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Marx OM, Mankarious MM, Koltun WA, Yochum GS. Identification of differentially expressed genes and splicing events in early-onset colorectal cancer. Front Oncol 2024; 14:1365762. [PMID: 38680862 PMCID: PMC11047122 DOI: 10.3389/fonc.2024.1365762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Accepted: 03/27/2024] [Indexed: 05/01/2024] Open
Abstract
Background The incidence of colorectal cancer (CRC) has been steadily increasing in younger individuals over the past several decades for reasons that are incompletely defined. Identifying differences in gene expression profiles, or transcriptomes, in early-onset colorectal cancer (EOCRC, < 50 years old) patients versus later-onset colorectal cancer (LOCRC, > 50 years old) patients is one approach to understanding molecular and genetic features that distinguish EOCRC. Methods We performed RNA-sequencing (RNA-seq) to characterize the transcriptomes of patient-matched tumors and adjacent, uninvolved (normal) colonic segments from EOCRC (n=21) and LOCRC (n=22) patients. The EOCRC and LOCRC cohorts were matched for demographic and clinical characteristics. We used The Cancer Genome Atlas Colon Adenocarcinoma (TCGA-COAD) database for validation. We used a series of computational and bioinformatic tools to identify EOCRC-specific differentially expressed genes, molecular pathways, predicted cell populations, differential gene splicing events, and predicted neoantigens. Results We identified an eight-gene signature in EOCRC comprised of ALDOB, FBXL16, IL1RN, MSLN, RAC3, SLC38A11, WBSCR27 and WNT11, from which we developed a score predictive of overall CRC patient survival. On the entire set of genes identified in normal tissues and tumors, cell type deconvolution analysis predicted a differential abundance of immune and non-immune populations in EOCRC versus LOCRC. Gene set enrichment analysis identified increased expression of splicing machinery in EOCRC. We further found differences in alternative splicing (AS) events, including one within the long non-coding RNA, HOTAIRM1. Additional analysis of AS found seven events specific to EOCRC that encode potential neoantigens. Conclusion Our transcriptome analyses identified genetic and molecular features specific to EOCRC which may inform future screening, development of prognostic indicators, and novel drug targets.
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Affiliation(s)
- Olivia M. Marx
- Koltun and Yochum Laboratory, Department of Surgery, Division of Colon & Rectal Surgery, Pennsylvania State University College of Medicine, Hershey, PA, United States
- Department of Biochemistry & Molecular Biology, Pennsylvania State University College of Medicine, Hershey, PA, United States
| | - Marc M. Mankarious
- Koltun and Yochum Laboratory, Department of Surgery, Division of Colon & Rectal Surgery, Pennsylvania State University College of Medicine, Hershey, PA, United States
| | - Walter A. Koltun
- Koltun and Yochum Laboratory, Department of Surgery, Division of Colon & Rectal Surgery, Pennsylvania State University College of Medicine, Hershey, PA, United States
| | - Gregory S. Yochum
- Koltun and Yochum Laboratory, Department of Surgery, Division of Colon & Rectal Surgery, Pennsylvania State University College of Medicine, Hershey, PA, United States
- Department of Biochemistry & Molecular Biology, Pennsylvania State University College of Medicine, Hershey, PA, United States
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Liu Z, Jin D, Wei X, Gao Y, Gao X, Li X, Wang X, Wei P, Liu T. ZBTB34 is a hepatocellular carcinoma-associated protein with a monopartite nuclear localization signal. Aging (Albany NY) 2023; 15:8487-8500. [PMID: 37650557 PMCID: PMC10496988 DOI: 10.18632/aging.204987] [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: 04/17/2023] [Accepted: 07/18/2023] [Indexed: 09/01/2023]
Abstract
ZBTB34 is a novel zinc finger protein with an unknown function. In this study, the gene expression and survival prognosis of ZBTB34 were analyzed across tumors based on the TCGA datasets. According to the bioinformatics analysis and qPCR results, liver hepatocellular carcinomas exhibit a high level of ZBTB34 expression. Additionally, the experiment supported the bioinformatics analysis findings that ZBTB34 expression was regulated by miR-125b-5p and that ZBTB34 affected ZBTB10, POLR1B, and AUH expression in HepG2 cells. Biological software analysis further revealed that ZBTB34 contains a monopartite nuclear localization signal (NLS). Arginine and lysine inside the putative NLS were substituted using the alanine-scanning mutagenesis method. The findings showed that the ability of ZBTB34 to enter the nucleus was abolished by the alanine substitution of the sequence 320RGGRARQKRA329 and the mutation of Lys327 and Arg328 residues. ZBTB34 was co-immunoprecipitated with importin α1, importin α3, importin α4, and importin β1, according to the results of the co-immunoprecipitation assay. In conclusion, ZBTB34 is a hepatocellular carcinoma-associated protein with a monopartite NLS. The nuclear import of ZBTB34 is mediated by importin α1, importin α3, importin α4, and importin β1. ZBTB34 performs its biological functions via a putative miR-125b-5p/ZBTB34/(ZBTB10, POLR1B, and AUH) signaling axis in HepG2 cells.
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Affiliation(s)
- Zheng Liu
- College of Medical Laboratory Science, Guilin Medical University, Guilin 541004, Guangxi, China
- Guihang Guiyang Hospital Affiliated to Zunyi Medical University, Guiyang 550027, Guizhou, China
| | - Di Jin
- College of Medical Laboratory Science, Guilin Medical University, Guilin 541004, Guangxi, China
| | - Xinran Wei
- College of Medical Laboratory Science, Guilin Medical University, Guilin 541004, Guangxi, China
| | - Yue Gao
- College of Medical Laboratory Science, Guilin Medical University, Guilin 541004, Guangxi, China
| | - Xiaodie Gao
- College of Medical Laboratory Science, Guilin Medical University, Guilin 541004, Guangxi, China
| | - Xia Li
- Clinical Laboratory, Hospital Affiliated to Guilin Medical University, Guilin 541001, Guangxi, China
| | - Xiujuan Wang
- College of Medical Laboratory Science, Guilin Medical University, Guilin 541004, Guangxi, China
| | - Pingying Wei
- College of Medical Laboratory Science, Guilin Medical University, Guilin 541004, Guangxi, China
| | - Tao Liu
- Guihang Guiyang Hospital Affiliated to Zunyi Medical University, Guiyang 550027, Guizhou, China
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Chen X, Yang M, Wang L, Wang Y, Tu J, Zhou X, Yuan X. Identification and in vitro and in vivo validation of the key role of GSDME in pyroptosis-related genes signature in hepatocellular carcinoma. BMC Cancer 2023; 23:411. [PMID: 37149620 PMCID: PMC10164321 DOI: 10.1186/s12885-023-10850-1] [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: 09/24/2022] [Accepted: 04/14/2023] [Indexed: 05/08/2023] Open
Abstract
We used pyroptosis-related genes to establish a risk-score model for prognostic prediction of liver hepatocellular carcinoma (LIHC) patients. A total of 52 pyroptosis-associated genes were identified. Then, data for 374 LIHC patients and 50 normal individuals were acquired from the TCGA database. Through gene expression analyses, differentially expressed genes (DEGs) were determined. The 13 pyroptosis-related genes (PRGs) confirmed as potential prognostic factors through univariate Cox regression analysis were entered into Lasso and multivariate Cox regression to build a PRGs prognostic signature, containing four PRGs (BAK1, GSDME, NLRP6, and NOD2) determined as independent prognostic factors. mRNA levels were evaluated by qRT-PCR, while overall survival (OS) rates were assessed by the Kaplan-Meier method. Enrichment analyses were done to establish the mechanisms associated with differential survival status of LIHC patients from a tumor immunology perspective. Additionally, a risk score determined by the prognostic model could divide LIHC patients into low- and high-risk groups using median risk score as cut-off. A prognostic nomogram, derived from the prognostic model and integrating clinical characteristics of patients, was constructed. The prognostic function of the model was also validated using GEO, ICGC cohorts, and online databases Kaplan-Meier Plotter. Small interfering RNA-mediated knockdown of GSDME, as well as lentivirus-mediated GSDME knockdown, were performed to validate that knockdown of GSDME markedly suppressed growth of HCC cells both in vivo and in vitro. Collectively, our study demonstrated a PRGs prognostic signature that had great clinical value in prognosis assessment.
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Affiliation(s)
- Xinyi Chen
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jie Fang Road 1095, Wuhan, Hubei, China
| | - Mu Yang
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jie Fang Road 1095, Wuhan, Hubei, China
| | - Lu Wang
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jie Fang Road 1095, Wuhan, Hubei, China
| | - Yuan Wang
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jie Fang Road 1095, Wuhan, Hubei, China
| | - Jingyao Tu
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jie Fang Road 1095, Wuhan, Hubei, China.
| | - Xiao Zhou
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jie Fang Road 1095, Wuhan, Hubei, China.
| | - Xianglin Yuan
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jie Fang Road 1095, Wuhan, Hubei, China.
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Horaira MA, Islam MA, Kibria MK, Alam MJ, Kabir SR, Mollah MNH. Bioinformatics screening of colorectal-cancer causing molecular signatures through gene expression profiles to discover therapeutic targets and candidate agents. BMC Med Genomics 2023; 16:64. [PMID: 36991484 PMCID: PMC10053149 DOI: 10.1186/s12920-023-01488-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 03/14/2023] [Indexed: 03/31/2023] Open
Abstract
BACKGROUND Detection of appropriate receptor proteins and drug agents are equally important in the case of drug discovery and development for any disease. In this study, an attempt was made to explore colorectal cancer (CRC) causing molecular signatures as receptors and drug agents as inhibitors by using integrated statistics and bioinformatics approaches. METHODS To identify the important genes that are involved in the initiation and progression of CRC, four microarray datasets (GSE9348, GSE110224, GSE23878, and GSE35279) and an RNA_Seq profiles (GSE50760) were downloaded from the Gene Expression Omnibus database. The datasets were analyzed by a statistical r-package of LIMMA to identify common differentially expressed genes (cDEGs). The key genes (KGs) of cDEGs were detected by using the five topological measures in the protein-protein interaction network analysis. Then we performed in-silico validation for CRC-causing KGs by using different web-tools and independent databases. We also disclosed the transcriptional and post-transcriptional regulatory factors of KGs by interaction network analysis of KGs with transcription factors (TFs) and micro-RNAs. Finally, we suggested our proposed KGs-guided computationally more effective candidate drug molecules compared to other published drugs by cross-validation with the state-of-the-art alternatives of top-ranked independent receptor proteins. RESULTS We identified 50 common differentially expressed genes (cDEGs) from five gene expression profile datasets, where 31 cDEGs were downregulated, and the rest 19 were up-regulated. Then we identified 11 cDEGs (CXCL8, CEMIP, MMP7, CA4, ADH1C, GUCA2A, GUCA2B, ZG16, CLCA4, MS4A12 and CLDN1) as the KGs. Different pertinent bioinformatic analyses (box plot, survival probability curves, DNA methylation, correlation with immune infiltration levels, diseases-KGs interaction, GO and KEGG pathways) based on independent databases directly or indirectly showed that these KGs are significantly associated with CRC progression. We also detected four TFs proteins (FOXC1, YY1, GATA2 and NFKB) and eight microRNAs (hsa-mir-16-5p, hsa-mir-195-5p, hsa-mir-203a-3p, hsa-mir-34a-5p, hsa-mir-107, hsa-mir-27a-3p, hsa-mir-429, and hsa-mir-335-5p) as the key transcriptional and post-transcriptional regulators of KGs. Finally, our proposed 15 molecular signatures including 11 KGs and 4 key TFs-proteins guided 9 small molecules (Cyclosporin A, Manzamine A, Cardidigin, Staurosporine, Benzo[A]Pyrene, Sitosterol, Nocardiopsis Sp, Troglitazone, and Riccardin D) were recommended as the top-ranked candidate therapeutic agents for the treatment against CRC. CONCLUSION The findings of this study recommended that our proposed target proteins and agents might be considered as the potential diagnostic, prognostic and therapeutic signatures for CRC.
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Affiliation(s)
- Md Abu Horaira
- Bioinformatics Lab, Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh
| | - Md Ariful Islam
- Bioinformatics Lab, Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh
| | - Md Kaderi Kibria
- Bioinformatics Lab, Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh
| | - Md Jahangir Alam
- Bioinformatics Lab, Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh
| | - Syed Rashel Kabir
- Department of Biochemistry and Molecular Biology, University of Rajshahi, Rajshahi, 6205, Bangladesh
| | - Md Nurul Haque Mollah
- Bioinformatics Lab, Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh.
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Zhang Q, Sun C, Liu X, Zhu C, Ma C, Feng R. Mechanism of immune infiltration in synovial tissue of osteoarthritis: a gene expression-based study. J Orthop Surg Res 2023; 18:58. [PMID: 36681837 PMCID: PMC9862811 DOI: 10.1186/s13018-023-03541-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/06/2022] [Accepted: 01/13/2023] [Indexed: 01/23/2023] Open
Abstract
BACKGROUND Osteoarthritis is a chronic degenerative joint disease, and increasing evidences suggest that the pathogenic mechanism involves immune system and inflammation. AIMS The aim of current study was to uncover hub genes linked to immune infiltration in osteoarthritis synovial tissue using comprehensive bioinformatics analysis and experimental confirmation. METHODS Multiple microarray datasets (GSE55457, GSE55235, GSE12021 and GSE1919) for osteoarthritis in Gene Expression Omnibus database were downloaded for analysis. Differentially expressed genes (DEGs) were identified using Limma package in R software, and immune infiltration was evaluated by CIBERSORT algorithm. Then weighted gene co-expression network analysis (WGCNA) was performed to uncover immune infiltration-associated gene modules. Protein-protein interaction (PPI) network was constructed to select the hub genes, and the tissue distribution of these genes was analyzed using BioGPS database. Finally, the expression pattern of these genes was confirmed by RT-qPCR using clinical samples. RESULTS Totally 181 DEGs between osteoarthritis and normal control were screened. Macrophages, mast cells, memory CD4 T cells and B cells accounted for the majority of immune cell composition in synovial tissue. Osteoarthritis synovial showed high abundance of infiltrating resting mast cells, B cells memory and plasma cells. WGCNA screened 93 DEGs related to osteoarthritis immune infiltration. These genes were involved in TNF signaling pathway, IL-17 signaling pathway, response to steroid hormone, glucocorticoid and corticosteroid. Ten hub genes including MYC, JUN, DUSP1, NFKBIA, VEGFA, ATF3, IL-6, PTGS2, IL1B and SOCS3 were selected by using PPI network. Among them, four genes (MYC, JUN, DUSP1 and NFKBIA) specifically expressed in immune system were identified and clinical samples revealed consistent change of these four genes in synovial tissue retrieved from patients with osteoarthritis. CONCLUSION A 4-gene-based diagnostic model was developed, which had well predictive performance in osteoarthritis. MYC, JUN, DUSP1 and NFKBIA might be biomarkers and potential therapeutic targets in osteoarthritis.
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Affiliation(s)
- Qingyu Zhang
- grid.460018.b0000 0004 1769 9639Department of Orthopedics, Shandong Provincial Hospital Affiliated to Shandong First Medical University, No. 324, Road Jing Wu Wei Qi, Jinan, 250021 Shandong China
| | - Chao Sun
- grid.460018.b0000 0004 1769 9639Department of Orthopedics, Shandong Provincial Hospital Affiliated to Shandong First Medical University, No. 324, Road Jing Wu Wei Qi, Jinan, 250021 Shandong China
| | - Xuchang Liu
- grid.460018.b0000 0004 1769 9639Department of Orthopedics, Shandong Provincial Hospital Affiliated to Shandong First Medical University, No. 324, Road Jing Wu Wei Qi, Jinan, 250021 Shandong China
| | - Chao Zhu
- grid.460018.b0000 0004 1769 9639Department of Orthopedics, Shandong Provincial Hospital Affiliated to Shandong First Medical University, No. 324, Road Jing Wu Wei Qi, Jinan, 250021 Shandong China
| | - Chuncheng Ma
- grid.460018.b0000 0004 1769 9639Department of Orthopedics, Shandong Provincial Hospital Affiliated to Shandong First Medical University, No. 324, Road Jing Wu Wei Qi, Jinan, 250021 Shandong China
| | - Rongjie Feng
- grid.460018.b0000 0004 1769 9639Department of Orthopedics, Shandong Provincial Hospital Affiliated to Shandong First Medical University, No. 324, Road Jing Wu Wei Qi, Jinan, 250021 Shandong China
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