Identification and Validation of Prognostically Relevant Gene Signature in Melanoma

Background. Currently, effective genetic markers are limited to predict the clinical outcome of melanoma. High-throughput multiomics sequencing data have provided a valuable approach for the identification of genes associated with cancer prognosis. Method. The multidimensional data of melanoma patients, including clinical, genomic, and transcriptomic data, were obtained from The Cancer Genome Atlas (TCGA). These samples were then randomly divided into two groups, one for training dataset and the other for validation dataset. In order to select reliable biomarkers, we screened prognosis-related genes, copy number variation genes, and SNP variation genes and integrated these genes to further select features using random forests in the training dataset. We screened for robust biomarkers and established a gene-related prognostic model. Finally, we verified the selected biomarkers in the test sets (GSE19234 and GSE65904) and on clinical samples extracted from melanoma patients using qRT-PCR and immunohistochemistry analysis. Results. We obtained 1569 prognostic-related genes and 1101 copy-amplification, 1093 copy-deletions, and 92 significant mutations in genomic variants. These genomic variant genes were closely related to the development of tumors and genes that integrate genomic variation. A total of 141 candidate genes were obtained from prognosis-related genes. Six characteristic genes (IQCE, RFX6, GPAA1, BAHCC1, CLEC2B, and AGAP2) were selected by random forest feature selection, many of which have been reported to be associated with tumor progression. Cox regression analysis was used to establish a 6-gene signature. Experimental verification with qRT-PCR and immunohistochemical staining proved that these selected genes were indeed expressed at a significantly higher level compared with the normal tissues. This signature comprised an independent prognostic factor for melanoma patients. Conclusions. We constructed a 6-gene signature (IQCE, RFX6, GPAA1, BAHCC1, CLEC2B, and AGAP2) as a novel prognostic marker for predicting the survival of melanoma patients.

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Systematic Characterization of Novel Immune Gene Signatures Predicts Prognostic Factors in Hepatocellular Carcinoma

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.686664 ◽

2021 ◽

Vol 9 ◽

Author(s):

Dafeng Xu ◽

Yu Wang ◽

Jincai Wu ◽

Yuliang Zhang ◽

Zhehao Liu ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Risk Score ◽

Cox Regression ◽

Expression Profiles ◽

Gene Signature ◽

Clinical Samples ◽

Immune Gene ◽

Cox Regression Analysis ◽

Immune Related Genes ◽

Cancer Tissues

Background: The prognosis of patients with hepatocellular carcinoma (HCC) is negatively affected by the lack of effective prognostic indicators. The change of tumor immune microenvironment promotes the development of HCC. This study explored new markers and predicted the prognosis of HCC patients by systematically analyzing immune characteristic genes.Methods: Immune-related genes were obtained, and the differentially expressed immune genes (DEIGs) between tumor and para-cancer samples were identified and analyzed using gene expression profiles from TCGA, HCCDB, and GEO databases. An immune prognosis model was also constructed to evaluate the predictive performance in different cohorts. The high and low groups were divided based on the risk score of the model, and different algorithms were used to evaluate the tumor immune infiltration cell (TIIC). The expression and prognosis of core genes in pan-cancer cohorts were analyzed, and gene enrichment analysis was performed using clusterProfiler. Finally, the expression of the hub genes of the model was validated by clinical samples.Results: Based on the analysis of 730 immune-related genes, we identified 64 common DEIGs. These genes were enriched in the tumor immunologic related signaling pathways. The first 15 genes were selected using RankAggreg analysis, and all the genes showed a consistent expression trend across multi-cohorts. Based on lasso cox regression analysis, a 5-gene signature risk model (ATG10, IL18RAP, PRKCD, SLC11A1, and SPP1) was constructed. The signature has strong robustness and can stabilize different cohorts (TCGA-LIHC, HCCDB18, and GSE14520). Compared with other existing models, our model has better performance. CIBERSORT was used to assess the landscape maps of 22 types of immune cells in TCGA, GSE14520, and HCCDB18 cohorts, and found a consistent trend in the distribution of TIIC. In the high-risk score group, scores of Macrophages M1, Mast cell resting, and T cells CD8 were significantly lower than those of the low-risk score group. Different immune expression characteristics, lead to the different prognosis. Western blot demonstrated that ATG10, PRKCD, and SPP1 were highly expressed in cancer tissues, while IL18RAP and SLC11A1 expression in cancer tissues was lower. In addition, IL18RAP has a highly positive correlation with B cell, macrophage, Neutrophil, Dendritic cell, CD8 cell, and CD4 cell. The SPP1, PRKCD, and SLC11A1 genes have the strongest correlation with macrophages. The expression of ATG10, IL18RAP, PRKCD, SLC11A1, and SPP1 genes varies among different immune subtypes and between different T stages.Conclusion: The 5-immu-gene signature constructed in this study could be utilized as a new prognostic marker for patients with HCC.

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An epithelial-mesenchymal transition-related 5-gene signature predicting the prognosis of hepatocellular carcinoma patients

Cancer Cell International ◽

10.1186/s12935-021-01864-5 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Gongmin Zhu ◽

Hongwei Xia ◽

Qiulin Tang ◽

Feng Bi

Keyword(s):

Tumor Metastasis ◽

Cox Regression ◽

Epithelial Mesenchymal Transition ◽

Risk Group ◽

Gene Signature ◽

High Risk Group ◽

Related Gene ◽

Mesenchymal Transition ◽

Cox Regression Analysis ◽

Qrt Pcr

Abstract Background Tumor metastasis is one of the leading reasons of the dismal prognosis of hepatocellular carcinoma (HCC). Epithelial-mesenchymal transition (EMT) is closely associated with tumor metastasis including HCC. The purpose of this study is to construct and validate an EMT-related gene signature for predicting the prognosis of HCC patients. Methods Gene expression data of HCC patients was downloaded from The Cancer Genome Atlas (TCGA) database. Gene set enrichment analysis (GSEA) was performed to found the EMT-related gene sets which were obviously distinct between normal samples and paired HCC samples. Cox regression analysis was used to develop an EMT-related prognostic signature, and the performance of the signature was evaluated by Kaplan–Meier curves and time-dependent receiver operating characteristic (ROC) curves. A nomogram incorporating the independent predictors was established. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to detect the expression levels of the hub genes in HCC cell lines, and the role of PDCD6 in the metastasis of HCC was determined by functional experiments. Results An EMT-related 5-gene signature (PDCD6, TCOF1, TRIM28, EZH2 and FAM83D) was constructed using univariate and multivariate Cox regression analysis. Based on the signature, the HCC patients were classified into high- and low-risk groups, and patients in high-risk group had a poor prognosis. Time-dependent ROC and Cox regression analyses suggested that the signature could predict HCC prognosis exactly and independently. The predictive capacity of the signature was also validated in two external cohorts. GSEA results showed that many cancer-related signaling pathways such as PI3K/Akt/mTOR pathway and TGF-β/SMAD pathway were enriched in high-risk group. The result of qRT-PCR revealed that PDCD6, TCOF1 and FAM83D were highly expressed in HCC cancer cells. Among them, PDCD6 were found to promote cell migration and invasion. Conclusion The EMT-related 5-gene signature can serve as a promising prognostic biomarker for HCC patients and may provide a novel mechanism of HCC metastasis.

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Development and Validation of a Prognostic Gene Signature Correlated With M2 Macrophage Infiltration in Esophageal Squamous Cell Carcinoma

Frontiers in Oncology ◽

10.3389/fonc.2021.769727 ◽

2021 ◽

Vol 11 ◽

Author(s):

Jiannan Yao ◽

Ling Duan ◽

Xuying Huang ◽

Jian Liu ◽

Xiaona Fan ◽

...

Keyword(s):

Regression Analysis ◽

Risk Score ◽

Cox Regression ◽

Risk Group ◽

Gene Signature ◽

M2 Macrophage ◽

Cox Regression Analysis ◽

Qrt Pcr ◽

Score Model ◽

Geo Database

BackgroundEsophageal squamous cell carcinoma (ESCC) is the most common type of esophageal cancer and the seventh most prevalent cause of cancer-related death worldwide. Tumor microenvironment (TME) has been confirmed to play an crucial role in ESCC progression, prognosis, and the response to immunotherapy. There is a need for predictive biomarkers of TME-related processes to better prognosticate ESCC outcomes.AimTo identify a novel gene signature linked with the TME to predict the prognosis of ESCC.MethodsWe calculated the immune/stromal scores of 95 ESCC samples from The Cancer Genome Atlas (TCGA) using the ESTIMATE algorithm, and identified differentially expressed genes (DEGs) between high and low immune/stromal score patients. The key prognostic genes were further analyzed by the intersection of protein–protein interaction (PPI) networks and univariate Cox regression analysis. Finally, a risk score model was constructed using multivariate Cox regression analysis. We evaluated the associations between the risk score model and immune infiltration via the CIBERSORT algorithm. Moreover, we validated the signature using the Gene Expression Omnibus (GEO) database. Within the ten gene signature, five rarely reported genes were further validated with quantitative real time polymerase chain reaction (qRT-PCR) using an ESCC tissue cDNA microarray.ResultsA total of 133 up-regulated genes were identified as DEGs. Ten prognostic genes were selected based on intersection analysis of univariate COX regression analysis and PPI, and consisted of C1QA, C1QB, C1QC, CD86, C3AR1, CSF1R, ITGB2, LCP2, SPI1, and TYROBP (HR>1, p<0.05). The expression of 9 of these genes in the tumor samples were significantly higher compared to matched adjacent normal tissue based on the GEO database (p<0.05). Next, we assessed the ability of the ten-gene signature to predict the overall survival of ESCC patients, and found that the high-risk group had significantly poorer outcomes compared to the low-risk group using univariate and multivariate analyses in the TCGA and GEO cohorts (HR=2.104, 95% confidence interval:1.343-3.295, p=0.001; HR=1.6915, 95% confidence interval:1.053-2.717, p=0.0297). Additionally, receiver operating characteristic (ROC) curve analysis demonstrated a relatively sensitive and specific profile for the signature (1-, 2-, 3-year AUC=0.672, 0.854, 0.81). To identify the basis for these differences in the TME, we performed correlation analyses and found a significant positive correlation with M1 and M2 macrophages and CD8+ T cells, as well as a strong correlation to M2 macrophage surface markers. A nomogram based on the risk score and select clinicopathologic characteristics was constructed to predict overall survival of ESCC patients. For validation, qRT-PCR of an ESCC patient cDNA microarray was performed, and demonstrated that C1QA, C3AR1, LCP2, SPI1, and TYROBP were up-regulated in tumor samples and predict poor prognosis.ConclusionThis study established and validated a novel 10-gene signature linked with M2 macrophages and poor prognosis in ESCC patients. Importantly, we identified C1QA, C3AR1, LCP2, SPI1, and TYROBP as novel M2 macrophage-correlated survival biomarkers. These findings may identify potential targets for therapy in ESCC patients.

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Weighted Gene Co-expression Network Analysis Identified a Novel Thirteen-Gene Signature Associated With Progression, Prognosis, and Immune Microenvironment of Colon Adenocarcinoma Patients

Frontiers in Genetics ◽

10.3389/fgene.2021.657658 ◽

2021 ◽

Vol 12 ◽

Author(s):

Cangang Zhang ◽

Zhe Zhao ◽

Haibo Liu ◽

Shukun Yao ◽

Dongyan Zhao

Keyword(s):

Network Analysis ◽

Cox Regression ◽

Colon Adenocarcinoma ◽

Gene Signature ◽

Training Dataset ◽

Migration And Invasion ◽

Overlapping Genes ◽

Characteristic Analysis ◽

Cox Regression Analysis ◽

Testing Dataset

Colon adenocarcinoma (COAD) is one of the most common malignant tumors and has high migration and invasion capacity. In this study, we attempted to establish a multigene signature for predicting the prognosis of COAD patients. Weighted gene co-expression network analysis and differential gene expression analysis methods were first applied to identify differentially co-expressed genes between COAD tissues and normal tissues from the Cancer Genome Atlas (TCGA)-COAD dataset and GSE39582 dataset, and a total of 309 overlapping genes were screened out. Then, our study employed TCGA-COAD cohort as the training dataset and an independent cohort by merging the GES39582 and GSE17536 datasets as the testing dataset. After univariate and multivariate Cox regression analyses were performed for these overlapping genes and overall survival (OS) of COAD patients in the training dataset, a 13-gene signature was constructed to divide COAD patients into high- and low-risk subgroups with significantly different OS. The testing dataset exhibited the same results utilizing the same predictive signature. The area under the curve of receiver operating characteristic analysis for predicting OS in the training and testing datasets were 0.789 and 0.868, respectively, which revealed the enhanced predictive power of the signature. Multivariate Cox regression analysis further suggested that the 13-gene signature could independently predict OS. Among the 13 prognostic genes, NAT1 and NAT2 were downregulated with deep deletions in tumor tissues in multiple COAD cohorts and exhibited significant correlations with poorer OS based on the GEPIA database. Notably, NAT1 and NAT2 expression levels were positively correlated with infiltrating levels of CD8+ T cells and dendritic cells, exhibiting a foundation for further research investigating the antitumor immune roles played by NAT1 and NAT2 in COAD. Taken together, the results of our study showed that the 13-gene signature could efficiently predict OS and that NAT1 and NAT2 could function as biomarkers for prognosis and the immune response in COAD.

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Co-amplification of Genes in Chromosome 8q24: a Robust Prognostic Marker in Hepatocellular Carcinoma

10.21203/rs.3.rs-272648/v2 ◽

2021 ◽

Author(s):

Yuan Cheng ◽

Yongjian Zheng ◽

Cheng Zhang ◽

Shunjun Fu ◽

Guolin He ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Prognostic Marker ◽

Copy Number ◽

Molecular Mechanisms ◽

Cox Regression ◽

Gene Signature ◽

Gene Set Enrichment Analysis ◽

Training Dataset ◽

Tissue Samples ◽

Cox Regression Analysis

Abstract Background: Hepatocellular carcinoma (HCC) is one of the major cause of cancer related deaths worldwide, due to high 5 year postoperative recurrence rate and individual heterogeneity. Thus, prognostic model has dramatically urgently needed on HCC in recent years. Serval research have reported that copy number amplification of the 8q24 chromosomal region is associated with low survival in many cancers. The objective of this study was to construct a multi-gene model to predict the prognosis of HCC. Methods: RNAseq and copy number variant (CNV) data of tumor tissue samples of HCC from TCGA (N = 328) was used to identify differentially expressed mRNAs of genes located on chromosomal 8q24 regions by Wilcox test. Univariate Cox and Lasso Cox regression were performed to screen and construct prognostic multi-gene signature in TCGA cohort (N = 119). The multi-gene signature was validated in ICGC cohort (N = 240).A nomogram for prognosis prediction was built and Gene Set Enrichment Analysis (GSEA) was used to further study the underlying molecular mechanisms. Results: A 7-gene prognosis signature model was established for predicting HCC prognosis. Using the model, we divided individuals into high-risk and low-risk sets with significantly different overall survival in training dataset(HR = 0.17, 95% CI 0.1–0.28; P ＜ 0.001) and in testing dataset (HR = 0.42, 95% CI 0.23–0.74; P = 0.002). Multivariate Cox regression analysis indicated that this signature was an independent prognostic factor of HCC survival. Nomogram including the prognostic signature was constructed and showed better predictive performance in short year (1 and 3 year) than long year (5 year) survival. Furthermore, GSEA revealed several significantly pathways, which may help explain the underlying molecular mechanism. Conclusions: The 7-gene signature was a reliable prognostic marker in HCC, which may provide meaningful information to therapeutic customization and treatment decision making.

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Co-amplification of Genes in Chromosome 8q24: a Robust Prognostic Marker in Hepatocellular Carcinoma

10.21203/rs.3.rs-272648/v1 ◽

2021 ◽

Author(s):

Yuan Cheng ◽

Yongjian Zheng ◽

Cheng Zhang ◽

Shunjun Fu ◽

Guolin He ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Copy Number ◽

Molecular Mechanisms ◽

Cox Regression ◽

Treatment Decision ◽

Gene Signature ◽

Gene Set Enrichment Analysis ◽

Training Dataset ◽

Tissue Samples ◽

Cox Regression Analysis

Abstract Background: Hepatocellular carcinoma (HCC) is one of the majorcause of cancer related deaths worldwide, due tohigh 5 yearpostoperativerecurrence rate and individualheterogeneity.Thus, prognostic modelhas dramaticallyurgently neededon HCCin recent years.Serval research have reported that copy number amplificationof the 8q24 chromosomalregion is associated with lowsurvival in many cancers. The objectiveof this study was to construct a multi-gene modelto predict the prognosis of HCC. Methods: RNAseq and copy number variant (CNV)data of tumor tissue samples of HCC from TCGA(N = 328)wasused to identify differentially expressed mRNAs of genes located on chromosomal 8q24 regionsby Wilcoxtest.Univariate Cox and Lasso Cox regressionwereperformed to screenand constructprognostic multi-gene signature in TCGA cohort (N = 119). The multi-gene signature was validated in ICGCcohort(N = 240).A nomogram for prognosis prediction was built and Gene Set Enrichment Analysis (GSEA) was usedto further studythe underlying molecular mechanisms.Results:A 7-gene prognosis signaturemodelwas established for predicting HCC prognosis. Using the model, we divided individualsinto high-risk and low-risk setswith significantly different overall survival in training dataset(HR = 0.17, 95% CI 0.1–0.28; P ＜0.001) and in testing dataset (HR = 0.42, 95% CI 0.23–0.74; P =0.002). Multivariate Cox regression analysis indicatedthat thissignature was an independentprognostic factor ofHCC survival. Nomogram including the prognostic signature was constructed and showed better predictive performance in short year (1and 3year) than long year (5 year) survival. Furthermore, GSEA revealed several significantly pathways, which may help explain the underlying molecular mechanism.Conclusions:The 7-gene signature was areliableprognosticmarkerinHCC, which may provide meaningful informationto therapeutic customization and treatment decision making.

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Identification and Validation a Costimulatory Molecule Gene Signature to Predict the Prognosis and Immunotherapy Response for Hepatocellular Carcinoma

10.21203/rs.3.rs-1064833/v1 ◽

2021 ◽

Author(s):

Yinan Hu ◽

Jingyi Liu ◽

Jiahao Yu ◽

Fangfang Yang ◽

Miao Zhang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Immune Cells ◽

Cox Regression ◽

Costimulatory Molecule ◽

Gene Signature ◽

Consensus Clustering ◽

Prognostic Signature ◽

Cox Regression Analysis ◽

Qrt Pcr

Abstract Background: Hepatocellular carcinoma (HCC) is one of the most common malignancies worldwide. Costimulatory molecules have been proven to be the foundation of immunotherapy. However, the potential roles of costimulatory molecule genes (CMGs) in HCC remain unclear. Our study is aimed to develop a costimulatory molecule-related gene signature that could evaluate the prognosis of HCC patients.Methods: Based on The Cancer Gene Atlas (TCGA) database, univariate Cox regression analysis was applied in CMGs to identify prognosis-related CMGs. Consensus clustering analysis was performed to stratify HCC patients into different subtypes and compared them in OS. Subsequently, the LASSO Cox regression analysis was performed to construct the CMGs-related prognostic signature and Kaplan–Meier survival curves as well as ROC curve were used to validate the predictive capability. Then we explored the correlations of the risk signature with tumor-infiltrating immune cells, tumor mutation burden (TMB) and response to immunotherapy. The expression levels of prognosis-related CMGs were validated in HCC using qRT-PCR method.Results: All HCC patients were classified into two clusters based on 11 CMGs with prognosis values and cluster 2 correlated with a poorer prognosis. Next, a prognostic signature of six CMGs was constructed, which was an independent risk factor for HCC patients. Patients with low-risk score were associated with better prognosis. The correlation analysis showed that the risk signature could predict the infiltration of immune cells and immune status of the immune microenvironment in HCC. The qRT-PCR indicated six CMGs with significantly differential expression in HCC tissues and normal tissues.Conclusion: In conclusion, our CMGs-related risk signature could be used as a prediction tool in survival assessment and immunotherapy for HCC patients.

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Identification of HBA2 and NR4A2 as Potential Prognostic Molecular Markers of MYCN Amplified Neuroblastoma

10.21203/rs.3.rs-877542/v1 ◽

2021 ◽

Author(s):

Junwei Peng ◽

Linya Lv

Keyword(s):

Regression Analysis ◽

Cox Regression ◽

Prognostic Significance ◽

Risk Scores ◽

Clinical Samples ◽

Signal Pathways ◽

Data Set ◽

Cox Regression Analysis ◽

Qrt Pcr ◽

Key Genes

Abstract Background Neuroblastoma is a common extracranial solid tumor in children, and the prognosis of children with MYCN amplified type is poor. The prognostic factors in children with MYCN amplified form are still unclear. Methods Download data sets GSE53371 and GSE49710 from GEO database. The GSE53371 data set is used to screen the differentially expressed genes (DEGs) of MYCN amplified and MYCN non-amplified NB patients, and then cross the key module genes in the WGCNA analysis results to obtain their key genes. Then these key genes were analyzed for GO and KEGG function enrichment to analyze their potential biological functions and signal pathways. Single-factor COX regression and multi-factor COX regression analysis are used to screen key genes related to prognosis from key genes to construct risk scores. Finally, qRT-PCR was used to verify the expression of HBA2 and NR4A2 in tissue samples collected from the clinic. Results 20 core genes were obtained after intersecting the differential genes between MYCN amplified and non-amplified samples with WGCNA analysis results. Univariate COX regression analysis showed that a total of 4 genes were closely related to the overall survival of MYCN amplified NB patients (p < 0.05). Further multivariate COX regression analysis showed that HBA2 and NR4A2 have independent prognostic significance. Finally, the qRT-PCR test results of clinical samples showed that, compared with the MYCN unamplified group, the expression of HBA2 in the MYCN amplified group increased, and the expression of NR4A2 decreased. Conclusion HBA2 and NR4A2 are diagnostic prognostic markers and potential therapeutic targets for patients with MYCN amplified NB.

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Identification and development of an independent immune-related genes prognostic model for breast cancer

BMC Cancer ◽

10.1186/s12885-021-08041-x ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Lin Chen ◽

Yuxiang Dong ◽

Yitong Pan ◽

Yuhan Zhang ◽

Ping Liu ◽

...

Keyword(s):

Breast Cancer ◽

Regression Analysis ◽

Cox Regression ◽

Gene Set Enrichment Analysis ◽

Risk Scores ◽

Validation Dataset ◽

Immune Gene ◽

Cox Regression Analysis ◽

Immune Genes ◽

Immune Related Genes

Abstract Background Breast cancer is one of the main malignant tumors that threaten the lives of women, which has received more and more clinical attention worldwide. There are increasing evidences showing that the immune micro-environment of breast cancer (BC) seriously affects the clinical outcome. This study aims to explore the role of tumor immune genes in the prognosis of BC patients and construct an immune-related genes prognostic index. Methods The list of 2498 immune genes was obtained from ImmPort database. In addition, gene expression data and clinical characteristics data of BC patients were also obtained from the TCGA database. The prognostic correlation of the differential genes was analyzed through Survival package. Cox regression analysis was performed to analyze the prognostic effect of immune genes. According to the regression coefficients of prognostic immune genes in regression analysis, an immune risk scores model was established. Gene set enrichment analysis (GSEA) was performed to probe the biological correlation of immune gene scores. P < 0.05 was considered to be statistically significant. Results In total, 556 immune genes were differentially expressed between normal tissues and BC tissues (p < 0. 05). According to the univariate cox regression analysis, a total of 66 immune genes were statistically significant for survival risk, of which 30 were associated with overall survival (P < 0.05). Finally, a 15 immune genes risk scores model was established. All patients were divided into high- and low-groups. KM survival analysis revealed that high immune risk scores represented worse survival (p < 0.001). ROC curve indicated that the immune genes risk scores model had a good reliability in predicting prognosis (5-year OS, AUC = 0.752). The established risk model showed splendid AUC value in the validation dataset (3-year over survival (OS) AUC = 0.685, 5-year OS AUC = 0.717, P = 0.00048). Moreover, the immune risk signature was proved to be an independent prognostic factor for BC patients. Finally, it was found that 15 immune genes and risk scores had significant clinical correlations, and were involved in a variety of carcinogenic pathways. Conclusion In conclusion, our study provides a new perspective for the expression of immune genes in BC. The constructed model has potential value for the prognostic prediction of BC patients and may provide some references for the clinical precision immunotherapy of patients.

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A glycolysis-based three-gene signature predicts survival in patients with lung squamous cell carcinoma

BMC Cancer ◽

10.1186/s12885-021-08360-z ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Guichuan Huang ◽

Jing Zhang ◽

Ling Gong ◽

Yi Huang ◽

Daishun Liu

Keyword(s):

Squamous Cell Carcinoma ◽

Cell Carcinoma ◽

Risk Score ◽

Squamous Cell ◽

Cox Regression ◽

Lung Squamous Cell Carcinoma ◽

Gene Signature ◽

Gene Set Enrichment Analysis ◽

Related Gene ◽

Cox Regression Analysis

Abstract Background Lung cancer is one of the most lethal and most prevalent malignant tumors worldwide, and lung squamous cell carcinoma (LUSC) is one of the major histological subtypes. Although numerous biomarkers have been found to be associated with prognosis in LUSC, the prediction effect of a single gene biomarker is insufficient, especially for glycolysis-related genes. Therefore, we aimed to develop a novel glycolysis-related gene signature to predict survival in patients with LUSC. Methods The mRNA expression files and LUSC clinical information were obtained from The Cancer Genome Atlas (TCGA) dataset. Results Based on Gene Set Enrichment Analysis (GSEA), we found 5 glycolysis-related gene sets that were significantly enriched in LUSC tissues. Univariate and multivariate Cox proportional regression models were performed to choose prognostic-related gene signatures. Based on a Cox proportional regression model, a risk score for a three-gene signature (HKDC1, ALDH7A1, and MDH1) was established to divide patients into high-risk and low-risk subgroups. Multivariate Cox regression analysis indicated that the risk score for this three-gene signature can be used as an independent prognostic indicator in LUSC. Additionally, based on the cBioPortal database, the rate of genomic alterations in the HKDC1, ALDH7A1, and MDH1 genes were 1.9, 1.1, and 5% in LUSC patients, respectively. Conclusion A glycolysis-based three-gene signature could serve as a novel biomarker in predicting the prognosis of patients with LUSC and it also provides additional gene targets that can be used to cure LUSC patients.

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