Identification of a Four-Gene-Based SERM Signature for Prognostic and Drug Sensitivity Prediction in Gastric Cancer

BackgroundGastric cancer (GC) is a highly molecular heterogeneous tumor with poor prognosis. Epithelial-mesenchymal transition (EMT) process and cancer stem cells (CSCs) are reported to share common signaling pathways and cause poor prognosis in GC. Considering about the close relationship between these two processes, we aimed to establish a gene signature based on both processes to achieve better prognostic prediction in GC.MethodsThe gene signature was constructed by univariate Cox and the least absolute shrinkage and selection operator (LASSO) Cox regression analyses by using The Cancer Genome Atlas (TCGA) GC cohort. We performed enrichment analyses to explore the potential mechanisms of the gene signature. Kaplan-Meier analysis and time-dependent receiver operating characteristic (ROC) curves were implemented to assess its prognostic value in TCGA cohort. The prognostic value of gene signature on overall survival (OS), disease-free survival (DFS), and drug sensitivity was validated in different cohorts. Quantitative reverse transcription polymerase chain reaction (RT-qPCR) validation of the prognostic value of gene signature for OS and DFS prediction was performed in the Fudan cohort.ResultsA prognostic signature including SERPINE1, EDIL3, RGS4, and MATN3 (SERM signature) was constructed to predict OS, DFS, and drug sensitivity in GC. Enrichment analyses illustrated that the gene signature has tight connection with the CSC and EMT processes in GC. Patients were divided into two groups based on the risk score obtained from the formula. The Kaplan-Meier analyses indicated high-risk group yielded significantly poor prognosis compared with low-risk group. Pearson’s correlation analysis indicated that the risk score was positively correlated with carboplatin and 5-fluorouracil IC50 of GC cell lines. Multivariate Cox regression analyses showed that the gene signature was an independent prognostic factor for predicting GC patients’ OS, DFS, and susceptibility to adjuvant chemotherapy.ConclusionsOur SERM prognostic signature is of great value for OS, DFS, and drug sensitivity prediction in GC, which may give guidance to the development of targeted therapy for CSC- and EMT-related gene in the future.

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Identification of a Potential Alternative Splicing Prognostic Signature Associated With Immune Infiltrates of Ovarian Cancer

10.21203/rs.3.rs-500677/v1 ◽

2021 ◽

Author(s):

Yan Li ◽

Yue Han ◽

Xiaoyin Wang

Keyword(s):

Ovarian Cancer ◽

Alternative Splicing ◽

Prognostic Value ◽

Clustering Algorithm ◽

Cox Regression ◽

Risk Group ◽

Gene Set Enrichment Analysis ◽

Consensus Clustering ◽

Regression Analyses ◽

Prognostic Signature

Abstract Background: The mortality rate of ovarian cancer (OC) ranks the first in gynecological tumors, which seriously threatens women's health and life. In recent years, alternative splicing (AS) has gradually been considered to play a key role in immune infiltrates of tumor. However, the prognostic significance of AS events related to immune infiltrates in OC remains unknow. The aim of our research was to investigate the potential prognostic value of AS events associated with immune infiltrates in OC.Methods: The RNA sequences (RNA-seq) and clinical data were downloaded from the Cancer Genome Atlas (TCGA) database. The AS event data was obtained from TCGA SpliceSeq database. Single sample gene set enrichment analysis (ssGSEA) was performed to calculate the abundance of 28 immune cell types in samples from TCGA-OV dataset. A consensus clustering algorithm was used to group the OC patients. Differential expression analysis was used to identify differentially expressed AS events (DEASs) between groups. Univariate Cox regression analysis was implemented to screen for AS events with prognostic value. A least absolute shrinkage and selection operator (LASSO) Cox regression and multivariate Cox regression analyses was used to further narrow the AS events with prognostic value and construct an alternative splicing prognostic signature for predicting the prognosis of OC patients. Results: The OC patients from TCGA database were classified into two groups (cluster.A and cluster.B) based on the 28 types of TIIC using a consensus clustering algorithm. The patients in the cluster.A group had increased immune infiltrates compared with the cluster.B group. 3616 DEASs were acquired and 171 DEASs have prognostic value (p<0.05). 28 DEASs with prognostic value (p<0.001) were fitted into LASSO Cox regression and multivariate Cox regression analyses. A prognostic signature with 18 DEASs was constructed to predict the prognosis of OC patients. Each patient obtained a riskscore and the patients were classified into high-and low-risk group using the median riskscore as a cutoff. Kaplan-Meier curve revealed that the patients in high-risk group have poor outcome. Conclusions: Collectively, our research identified an alternative splicing prognostic signature associated with immune infiltrates of OC, which may provide new directions for the immunotherapy of OC patients.

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Exploration of a Metabolism-Related Gene Signature Predicting Prognosis for Ovarian Cancer

10.21203/rs.3.rs-917465/v1 ◽

2021 ◽

Author(s):

Jinling Zhang ◽

Yifan Zhang ◽

Lin Zhang ◽

Haili Li

Keyword(s):

Ovarian Cancer ◽

Overall Survival ◽

Tumor Microenvironment ◽

Drug Sensitivity ◽

Cox Regression ◽

Risk Group ◽

Gene Signature ◽

Prognostic Signature ◽

Immune Infiltration ◽

Metabolic Genes

Abstract Background: Dysregulation of metabolism plays a critical role in the pathogenesis and progression of ovarian cancer (OC). However, the expression pattern of metabolic genes in OC and the prognostic value of metabolism-related genes for OC patients remains to be elucidated. Thus, this study aimed to identify a metabolism-related prognostic gene signature for OC. Methods: The expression profiles of metabolism-related genes and associated clinicopathological characteristics were obtained from online datasets (The Cancer Genome Atlas, TCGA and The Genotype-Tissue Expression, GTEx). The differently expressed genes were subjected to functional enrichment analysis. By means of LASSO, univariate and multivariate Cox regression analyses, this predictive signature was constructed and validated by internal and external databases. Genomic alterations, immune infiltration, tumor microenvironment, and drug sensitivity associated with the signature were also described.Results: A total of 302 metabolism-related differentially expressed genes were identified. These genes were mainly enriched in functions associated with substance and energy metabolism. Based on the 302 identified genes, a prognostic signature of 21 metabolic genes was constructed and validated across internal and external cohorts. The patients in the high-risk group had significantly longer overall survival compared to those in the low-risk group. By univariate and multivariate Cox regression, the signature was identified as an independent prognostic predictor for overall survival. There were also noticeable differences with regards to genetic alteration, immune infiltration, tumor microenvironment and drug sensitivity between the two groups.Conclusion: Our study suggests that clinical outcomes of OC patients are associated with dysregulated metabolic genes and that the metabolism-related prognostic signature can be used as a predictor for the overall survival of OC patients.

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Identification and Validation of a Seven m6A-related lncRNAs Signature Predicting Prognosis of Ovarian Cancer

10.21203/rs.3.rs-915700/v1 ◽

2021 ◽

Author(s):

Yan Li ◽

Xiaoying Wang ◽

Yue Han ◽

Xun Li

Keyword(s):

Ovarian Cancer ◽

Regression Analysis ◽

Correlation Analysis ◽

Prognostic Value ◽

Cox Regression ◽

Risk Group ◽

Pearson Correlation ◽

Regression Analyses ◽

Cox Regression Analysis ◽

Pearson Correlation Analysis

Abstract Background: Long non-coding RNAs (lncRNAs) play an important role in angiogenesis, immune response, inflammatory response and tumor development and metastasis. m6 A (N6 - methyladenosine) is one of the most common RNA modifications in eukaryotes. The aim of our research was to investigate the potential prognostic value of m6A-related lncRNAs in ovarian cancer (OC).Methods: The data we need for our research was downloaded from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database. Pearson correlation analysis between 21 m6A regulators and lncRNAs was performed to identify m6A-related lncRNAs. Univariate Cox regression analysis was implemented to screen for lncRNAs with prognostic value. A least absolute shrinkage and selection operator (LASSO) Cox regression and multivariate Cox regression analyses was used to further reduct the lncRNAs with prognostic value and construct a m6A-related lncRNAs signature for predicting the prognosis of OC patients. Results: 275 m6A-related lncRNAs were obtained using pearson correlation analysis. 29 m6A-related lncRNAs with prognostic value was selected through univariate Cox regression analysis. Then, a seven m6A-related lncRNAs signature was identified by LASSO Cox regression. Each patient obtained a riskscore through multivariate Cox regression analyses and the patients were classified into high-and low-risk group using the median riskscore as a cutoff. Kaplan-Meier curve revealed that the patients in high-risk group have poor outcome. The receiver operating characteristic curve revealed that the predictive potential of the m6A-related lncRNAs signature for OC was powerful. The predictive potential of the m6A-related lncRNAs signature was successfully validated in the GSE9891, GSE26193 datasets and our clinical specimens. Multivariate analyses suggested that the m6A-related lncRNAs signature was an independent prognostic factor for OC patients. Moreover, a nomogram based on the expression level of the seven m6A-related lncRNAs was established to predict survival rate of patients with OC. Finally, a competing endogenous RNA (ceRNA) network associated with the seven m6A-related lncRNAs was constructed to understand the possible mechanisms of the m6A-related lncRNAs involed in the progression of OC.Conclusions: In conclusion, our research revealed that the m6A-related lncRNAs may affect the prognosis of OC patients and identified a seven m6A-related lncRNAs signature to predict the prognosis of OC patients.

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Identification of a Liver Progenitor Cell-Related Genes Signature Predicting Overall Survival for Hepatocellular Carcinoma

Technology in Cancer Research & Treatment ◽

10.1177/15330338211041425 ◽

2021 ◽

Vol 20 ◽

pp. 153303382110414

Author(s):

Xiaoyong Li ◽

Jiaqong Lin ◽

Yuguo pan ◽

Peng Cui ◽

Jintang Xia

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

Regression Analysis ◽

Prognostic Model ◽

Cox Regression ◽

Risk Group ◽

Gene Signature ◽

Regression Analyses ◽

Related Gene ◽

Cox Regression Analysis

Background: Liver progenitor cells (LPCs) play significant roles in the development and progression of hepatocellular carcinoma (HCC). However, no studies on the value of LPC-related genes for evaluating HCC prognosis exist. We developed a gene signature of LPC-related genes for prognostication in HCC. Methods: To identify LPC-related genes, we analyzed mRNA expression arrays from a dataset (GSE57812 & GSE 37071) containing LPCs, mature hepatocytes, and embryonic stem cell samples. HCC RNA-Seq data from The Cancer Genome Atlas (TCGA) were used to explore the differentially expressed genes (DEGs) related to prognosis through DEG analysis and univariate Cox regression analysis. Lasso and multivariate Cox regression analyses were performed to construct the LPC-related gene prognostic model in the TCGA training dataset. This model was validated in the TCGA testing set and an external dataset (International Cancer Genome Consortium [ICGC] dataset). Finally, we investigated the relationship between this prognostic model with tumor-node-metastasis stage, tumor grade, and vascular invasion of HCC. Results: Overall, 1770 genes were identified as LPC-related genes, of which 92 genes were identified as DEGs in HCC tissues compared with normal tissues. Furthermore, we randomly assigned patients from the TCGA dataset to the training and testing cohorts. Twenty-six DEGs correlated with overall survival (OS) in the univariate Cox regression analysis. Lasso and multivariate Cox regression analyses were performed in the TCGA training set, and a 3-gene signature was constructed to stratify patients into 2 risk groups: high-risk and low-risk. Patients in the high-risk group had significantly lower OS than those in the low-risk group. Receiver operating characteristic curve analysis confirmed the signature's predictive capacity. Moreover, the risk score was confirmed to be an independent predictor for patients with HCC. Conclusion: We demonstrated that the LPC-related gene signature can be used for prognostication in HCC. Thus, targeting LPCs may serve as a therapeutic alternative for HCC.

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Analysis of DNA damage response gene signature for prognosis prediction of esophageal squamous cell carcinoma.

Journal of Clinical Oncology ◽

10.1200/jco.2021.39.15_suppl.e16073 ◽

2021 ◽

Vol 39 (15_suppl) ◽

pp. e16073-e16073

Author(s):

Weitao Zhuang ◽

Xiao-song Ben ◽

Dan Tian ◽

Zihao Zhou ◽

Gang Chen ◽

...

Keyword(s):

High Risk ◽

Dna Damage Response ◽

Squamous Cell ◽

Cox Regression ◽

Risk Group ◽

Gene Signature ◽

Prognostic Signature ◽

Training Set ◽

Prognosis Prediction ◽

Damage Response

e16073 Background: Esophageal squamous cell cancer (ESCC) is a malignant tumor with a poor 5-year relative survival. A prognosis prediction signature associated with DNA Damage Response (DDR) genes in ESCC was explored in this study. Methods: The clinical and gene expression profiles of ESCC patients were downloaded from the GEO and TCGA database. Univariate Cox regression and 1000 iterations of 10-fold cross-validation of LASSO Cox regression with binomial deviance minimization criteria were used to identify DDR genes as potential object and a prognostic signature for ESCC survival prediction, followed by validation of the signature via TCGA cohort and identification of independent prognostic predictors. A nomogram for prognosis prediction was built and Gene Set Enrichment Analysis (GSEA) was performed to further understand the underlying molecular mechanisms. Results: A signature of 8 DDR genes were constructed as being significantly associated with overall survival (OS) among patients with esophageal squamous cell carcinoma. The pronostic signature stratified ESCC patients into low- vs high-risk groups in terms of OS in the training set, testing set and the validation cohorts, and remained as an independent prognostic factor in multivariate analyses (hazard ratio (HR) in training set, 0.17 [95% CI, 0.09-0.35; P < 0 .001], HR in testing set, 0.38 [95% CI, 0.16-0.93; P = 0.029], HR in discovery cohort, 0.171 [95% CI, 0.03-0.48; P < 0 .001]) after adjusting for clinicopathological factors. The 8-DDR gene signature achieved a higher accuracy (C-index, 0.69; AUCs for 1-, 3- and 5-year OS, 0.74, 0.77 and 0.76, respectively) than 7 previously reported multigene signatures (C-index range, 0.53 to 0.60; AUCs range, 0.55to 0.66, 0.54 to 0.64 and 0.62 to 0.66, respectively) for estimation of survival in comparable cohorts. A nomogram incorporating tumor location, grade, adjuvant therapy and signature-based risk group showed better predictive performance for 1- and 3- year survival than for 5 year survival. Moreover, GSEA revealed that the DNA repair was more prominently enriched in the high-risk group while the low-risk group had not enrichment of any process (P > 0.05 for all). Conclusions: Taken together, our study identified 8 DDR genes related to the prognosis of ESCC patients, and constructed a robust prognostic signature to effectively stratify ESCC patients with different survival rates, which may help recognize high-risk patients potentially benefiting from more aggressive treatment.

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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 4-gene signature predicts prognosis of uterine serous carcinoma

10.21203/rs.3.rs-42551/v3 ◽

2020 ◽

Author(s):

Hui Chen ◽

Lingjun Li ◽

Ping Qin ◽

Hanzhen Xiong ◽

Ruichao Chen ◽

...

Keyword(s):

Endometrial Cancer ◽

Roc Curve ◽

Cox Regression ◽

Risk Group ◽

Gene Signature ◽

Functional Enrichment ◽

Serous Carcinoma ◽

Regression Analyses ◽

Uterine Serous Carcinoma ◽

Clinical Records

Abstract Background: Uterine serous carcinoma (USC) is an aggressive type of endometrial cancer that accounts for up to 40% of endometrial cancer deaths, creating an urgent need for prognostic biomarkers. Methods: USC RNA-Seq data and corresponding patients’ clinical records were obtained from The Cancer Genome Atlas and Genotype-Tissue Expression datasets. Univariate cox, Lasso, and Multivariate cox regression analyses were conducted to forge a prognostic signature. Multivariable and univariable cox regression analysis and ROC curve evaluated the prediction efficiency both in the training and testing sets. Results: We uncovered 1385 genes dysregulated in 110 cases of USC tissue relative to 113 cases of normal uterine tissue. Functional enrichment analysis of these genes revealed the involvement of various cancer-related pathways in USC. A novel 4‐gene signature (KRT23, CXCL1, SOX9 and ABCA10) of USC prognosis was finally forged by serial regression analyses. Overall patient survival (OS) and recurrence-free survival (RFS) were significantly lower in the high-risk group relative to the low-risk group in both the training and testing sets. The area under the ROC curve of the 4-gene signature was highest among clinicopathological features in predicting OS and RFS. The 4-gene signature was found to be an independent prognostic indicator in USC and was a superior predictor of OS in early stage of USC. Conclusions: Our findings highlight the potential of the 4-gene signature as a guide for personalized USC treatment.

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Identification of CDC20 As an Immune Infiltration-Correlated Prognostic Biomarker in Hepatocellular Carcinoma

10.21203/rs.3.rs-400598/v1 ◽

2021 ◽

Author(s):

Chen Xiong ◽

Zhihuai Wang ◽

Guifu Wang ◽

Chi Zhang ◽

Shengjie Jin ◽

...

Keyword(s):

Gene Expression ◽

Hepatocellular Carcinoma ◽

T Cells ◽

Poor Prognosis ◽

Prognostic Value ◽

Cox Regression ◽

Cancer Genome ◽

Regression Analyses ◽

Ubiquitin Protein Ligase ◽

Immune Infiltration

Abstract Hepatocellular carcinoma (HCC) is a malignancy with a poor prognosis. Some E3 ubiquitin-protein ligases play essential roles in HCC development. We aimed to explore a hub E3 ubiquitin-protein ligase gene and verify its association with prognosis and immune cell infiltration in HCC. We identified cell division cycle 20 (CDC20) as a hub E3 ubiquitin-protein ligase in HCC by determining the intersecting genes in a protein-protein interaction (PPI) network of differentially expressed genes (DEGs) in HCC data from the International Cancer Genome Consortium (ICGC) and 919 E3 ubiquitin-protein ligase genes from the Integrated annotations for Ubiquitin and Ubiquitin-like Conjugation Database (IUUCD). DEGs and their correlations with clinicopathological features were explored in The Cancer Genome Atlas (TCGA), ICGC, and Gene Expression Omnibus (GEO) databases via the Wilcoxon signed-rank test. The prognostic value of CDC20 was illustrated by Kaplan-Meier (K-M) curves and Cox regression analyses. Subsequently, the correlation between CDC20 and immune infiltration was demonstrated via the Tumor Immune Estimation Resource (TIMER) and Gene Expression Profiling Interactive Analysis (GEPIA). CDC20 expression was significantly higher in HCC than in normal tissues (all P < 0.05). K-M curves and Cox regression analyses showed that high CDC20 expression predicted a poor prognosis and might be an independent risk factor for HCC prognosis (P < 0.05). Additionally, the TIMER and GEPIA results indicated that CDC20 is correlated with the immune infiltration of CD8 + T cells, T cells (general), monocytes, and exhausted T cells. This research revealed the potential prognostic value of CDC20 in HCC and demonstrated that CDC20 might be an immune-associated therapeutic target in HCC because of its correlation with immune infiltration.

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Identification of Prognostic Markers For Hepatocellular Carcinoma Based On the Epithelial-Mesenchymal Transition-Related Gene BIRC5

10.21203/rs.3.rs-390539/v1 ◽

2021 ◽

Author(s):

yan li ◽

yiping li ◽

rongzhong xu ◽

liubing lin ◽

bo zhang ◽

...

Keyword(s):

Risk Score ◽

Poor Prognosis ◽

Prognostic Markers ◽

Cox Regression ◽

Epithelial Mesenchymal Transition ◽

Risk Group ◽

Enrichment Analysis ◽

Risk Groups ◽

Regression Analyses ◽

Mesenchymal Transition

Abstract Background: The baculoviral IAP repeat containing 5 (BIRC5) related to epithelial-mesenchymal transition (EMT) plays a crucial role in the pathogenesis of hepatocellular carcinoma (HCC). However, it remains unclear whether BIRC5-related genes can be used as prognostic markers of HCC. Methods: Kaplan-Meier (K-M) survival curve was used to assess the Overall Survival (OS) of high- and low-expression group divided by the median of BIRC5 expression. The differentially expressed genes (DEGs) between the two groups were screened using the limma package, and performed the functional enrichment analysis by the clusterProfiler package. WGCNA was used to analyze the relationship of the module and the clinical traits. The risk signature was constructed by univariate and multivariate Cox regression analyses and the enrichment analysis of genes in the risk signature was performed by the Intelligent pathway analysis (IPA). The immunophenoscore (IPS) and the tumor immune dysfunction and exclusion (TIDE) were used to estimate the clinical significance of the risk groups.Results: BIRC5 was high-expressed in HCC samples and associated with a poor prognosis (p-value < 0.0001). WGCNA screened 180 module genes which were overlapped with the 241 DEGs, ultimately getting 33 candidate genes. After the Cox regression analyses, CENPA, CDCA8, EZH2, KIF20A, KPNA2, CCNB1, KIF18B and MCM4 were preserved and used to construct risk signature, followed by calculating the risk score. The patients in high-risk groups stratified by median of the risk score were associated with a poor prognosis. The risk score had high accuracy [the area under the curve (AUC) >0.72] and was closely associated with clinicopathological characteristics of HCC patients. IPA suggested that the 8 genes were enriched in Cancer and Immunological disease related pathways. IPS and TIDE score indicated that the genes in low-risk group could cause an immune response, and patients in the low-risk group may be more sensitive to the immune checkpoint blockade (ICB) therapy.Conclusion: The risk score constructed by the 8 genes could not only predict the clinical outcome but also distinguish the cohort of ICB therapy in HCC, which exerted a vital value in treatment and prognosis of HCC.

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