell ◽

Risk Group ◽

Roc Curves ◽

Risk Groups ◽

High Risk Group ◽

The Cancer Genome Atlas ◽

Cancer Genome Atlas

<abstract> <sec><title>Background</title>Hypoxia is a crucial factor in the development of esophageal cancer. The relationship between hypoxia and immune status in the esophageal cancer microenvironment is becoming increasingly important in clinical practice. This study aims to clarify and investigate the possible connection between immunotherapy and hypoxia in esophageal cancer. </sec> <sec><title>Methods</title>The Cancer Genome Atlas databases are used to find two types of esophageal cancer cases. Cox regressions analyses are used to screen genes for hypoxia-related traits. After that, the genetic signature is validated by survival analysis and the construction of ROC curves. GSEA is used to compare differences in enrichment in the two groups and is followed by the CIBERSORT tool to investigate a potentially relevant correlation between immune cells and gene signatures. </sec> <sec><title>Results</title>We found that the esophageal adenocarcinoma hypoxia model contains 3 genes (PGK1, PGM1, SLC2A3), and the esophageal squamous cell carcinoma hypoxia model contains 2 genes (EGFR, ATF3). The findings demonstrated that the survival rate of patients in the high-risk group is lower than in the lower-risk group. Furthermore, we find that three kinds of immune cells (memory activated CD4+ T cells, activated mast cells, and M2 macrophages) have a marked infiltration in the tissues of patients in the high-risk group. Moreover, we find that PD-L1 and CD244 are highly expressed in high-risk groups. </sec> <sec><title>Conclusions</title>Our data demonstrate that oxygen deprivation is correlated with prognosis and the incidence of immune cell infiltration in patients with both types of esophageal cancer, which provides an immunological perspective for the development of personalized therapy. </sec> </abstract>

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Ferroptosis-related gene signature predicts the prognosis of papillary thyroid carcinoma

Cancer Cell International ◽

10.1186/s12935-021-02389-7 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Jinyuan Shi ◽

Pu Wu ◽

Lei Sheng ◽

Wei Sun ◽

Hao Zhang

Keyword(s):

High Risk ◽

Immune Cells ◽

Risk Group ◽

Gene Signature ◽

High Risk Group ◽

Low Risk ◽

Lasso Regression ◽

Papillary Thyroid ◽

Related Gene ◽

Normal Thyroid

Abstract Background Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer (TC), accounting for more than 80% of all cases. Ferroptosis is a novel iron-dependent and Reactive oxygen species (ROS) reliant type of cell death which is distinct from the apoptosis, necroptosis and pyroptosis. Considerable studies have demonstrated that ferroptosis is involved in the biological process of various cancers. However, the role of ferroptosis in PTC remains unclear. This study aims at exploring the expression of ferroptosis-related genes (FRG) and their prognostic values in PTC. Methods A ferroptosis-related gene signature was constructed using lasso regression analysis through the PTC datasets of the Cancer Genome Atlas (TCGA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to investigate the bioinformatics functions of significantly different genes (SDG) of ferroptosis. Additionally, the correlations of ferroptosis and immune cells were assessed through the single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT database. Finally, SDG were test in clinical PTC specimens and normal thyroid tissues. Results LASSO regression model was utilized to establish a novel FRG signature with 10 genes (ANGPTL7, CDKN2A, DPP4, DRD4, ISCU, PGD, SRXN1, TF, TFRC, TXNRD1) to predicts the prognosis of PTC, and the patients were separated into high-risk and low-risk groups by the risk score. The high-risk group had poorer survival than the low-risk group (p < 0.001). Receiver operating characteristic (ROC) curve analysis confirmed the signature's predictive capacity. Multivariate regression analysis identified the prognostic signature-based risk score was an independent prognostic indicator for PTC. The functional roles of the DEGs in the TGCA PTC cohort were explored using GO enrichment and KEGG pathway analyses. Immune related analysis demonstrated that the most types of immune cells and immunological function in the high-risk group were significant different with those in the low-risk group. Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) verified the SDG have differences in expression between tumor tissue and normal thyroid tissue. In addition, cell experiments were conducted to observe the changes in cell morphology and expression of signature’s genes with the influence of ferroptosis induced by sorafenib. Conclusions We identified differently expressed FRG that may involve in PTC. A ferroptosis-related gene signature has significant values in predicting the patients’ prognoses and targeting ferroptosis may be an alternative for PTC’s therapy.

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Identification of a Five-Autophagy-Related-lncRNA Signature as a Novel Prognostic Biomarker for Hepatocellular Carcinoma

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2020.611626 ◽

2021 ◽

Vol 7 ◽

Author(s):

Xiaoyu Deng ◽

Qinghua Bi ◽

Shihan Chen ◽

Xianhua Chen ◽

Shuhui Li ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

High Risk ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Prognostic Signature ◽

Prediction Ability ◽

Cox Regression Analysis

Although great progresses have been made in the diagnosis and treatment of hepatocellular carcinoma (HCC), its prognostic marker remains controversial. In this current study, weighted correlation network analysis and Cox regression analysis showed significant prognostic value of five autophagy-related long non-coding RNAs (AR-lncRNAs) (including TMCC1-AS1, PLBD1-AS1, MKLN1-AS, LINC01063, and CYTOR) for HCC patients from data in The Cancer Genome Atlas. By using them, we constructed a five-AR-lncRNA prognostic signature, which accurately distinguished the high- and low-risk groups of HCC patients. All of the five AR lncRNAs were highly expressed in the high-risk group of HCC patients. This five-AR-lncRNA prognostic signature showed good area under the curve (AUC) value (AUC = 0.751) for the overall survival (OS) prediction in either all HCC patients or HCC patients stratified according to several clinical traits. A prognostic nomogram with this five-AR-lncRNA signature predicted the 3- and 5-year OS outcomes of HCC patients intuitively and accurately (concordance index = 0.745). By parallel comparison, this five-AR-lncRNA signature has better prognosis accuracy than the other three recently published signatures. Furthermore, we discovered the prediction ability of the signature on therapeutic outcomes of HCC patients, including chemotherapy and immunotherapeutic responses. Gene set enrichment analysis and gene mutation analysis revealed that dysregulated cell cycle pathway, purine metabolism, and TP53 mutation may play an important role in determining the OS outcomes of HCC patients in the high-risk group. Collectively, our study suggests a new five-AR-lncRNA prognostic signature for HCC patients.

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A Four-Gene-Based Prognostic Model Predicts Overall Survival in Patients With Cutaneous Melanoma

Frontiers in Oncology ◽

10.3389/fonc.2021.639874 ◽

2021 ◽

Vol 11 ◽

Author(s):

Xiaoxia Tong ◽

Xiaofei Qu ◽

Mengyun Wang

Keyword(s):

Gene Expression ◽

High Risk ◽

Risk Score ◽

Cutaneous Melanoma ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Low Risk ◽

Rank Test ◽

Score Model

BackgroundCutaneous melanoma (CM) is one of the most aggressive cancers with highly metastatic ability. To make things worse, there are limited effective therapies to treat advanced CM. Our study aimed to investigate new biomarkers for CM prognosis and establish a novel risk score system in CM.MethodsGene expression data of CM from Gene Expression Omnibus (GEO) datasets were downloaded and analyzed to identify differentially expressed genes (DEGs). The overlapped DEGs were then verified for prognosis analysis by univariate and multivariate COX regression in The Cancer Genome Atlas (TCGA) datasets. Based on the gene signature of multiple survival associated DEGs, a risk score model was established, and its prognostic and predictive role was estimated through Kaplan-Meier (K-M) analysis and log-rank test. Furthermore, the correlations between prognosis related genes expression and immune infiltrates were analyzed via Tumor Immune Estimation Resource (TIMER) site.ResultsA total of 103 DEGs were obtained based on GEO cohorts, and four genes were verified in TCGA datasets. Subsequently, four genes (ADAMDEC1, GNLY, HSPA13, and TRIM29) model was developed by univariate and multivariate Cox regression analyses. The K-M plots showed that the high-risk group was associated with shortened survival than that in the low-risk group (P < 0.0001). Multivariate analysis suggested that the model was an independent prognostic factor (high-risk vs. low-risk, HR= 2.06, P < 0.001). Meanwhile, the high-risk group was prone to have larger breslow depth (P< 0.001) and ulceration (P< 0.001).ConclusionsThe four-gene risk score model functions well in predicting the prognosis and treatment response in CM and will be useful for guiding therapeutic strategies for CM patients. Additional clinical trials are needed to verify our findings.

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