scholarly journals Identification of Molecular Subtypes and Potential Small-Molecule Drugs for Esophagus Cancer Treatment Based on m6A Regulators

2022 ◽  
Vol 2022 ◽  
pp. 1-13
Author(s):  
Jianjun Li ◽  
Hongbo Zhu ◽  
Qiao Yang ◽  
Hua Xiao ◽  
Haibiao Wu ◽  
...  
Keyword(s):  
Cancer Progression ◽  
Small Molecule ◽  
Cox Regression ◽  
Molecular Subtypes ◽  
Gene Set Enrichment Analysis ◽  
Hub Genes ◽  
Cox Regression Analysis ◽  
Esophagus Cancer ◽  
Small Molecule Drugs ◽  
Drug Databases

Background. Esophagus cancer (ESCA) is the sixth most frequent cancer in males, with 5-year overall survival of 15%–25%. RNA modifications function critically in cancer progression, and m6A regulators are associated with ESCA prognosis. This study further revealed correlations between m6A and ESCA development. Methods. Univariate Cox regression analysis and consensus clustering were applied to determine molecular subtypes. Functional pathways and gene ontology terms were enriched by gene set enrichment analysis. Protein-protein interaction (PPI) analysis on differentially expressed genes (DEGs) was conducted for hub gene screening. Public drug databases were employed to study the interactions between hub genes and small molecules. Results. Three molecular subtypes related to ESCA prognosis were determined. Based on multiple analyses among molecular subtypes, 146 DEGs were screened, and a PPT network of 15 hub genes was visualized. Finally, 8 potential small-molecule drugs (BMS-754807, gefitinib, neratinib, zuclopenthixol, puromycin, sulfasalazine, and imatinib) were identified for treating ESCA. Conclusions. This study applied a new approach to analyzing the relation between m6A and ESCA prognosis, providing a reference for exploring potential targets and drugs for ESCA treatment.

scholarly journals Identification of a five-gene signature in association with overall survival for hepatocellular carcinoma

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11273
Author(s):  
Lei Yang ◽  
Weilong Yin ◽  
Xuechen Liu ◽  
Fangcun Li ◽  
Li Ma ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cell Cycle ◽  
Overall Survival ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
Enrichment Score ◽  
Hub Genes ◽  
Cox Regression Analysis

Background Hepatocellular carcinoma (HCC) is considered to be a malignant tumor with a high incidence and a high mortality. Accurate prognostic models are urgently needed. The present study was aimed at screening the critical genes for prognosis of HCC. Methods The GSE25097, GSE14520, GSE36376 and GSE76427 datasets were obtained from Gene Expression Omnibus (GEO). We used GEO2R to screen differentially expressed genes (DEGs). A protein-protein interaction network of the DEGs was constructed by Cytoscape in order to find hub genes by module analysis. The Metascape was performed to discover biological functions and pathway enrichment of DEGs. MCODE components were calculated to construct a module complex of DEGs. Then, gene set enrichment analysis (GSEA) was used for gene enrichment analysis. ONCOMINE was employed to assess the mRNA expression levels of key genes in HCC, and the survival analysis was conducted using the array from The Cancer Genome Atlas (TCGA) of HCC. Then, the LASSO Cox regression model was performed to establish and identify the prognostic gene signature. We validated the prognostic value of the gene signature in the TCGA cohort. Results We screened out 10 hub genes which were all up-regulated in HCC tissue. They mainly enrich in mitotic cell cycle process. The GSEA results showed that these data sets had good enrichment score and significance in the cell cycle pathway. Each candidate gene may be an indicator of prognostic factors in the development of HCC. However, hub genes expression was weekly associated with overall survival in HCC patients. LASSO Cox regression analysis validated a five-gene signature (including CDC20, CCNB2, NCAPG, ASPM and NUSAP1). These results suggest that five-gene signature model may provide clues for clinical prognostic biomarker of HCC.

scholarly journals A Tumor Progression Related 7-Gene Signature Indicates Prognosis and Tumor Immune Characteristics of Gastric Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Fen Liu ◽  
Zongcheng Yang ◽  
Lixin Zheng ◽  
Wei Shao ◽  
Xiujie Cui ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
High Risk ◽  
Cancer Progression ◽  
Risk Score ◽  
Risk Model ◽  
Cox Regression ◽  
Hub Genes ◽  
Cox Regression Analysis ◽  
High Risk Patients ◽  
Risk Patients

BackgroundGastric cancer is a common gastrointestinal malignancy. Since it is often diagnosed in the advanced stage, its mortality rate is high. Traditional therapies (such as continuous chemotherapy) are not satisfactory for advanced gastric cancer, but immunotherapy has shown great therapeutic potential. Gastric cancer has high molecular and phenotypic heterogeneity. New strategies for accurate prognostic evaluation and patient selection for immunotherapy are urgently needed.MethodsWeighted gene coexpression network analysis (WGCNA) was used to identify hub genes related to gastric cancer progression. Based on the hub genes, the samples were divided into two subtypes by consensus clustering analysis. After obtaining the differentially expressed genes between the subtypes, a gastric cancer risk model was constructed through univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analysis. The differences in prognosis, clinical features, tumor microenvironment (TME) components and immune characteristics were compared between subtypes and risk groups, and the connectivity map (CMap) database was applied to identify potential treatments for high-risk patients.ResultsWGCNA and screening revealed nine hub genes closely related to gastric cancer progression. Unsupervised clustering according to hub gene expression grouped gastric cancer patients into two subtypes related to disease progression, and these patients showed significant differences in prognoses, TME immune and stromal scores, and suppressive immune checkpoint expression. Based on the different expression patterns between the subtypes, we constructed a gastric cancer risk model and divided patients into a high-risk group and a low-risk group based on the risk score. High-risk patients had a poorer prognosis, higher TME immune/stromal scores, higher inhibitory immune checkpoint expression, and more immune characteristics suitable for immunotherapy. Multivariate Cox regression analysis including the age, stage and risk score indicated that the risk score can be used as an independent prognostic factor for gastric cancer. On the basis of the risk score, we constructed a nomogram that relatively accurately predicts gastric cancer patient prognoses and screened potential drugs for high-risk patients.ConclusionsOur results suggest that the 7-gene signature related to tumor progression could predict the clinical prognosis and tumor immune characteristics of gastric cancer.

scholarly journals Gene Expression Analysis Reveals Novel Gene Prognostic Signatures Involved in Medulloblastoma Subgroups

2021 ◽  
Author(s):  
Fangcheng Li ◽  
Hong-Yao Yuan ◽  
Cheng Chen ◽  
Jun-Ping Pan ◽  
Xin-Ke Xu ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Gene Expression Analysis ◽  
Cox Regression ◽  
Molecular Subtypes ◽  
Molecular Subtype ◽  
Specific Treatment ◽  
Single Factor ◽  
Hub Genes ◽  
Cox Regression Analysis ◽  
Pathway Enrichment

Abstract PurposeMedulloblastoma is a malignant childhood tumor with four molecular subtypes: WNT, SHH, G3, and G4. The prognosis of these four molecular subtypes is different. WNT has the best prognosis, followed by SHH, and G3 and G4 subtypes have the worst prognosis. This study aimed to identify various molecular subtypes of medulloblastoma can independently predict the prognosis of patients and provide specific treatment way for them.MethodsBased on the data in the GSE37418 dataset, the WGCNA method was used to find the genes most related to these molecular subtypes, and then the top ten hub genes in each subtype were found through the cytohubba plug-in of cytoscape. GO pathway enrichment of four interested modules was used, and then GSE85217 was used for clinical trials in single-factor Cox regression analysis .ResultsThe information was subjected to single-factor Cox regression analysis, and twelve hub genes with the most significant prognostic effects on medulloblastoma were found, and then these genes were subjected to multi-factor Cox regression analysis on each molecular subtype, and finally GNG3 was determined .The combination of CALCB and GCGR can predict the development of SHH well (p=0.0011, AUC=0.734), SOCS3 and HOXC10 can better predict the development of G4 (p=0.044, AUC=0.618), and the combination of ADCY8 and LHX3 can predict G3 Development (p=0.034, AUC=0.675).ConclusionThis report showed a possible evidence that OS-related features of various molecular subtypes of medulloblastoma can independently predict the prognosis of patients with each subtype of medulloblastoma, and provide new therapeutic targets for them.

scholarly journals Identification of Core Genes Related to Progression and Prognosis of Hepatocellular Carcinoma and Small-Molecule Drug Predication

2021 ◽  
Vol 12 ◽  
Author(s):  
Nan Jiang ◽  
Xinzhuo Zhang ◽  
Dalian Qin ◽  
Jing Yang ◽  
Anguo Wu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Cell Cycle ◽  
Small Molecule ◽  
Molecular Mechanisms ◽  
Cox Regression ◽  
Disease Free Survival ◽  
Gene Expression Signature ◽  
Cox Regression Analysis ◽  
Small Molecule Drugs

BackgroundHepatocellular carcinoma (HCC) is one of the most leading causes of cancer death with a poor prognosis. However, the underlying molecular mechanisms are largely unclear, and effective treatment for it is limited. Using an integrated bioinformatics method, the present study aimed to identify the key candidate prognostic genes that are involved in HCC development and identify small-molecule drugs with treatment potential.Methods and ResultsIn this study, by using three expression profile datasets from Gene Expression Omnibus database, 1,704 differentially expressed genes were identified, including 671 upregulated and 1,033 downregulated genes. Then, weighted co-expression network analysis revealed nine modules are related with pathological stage; turquoise module was the most associated module. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway analyses (KEGG) indicated that these genes were enriched in cell division, cell cycle, and metabolic related pathways. Furthermore, by analyzing the turquoise module, 22 genes were identified as hub genes. Based on HCC data from gene expression profiling interactive analysis (GEPIA) database, nine genes associated with progression and prognosis of HCC were screened, including ANLN, BIRC5, BUB1B, CDC20, CDCA5, CDK1, NCAPG, NEK2, and TOP2A. According to the Human Protein Atlas and the Oncomine database, these genes were highly upregulated in HCC tumor samples. Moreover, multivariate Cox regression analysis showed that the risk score based on the gene expression signature of these nine genes was an independent prognostic factor for overall survival and disease-free survival in HCC patients. In addition, the candidate small-molecule drugs for HCC were identified by the CMap database.ConclusionIn conclusion, the nine key gene signatures related to HCC progression and prognosis were identified and validated. The cell cycle pathway was the core pathway enriched with these key genes. Moreover, several candidate molecule drugs were identified, providing insights into novel therapeutic approaches for HCC.

scholarly journals Low CCL19 Expression is Associated With Adverse Clinical Outcomes For Follicular Lymphoma Patients Treated With Chemoimmunotherapy

2021 ◽  
Author(s):  
Yu Zhou ◽  
Shasha Wang ◽  
Yunxia Tao ◽  
Haizhu Chen ◽  
Yan Qin ◽  
...  
Keyword(s):  
Follicular Lymphoma ◽  
Plasma Cells ◽  
Cox Regression ◽  
Progression Free Survival ◽  
Gene Set Enrichment Analysis ◽  
Sequencing Data ◽  
First Line ◽  
Hub Genes ◽  
Cox Regression Analysis ◽  
Subset Analysis

Abstract Background: This study aimed to recognize the hub genes associated with prognosis in follicular lymphoma (FL) treated with first-line rituximab combined with chemotherapy.Method: RNA sequencing data of dataset GSE65135 (n=24) were included in differentially expressed genes (DEGs) analysis. Weighted gene co-expression network analysis (WGCNA) was applied for exploring the coexpression network and identifying hub genes. Validation of hub genes expression and prognosis were applied in dataset GSE119214 (n=137) and independent patient cohort from Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College (n=32), respectively, by analyzing RNAseq expression data and serum protein concentration quantified by ELISA. The Gene Set Enrichment Analysis (GSEA), gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments analysis were performed. CIBERSORT was applied for tumor-infiltrating immune cells (TIICs) subset analysis. Results: A total of 3260 DEGs were obtained, with 1861 genes upregulated and 1399 genes downregulated. Using WGCNA, eight hub genes, PLA2G2D, MMP9, PTGDS, CCL19, NFIB, YAP1, RGL1, and TIMP3 were identified. Kaplan-Meier analysis and multivariate COX regression analysis indicated that CCL19 independently associated with overall survival (OS) for FL patients treated with rituximab and chemotherapy (HR = 0.47, 95%CI [0.25-0.86], p = 0.014). Higher serum CCL19 concentration was associated with longer progression-free survival (PFS, p=0.014) and OS (p=0.039). TIICs subset analysis showed that CCL19 expression had a positive correlation with monocytes and macrophages M1, and a negative correlation with naïve B cells and plasma cells. Conclusion: CCL19 expression was associated with survival outcomes and might be a potential prognostic biomarker for FL treated with first-line chemoimmunotherapy.

scholarly journals Identification of Potential Biomarkers Associated With Immune Infiltration in Papillary Renal Cell Carcinoma

2021 ◽  
Author(s):  
Ran Deng ◽  
Jianpeng Li ◽  
Hong Zhao ◽  
Zhirui Zou ◽  
Jiangwei Man ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Immune Cells ◽  
Renal Cell ◽  
Cox Regression ◽  
Papillary Renal Cell Carcinoma ◽  
Risk Groups ◽  
Gene Set Enrichment Analysis ◽  
Hub Genes ◽  
Cox Regression Analysis

Abstract Background: Immunotherapeutic approaches have recently emerged as effective treatment regimens against various types of cancer. However, the immune-mediated mechanisms surrounding papillary renal cell carcinoma (pRCC) remain unclear. This study aimed to investigate the tumor microenvironment (TME) and identify the potential immune-related biomarkers for pRCC.Methods: The CIBERSORT algorithm was used to calculate the abundance ratio of immune cells in each pRCC sample downloaded from the database UCSC Xena. Univariate Cox analysis was used to select the prognostic-related tumor-infiltrating immune cells (TIICs). Multivariate Cox regression analysis was performed to develop a signature based on the selected prognostic-related TIICs. Then, these pRCC samples were divided into low- and high-risk groups according to the obtained signature. Analyses using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) were performed to investigate the biological function of the DEGs (differentially expressed genes) between the high- and low-risk groups. The hub genes were identified using a Weighted Gene Co-Expression Network Analysis (WGCNA) and a Protein-Protein Interaction (PPI) analysis. The hub genes were subsequently validated using Kaplan-Meier survival analysis, Receiver Operating Characteristic (ROC) analysis, a nomogram prediction model, and via the Gene Expression Omnibus (GEO) database, and the Human Protein Atlas (HPA) database. Finally, we validated the correlation between the nine hub genes and immune cells using the XCELL algorithm.Results: According to our analyses, nine immune cells play a vital role in the TME of pRCC. Our analyses also obtained nine potential immune-related biomarkers for pRCC, including TOP2A, BUB1B, BUB1, TPX2, PBK, CEP55, ASPM, RRM2, and CENPF.Conclusion: In this study, our data revealed the crucial TIICs and potential immune-related biomarkers for pRCC and provided compelling insights into the pathogenesis and potential therapeutic targets for pRCC.

scholarly journals Low CCL19 expression is associated with adverse clinical outcomes for follicular lymphoma patients treated with chemoimmunotherapy

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Yu Zhou ◽  
Shasha Wang ◽  
Yunxia Tao ◽  
Haizhu Chen ◽  
Yan Qin ◽  
...  
Keyword(s):  
Follicular Lymphoma ◽  
Plasma Cells ◽  
Cox Regression ◽  
Progression Free Survival ◽  
Gene Set Enrichment Analysis ◽  
Sequencing Data ◽  
First Line ◽  
Hub Genes ◽  
Cox Regression Analysis ◽  
Subset Analysis

Abstract Background This study aimed to recognize the hub genes associated with prognosis in follicular lymphoma (FL) treated with first-line rituximab combined with chemotherapy. Method RNA sequencing data of dataset GSE65135 (n = 24) were included in differentially expressed genes (DEGs) analysis. Weighted gene co-expression network analysis (WGCNA) was applied for exploring the coexpression network and identifying hub genes. Validation of hub genes expression and prognosis were applied in dataset GSE119214 (n = 137) and independent patient cohort from Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College (n = 32), respectively, by analyzing RNAseq expression data and serum protein concentration quantified by ELISA. The Gene Set Enrichment Analysis (GSEA), gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments analysis were performed. CIBERSORT was applied for tumor-infiltrating immune cells (TIICs) subset analysis. Results A total of 3260 DEGs were obtained, with 1861 genes upregulated and 1399 genes downregulated. Using WGCNA, eight hub genes, PLA2G2D, MMP9, PTGDS, CCL19, NFIB, YAP1, RGL1, and TIMP3 were identified. Kaplan–Meier analysis and multivariate COX regression analysis indicated that CCL19 independently associated with overall survival (OS) for FL patients treated with rituximab and chemotherapy (HR = 0.47, 95% CI [0.25–0.86], p = 0.014). Higher serum CCL19 concentration was associated with longer progression-free survival (PFS, p = 0.014) and OS (p = 0.039). TIICs subset analysis showed that CCL19 expression had a positive correlation with monocytes and macrophages M1, and a negative correlation with naïve B cells and plasma cells. Conclusion CCL19 expression was associated with survival outcomes and might be a potential prognostic biomarker for FL treated with first-line chemoimmunotherapy.

scholarly journals A Prognostic Autophagy-Related Gene Pair Signature and Small-Molecule Drugs for Hepatocellular Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
ZeBing Song ◽  
GuoPei Zhang ◽  
Yang Yu ◽  
ShaoQiang Li
Keyword(s):  
Hepatocellular Carcinoma ◽  
Small Molecule ◽  
Risk Score ◽  
Cox Regression ◽  
Cancer Genome ◽  
Related Gene ◽  
Prognostic Signature ◽  
Aberrant Expression ◽  
Cox Regression Analysis ◽  
Small Molecule Drugs

Dysregulation of autophagy-related genes (ARGs) is related to the prognosis of cancers. However, the aberrant expression of ARGs signature in the prognosis of hepatocellular carcinoma (HCC) remain unclear. Using The Cancer Genome Atlas and the International Cancer Genome Consortium database, 188 common autophagy-related gene pairs (ARGPs) were identified. Through univariate, least absolute shrinkage and selection operator analysis, and multivariate Cox regression analysis, a prognostic signature of the training set was constructed on the basis of 6 ARGPs. Further analysis revealed that the ARGP based signature performed more accurately in overall survival (OS) prediction compared to other published gene signatures. In addition, a high risk of HCC was closely related to CTLA4 upregulation, LC3 downregulation, low-response to axitinib, rapamycin, temsirolimus, docetaxel, metformin, and high-response to bleomycin. Univariate Cox and multivariate Cox analysis revealed that the risk score was an independent prognostic factor for HCC. These results were internally validated in the test and TCGA sets and externally validated in the ICGC set. A nomogram, consisting of the risk score and the TNM stage, performed well when compared to an ideal nomogram. In conclusion, a 6-ARGP-based prognostic signature was identified and validated as an effective predictor of OS of patients with HCC. Furthermore, we recognized six small-molecule drugs, which may be potentially effective in treating HCC.

KIAA0101 in Malignant Pleural Mesothelioma: a Potential Diagnostic and Prognostic Marker

2021 ◽  
Vol 24 ◽  
Author(s):  
Ping Lin ◽  
Yuean Zhao ◽  
Xiaoqian Li ◽  
Zongan Liang
Keyword(s):  
Gene Expression ◽  
Regression Analysis ◽  
Malignant Pleural Mesothelioma ◽  
Cox Regression ◽  
Expression Profiles ◽  
Gene Set Enrichment Analysis ◽  
High Expression ◽  
Pleural Mesothelioma ◽  
Hub Genes ◽  
Cox Regression Analysis

Background: Currently, there are no reliable diagnostic and prognostic markers for malignant pleural mesothelioma (MPM). The objective of this study was to identify hub genes that could be helpful for diagnosis and prognosis in MPM by using bioinformatics analysis. Materials and Methods: The gene expression profiles were downloaded from the Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA). Weighted gene co-expression network analysis (WGCNA), LASSO regression analysis, Cox regression analysis, and Gene Set Enrichment Analysis (GSEA) were performed to identify hub genes and their functions. Results: A total of 430 up-regulated and 867 downregulated genes in MPM were identified based on the GSE51024 dataset. According to the WGCNA analysis, differentially expressed genes were classified into 8 modules. Among them, the pink module was most closely associated with MPM. According to genes with GS > 0.8 and MM > 0.8, six genes were selected as candidate hub genes (NUSAP1, TOP2A, PLOD2, BUB1B, UHRF1, KIAA0101) in the pink module. In the LASSO model, three genes (NUSAP1, PLOD2, and KIAA0101) were identified with non-zero regression coefficients and were considered hub genes among the 6 candidates. The hub gene-based LASSO model can accurately distinguish MPM from controls (AUC = 0.98). Moreover, the high expression level of KIAA0101, PLOD2, and NUSAP1 were all associated with poor prognosis compared to the low level in Kaplan–Meier survival analyses. After further multivariate Cox analysis, only KIAA0101 (HR = 1.55, 95% CI = 1.05-2.29) was identified as an independent prognostic factor among these hub genes. Finally, GSEA revealed that high expression of KIAA0101 was closely associated with 10 signaling pathways. Conclusion: Our study identified several hub genes relevant to MPM, including NUSAP1, PLOD2, and KIAA0101. Among these genes, KIAA0101 appears to be a useful diagnostic and prognostic biomarker for MPM, which may provide new clues for MPM diagnosis and therapy.

scholarly journals Identification of a Novel Lipid Metabolism-Related Gene Signature in Prognosis of Bladder Cancer

2021 ◽  
Author(s):  
Ke Zhu ◽  
Liu Xiaoqiang ◽  
Wen Deng ◽  
Gongxian Wang ◽  
Bin Fu
Keyword(s):  
Bladder Cancer ◽  
Regression Analysis ◽  
Lipid Metabolism ◽  
Signaling Pathway ◽  
Small Molecule ◽  
Enrichment Analysis ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
Cox Regression Analysis ◽  
Small Molecule Drugs

Abstract BackgroundBladder cancer (BLCA) is a destructive cancer with unfavorable prognosis. Mounting studies have demonstrated that lipid metabolism affected the progression and treatment of tumor. Therefore, we aimed to explore the function and prognostic value of lipid metabolism-related genes in patients with bladder cancer.MethodsLipid metabolism-related genes (LRGs) were acquired from Molecular Signature Database (MSigDB). LRG mRNA expression and clinical data were obtained from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) dataset. Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis were used to identify the prognostic gene for predicting overall survival in BLCA. The Kaplan-Meier analysis was performed to assess the prognosis. The function of LRGs was explored via enrichment analysis and single sample Gene Set Enrichment Analysis (ssGSEA). CMAP database was used to find the small molecule drugs for treatment.ResultsWe successfully constructed and validated a 11-LRG risk model for predicting the prognosis of BLCA patients. Furthermore, we also found that the 11-gene signature was an independent hazardous factor. Functional analysis suggested that LRGs were closely related with the PPAR signaling pathway, fatty acid metabolism and AMPK signaling pathway. LRGs were also involved in immune cells infiltration. Five small molecule drugs could be the candidate treatment for BLCA patients based on CMAP dataset.ConclusionIn conclusion, we identified a reliable prognostic biomarker based on11-LRG signature and found five small molecule drugs for BLCA patient treatments.

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