Identification of stem cell-related subtypes and risk scoring for gastric cancer based on stem genomic profiling

Abstract Background Although numerous studies demonstrate the role of cancer stem cells in occurrence, recurrence, and distant metastases in gastric cancer (GC), little is known about the evolving genetic and epigenetic changes in the stem and progenitor cells. The purpose of this study was to identify the stem cell subtypes in GC and examine their clinical relevance. Methods Two publicly available datasets were used to identify GC stem cell subtypes, and consensus clustering was performed by unsupervised machine learning methods. The cancer stem cell (CSC) typing-related risk scoring (RS) model was established through multivariate Cox regression analysis. Results Cross-platform dataset-based two stable GC stem cell subtypes, namely low stem cell enrichment (SCE_L) and high stem cell enrichment (SCE_H), were prudently identified. Gene set enrichment analysis revealed that the classical oncogenic pathways, immune-related pathways, and regulation of stem cell division were active in SCE_H; ferroptosis, NK cell activation, and post-mutation repair pathways were active in SCE_L. GC stem cell subtypes could accurately predict clinical outcomes in patients, tumor microenvironment cell-infiltration characteristics, somatic mutation landscape, and potential responses to immunotherapy, targeted therapy, and chemotherapy. Additionally, a CSC typing-related RS model was established; it was strongly independent and could accurately predict the patient’s overall survival. Conclusions This study demonstrated the complex oncogenic mechanisms underlying GC. The findings provide a basis and reference for the diagnosis and treatment of GC.

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Expression of LOX Suggests Poor Prognosis in Gastric Cancer

Frontiers in Medicine ◽

10.3389/fmed.2021.718986 ◽

2021 ◽

Vol 8 ◽

Author(s):

Jinfeng Zhu ◽

Chen Luo ◽

Jiefeng Zhao ◽

Xiaojian Zhu ◽

Kang Lin ◽

...

Keyword(s):

Gene Expression ◽

Gastric Cancer ◽

Logistic Regression ◽

Regression Analysis ◽

Poor Prognosis ◽

Cox Regression ◽

Gene Set Enrichment Analysis ◽

High Expression ◽

Expression Level ◽

Cox Regression Analysis

Background: Lysyl oxidase (LOX) is a key enzyme for the cross-linking of collagen and elastin in the extracellular matrix. This study evaluated the prognostic role of LOX in gastric cancer (GC) by analyzing the data of The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) dataset.Methods: The Wilcoxon rank-sum test was used to calculate the expression difference of LOX gene in gastric cancer and normal tissues. Western blot and immunohistochemical staining were used to evaluate the expression level of LOX protein in gastric cancer. Kaplan-Meier analysis was used to calculate the survival difference between the high expression group and the low expression group in gastric cancer. The relationship between statistical clinicopathological characteristics and LOX gene expression was analyzed by Wilcoxon or Kruskal-Wallis test and logistic regression. Univariate and multivariate Cox regression analysis was used to find independent risk factors affecting the prognosis of GC patients. Gene set enrichment analysis (GSEA) was used to screen the possible mechanisms of LOX and GC. The CIBERSORT calculation method was used to evaluate the distribution of tumor-infiltrating immune cell (TIC) abundance.Results: LOX is highly expressed in gastric cancer tissues and is significantly related to poor overall survival. Wilcoxon or Kruskal-Wallis test and Logistic regression analysis showed, LOX overexpression is significantly correlated with T-stage progression in gastric cancer. Multivariate Cox regression analysis on TCGA and GEO data found that LOX (all p < 0.05) is an independent factor for poor GC prognosis. GSEA showed that high LOX expression is related to ECM receptor interaction, cancer, Hedgehog, TGF-beta, JAK-STAT, MAPK, Wnt, and mTOR signaling pathways. The expression level of LOX affects the immune activity of the tumor microenvironment in gastric cancer.Conclusion: High expression of LOX is a potential molecular indicator for poor prognosis of gastric cancer.

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Identification of 10 Gene Signatures as Prognostic Biomarkers for Peritoneal Metastatic Gastric Cancer

10.21203/rs.3.rs-1093123/v1 ◽

2021 ◽

Author(s):

Junliang Li ◽

Lingfang Zhang ◽

Tiankang Guo

Keyword(s):

Gastric Cancer ◽

Clustering Algorithm ◽

Transforming Growth Factor ◽

Cox Regression ◽

Risk Groups ◽

Metastatic Gastric Cancer ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

Cox Regression Analysis ◽

Gene Signatures

Abstract Background. Peritoneal metastatic gastric cancer (PMGC) is very common, and usually, the prognosis is poor. There is currently an absence of accurate methods for the early diagnosis and prediction of peritoneal metastasis (PM). This highlights the need to develop strategies to identify the risk of PMGC. Methods. We performed a comprehensive discovery of biomarkers to predict PM by analyzing profiling datasets from GSE62254. The prognostic PM-related genes were obtained using the univariate Cox regression analysis, followed by a least absolute shrinkage and selection operator regression (LASSO) to establish a risk score model. The gene set enrichment analysis (GSEA) was used to determine the pathway enrichment in both the high- and low-risk groups. The 1-, 3-, and 5-year overall survival (OS) rates and area under the receiver operating characteristic curve (ROC) were used to compare the predictive accuracy-based risk stratification. In addition, an unsupervised clustering algorithm was applied to divide patients into subgroups according to the PM-related genes. Results. We identified 10 genes (MMP12, TAC1, TSPYL5, PPP1R14A, TMSB15B, NPY1R, PCDH9, EPM2AIP1, TIG7, and DYNC1I1) for PMGC diagnosis. The OS rates between the high- and low-risk groups at 1-, 3-, and 5-years were significantly different in the training and validation sets. The AUCs at 1-, 3-, and 5-years in the training set were 0.71, 0.74, and 0.73, respectively. In the validation set, the AUCs at 1-, 3-, and 5-years were 0.68, 0.66, and 0.69, respectively. The 10 gene signatures were correlated with immune cell infiltration in both the high- and low-risk groups. In addition, based on the GSEA, several significant pathways were enriched in the high-risk PMGC group, such as the Wnt and transforming growth factor beta (TGF-β) signaling pathway and leukocyte transendothelial migration pathway. Furthermore, unsupervised cluster analysis showed that the model could distinguish the level of risk among patients with PMGC. Conclusions. Overall, 10 gene signatures were identified for PMGC risk prediction. These may be valuable in making clinical decisions to improve treatment outcomes in patients with PMGC.

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A Signature of Autophagy-Related Long Non-coding RNA to Predict the Prognosis of Breast Cancer

Frontiers in Genetics ◽

10.3389/fgene.2021.569318 ◽

2021 ◽

Vol 12 ◽

Author(s):

Xiaoping Li ◽

Jishang Chen ◽

Qihe Yu ◽

Hui Huang ◽

Zhuangsheng Liu ◽

...

Keyword(s):

Breast Cancer ◽

Regression Analysis ◽

High Risk ◽

Cox Regression ◽

Enrichment Analysis ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

Lasso Regression ◽

Cox Regression Analysis ◽

Risk Scoring

Background: A surge in newly diagnosed breast cancer has overwhelmed the public health system worldwide. Joint effort had beed made to discover the genetic mechanism of these disease globally. Accumulated research has revealed autophagy may act as a vital part in the pathogenesis of breast cancer.Objective: Aim to construct a prognostic model based on autophagy-related lncRNAs and investigate their potential mechanisms in breast cancer.Methods: The transcriptome data and clinical information of patients with breast cancer were obtained from The Cancer Genome Atlas (TCGA) database. Autophagy-related genes were obtained from the Human Autophagy Database (HADb). Long non-coding RNAs (lncRNAs) related to autophagy were acquired through the Pearson correlation analysis. Univariate Cox regression analysis as well as the least absolute shrinkage and selection operator (LASSO) regression analysis were used to identify autophagy-related lncRNAs with prognostic value. We constructed a risk scoring model to assess the prognostic significance of the autophagy-related lncRNAs signatures. The nomogram was then established based on the risk score and clinical indicators. Through the calibration curve, the concordance index (C-index) and receiver operating characteristic (ROC) curve analysis were evaluated to obtain the model's predictive performance. Subgroup analysis was performed to evaluate the differential ability of the model. Subsequently, gene set enrichment analysis was conducted to investigate the potential functions of these lncRNAs.Results: We attained 1,164 breast cancer samples from the TCGA database and 231 autophagy-related genes from the HAD database. Through correlation analysis, 179 autophagy-related lncRNAs were finally identified. Univariate Cox regression analysis and LASSO regression analysis further screened 18 prognosis-associated lncRNAs. The risk scoring model was constructed to divide patients into high-risk and low-risk groups. It was found that the low-risk group had better overall survival (OS) than those of the high-risk group. Then, the nomogram model including age, tumor stage, TNM stage and risk score was established. The evaluation index (C-index: 0.78, 3-year OS AUC: 0.813 and 5-year OS AUC: 0.785) showed that the nomogram had excellent predictive power. Subgroup analysis showed there were difference in OS between high-risk and low-risk patients in different subgroups (stage I-II, ER positive, Her-2 negative and non-TNBC subgroups; all P < 0.05). According to the results of gene set enrichment analysis, these lncRNAs were involved in the regulation of multicellular organismal macromolecule metabolic process in multicellular organisms, nucleotide excision repair, oxidative phosphorylation, and TGF-β signaling pathway.Conclusions: We identified 18 autophagy-related lncRNAs with prognostic value in breast cancer, which may regulate tumor growth and progression in multiple ways.

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Comprehensive Analysis and Prognosis Prediction of N6-methyladenosine-related Lncrnas in Immune Microenvironment Infiltration of Gastric Cancer

10.21203/rs.3.rs-1021150/v1 ◽

2021 ◽

Author(s):

Jianfeng Huang ◽

Wenzheng Chen ◽

Changyu Chen ◽

Tao Xiao ◽

Zhigang Jie

Keyword(s):

Gastric Cancer ◽

Risk Model ◽

Cox Regression ◽

Cytotoxic T Lymphocyte ◽

The Cancer Genome Atlas ◽

Risk Scores ◽

Rna Modification ◽

Consensus Clustering ◽

Immune Microenvironment ◽

Cox Regression Analysis

Abstract BackgroundN6-methyladenosine (m6A) RNA modification plays an important role in regulating tumor microenvironment (TME) infiltration. However, the relationship between the expression pattern of m6A-related long non-coding RNAs (lncRNAs) and the immune microenvironment of gastric cancer (GC) is unclear. MethodsIn this study, 23 m6A-related lncRNAs were identified by Pearson’s correlation analysis and univariate Cox regression analysis. According to the expression of these lncRNAs, we identified two distinct molecular clusters by consensus clustering and compared the differences of the TME and enriched pathways between the two clusters. We further constructed a prognostic risk signature and verified it using The Cancer Genome Atlas training and testing cohorts. ResultsThe results showed that cluster 1 was associated with tumor-related and immune activation-related pathways. In addition, cluster 1 was also associated with higher ImmuneScore, StromalScore, and ESTIMATEScore. The results of the stratified survival analysis and independent prognosis analysis indicated that the risk signature is an independent prognostic indicator for patients with GC. In addition, it can effectively predict survival status in patients with different clinical characteristics. Furthermore, our risk model showed that low risk scores were significantly correlated with high expression of programmed death-1 (PD-1) and cytotoxic T-lymphocyte associated protein 4 (CTLA4), as well as sensitivity to chemotherapeutic drugs (e.g., paclitaxel and oxaliplatin). ConclusionsThis evidence contributes to our understanding of the regulation of TME infiltration by m6A-related lncRNAs and my lead to more effective immunotherapy and chemotherapy for patients with GC.

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FABP4 Might be an Indicator of the Status of Tumor Microenvironment in Colorectal Cancer

10.21203/rs.3.rs-498848/v1 ◽

2021 ◽

Author(s):

Lijun Ning ◽

Yuqing Yan ◽

Tianying Tong ◽

Ziyun Gao ◽

Zhe Cui ◽

...

Keyword(s):

Colorectal Cancer ◽

T Cells ◽

Tumor Microenvironment ◽

Cox Regression ◽

Nk Cell ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Fatty Acid Binding ◽

Cox Regression Analysis ◽

The Status

Abstract Background: As tumor microenvironment (TME) play an indispensable role in tumorigenesis of colorectal cancer, this study performs a bunch of bioinformatics analysis to identify the indicator of the status of TME in Colorectal cancer (CRC). Results: In the presented study, we applied CIBERSORT and ESTIMATE computational methods to calculate the proportion of tumor-infiltrating immune cells (TICs) and the amount of immune and stromal components in 444 COAD-READ cases from The Cancer Genome Atlas (TCGA) database. The differentially expressed genes (DEGs) were analyzed by COX regression analysis and protein–protein interaction (PPI) network construction. Then, fatty acid-binding protein four ( FABP4 ) was determined as a predictive factor by the intersection analysis of univariate COX and PPI. Further analysis revealed that FABP4 expression was positively correlated with the clinical pathologic characteristics (clinical stage, distant metastasis) and negatively correlated with the survival of CRC patients. Gene Set Enrichment Analysis (GSEA) showed that the genes in the high-expression FABP4 group were mainly enriched in immune-related activities. In the low-expression FABP4 group, the genes were enriched in metabolic pathways. CIBERSORT analysis for the proportion of TICs revealed that NK cell, CD4 + T cells and CD8 + T cells were negatively correlated with FABP4 expression, suggesting that FABP4 might be a potential prognostic factor of CRC patients. Conclusion: Our study has developed a new biomarker (FABP4) that can predict the status of tumor microenvironment in Colorectal cancer. Keywords: FABP4, tumor microenvironment, ESTIMATE, CIBERSORT, colorectal cancer

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Angiogenesis-Related Gene Expression Signatures Predicting Prognosis in Gastric Cancer Patients

Cancers ◽

10.3390/cancers12123685 ◽

2020 ◽

Vol 12 (12) ◽

pp. 3685

Author(s):

Haoyu Ren ◽

Jiang Zhu ◽

Haochen Yu ◽

Alexandr Bazhin ◽

Christoph Westphalen ◽

...

Keyword(s):

Gastric Cancer ◽

Risk Score ◽

Cox Regression ◽

Clinical Stage ◽

External Validation ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Consensus Clustering ◽

Related Gene ◽

Angiogenesis Related Gene

Increasing evidence indicates that angiogenesis is crucial in the development and progression of gastric cancer (GC). This study aimed to develop a prognostic relevant angiogenesis-related gene (ARG) signature and a nomogram. The expression profile of the 36 ARGs and clinical information of 372 GC patients were extracted from The Cancer Genome Atlas (TCGA). Consensus clustering was applied to divide patients into clusters 1 and 2. Least absolute shrinkage and selection operator (LASSO) Cox regression analyses were used to identify the survival related ARGs and establish prognostic gene signatures, respectively. The Asian Cancer Research Group (ACRG) (n = 300) was used for external validation. Risk score of ARG signatures was calculated, and a prognostic nomogram was developed. Gene set enrichment analysis of the ARG model risk score was performed. Cluster 2 patients had more advanced clinical stage and shorter survival rates. ARG signatures carried prognostic relevance in both cohorts. Moreover, ARG-risk score was proved as an independent prognostic factor. The predictive value of the nomogram incorporating the risk score and clinicopathological features was superior to tumor, lymph node, metastasis (TNM) staging. The high-risk score group was associated with several cancer and metastasis-related pathways. The present study suggests that ARG-based nomogram could serve as effective prognostic biomarkers and allow a more precise risk stratification.

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Upregulation of FGD6 Predicts Poor Prognosis in Gastric Cancer

Frontiers in Medicine ◽

10.3389/fmed.2021.672595 ◽

2021 ◽

Vol 8 ◽

Author(s):

Jianmin Zeng ◽

Man Li ◽

Huasheng Shi ◽

Jianhui Guo

Keyword(s):

Gastric Cancer ◽

Regression Analysis ◽

Prognostic Factor ◽

Poor Prognosis ◽

Cox Regression ◽

Independent Prognostic Factor ◽

Gene Set Enrichment Analysis ◽

High Expression ◽

Cox Regression Analysis ◽

The Relationship

Background: The aim of this study was to investigate the prognostic significance of faciogenital dysplasia 6 (FGD6) in gastric cancer (GC).Methods: The data of GC patients from The Cancer Genome Atlas (TCGA) database were used for the primary study. Then, our data were validated by the GEO database and RuiJin cohort. The relationship between the FGD6 level and various clinicopathological features was analyzed by logistic regression and univariate Cox regression. Multivariate Cox regression analysis was used to evaluate whether FGD6 was an independent prognostic factor for survival of patients with GC. The relationship between FGD6 and overall survival time was explored by the Kaplan–Meier method. In addition, gene set enrichment analysis (GSEA) was performed to investigate the possible biological processes of FGD6.Results: The FGD6 level was significantly overexpressed in GC tissues, compared with adjacent normal tissues. The high expression of FGD6 was related to a high histological grade, stage, and T classification and poor prognosis of GC. Multivariate Cox regression analysis showed that FGD6 was an independent prognostic factor for survival of patients with GC. GSEA identified that the high expression of FGD6 was mainly enriched in regulation of actin cytoskeleton.Conclusion: FGD6 may be a prognostic biomarker for predicting the outcome of patients with GC.

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High expression of oncogene cadherin-6 correlates with tumor progression and a poor prognosis in gastric cancer

Cancer Cell International ◽

10.1186/s12935-021-02071-y ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Zongxian Zhao ◽

Shuliang Li ◽

Shilong Li ◽

Jun Wang ◽

Hai Lin ◽

...

Keyword(s):

Gastric Cancer ◽

Overall Survival ◽

Roc Curve ◽

Cox Regression ◽

Gene Set Enrichment Analysis ◽

Gastric Tissue ◽

Tissue Samples ◽

Cox Regression Analysis ◽

Expression Levels ◽

Worse Prognosis

Abstract Background Gastric cancer (GC) is one of the most common and fatal cancers worldwide. Effective biomarkers to aid the early diagnosis of GC, as well as predict the course of disease, are urgently needed. Hence, we explored the role and function of cadherin-6 (CDH6) in the diagnosis and prognosis of gastric cancer. Methods The expression levels of CDH6 in cancerous and normal gastric tissue were analyzed using multiple public databases. Gene set enrichment analysis (GSEA) was performed using The Cancer Genome Atlas (TCGA) dataset. The diagnostic efficiency of CDH6 expression in GC patients was determined through receiver operating characteristic (ROC) curve analysis. The associations between clinical variables and CDH6 expression were evaluated statistically, and the prognostic factors for overall survival were analyzed by univariate and multivariate Cox regression. 44 GC tissue samples, 20 donor-matched adjacent normal tissue samples, and associated detailed clinical information, were collected from the Tianjin Medical University General Hospital. CDH6 expression levels were determined for further validation. Results CDH6 was upregulated in GC samples compared to normal gastric tissue. Furthermore, GSEA identified the tricarboxylic acid (TCA) cycle, extracellular matrix (ECM) receptor interaction, glyoxylate and dicarboxylate metabolism, oxidative phosphorylation, and the pentose phosphate pathway as differentially enriched in GC tissue samples. According to the area under the ROC curve (AUC) values (AUC = 0.829 in the TCGA and 0.966 in the GSE54129 dataset), CDH6 expression was associated with high diagnostic efficacy. Patients with high CDH6 levels in their GC tissues had a higher T number (according to the TNM classification) and a worse prognosis than those with low CDH6 expression. Univariate and multivariate Cox regression analysis showed that CDH6 was an independent risk factor for overall survival (univariate: HR = 1.305, P = 0.002, multivariate: HR = 1.481, P < 0.001). Conclusion CDH6 was upregulated in GC, and high CDH6 expression was indicative of a higher T number and a worse prognosis. Therefore, CDH6 represents a potentially independent molecular biomarker for the diagnostic and prognostic prediction of GC.

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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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