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Chen Y, Zhang Y, Zhang S, Ren H. Molecular insights into sarcopenia: ferroptosis-related genes as diagnostic and therapeutic targets. J Biomol Struct Dyn 2024:1-19. [PMID: 38229237 DOI: 10.1080/07391102.2023.2298390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 10/26/2023] [Indexed: 01/18/2024]
Abstract
Ferroptosis, characterized by iron accumulation and lipid peroxidation, leads to cell death. Growing evidence suggests the involvement of ferroptosis in sarcopenia. However, the fundamental ferroptosis-related genes (FRGs) for sarcopenia diagnosis, prognosis, and therapy remain elusive. This study aimed to identify molecular biomarkers of ferroptosis in sarcopenia patients. Gene expression profiles were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between normal and sarcopenia samples were identified using the 'limma' package in R software. FRGs were extracted from GeneCards and FerrDB databases. Functional enrichment analysis determined the roles of DEGs using the 'clusterProfiler' package. A protein-protein network was constructed using Cytoscape software. Immune infiltration analysis and receiver operating characteristic (ROC) analysis were performed. mRNA-miRNA, mRNA-TF, and mRNA-drug interactions were predicted using ENCORI, hTFtarget, and CHIPBase databases. The network was visualized using Cytoscape. We identified 46 FRGs in sarcopenia. Functional enrichment analysis revealed their involvement in critical biological processes, including responses to steroid hormones and glucocorticoids. KEGG enrichment analysis implicated pathways such as carbon metabolism, ferroptosis, and glyoxylate in sarcopenia. Totally, 11 hub genes were identified, and ROC analysis demonstrated their potential as sensitive and specific markers for sarcopenia in both datasets. Additionally, differences in immune cell infiltration were observed between normal and sarcopenia samples. The hub genes identified in this study are closely associated with ferroptosis in sarcopenia and can effectively differentiate sarcopenia from controls. CDKN1A, CS, DLD, FOXO1, HSPB1, LDHA, MDH2, and YWHAZ show high sensitivity and specificity for sarcopenia diagnosis.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Yanzhong Chen
- School of Sport Science, Beijing Sport University, Beijing, China
- Key Laboratory of Physical Fitness and Exercise, Ministry of Education, Beijing Sport University, Beijing, China
| | - Yaonan Zhang
- School of Sport Science, Beijing Sport University, Beijing, China
- Key Laboratory of Physical Fitness and Exercise, Ministry of Education, Beijing Sport University, Beijing, China
- Department of orthopedics, Beijing Hospital, Beijing, China
| | - Sihan Zhang
- School of Sport Science, Beijing Sport University, Beijing, China
- Key Laboratory of Physical Fitness and Exercise, Ministry of Education, Beijing Sport University, Beijing, China
| | - Hong Ren
- School of Sport Science, Beijing Sport University, Beijing, China
- Key Laboratory of Physical Fitness and Exercise, Ministry of Education, Beijing Sport University, Beijing, China
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Wu Z, Li G, Wang W, Zhang K, Fan M, Jin Y, Lin R. Immune checkpoints signature-based risk stratification for prognosis of patients with gastric cancer. Cell Signal 2024; 113:110976. [PMID: 37981068 DOI: 10.1016/j.cellsig.2023.110976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 11/07/2023] [Accepted: 11/13/2023] [Indexed: 11/21/2023]
Abstract
Until now, few researches have comprehensive explored the role of immune checkpoints (ICIs) and tumor microenvironment (TME) in gastric cancer (GC) patients based on the genomic data. RNA-sequence data and clinical information were obtained from The Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD) database, GSE84437 and GSE84433. Univariate Cox analysis identified 60 ICIs with prognostic values, and these genes were then subjected to NMF cluster analysis and the GC samples (n = 804) were classified into two distinct subtypes (Cluster 1: n = 583; Cluster 2: n = 221). The Kaplan-Meier curves for OS analysis indicated that C1 predicted a poorer prognosis. The C2 subtype illustrated a relatively better prognosis and characteristics of "hot tumors," including high immune score, overexpression of immune checkpoint molecules, and enriched tumor-infiltrated immune cells, indicating that the NMF clustering in GC was robust and stable. Regarding the patient's heterogeneity, an ICI-score was constructed to quantify the ICI patterns in individual patients. Moreover, the study found that the low ICI-score group contained mostly MSI-low events, and the high ICI-score group contained predominantly MSI-high events. In addition, the ICI-score groups had good responsiveness to CTLA4 and PD-1 based on The Cancer Immunome Atlas (TCIA) database. Our research firstly constructed ICIs signature, as well as identified some hub genes in GC patients.
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Affiliation(s)
- Zenghong Wu
- Division of Gastroenterology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Gangping Li
- Division of Gastroenterology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Weijun Wang
- Division of Gastroenterology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Kun Zhang
- Division of Gastroenterology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Mengke Fan
- Division of Gastroenterology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yu Jin
- Division of Gastroenterology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Rong Lin
- Division of Gastroenterology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
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Xiong Z, Xing C, Zhang P, Diao Y, Guang C, Ying Y, Zhang W. Identification of a Novel Protein-Based Prognostic Model in Gastric Cancers. Biomedicines 2023; 11:biomedicines11030983. [PMID: 36979962 PMCID: PMC10046574 DOI: 10.3390/biomedicines11030983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Revised: 02/14/2023] [Accepted: 03/13/2023] [Indexed: 03/30/2023] Open
Abstract
Gastric cancer (GC) is the third leading cause of cancer-related deaths worldwide. However, there are still no reliable biomarkers for the prognosis of this disease. This study aims to construct a robust protein-based prognostic prediction model for GC patients. The protein expression data and clinical information of GC patients were downloaded from the TCPA and TCGA databases, and the expressions of 218 proteins in 352 GC patients were analyzed using bioinformatics methods. Additionally, Kaplan-Meier (KM) survival analysis and univariate and multivariate Cox regression analysis were applied to screen the prognosis-related proteins for establishing the prognostic prediction risk model. Finally, five proteins, including NDRG1_pT346, SYK, P90RSK, TIGAR, and XBP1, were related to the risk prognosis of gastric cancer and were selected for model construction. Furthermore, a significant trend toward worse survival was found in the high-risk group (p = 1.495 × 10-7). The time-dependent ROC analysis indicated that the model had better specificity and sensitivity compared to the clinical features at 1, 2, and 3 years (AUC = 0.685, 0.673, and 0.665, respectively). Notably, the independent prognostic analysis results revealed that the model was an independent prognostic factor for GC patients. In conclusion, the robust protein-based model based on five proteins was established, and its potential benefits in the prognostic prediction of GC patients were demonstrated.
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Affiliation(s)
- Zhijuan Xiong
- Department of Gastroenterology, The First Affiliated Hospital of Nanchang University, Nanchang 330006, China
- Jiangxi Medical Center for Major Public Health Events, The First Affiliated Hospital of Nanchang University, Nanchang 330006, China
- The Department of Respiratory and Intensive Care Medicine, The First Affiliated Hospital of Nanchang University, Nanchang 330006, China
| | - Chutian Xing
- Queen Mary School, Nanchang University, Nanchang 330006, China
| | - Ping Zhang
- The Department of Respiratory and Intensive Care Medicine, The First Affiliated Hospital of Nanchang University, Nanchang 330006, China
| | - Yunlian Diao
- The Department of Respiratory and Intensive Care Medicine, The First Affiliated Hospital of Nanchang University, Nanchang 330006, China
| | - Chenxi Guang
- Jiangxi Medical Center for Major Public Health Events, The First Affiliated Hospital of Nanchang University, Nanchang 330006, China
| | - Ying Ying
- Jiangxi Medical Center for Major Public Health Events, The First Affiliated Hospital of Nanchang University, Nanchang 330006, China
| | - Wei Zhang
- Jiangxi Medical Center for Major Public Health Events, The First Affiliated Hospital of Nanchang University, Nanchang 330006, China
- The Department of Respiratory and Intensive Care Medicine, The First Affiliated Hospital of Nanchang University, Nanchang 330006, China
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Wei X, Liu J, Hong Z, Chen X, Wang K, Cai J. Identification of novel tumor microenvironment-associated genes in gastric cancer based on single-cell RNA-sequencing datasets. Front Genet 2022; 13:896064. [PMID: 36046240 PMCID: PMC9421061 DOI: 10.3389/fgene.2022.896064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 07/06/2022] [Indexed: 11/13/2022] Open
Abstract
Tumor microenvironment and heterogeneity play vital roles in the development and progression of gastric cancer (GC). In the past decade, a considerable amount of single-cell RNA-sequencing (scRNA-seq) studies have been published in the fields of oncology and immunology, which improve our knowledge of the GC immune microenvironment. However, much uncertainty still exists about the relationship between the macroscopic and microscopic data in transcriptomics. In the current study, we made full use of scRNA-seq data from the Gene Expression Omnibus database (GSE134520) to identify 25 cell subsets, including 11 microenvironment-related cell types. The MIF signaling pathway network was obtained upon analysis of receptor–ligand pairs and cell–cell interactions. By comparing the gene expression in a wide variety of cells between intestinal metaplasia and early gastric cancer, we identified 64 differentially expressed genes annotated as immune response and cellular communication. Subsequently, we screened these genes for prognostic clinical value based on the patients’ follow-up data from The Cancer Genome Atlas. TMPRSS15, VIM, APOA1, and RNASE1 were then selected for the construction of LASSO risk scores, and a nomogram model incorporating another five clinical risk factors was successfully created. The effectiveness of least absolute shrinkage and selection operator risk scores was validated using gene set enrichment analysis and levels of immune cell infiltration. These findings will drive the development of prognostic evaluations affected by the immune tumor microenvironment in GC.
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Affiliation(s)
- Xujin Wei
- The Graduate School of Fujian Medical University, Fuzhou, China
- Department of Gastrointestinal Surgery, Zhongshan Hospital of Xiamen University, Institute of Gastrointestinal Oncology, School of Medicine, Xiamen University, Xiamen, China
- Xiamen Municipal Key Laboratory of Gastrointestinal Oncology, Xiamen, China
| | - Jie Liu
- The Graduate School of Fujian Medical University, Fuzhou, China
| | - Zhijun Hong
- Department of Gastrointestinal Surgery, Zhongshan Hospital of Xiamen University, Institute of Gastrointestinal Oncology, School of Medicine, Xiamen University, Xiamen, China
- Xiamen Municipal Key Laboratory of Gastrointestinal Oncology, Xiamen, China
| | - Xin Chen
- The Graduate School of Fujian Medical University, Fuzhou, China
- Department of Gastrointestinal Surgery, Zhongshan Hospital of Xiamen University, Institute of Gastrointestinal Oncology, School of Medicine, Xiamen University, Xiamen, China
- Xiamen Municipal Key Laboratory of Gastrointestinal Oncology, Xiamen, China
| | - Kang Wang
- Department of Gastrointestinal Surgery, Zhongshan Hospital of Xiamen University, Institute of Gastrointestinal Oncology, School of Medicine, Xiamen University, Xiamen, China
- Xiamen Municipal Key Laboratory of Gastrointestinal Oncology, Xiamen, China
| | - Jianchun Cai
- The Graduate School of Fujian Medical University, Fuzhou, China
- Department of Gastrointestinal Surgery, Zhongshan Hospital of Xiamen University, Institute of Gastrointestinal Oncology, School of Medicine, Xiamen University, Xiamen, China
- Xiamen Municipal Key Laboratory of Gastrointestinal Oncology, Xiamen, China
- *Correspondence: Jianchun Cai,
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Chen Q, Wang Y, Liu Y, Xi B. ESRRG, ATP4A, and ATP4B as Diagnostic Biomarkers for Gastric Cancer: A Bioinformatic Analysis Based on Machine Learning. Front Physiol 2022; 13:905523. [PMID: 35812327 PMCID: PMC9262247 DOI: 10.3389/fphys.2022.905523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Accepted: 05/10/2022] [Indexed: 11/13/2022] Open
Abstract
Based on multiple bioinformatics methods and machine learning techniques, this study was designed to explore potential hub genes of gastric cancer with a diagnostic value. The novel biomarkers were detected through multiple databases of gastric cancer–related genes. The NCBI Gene Expression Omnibus (GEO) database was used to obtain gene expression files. Three hub genes (ESRRG, ATP4A, and ATP4B) were detected through a combination of weighted gene co-expression network analysis (WGCNA), gene–gene interaction network analysis, and supervised feature selection method. GEPIA2 was used to verify the differences in the expression levels of the hub genes in normal and cancer tissues in the RNA-seq levels of Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) databases. The objectivity of potential hub genes was also verified by immunohistochemistry in the Human Protein Atlas (HPA) database and transcription factor–hub gene regulatory network. Machine learning (ML) methods including data pre-processing, model selection and cross-validation, and performance evaluation were examined on the hub-gene expression profiles in five Gene Expression Omnibus datasets and verified on a GEO external validation (EV) dataset. Six supervised learning models (support vector machine, random forest, k-nearest neighbors, neural network, decision tree, and eXtreme Gradient Boosting) and one semi-supervised learning model (label spreading) were established to evaluate the diagnostic value of biomarkers. Among the six supervised models, the support vector machine (SVM) algorithm was the most effective one according to calculated performance metrics, including 0.93 and 0.99 area under the curve (AUC) scores on the test and external validation datasets, respectively. Furthermore, the semi-supervised model could also successfully learn and predict sample types, achieving a 0.986 AUC score on the EV dataset, even when 10% samples in the five GEO datasets were labeled. In conclusion, three hub genes (ATP4A, ATP4B, and ESRRG) closely related to gastric cancer were mined, based on which the ML diagnostic model of gastric cancer was conducted.
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Affiliation(s)
- Qiu Chen
- Medical College, Yangzhou University, Yangzhou, China
| | - Yu Wang
- College of Physics Science and Technology, Yangzhou University, Yangzhou, China
| | - Yongjun Liu
- College of Physics Science and Technology, Yangzhou University, Yangzhou, China
| | - Bin Xi
- College of Physics Science and Technology, Yangzhou University, Yangzhou, China
- *Correspondence: Bin Xi,
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