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Liu X, Zhang S, Qiu H, Xie ZQ, Tang WF, Chen Y, Wei X. Investigation of high-mobility group box 1 variants with lymph node status and colorectal cancer risk. World J Gastrointest Oncol 2025; 17:102584. [DOI: 10.4251/wjgo.v17.i4.102584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/23/2024] [Revised: 12/31/2024] [Accepted: 01/22/2025] [Indexed: 03/25/2025] Open
Abstract
BACKGROUND Accumulating studies indicated that maintain nuclei homeostasis was deemed to the protective factors for the occurrence of cancer. Thus, high-mobility group box 1 (HMGB1) might influence the risk and poorer prognoses of colorectal cancer (CRC).
AIM This study was designed to investigate whether HMGB1 polymorphisms influence the risk and lymph node metastasis (LNM) of CRC.
METHODS Firstly, we designed an investigation with 1003 CRC patients and 1303 cancer-free controls to observe whether HMGB1 rs1412125 T > C and rs1045411 C > T SNPs could influence the risk of cancer. Subsequently, we carried out a correlation-analysis to assess whether these SNPs could alter the risk of LNM.
RESULTS The current investigation suggested a relationship of HMGB1 rs1412125 SNP with the increased susceptibility of CRC. In a subgroup analysis, our findings suggested that this SNP could enhance an occurrence of CRC in ≥ 61 years, non-drinker and body mass index < 24 kg/m2 subgroups. However, we found that there was null association between HMGB1 rs1412125 SNP and LNM, even in different CRC region. These observations were confirmed by calculating the power value (more than 0.8). The association of HMGB1 rs1045411 C > T SNP with CRC risk and LNM was not found in any compare.
CONCLUSION This study highlights a possible association between HMGB1 rs1412125 polymorphism and the increased risk of CRC. In the future, more studies should be conducted to explore HMGB1 rs1412125 polymorphism in relation to CRC development.
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Affiliation(s)
- Xin Liu
- Department of General Surgery, Changzhou Third People’s Hospital, Changzhou 213001, Jiangsu Province, China
| | - Sheng Zhang
- Department of General Surgery, Changzhou Third People’s Hospital, Changzhou 213001, Jiangsu Province, China
| | - Hao Qiu
- Laboratory Medicine, School of Medicine, Jiangsu University, Zhenjiang 212000, Jiangsu Province, China
| | - Zhi-Qiang Xie
- Department of Clinical Laboratory, Fujian Medical University Union Hospital, Fuzhou 350001, Fujian Province, China
| | - Wei-Feng Tang
- Department of Cardiothoracic Surgery, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing 210000, Jiangsu Province, China
| | - Yu Chen
- Department of Medical Oncology, Fujian Cancer Hospital and Fujian Medical University Cancer Hospital, Fuqing 350014, Fujian Province, China
| | - Xi Wei
- Department of Pathology, Affiliated People’s Hospital of Jiangsu University, Zhenjiang 212002, Jiangsu Province, China
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Wei J, Ge X, Qian Y, Jiang K, Chen X, Lu W, Yang H, Fu D, Fang Y, Zhou X, Xiao Q, Tang Y, Ding K. Development and verification of a combined immune- and cancer-associated fibroblast related prognostic signature for colon adenocarcinoma. Front Immunol 2024; 15:1291938. [PMID: 38312843 PMCID: PMC10834644 DOI: 10.3389/fimmu.2024.1291938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Accepted: 01/04/2024] [Indexed: 02/06/2024] Open
Abstract
Introduction To better understand the role of immune escape and cancer-associated fibroblasts (CAFs) in colon adenocarcinoma (COAD), an integrative analysis of the tumor microenvironment was performed using a set of 12 immune- and CAF-related genes (ICRGs). Methods Univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses were used to establish a prognostic signature based on the expression of these 12 genes (S1PR5, AEN, IL20RB, FGF9, OSBPL1A, HSF4, PCAT6, FABP4, KIF15, ZNF792, CD1B and GLP2R). This signature was validated in both internal and external cohorts and was found to have a higher C-index than previous COAD signatures, confirming its robustness and reliability. To make use of this signature in clinical settings, a nomogram incorporating ICRG signatures and key clinical parameters, such as age and T stage, was developed. Finally, the role of S1PR5 in the immune response of COAD was validated through in vitro cytotoxicity experiments. Results The developed nomogram exhibited slightly improved predictive accuracy compared to the ICRG signature alone, as indicated by the areas under the receiver operating characteristic curves (AUC, nomogram:0.838; ICRGs:0.807). The study also evaluated the relationships between risk scores (RS) based on the expression of the ICRGs and other key immunotherapy variables, including immune checkpoint expression, immunophenoscore (IPS), and microsatellite instability (MSI). Integration of these variables led to more precise prediction of treatment efficacy, enabling personalized immunotherapy for COAD patients. Knocking down S1PR5 can enhance the efficacy of PD-1 monoclonal antibody, promoting the cytotoxicity of T cells against HCT116 cells ((p<0.05). Discussion These findings indicate that the ICRG signature may be a valuable tool for predicting prognostic risk, evaluating the efficacy of immunotherapy, and tailoring personalized treatment options for patients with COAD.
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Affiliation(s)
- Jingsun Wei
- Department of Colorectal Surgery and Oncology (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Department of Colorectal Surgery and Oncology, Zhejiang Provincial Clinical Research Center for Cancer, Hangzhou, Zhejiang, China
- Department of Colorectal Surgery and Oncology, Cancer Center of Zhejiang University, Hangzhou, Zhejiang, China
| | - Xiaoxu Ge
- Department of Colorectal Surgery and Oncology (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Department of Colorectal Surgery and Oncology, Zhejiang Provincial Clinical Research Center for Cancer, Hangzhou, Zhejiang, China
- Department of Colorectal Surgery and Oncology, Cancer Center of Zhejiang University, Hangzhou, Zhejiang, China
| | - Yucheng Qian
- Department of Colorectal Surgery and Oncology (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Department of Colorectal Surgery and Oncology, Zhejiang Provincial Clinical Research Center for Cancer, Hangzhou, Zhejiang, China
- Department of Colorectal Surgery and Oncology, Cancer Center of Zhejiang University, Hangzhou, Zhejiang, China
| | - Kai Jiang
- Department of Colorectal Surgery and Oncology (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Department of Colorectal Surgery and Oncology, Zhejiang Provincial Clinical Research Center for Cancer, Hangzhou, Zhejiang, China
- Department of Colorectal Surgery and Oncology, Cancer Center of Zhejiang University, Hangzhou, Zhejiang, China
| | - Xin Chen
- Department of Colorectal Surgery and Oncology (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Department of Colorectal Surgery and Oncology, Zhejiang Provincial Clinical Research Center for Cancer, Hangzhou, Zhejiang, China
- Department of Colorectal Surgery and Oncology, Cancer Center of Zhejiang University, Hangzhou, Zhejiang, China
| | - Wei Lu
- Department of Colorectal Surgery and Oncology (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Department of Colorectal Surgery and Oncology, Zhejiang Provincial Clinical Research Center for Cancer, Hangzhou, Zhejiang, China
- Department of Colorectal Surgery and Oncology, Cancer Center of Zhejiang University, Hangzhou, Zhejiang, China
| | - Hang Yang
- Department of Colorectal Surgery and Oncology (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Department of Colorectal Surgery and Oncology, Zhejiang Provincial Clinical Research Center for Cancer, Hangzhou, Zhejiang, China
- Department of Colorectal Surgery and Oncology, Cancer Center of Zhejiang University, Hangzhou, Zhejiang, China
| | - Dongliang Fu
- Department of Colorectal Surgery and Oncology (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Department of Colorectal Surgery and Oncology, Zhejiang Provincial Clinical Research Center for Cancer, Hangzhou, Zhejiang, China
- Department of Colorectal Surgery and Oncology, Cancer Center of Zhejiang University, Hangzhou, Zhejiang, China
| | - Yimin Fang
- Department of Colorectal Surgery and Oncology (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Department of Colorectal Surgery and Oncology, Zhejiang Provincial Clinical Research Center for Cancer, Hangzhou, Zhejiang, China
- Department of Colorectal Surgery and Oncology, Cancer Center of Zhejiang University, Hangzhou, Zhejiang, China
| | - Xinyi Zhou
- Department of Colorectal Surgery and Oncology (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Department of Colorectal Surgery and Oncology, Zhejiang Provincial Clinical Research Center for Cancer, Hangzhou, Zhejiang, China
- Department of Colorectal Surgery and Oncology, Cancer Center of Zhejiang University, Hangzhou, Zhejiang, China
| | - Qian Xiao
- Department of Colorectal Surgery and Oncology (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Department of Colorectal Surgery and Oncology, Zhejiang Provincial Clinical Research Center for Cancer, Hangzhou, Zhejiang, China
- Department of Colorectal Surgery and Oncology, Cancer Center of Zhejiang University, Hangzhou, Zhejiang, China
| | - Yang Tang
- Department of Colorectal Surgery and Oncology (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Department of Colorectal Surgery and Oncology, Zhejiang Provincial Clinical Research Center for Cancer, Hangzhou, Zhejiang, China
- Department of Colorectal Surgery and Oncology, Cancer Center of Zhejiang University, Hangzhou, Zhejiang, China
| | - Kefeng Ding
- Department of Colorectal Surgery and Oncology (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Department of Colorectal Surgery and Oncology, Zhejiang Provincial Clinical Research Center for Cancer, Hangzhou, Zhejiang, China
- Department of Colorectal Surgery and Oncology, Cancer Center of Zhejiang University, Hangzhou, Zhejiang, China
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Zhang YJ, Yi DH. CDK1-SRC Interaction-Dependent Transcriptional Activation of HSP90AB1 Promotes Antitumor Immunity in Hepatocellular Carcinoma. J Proteome Res 2023; 22:3714-3729. [PMID: 37949475 DOI: 10.1021/acs.jproteome.3c00379] [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] [Indexed: 11/12/2023]
Abstract
This study aimed to analyze multiomics data and construct a regulatory network involving kinases, transcription factors, and immune genes in hepatocellular carcinoma (HCC) prognosis. The researchers used transcriptomic, proteomic, and clinical data from TCGA and GEO databases to identify immune genes associated with HCC. Statistical analysis, meta-analysis, and protein-protein interaction analyses were performed to identify key immune genes and their relationships. In vitro and in vivo experiments validated the CDK1-SRC-HSP90AB1 network's effects on HCC progression and antitumor immunity. A prognostic risk model was developed using clinicopathological features and immune infiltration. The immune genes LPA, BIRC5, HSP90AB1, ROBO1, and CCL20 were identified as the key prognostic factors. The CDK1-SRC-HSP90AB1 network promoted HCC cell proliferation and migration, with HSP90AB1 being transcriptionally activated by the CDK1-SRC interaction. Manipulating SRC or HSP90AB1 reversed the effects of CDK1 and SRC on HCC. The CDK1-SRC-HSP90AB1 network also influenced HCC tumor formation and antitumor immunity. Overall, this study highlights the importance of the CDK1-SRC-HSP90AB1 network as a crucial immune-regulatory network in the HCC prognosis.
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Affiliation(s)
- Yi-Jie Zhang
- Department of Hepatobiliary and Organ Transplantation, The First Affiliated Hospital of China Medical University, Shenyang 110001, P. R. China
- The Key Laboratory of Organ Transplantation of Liaoning Province, The First Affiliated Hospital of China Medical University, Shenyang 110001, P. R. China
| | - De-Hui Yi
- Department of Hepatobiliary and Organ Transplantation, The First Affiliated Hospital of China Medical University, Shenyang 110001, P. R. China
- The Key Laboratory of Organ Transplantation of Liaoning Province, The First Affiliated Hospital of China Medical University, Shenyang 110001, P. R. China
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Weighted gene co-expression network analysis combined with machine learning validation to identify key hub biomarkers in colorectal cancer. Funct Integr Genomics 2022; 23:24. [PMID: 36576616 DOI: 10.1007/s10142-022-00949-2] [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: 06/24/2022] [Revised: 12/21/2022] [Accepted: 12/21/2022] [Indexed: 12/29/2022]
Abstract
Colorectal cancer (CRC) is one of the most common malignancies worldwide; however, the potentially possible molecular biological mechanism of CRC is still not completely comprehended. This study aimed to confirm candidate key hub genes involved in the growth and development of CRC and their connection with immune infiltration as well as the related pathways. Gene expression data were selected from the GEO dataset. Hub genes for CRC were identified on the basis of differential expression analysis, weighted gene co-expression network analysis (WGCNA), and LASSO regression. Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), and Gene Set Enrichment Analysis (GSEA) were applied to reveal possible functions of the differential genes. Single-sample GSEA (ssGSEA) was implemented to identify the relationship between immune cells infiltration and hub genes. Two hundred and sixty-two differentially expressed genes (DEGs) were identified. Three modules were acquired based on WGCNA, and the blue module presented the highest relevance with CRC. Ten hub genes (AQP8, B3GALT5, CDH3, CEMIP, CPM, FOXQ1, PLAC8, SCNN1B, SPINK5, and SST) were acquired with LASSO analysis as underlying biomarkers for CRC. Compared with normal tissues, CRC tissues presented significantly higher numbers of CD4 T cells, CD8 T cells, B cells, natural regulatory T (Treg) cells, and monocytes. The functional enrichment analyses demonstrated that hub genes were primarily enriched in metabolic process, inflammatory-related, and immune-related response. Ten hub genes were identified to be involved in the occurrence and development of CRC and may be deemed as novel biomarkers for clinical diagnosis and treatment.
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Wang D, Liufu J, Yang Q, Dai S, Wang J, Xie B. Identification and validation of a novel signature as a diagnostic and prognostic biomarker in colorectal cancer. Biol Direct 2022; 17:29. [PMID: 36319976 PMCID: PMC9628086 DOI: 10.1186/s13062-022-00342-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 10/14/2022] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND Colorectal cancer (CRC) is one of the most common malignant neoplasms worldwide. Although marker genes associated with CRC have been identified previously, only a few have fulfilled the therapeutic demand. Therefore, based on differentially expressed genes (DEGs), this study aimed to establish a promising and valuable signature model to diagnose CRC and predict patient's prognosis. METHODS The key genes were screened from DEGs to establish a multiscale embedded gene co-expression network, protein-protein interaction network, and survival analysis. A support vector machine (SVM) diagnostic model was constructed by a supervised classification algorithm. Univariate Cox analysis was performed to construct two prognostic signatures for overall survival and disease-free survival by Kaplan-Meier analysis, respectively. Independent clinical prognostic indicators were identified, followed by univariable and multivariable Cox analysis. GSEA was used to evaluate the gene enrichment analysis and CIBERSORT was used to estimate the immune cell infiltration. Finally, key genes were validated by qPCR and IHC. RESULTS In this study, four key genes (DKC1, FLNA, CSE1L and NSUN5) were screened. The SVM diagnostic model, consisting of 4-gene signature, showed a good performance for the diagnostic (AUC = 0.9956). Meanwhile, the four-gene signature was also used to construct a risk score prognostic model for disease-free survival (DFS) and overall survival (OS), and the results indicated that the prognostic model performed best in predicting the DFS and OS of CRC patients. The risk score was validated as an independent prognostic factor to exhibit the accurate survival prediction for OS according to the independent prognostic value. Furthermore, immune cell infiltration analysis demonstrated that the high-risk group had a higher proportion of macrophages M0, and T cells CD4 memory resting was significantly higher in the low-risk group than in the high-risk group. In addition, functional analysis indicated that WNT and other four cancer-related signaling pathways were the most significantly enriched pathways in the high-risk group. Finally, qRT-PCR and IHC results demonstrated that the high expression of DKC1, CSE1L and NSUN5, and the low expression of FLNA were risk factors of CRC patients with a poor prognosis. CONCLUSION In this study, diagnosis and prognosis models were constructed based on the screened genes of DKC1, FLNA, CSE1L and NSUN5. The four-gene signature exhibited an excellent ability in CRC diagnosis and prognostic prediction. Our study supported and highlighted that the four-gene signature is conducive to better prognostic risk stratification and potential therapeutic targets for CRC patients.
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Affiliation(s)
- Di Wang
- Department of Gastroenterology, People's Hospital of Longhua, NO.38 Jinglong Construction Road, Longhua District, 518109, Shenzhen, P.R. China
| | - Junye Liufu
- Department of Gastroenterology, People's Hospital of Longhua, NO.38 Jinglong Construction Road, Longhua District, 518109, Shenzhen, P.R. China
| | - Qiyuan Yang
- Department of Gastroenterology, People's Hospital of Longhua, NO.38 Jinglong Construction Road, Longhua District, 518109, Shenzhen, P.R. China
| | - Shengqun Dai
- Department of Gastroenterology, People's Hospital of Longhua, NO.38 Jinglong Construction Road, Longhua District, 518109, Shenzhen, P.R. China
| | - Jiaqi Wang
- Department of Gastroenterology, Guangzhou First People's Hospital, 511458, Guangzhou, P.R. China
| | - Biao Xie
- Department of Gastroenterology, People's Hospital of Longhua, NO.38 Jinglong Construction Road, Longhua District, 518109, Shenzhen, P.R. China.
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Jiang HZ, Yang B, Jiang YL, Liu X, Chen DL, Long FX, Yang Z, Tang DX. Development and validation of prognostic models for colon adenocarcinoma based on combined immune-and metabolism-related genes. Front Oncol 2022; 12:1025397. [PMID: 36387195 PMCID: PMC9661394 DOI: 10.3389/fonc.2022.1025397] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 09/30/2022] [Indexed: 11/02/2023] Open
Abstract
Background The heterogeneity of tumor tissue is one of the reasons for the poor effect of tumor treatment, which is mainly affected by the tumor immune microenvironment and metabolic reprogramming. But more research is needed to find out how the tumor microenvironment (TME) and metabolic features of colon adenocarcinoma (COAD) are related. Methods We obtained the transcriptomic and clinical data information of COAD patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Consensus clustering analysis was used to identify different molecular subtypes, identify differentially expressed genes (DEGs) associated with immune-and metabolism-related genes (IMRGs) prognosis. Univariate and multivariable Cox regression analysis and Lasso regression analysis were applied to construct the prognostic models based on the IMRG risk score. The correlations between risk scores and TME, immune cell infiltration, and immune checkpoint genes were investigated. Lastly, potential appropriate drugs related to the risk score were screened by drug sensitivity analysis. Results By consensus clustering analysis, we identified two distinct molecular subtypes. It was also found that the multilayered IMRG subtypes were associated with the patient's clinicopathological characteristics, prognosis, and TME cell infiltration characteristics. Meanwhile, a prognostic model based on the risk score of IMRGs was constructed and its predictive power was verified internally and externally. Clinicopathological analysis and nomogram give it better clinical guidance. The IMRG risk score plays a key role in immune microenvironment infiltration. Patients in the high-risk groups of microsatellite instability (MSI) and tumor mutational burden (TMB) were found to, although with poor prognosis, actively respond to immunotherapy. Furthermore, IMRG risk scores were significantly associated with immune checkpoint gene expression. The potential drug sensitivity study helps come up with and choose a chemotherapy treatment plan. Conclusion Our comprehensive analysis of IMRG signatures revealed a broad range of regulatory mechanisms affecting the tumor immune microenvironment (TIME), immune landscape, clinicopathological features, and prognosis. And to explore the potential drugs for immunotherapy. It will help to better understand the molecular mechanisms of COAD and provide new directions for disease treatment.
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Affiliation(s)
- Hui-zhong Jiang
- College of Graduate, Guizhou University of Traditional Chinese Medicine, Guiyang, China
| | - Bing Yang
- College of Graduate, Guizhou University of Traditional Chinese Medicine, Guiyang, China
| | - Ya-li Jiang
- College of Graduate, Guizhou University of Traditional Chinese Medicine, Guiyang, China
| | - Xun Liu
- College of Graduate, Guizhou University of Traditional Chinese Medicine, Guiyang, China
| | - Da-lin Chen
- College of Graduate, Guizhou University of Traditional Chinese Medicine, Guiyang, China
| | - Feng-xi Long
- College of Graduate, Guizhou University of Traditional Chinese Medicine, Guiyang, China
| | - Zhu Yang
- College of Graduate, Guizhou University of Traditional Chinese Medicine, Guiyang, China
| | - Dong-xin Tang
- College of Graduate, Guizhou University of Traditional Chinese Medicine, Guiyang, China
- The First Affiliated Hospital of Guizhou University of Traditional Chinese Medicine, Guiyang, China
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Ouyang Y, Huang J, Wang Y, Tang F, Hu Z, Zeng Z, Zhang S. Bioinformatic analysis of RNA-seq data from TCGA database reveals prognostic significance of immune-related genes in colon cancer. Medicine (Baltimore) 2022; 101:e29962. [PMID: 35945793 PMCID: PMC9351934 DOI: 10.1097/md.0000000000029962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
The tumor immune microenvironment is of crucial importance in cancer progression and anticancer immune responses. Thus, systematic exploration of the expression landscape and prognostic significance of immune-related genes (IRGs) to assist in the prognosis of colon cancer is valuable and significant. The transcriptomic data of 470 colon cancer patients were obtained from The Cancer Genome Atlas database and the differentially expressed genes were analyzed. After an intersection analysis, the hub IRGs were identified and a prognostic index was further developed using multivariable Cox analysis. In addition, the discriminatory ability and prognostic significance of the constructed model were validated and the characteristics of IRGs associated overall survival were analyzed to elucidate the underlying molecular mechanisms. A total of 465 differentially expressed IRGs and 130 survival-associated IRGs were screened. Then, 46 hub IRGs were identified by an intersection analysis. A regulatory network displayed that most of these genes were unfavorable for the prognosis of colon cancer and were regulated by transcription factors. After a least absolute shrinkage and selection operator regression analysis, 14 hub IRGs were ultimately chose to construct a prognostic index. The validation results illustrated that this model could act as an independent indicator to moderately separate colon cancer patients into low- and high-risk groups. This study ascertained the prognostic significance of IRGs in colon cancer and successfully constructed an IRG-based prognostic signature for clinical prediction. Our results provide promising insight for the exploration of diagnostic markers and immunotherapeutic targets in colon cancer.
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Affiliation(s)
- Yan Ouyang
- Key Laboratory of Infectious Immune and Antibody Engineering of Guizhou Province/Immune Cells and Antibody Engineering Research Center of Guizhou Province, School of Biology and Engineering/School of Basic Medical Sciences, Guizhou Medical University, Guiyang, China
| | - Jiangtao Huang
- Key Laboratory of Infectious Immune and Antibody Engineering of Guizhou Province/Immune Cells and Antibody Engineering Research Center of Guizhou Province, School of Biology and Engineering/School of Basic Medical Sciences, Guizhou Medical University, Guiyang, China
| | - Yun Wang
- Key Laboratory of Infectious Immune and Antibody Engineering of Guizhou Province/Immune Cells and Antibody Engineering Research Center of Guizhou Province, School of Biology and Engineering/School of Basic Medical Sciences, Guizhou Medical University, Guiyang, China
| | - Fuzhou Tang
- Key Laboratory of Infectious Immune and Antibody Engineering of Guizhou Province/Immune Cells and Antibody Engineering Research Center of Guizhou Province, School of Biology and Engineering/School of Basic Medical Sciences, Guizhou Medical University, Guiyang, China
| | - Zuquan Hu
- Key Laboratory of Infectious Immune and Antibody Engineering of Guizhou Province/Immune Cells and Antibody Engineering Research Center of Guizhou Province, School of Biology and Engineering/School of Basic Medical Sciences, Guizhou Medical University, Guiyang, China
- Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education of China, Guizhou Medical University, Guiyang, China
- *Correspondence: Zuquan Hu, Department of Medical Biotechnology, School of Biology and Engineering, Guizhou Medical University, Guiyang, Guizhou, China (e-mail: )
| | - Zhu Zeng
- Key Laboratory of Infectious Immune and Antibody Engineering of Guizhou Province/Immune Cells and Antibody Engineering Research Center of Guizhou Province, School of Biology and Engineering/School of Basic Medical Sciences, Guizhou Medical University, Guiyang, China
- State Key Laboratory of Functions and Applications of Medicinal Plants, Engineering Center of Cellular Immunotherapy of Guizhou Province, Guizhou Medical University, Guiyang, China
| | - Shichao Zhang
- Key Laboratory of Infectious Immune and Antibody Engineering of Guizhou Province/Immune Cells and Antibody Engineering Research Center of Guizhou Province, School of Biology and Engineering/School of Basic Medical Sciences, Guizhou Medical University, Guiyang, China
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Lin Z, Wang R, Huang C, He H, Ouyang C, Li H, Zhong Z, Guo J, Chen X, Yang C, Yang X. Identification of an Immune-Related Prognostic Risk Model in Glioblastoma. Front Genet 2022; 13:926122. [PMID: 35783263 PMCID: PMC9247349 DOI: 10.3389/fgene.2022.926122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 05/06/2022] [Indexed: 11/29/2022] Open
Abstract
Background: Glioblastoma (GBM) is the most common and malignant type of brain tumor. A large number of studies have shown that the immunotherapy of tumors is effective, but the immunotherapy effect of GBM is not poor. Thus, further research on the immune-related hub genes of GBM is extremely important. Methods: The GBM highly correlated gene clusters were screened out by differential expression, mutation analysis, and weighted gene co-expression network analysis (WGCNA). Least absolute shrinkage and selection operator (LASSO) and proportional hazards model (COX) regressions were implemented to construct prognostic risk models. Survival, receiver operating characteristic (ROC) curve, and compound difference analyses of tumor mutation burden were used to further verify the prognostic risk model. Then, we predicted GBM patient responses to immunotherapy using the ESTIMATE algorithm, GSEA, and Tumor Immune Dysfunction and Exclusion (TIDE) algorithm. Results: A total of 834 immune-related differentially expressed genes (DEGs) were identified. The five hub genes (STAT3, SEMA4F, GREM2, MDK, and SREBF1) were identified as the prognostic risk model (PRM) screened out by WGCNA and LASSO analysis of DEGs. In addition, the PRM has a significant positive correlation with immune cell infiltration of the tumor microenvironment (TME) and expression of critical immune checkpoints, indicating that the poor prognosis of patients is due to TIDE. Conclusion: We constructed the PRM composed of five hub genes, which provided a new strategy for developing tumor immunotherapy.
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Liu Y, Chen L, Meng X, Ye S, Ma L. Identification of Hub Genes in Colorectal Adenocarcinoma by Integrated Bioinformatics. Front Cell Dev Biol 2022; 10:897568. [PMID: 35693937 PMCID: PMC9184445 DOI: 10.3389/fcell.2022.897568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Accepted: 04/20/2022] [Indexed: 11/13/2022] Open
Abstract
An improved understanding of the molecular mechanism of colorectal adenocarcinoma is necessary to predict the prognosis and develop new target gene therapy strategies. This study aims to identify hub genes associated with colorectal adenocarcinoma and further analyze their prognostic significance. In this study, The Cancer Genome Atlas (TCGA) COAD-READ database and the gene expression profiles of GSE25070 from the Gene Expression Omnibus were collected to explore the differentially expressed genes between colorectal adenocarcinoma and normal tissues. The weighted gene co-expression network analysis (WGCNA) and differential expression analysis identified 82 differentially co-expressed genes in the collected datasets. Enrichment analysis was applied to explore the regulated signaling pathway in colorectal adenocarcinoma. In addition, 10 hub genes were identified in the protein–protein interaction (PPI) network by using the cytoHubba plug-in of Cytoscape, where five genes were further proven to be significantly related to the survival rate. Compared with normal tissues, the expressions of the five genes were both downregulated in the GSE110224 dataset. Subsequently, the expression of the five hub genes was confirmed by the Human Protein Atlas database. Finally, we used Cox regression analysis to identify genes associated with prognosis, and a 3-gene signature (CLCA1–CLCA4–GUCA2A) was constructed to predict the prognosis of patients with colorectal cancer. In conclusion, our study revealed that the five hub genes and CLCA1–CLCA4–GUCA2A signature are highly correlated with the development of colorectal adenocarcinoma and can serve as promising prognosis factors to predict the overall survival rate of patients.
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Affiliation(s)
- Yang Liu
- Endoscopy Center, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Lanlan Chen
- Department of Hepatobiliary and Pancreatic Surgery, The First Hospital of Jilin University, Changchun, China
| | - Xiangbo Meng
- Endoscopy Center, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Shujun Ye
- Endoscopy Center, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Lianjun Ma
- Endoscopy Center, China-Japan Union Hospital of Jilin University, Changchun, China
- *Correspondence: Lianjun Ma,
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10
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Zhang Z, Huang L, Li J, Wang P. Bioinformatics analysis reveals immune prognostic markers for overall survival of colorectal cancer patients: a novel machine learning survival predictive system. BMC Bioinformatics 2022; 23:124. [PMID: 35395711 PMCID: PMC8991575 DOI: 10.1186/s12859-022-04657-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 03/11/2022] [Indexed: 12/13/2022] Open
Abstract
Objectives Immune microenvironment was closely related to the occurrence and progression of colorectal cancer (CRC). The objective of the current research was to develop and verify a Machine learning survival predictive system for CRC based on immune gene expression data and machine learning algorithms. Methods The current study performed differentially expressed analyses between normal tissues and tumor tissues. Univariate Cox regression was used to screen prognostic markers for CRC. Prognostic immune genes and transcription factors were used to construct an immune-related regulatory network. Three machine learning algorithms were used to create an Machine learning survival predictive system for CRC. Concordance indexes, calibration curves, and Brier scores were used to evaluate the performance of prognostic model. Results Twenty immune genes (BCL2L12, FKBP10, XKRX, WFS1, TESC, CCR7, SPACA3, LY6G6C, L1CAM, OSM, EXTL1, LY6D, FCRL5, MYEOV, FOXD1, REG3G, HAPLN1, MAOB, TNFSF11, and AMIGO3) were recognized as independent risk factors for CRC. A prognostic nomogram was developed based on the previous immune genes. Concordance indexes were 0.852, 0.778, and 0.818 for 1-, 3- and 5-year survival. This prognostic model could discriminate high risk patients with poor prognosis from low risk patients with favorable prognosis. Conclusions The current study identified twenty prognostic immune genes for CRC patients and constructed an immune-related regulatory network. Based on three machine learning algorithms, the current research provided three individual mortality predictive curves. The Machine learning survival predictive system was available at: https://zhangzhiqiao8.shinyapps.io/Artificial_Intelligence_Survival_Prediction_for_CRC_B1005_1/, which was valuable for individualized treatment decision before surgery. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04657-3.
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Affiliation(s)
- Zhiqiao Zhang
- Department of Infectious Diseases, Shunde Hospital, Southern Medical University, Shunde, Guangdong, China
| | - Liwen Huang
- Department of Infectious Diseases, Shunde Hospital, Southern Medical University, Shunde, Guangdong, China
| | - Jing Li
- Department of Infectious Diseases, Shunde Hospital, Southern Medical University, Shunde, Guangdong, China
| | - Peng Wang
- Department of Infectious Diseases, Shunde Hospital, Southern Medical University, Shunde, Guangdong, China.
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Ahluwalia P, Mondal AK, Ahluwalia M, Sahajpal NS, Jones K, Jilani Y, Gahlay GK, Barrett A, Kota V, Rojiani AM, Kolhe R. Clinical and molecular assessment of an onco-immune signature with prognostic significance in patients with colorectal cancer. Cancer Med 2022; 11:1573-1586. [PMID: 35137551 PMCID: PMC8921909 DOI: 10.1002/cam4.4568] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 11/24/2021] [Accepted: 12/28/2021] [Indexed: 12/22/2022] Open
Abstract
Understanding the complex tumor microenvironment is key to the development of personalized therapies for the treatment of cancer including colorectal cancer (CRC). In the past decade, significant advances in the field of immunotherapy have changed the paradigm of cancer treatment. Despite significant improvements, tumor heterogeneity and lack of appropriate classification tools for CRC have prevented accurate risk stratification and identification of a wider patient population that may potentially benefit from targeted therapies. To identify novel signatures for accurate prognostication of CRC, we quantified gene expression of 12 immune‐related genes using a medium‐throughput NanoString quantification platform in 93 CRC patients. Multivariate prognostic analysis identified a combined four‐gene prognostic signature (TGFB1, PTK2, RORC, and SOCS1) (HR: 1.76, 95% CI: 1.05–2.95, *p < 0.02). The survival trend was captured in an independent gene expression data set: GSE17536 (177 patients; HR: 3.31, 95% CI: 1.99–5.55, *p < 0.01) and GSE14333 (226 patients; HR: 2.47, 95% CI: 1.35–4.53, *p < 0.01). Further, gene set enrichment analysis of the TCGA data set associated higher prognostic scores with epithelial–mesenchymal transition (EMT) and inflammatory pathways. Comparatively, a lower prognostic score was correlated with oxidative phosphorylation and MYC and E2F targets. Analysis of immune parameters identified infiltration of T‐reg cells, CD8+ T cells, M2 macrophages, and B cells in high‐risk patient groups along with upregulation of immune exhaustion genes. This molecular study has identified a novel prognostic gene signature with clinical utility in CRC. Therefore, along with prognostic features, characterization of immune cell infiltrates and immunosuppression provides actionable information that should be considered while employing personalized medicine.
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Affiliation(s)
- Pankaj Ahluwalia
- Department of Pathology, Medical College of Georgia at Augusta University, Augusta, Georgia, USA
| | - Ashis K Mondal
- Department of Pathology, Medical College of Georgia at Augusta University, Augusta, Georgia, USA
| | | | - Nikhil S Sahajpal
- Department of Pathology, Medical College of Georgia at Augusta University, Augusta, Georgia, USA
| | - Kimya Jones
- Department of Pathology, Medical College of Georgia at Augusta University, Augusta, Georgia, USA
| | - Yasmeen Jilani
- Department of Pathology, Medical College of Georgia at Augusta University, Augusta, Georgia, USA
| | - Gagandeep K Gahlay
- Department of Molecular Biology and Biochemistry, Guru Nanak Dev University, Amritsar, India
| | - Amanda Barrett
- Department of Pathology, Medical College of Georgia at Augusta University, Augusta, Georgia, USA
| | - Vamsi Kota
- Department of Medicine, Medical College of Georgia at Augusta University, Augusta, Georgia, USA
| | - Amyn M Rojiani
- Department of Pathology, Penn State College of Medicine, Hershey, USA
| | - Ravindra Kolhe
- Department of Pathology, Medical College of Georgia at Augusta University, Augusta, Georgia, USA
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