1
|
Kwiatkowski M, Krajewski A, Durślewicz J, Buchholz K, Grzanka D, Gagat M, Zabrzyński J, Klimaszewska-Wiśniewska A. Overexpression of cyclin F/CCNF as an independent prognostic factor for poor survival in clear cell renal cell carcinoma. Sci Rep 2024; 14:9280. [PMID: 38654021 PMCID: PMC11039610 DOI: 10.1038/s41598-024-59437-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 04/10/2024] [Indexed: 04/25/2024] Open
Abstract
Cyclin F (encoded by CCNF gene) has been reported to be implicated in the pathobiology of several human cancers. However, its potential clinical significance in clear cell renal cell carcinoma (ccRCC) remains unknown. The present study aimed to evaluate the potential significance of cyclin F, assessed by immunohistochemical (IHC) staining and molecular (bioinformatics) techniques, as a prognostic marker in ccRCC in relation to clinicopathological features and outcomes. IHC staining was performed using two independent ccRCC tissue array cohorts, herein called tissue macroarray (TMA)_1 and tissue microarray (TMA)_2, composed of 108 ccRCCs and 37 histologically normal tissues adjacent to the tumor (NAT) and 192 ccRCCs and 16 normal kidney samples, respectively. The mRNA expression data were obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) public datasets, followed by bioinformatics analysis of biological mechanisms underlying prognosis. The relationship between immune cell infiltration level and CCNF expression in ccRCC was investigated using the Tumor Immune Estimation Resource 2.0 (TIMER2) and Gene Expression Profiling Interactive Analysis 2 (GEPIA2). Cyclin F expression was significantly elevated in ccRCC lesions compared to both NAT and normal renal tissues. Likewise, CCNF mRNA was markedly increased in ccRCCs relative to non-cancerous tissues. In all analyzed cohorts, tumors with features of more aggressive behavior were more likely to display cyclin F/CCNF-high expression than low. Furthermore, patients with high cyclin F/CCNF expression had shorter overall survival (OS) times than those with low expression. In addition, multivariable analysis revealed that cyclin F/CCNF-high expression was an independent prognostic factor for poor OS in ccRCC. Enrichment analysis for mechanistically relevant processes showed that CCNF and its highly correlated genes initiate the signaling pathways that eventually result in uncontrolled cell proliferation. CCNF expression was also correlated with immune cell infiltration and caused poor outcomes depending on the abundance of tumor-infiltrating immune cells in ccRCC. Our findings suggest that cyclin F/CCNF expression is likely to have an essential role in ccRCC pathobiology through regulating multiple oncogenic signaling pathways and affecting the tumor immune microenvironment and may serve as prognostic biomarker and promising therapeutic target in ccRCC.
Collapse
Affiliation(s)
- Maciej Kwiatkowski
- Department of Urology and Urological Oncology, Multidisciplinary Hospital of Ludwik Blażek, Inowrocław, Poland
| | - Adrian Krajewski
- Department of Histology and Embryology, Faculty of Medicine, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, Bydgoszcz, Poland
| | - Justyna Durślewicz
- Department of Clinical Pathomorphology, Faculty of Medicine, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, Bydgoszcz, Poland
| | - Karolina Buchholz
- Department of Clinical Pathomorphology, Faculty of Medicine, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, Bydgoszcz, Poland
- Department of Histology and Embryology, Faculty of Medicine, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, Bydgoszcz, Poland
| | - Dariusz Grzanka
- Department of Clinical Pathomorphology, Faculty of Medicine, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, Bydgoszcz, Poland
| | - Maciej Gagat
- Department of Histology and Embryology, Faculty of Medicine, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, Bydgoszcz, Poland
- Faculty of Medicine, Collegium Medicum, Mazovian Academy, Płock, Poland
| | - Jan Zabrzyński
- Department of Orthopaedics and Traumatology, Faculty of Medicine, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, Bydgoszcz, Poland
| | - Anna Klimaszewska-Wiśniewska
- Department of Clinical Pathomorphology, Faculty of Medicine, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, Bydgoszcz, Poland.
| |
Collapse
|
2
|
Wang X, Lin Y, Li Z, Li Y, Chen M. Alternative Polyadenylation Regulatory Factors Signature for Survival Prediction in Kidney Renal Cell Carcinoma. Cancer Inform 2024; 23:11769351231180789. [PMID: 38617569 PMCID: PMC11015750 DOI: 10.1177/11769351231180789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 05/22/2023] [Indexed: 04/16/2024] Open
Abstract
Background Alternative polyadenylation (APA) plays a vital regulatory role in various diseases. It is widely accepted that APA is regulated by APA regulatory factors. Objective Whether APA regulatory factors affect the prognosis of renal cell carcinoma remains unclear, and this is the main topic of this study. Methods We downloaded the transcriptome and clinical data from The Cancer Genome Atlas (TCGA) database. We used the Lasso regression system to construct an APA model for analyzing the relationship between common APA regulatory factors and renal cell carcinoma. We also validated our APA model using independent GEO datasets (GSE29609, GSE76207). Results It was found that the expression levels of 5 APA regulatory factors (CPSF1, CPSF2, CSTF2, PABPC1, and PABPC4) were significantly associated with tumor gene mutation burden (TMB) score in renal clear cell carcinoma, and the risk score constructed using the expression level of 5 key APA regulatory factors could be used to predict the outcome of renal clear cell carcinoma. The TMB score is associated with the remodeling of the immune microenvironment. Conclusions By identifying key APA regulatory factors in renal cell carcinoma and constructing risk scores for key APA regulatory factors, we showed that key APA regulators affect prognosis of renal clear cell carcinoma patients. In addition, the risk score level is associated with TMB, indicating that APA may affect the efficacy of immunotherapy through immune microenvironment-related genes. This helps us better understand the mRNA processing mechanism of renal clear cell carcinoma.
Collapse
Affiliation(s)
- Xiaoyu Wang
- Biosafety Level-3 Laboratory, Life Sciences Institute & Guangxi Collaborative Innovation Center for Biomedicine, Guangxi Medical University, Nanning, Guangxi, China
| | - Yao Lin
- Biosafety Level-3 Laboratory, Life Sciences Institute & Guangxi Collaborative Innovation Center for Biomedicine, Guangxi Medical University, Nanning, Guangxi, China
| | - Zheng Li
- Biosafety Level-3 Laboratory, Life Sciences Institute & Guangxi Collaborative Innovation Center for Biomedicine, Guangxi Medical University, Nanning, Guangxi, China
| | - Yueqi Li
- Biosafety Level-3 Laboratory, Life Sciences Institute & Guangxi Collaborative Innovation Center for Biomedicine, Guangxi Medical University, Nanning, Guangxi, China
| | - Mingcong Chen
- Department of Orthopedics and Traumatology, Shenzhen University General Hospital, Shenzhen, China
| |
Collapse
|
3
|
Li H, Li C, Zhang Y, Jiang W, Zhang F, Tang X, Sun G, Xu S, Dong X, Shou J, Yang Y, Chen M. Comprehensive analysis of m 6 A methylome and transcriptome by Nanopore sequencing in clear cell renal carcinoma. Mol Carcinog 2024; 63:677-687. [PMID: 38362848 DOI: 10.1002/mc.23680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 12/01/2023] [Accepted: 01/02/2024] [Indexed: 02/17/2024]
Abstract
N6 -methyladenosine (m6 A) is the most prevalent epigenetic modification on eukaryotic messenger RNAs. Recent studies have focused on elucidating the key role of m6 A modification patterns in tumor progression. However, the relationship between m6 A and transcriptional regulation remains elusive. Nanopore technology enables the quantification of m6 A levels at each genomic site. In this study, a pair of tumor tissues and adjacent normal tissues from clear cell renal cell carcinoma (ccRCC) surgical samples were collected for Nanopore direct RNA sequencing. We identified 9644 genes displaying anomalous m6 A modifications, with 5343 genes upregulated and 4301 genes downregulated. Among these, 5224 genes were regarded as dysregulated genes, encompassing abnormal regulation of both m6 A modification and RNA expression. Gene Set Enrichment Analysis revealed an enrichment of these genes in pathways related to renal system progress and fatty acid metabolic progress. Furthermore, the χ2 test demonstrated a significant association between the levels of m6 A in dysregulated genes and their transcriptional expression levels. Additionally, we identified four obesity-associated genes (FTO, LEPR, ADIPOR2, and NPY5R) among the dysregulated genes. Further analyses using public databases revealed that these four genes were all related to the prognosis and diagnosis of ccRCC. This study introduced the novel approach of employing conjoint analysis of m6 A modification and RNA expression based on Nanopore sequencing to explore potential disease-related genes. Our work demonstrates the feasibility of the application of Nanopore sequencing technology in RNA epigenetic regulation research and identifies new potential therapeutic targets for ccRCC.
Collapse
Affiliation(s)
- Hexin Li
- Clinical Biobank, Beijing Hospital, National Center of Gerontology, National Health Commission, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Chang Li
- Clinical Biobank, Beijing Hospital, National Center of Gerontology, National Health Commission, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Yuxiang Zhang
- Cancer Data Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Weixing Jiang
- Cancer Data Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Fubo Zhang
- Cancer Data Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiaokun Tang
- Clinical Biobank, Beijing Hospital, National Center of Gerontology, National Health Commission, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Gaoyuan Sun
- Clinical Biobank, Beijing Hospital, National Center of Gerontology, National Health Commission, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Siyuan Xu
- Clinical Biobank, Beijing Hospital, National Center of Gerontology, National Health Commission, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Xin Dong
- Cancer Data Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jianzhong Shou
- Cancer Data Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yong Yang
- Department of Oncology, Huai'an TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Jiangsu, China
| | - Meng Chen
- Cancer Data Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| |
Collapse
|
4
|
Niu T, Zhou F. Inflammation and tumor microenvironment. ZHONG NAN DA XUE XUE BAO. YI XUE BAN = JOURNAL OF CENTRAL SOUTH UNIVERSITY. MEDICAL SCIENCES 2023; 48:1899-1913. [PMID: 38448384 PMCID: PMC10930746 DOI: 10.11817/j.issn.1672-7347.2023.230231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Indexed: 03/08/2024]
Abstract
There is a connection between inflammation and cancer. Inflammation is one of the hallmarks of cancer, affecting tumor progression, transition to a malignant phenotype, and the efficacy of tumor chemotherapy. The tumor microenvironment impacts the biological characteristics of tumors through various specific factors and signaling mechanisms. The interaction between inflammation and the tumor microenvironment involves inflammation affecting the tumor microenvironment by inducing immune suppression, while acute inflammation promotes tumor suppression by producing anti-tumor immune responses. This review elaborates on how inflammation affects the tumor microenvironment and thus affects the progression and treatment of tumors, starting from the components of the tumor microenvironment, inflammasomes, cytokines, non-coding RNAs, and other aspects. Inflammatory factors play an important role in regulating inflammatory responses and immune reactions, and they also affect the development of tumors through various pathways in the tumor microenvironment. In addition, non-coding RNAs play an important role in the tumor microenvironment, regulating tumors and inflammation. They are involved in regulating the occurrence, development of tumors, the process of inflammation, as well as regulating inflammation-induced cancer or tumor-related inflammation, and the interaction between the tumor microenvironment, inflammatory factors, and immune cells. Therefore, gaining a deeper understanding of the interaction between inflammation and the tumor microenvironment and its connection to the occurrence and development of cancer can provide a theoretical basis for combating tumors and finding new therapeutic strategies.
Collapse
Affiliation(s)
- Tao Niu
- First Clinical Medical College, Gansu University of Chinese Medicine, Lanzhou 730000.
| | - Fenghai Zhou
- Department of Cadre Urology, Gansu Provincial People's Hospital, Lanzhou 730000, China.
| |
Collapse
|
5
|
Wu Y, Chen S, Yang X, Sato K, Lal P, Wang Y, Shinkle AT, Wendl MC, Primeau TM, Zhao Y, Gould A, Sun H, Mudd JL, Hoog J, Mashl RJ, Wyczalkowski MA, Mo CK, Liu R, Herndon JM, Davies SR, Liu D, Ding X, Evrard YA, Welm BE, Lum D, Koh MY, Welm AL, Chuang JH, Moscow JA, Meric-Bernstam F, Govindan R, Li S, Hsieh J, Fields RC, Lim KH, Ma CX, Zhang H, Ding L, Chen F. Combining the Tyrosine Kinase Inhibitor Cabozantinib and the mTORC1/2 Inhibitor Sapanisertib Blocks ERK Pathway Activity and Suppresses Tumor Growth in Renal Cell Carcinoma. Cancer Res 2023; 83:4161-4178. [PMID: 38098449 PMCID: PMC10722140 DOI: 10.1158/0008-5472.can-23-0604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 07/17/2023] [Accepted: 09/25/2023] [Indexed: 12/18/2023]
Abstract
Current treatment approaches for renal cell carcinoma (RCC) face challenges in achieving durable tumor responses due to tumor heterogeneity and drug resistance. Combination therapies that leverage tumor molecular profiles could offer an avenue for enhancing treatment efficacy and addressing the limitations of current therapies. To identify effective strategies for treating RCC, we selected ten drugs guided by tumor biology to test in six RCC patient-derived xenograft (PDX) models. The multitargeted tyrosine kinase inhibitor (TKI) cabozantinib and mTORC1/2 inhibitor sapanisertib emerged as the most effective drugs, particularly when combined. The combination demonstrated favorable tolerability and inhibited tumor growth or induced tumor regression in all models, including two from patients who experienced treatment failure with FDA-approved TKI and immunotherapy combinations. In cabozantinib-treated samples, imaging analysis revealed a significant reduction in vascular density, and single-nucleus RNA sequencing (snRNA-seq) analysis indicated a decreased proportion of endothelial cells in the tumors. SnRNA-seq data further identified a tumor subpopulation enriched with cell-cycle activity that exhibited heightened sensitivity to the cabozantinib and sapanisertib combination. Conversely, activation of the epithelial-mesenchymal transition pathway, detected at the protein level, was associated with drug resistance in residual tumors following combination treatment. The combination effectively restrained ERK phosphorylation and reduced expression of ERK downstream transcription factors and their target genes implicated in cell-cycle control and apoptosis. This study highlights the potential of the cabozantinib plus sapanisertib combination as a promising treatment approach for patients with RCC, particularly those whose tumors progressed on immune checkpoint inhibitors and other TKIs. SIGNIFICANCE The molecular-guided therapeutic strategy of combining cabozantinib and sapanisertib restrains ERK activity to effectively suppress growth of renal cell carcinomas, including those unresponsive to immune checkpoint inhibitors.
Collapse
Affiliation(s)
- Yige Wu
- Department of Medicine, Washington University in St. Louis, St. Louis, Missouri
- McDonnell Genome Institute, Washington University in St. Louis, St. Louis, Missouri
| | - Siqi Chen
- Department of Medicine, Washington University in St. Louis, St. Louis, Missouri
- McDonnell Genome Institute, Washington University in St. Louis, St. Louis, Missouri
| | - Xiaolu Yang
- Department of Medicine, Washington University in St. Louis, St. Louis, Missouri
| | - Kazuhito Sato
- Department of Medicine, Washington University in St. Louis, St. Louis, Missouri
- McDonnell Genome Institute, Washington University in St. Louis, St. Louis, Missouri
| | - Preet Lal
- Department of Medicine, Washington University in St. Louis, St. Louis, Missouri
| | - Yuefan Wang
- Department of Pathology, Johns Hopkins University, Baltimore, Maryland
| | - Andrew T. Shinkle
- Department of Medicine, Washington University in St. Louis, St. Louis, Missouri
| | - Michael C. Wendl
- Department of Medicine, Washington University in St. Louis, St. Louis, Missouri
- McDonnell Genome Institute, Washington University in St. Louis, St. Louis, Missouri
- Department of Genetics, Washington University in St. Louis, St. Louis, Missouri
- McKelvey School of Engineering, Washington University in St. Louis, St. Louis, Missouri
| | - Tina M. Primeau
- Department of Medicine, Washington University in St. Louis, St. Louis, Missouri
| | - Yanyan Zhao
- Department of Medicine, Washington University in St. Louis, St. Louis, Missouri
| | - Alanna Gould
- Department of Medicine, Washington University in St. Louis, St. Louis, Missouri
| | - Hua Sun
- Department of Medicine, Washington University in St. Louis, St. Louis, Missouri
- McDonnell Genome Institute, Washington University in St. Louis, St. Louis, Missouri
| | - Jacqueline L. Mudd
- Department of Medicine, Washington University in St. Louis, St. Louis, Missouri
| | - Jeremy Hoog
- Department of Medicine, Washington University in St. Louis, St. Louis, Missouri
| | - R. Jay Mashl
- Department of Medicine, Washington University in St. Louis, St. Louis, Missouri
- McDonnell Genome Institute, Washington University in St. Louis, St. Louis, Missouri
| | - Matthew A. Wyczalkowski
- Department of Medicine, Washington University in St. Louis, St. Louis, Missouri
- McDonnell Genome Institute, Washington University in St. Louis, St. Louis, Missouri
| | - Chia-Kuei Mo
- Department of Medicine, Washington University in St. Louis, St. Louis, Missouri
- McDonnell Genome Institute, Washington University in St. Louis, St. Louis, Missouri
| | - Ruiyang Liu
- Department of Medicine, Washington University in St. Louis, St. Louis, Missouri
- McDonnell Genome Institute, Washington University in St. Louis, St. Louis, Missouri
| | - John M. Herndon
- Siteman Cancer Center, Washington University in St. Louis, St. Louis, Missouri
- Department of Surgery, Washington University in St. Louis, St. Louis, Missouri
| | - Sherri R. Davies
- Department of Medicine, Washington University in St. Louis, St. Louis, Missouri
| | - Di Liu
- Department of Medicine, Washington University in St. Louis, St. Louis, Missouri
| | - Xi Ding
- Department of Medicine, Washington University in St. Louis, St. Louis, Missouri
| | - Yvonne A. Evrard
- Frederick National Laboratory for Cancer Research, Frederick, Maryland
| | - Bryan E. Welm
- Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah
| | - David Lum
- Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah
| | - Mei Yee Koh
- Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah
| | - Alana L. Welm
- Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah
| | - Jeffrey H. Chuang
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut
| | - Jeffrey A. Moscow
- Investigational Drug Branch, National Cancer Institute, Bethesda, Maryland
| | | | - Ramaswamy Govindan
- Department of Medicine, Washington University in St. Louis, St. Louis, Missouri
- Department of Genetics, Washington University in St. Louis, St. Louis, Missouri
| | - Shunqiang Li
- Department of Medicine, Washington University in St. Louis, St. Louis, Missouri
- Department of Genetics, Washington University in St. Louis, St. Louis, Missouri
| | - James Hsieh
- Department of Medicine, Washington University in St. Louis, St. Louis, Missouri
| | - Ryan C. Fields
- Department of Genetics, Washington University in St. Louis, St. Louis, Missouri
| | - Kian-Huat Lim
- Department of Medicine, Washington University in St. Louis, St. Louis, Missouri
- Department of Genetics, Washington University in St. Louis, St. Louis, Missouri
| | - Cynthia X. Ma
- Department of Medicine, Washington University in St. Louis, St. Louis, Missouri
- Department of Genetics, Washington University in St. Louis, St. Louis, Missouri
| | - Hui Zhang
- Department of Pathology, Johns Hopkins University, Baltimore, Maryland
| | - Li Ding
- Department of Medicine, Washington University in St. Louis, St. Louis, Missouri
- McDonnell Genome Institute, Washington University in St. Louis, St. Louis, Missouri
- Department of Genetics, Washington University in St. Louis, St. Louis, Missouri
- Siteman Cancer Center, Washington University in St. Louis, St. Louis, Missouri
| | - Feng Chen
- Department of Medicine, Washington University in St. Louis, St. Louis, Missouri
- Department of Genetics, Washington University in St. Louis, St. Louis, Missouri
| |
Collapse
|
6
|
Ahmadi M, Najari-Hanjani P, Ghaffarnia R, Ghaderian SMH, Mousavi P, Ghafouri-Fard S. The hsa-miR-3613-5p, a potential oncogene correlated with diagnostic and prognostic merits in kidney renal clear cell carcinoma. Pathol Res Pract 2023; 251:154903. [PMID: 37879147 DOI: 10.1016/j.prp.2023.154903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Revised: 10/14/2023] [Accepted: 10/20/2023] [Indexed: 10/27/2023]
Abstract
MicroRNA-3613 (hsa-miR-3613-5p), a biomarker with a dual role as an oncogenic or tumor suppressor, is associated with different types of cancer. This study aimed to determine the correlation between the hsa-miR-3613-5p gene expression and Kidney renal clear cell carcinoma (KIRC). Utilizing several bioinformatics tools, we examined the expression level and clinicopathological value of hsa-miR-3613-5p in patients with KIRC compared to normal tissues. Other bioinformatic measures, including survival analysis, diagnostic merit of hsa-miR-3613-5p, downstream target prediction, potential upstream lncRNAs, network construction, and functional enrichment analysis of hsa-miR-3613-5p, were performed. We observed that overexpression of hsa-miR-3613-5p in KIRC tissues had valuable diagnostic merit and was significantly correlated with the poor overall survival of KIRC patients. We also realized a correlation between abnormal expression of hsa-miR-3613-5p and several clinical parameters such as pathological stage, race, age, and histological grades in patients with KIRC. Moreover, we constructed the most potential regulatory network of hsa-miR-3613-5p in KIRC with 17 different axes, including four pseudogenes, two lncRNAs, and three mRNAs. Besides, we uncovered six variants in the mature form of hsa-miR-3613-5p. Finally, pathway enrichment analysis demonstrated that the top-ranked pathways for hsa-miR-3613-5p are cell cycle, cell adhesion molecules (CAMs), and hepatocellular carcinoma pathways. The present report suggests that the higher expression of hsa-miR-3613-5p is associated with the progression of KIRC. Therefore, it may be considered a valuable indicator for the early detection, risk stratification, and targeted treatment of patients with KIRC.
Collapse
Affiliation(s)
- Mohsen Ahmadi
- Department of Medical Genetics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Parisa Najari-Hanjani
- Department of Genetics, Faculty of Advanced Technologies in Medicine, Golestan University of Medical Science, Gorgan, Iran
| | - Roya Ghaffarnia
- Department of Medical Genetics, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Pegah Mousavi
- Molecular Medicine Research Center, Hormozgan Health Institute, Hormozgan University of Medical Sciences, Bandar Abbas, Iran.
| | - Soudeh Ghafouri-Fard
- Department of Medical Genetics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| |
Collapse
|
7
|
Gou H, Chen P, Wu W. FAM72 family proteins as poor prognostic markers in clear cell renal carcinoma. Biochem Biophys Rep 2023; 35:101506. [PMID: 37457361 PMCID: PMC10344709 DOI: 10.1016/j.bbrep.2023.101506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Revised: 06/08/2023] [Accepted: 06/24/2023] [Indexed: 07/18/2023] Open
Abstract
Purpose This study aimed to investigate the prognostic significance of the Family with Sequence Similarity 72 member (FAM72) gene family in clear cell renal carcinoma (ccRCC) using a bioinformatic approach. Patients and methods To investigate the association between FAM72 and ccRCC, we utilized various databases and analysis tools, including TCGA, GEPIA, Metscape, cBioPortal, and MethSurv. We conducted an analysis of FAM72 expression levels in ccRCC tissues compared to normal kidney tissues and performed univariate and multivariate Cox analysis to determine the relationship between FAM72 expression and patient prognosis. Furthermore, we carried out Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA) to identify enriched biological processes associated with FAM72 expression. Additionally, we analyzed immune cell infiltration and the level of methylation in ccRCC patients. Our bioinformatic analysis revealed that FAM72 expression levels were significantly higher in ccRCC tissues than in normal kidney tissues. High expression of FAM72 was associated with poor prognosis in ccRCC patients and was found to be an independent prognostic factor for ccRCC. GO and GSEA analyses indicated that FAM72 was enriched in biological processes related to mitosis, cell cycle, and DNA metabolism. Moreover, we found a significant correlation between FAM72 and immune cell infiltration and the level of methylation in ccRCC patients. Conclusion Our findings suggest that FAM72 could serve as an unfavorable prognostic molecular marker for ccRCC. A comprehensive understanding of FAM72 could provide crucial insights into tumor progression and prognosis.
Collapse
Affiliation(s)
- Hui Gou
- Department of Pharmacy, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, China
| | - Ping Chen
- Department of Pharmacy, Suining Central Hospital, Suining, 629000, China
| | - Wenbing Wu
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southwest Medical University, Luzhou, 646000, China
| |
Collapse
|
8
|
Mathur Y, Shafie A, Alharbi B, Ashour AA, Al-Soud WA, Alhassan HH, Alharethi SH, Anjum F. Genome-Wide Analysis of Kidney Renal Cell Carcinoma: Exploring Differentially Expressed Genes for Diagnostic and Therapeutic Targets. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2023; 27:393-401. [PMID: 37624678 DOI: 10.1089/omi.2023.0056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/27/2023]
Abstract
Kidney renal cell carcinoma (KIRC) is the most common type of renal cancer. Kidney malignancies have been ranked in the top 10 most frequently occurring cancers. KIRC is a prevalent malignancy with a poor prognosis. The disease has risen for the last 40 years, and robust biomarkers for KIRC are needed for precision/personalized medicine. In this bioinformatics study, we utilized genomic data of KIRC patients from The Cancer Genome Atlas for biomarker discovery. A total of 314 samples were used in this study. We identified many differentially expressed genes (DEGs) categorized as upregulated or downregulated. A protein-protein interaction network for the DEGs was then generated and analyzed using the Search Tool for the Retrieval of Interacting Genes plugin of Cytoscape. A set of 10 hub genes was selected based on the Maximum Clique Centrality score defined by the CytoHubba plugin. The elucidated set of genes, that is, CALCA, CRH, TH, CHAT, SLC18A3, FSHB, MYH6, CAV3, KCNA4, and GBX2, were then categorized as potential candidates to be explored as KIRC biomarkers. The survival analysis plots for each gene suggested that alterations in CHAT, CAV3, CRH, MYH6, SLC18A3, and FSHB resulted in decreased survival of KIRC patients. In all, the results suggest that genomic alterations in selected genes can be explored to inform biomarker discovery and for therapeutic predictions in KIRC.
Collapse
Affiliation(s)
- Yash Mathur
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, Jamia Nagar, New Delhi, India
| | - Alaa Shafie
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Taif University, Taif, Saudi Arabia
| | - Bandar Alharbi
- Department of Medical Laboratory Science, College of Applied Medical Sciences, University of Ha'il, Hail, Saudi Arabia
| | - Amal Adnan Ashour
- Department of Oral and Maxillofacial Surgery and Diagnostic Sciences, Faculty of Dentistry, Taif University, Taif, Saudi Arabia
| | - Waleed Abu Al-Soud
- Department of Clinical Laboratory Science, College of Applied Sciences, Jouf University, Sakaka, Saudi Arabia
| | - Hassan H Alhassan
- Department of Clinical Laboratory Science, College of Applied Sciences, Jouf University, Sakaka, Saudi Arabia
| | - Salem Hussain Alharethi
- Department of Biological Science, College of Arts and Science, Najran University, Najran, Saudi Arabia
| | - Farah Anjum
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Taif University, Taif, Saudi Arabia
| |
Collapse
|
9
|
Fu Y, Jia X, Yuan J, Yang Y, Zhang T, Yu Q, Zhou J, Wang T. Fam72a functions as a cell-cycle-controlled gene during proliferation and antagonizes apoptosis through reprogramming PP2A substrates. Dev Cell 2023; 58:398-415.e7. [PMID: 36868233 DOI: 10.1016/j.devcel.2023.02.006] [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: 04/20/2022] [Revised: 06/28/2022] [Accepted: 02/09/2023] [Indexed: 03/05/2023]
Abstract
The cell cycle is key to life. After decades of research, it is unclear whether any parts of this process have yet to be identified. Fam72a is a poorly characterized gene and is evolutionarily conserved across multicellular organisms. Here, we have found that Fam72a is a cell-cycle-regulated gene that is transcriptionally and post-transcriptionally regulated by FoxM1 and APC/C, respectively. Functionally, Fam72a directly binds to tubulin and both the Aα and B56 subunits of PP2A-B56 to modulate tubulin and Mcl1 phosphorylation, which in turn affects the progression of the cell cycle and signaling of apoptosis. Moreover, Fam72a is involved in early responses to chemotherapy, and it efficiently antagonizes various anticancer compounds such as CDK and Bcl2 inhibitors. Thus, Fam72a switches the tumor-suppressive PP2A to be oncogenic by reprogramming its substrates. These findings identify a regulatory axis of PP2A and a protein member in the cell cycle and tumorigenesis regulatory network in human cells.
Collapse
Affiliation(s)
- Yuan Fu
- Department of Pharmacology and Tianjin Key Laboratory of Inflammation Biology, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China; Department of Thoracic Oncology, Tianjin Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Lung Cancer Center, Tianjin Medical University, Tianjin 300070, China.
| | - Xiaofan Jia
- Department of Pharmacology and Tianjin Key Laboratory of Inflammation Biology, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Jinwei Yuan
- Department of Pharmacology and Tianjin Key Laboratory of Inflammation Biology, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Yuting Yang
- Department of Pharmacology and Tianjin Key Laboratory of Inflammation Biology, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Teng Zhang
- Department of Pharmacology and Tianjin Key Laboratory of Inflammation Biology, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Qiujing Yu
- Department of Immunology and Key Laboratory of Immune Microenvironment and Disease, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Jun Zhou
- Department of Genetics and Cell Biology, State Key Laboratory of Medicinal Chemical Biology, Haihe Laboratory of Cell Ecosystem, College of Life Sciences, Nankai University, Tianjin 300071, China
| | - Ting Wang
- Department of Pharmacology and Tianjin Key Laboratory of Inflammation Biology, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China; Department of Thoracic Oncology, Tianjin Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Lung Cancer Center, Tianjin Medical University, Tianjin 300070, China.
| |
Collapse
|
10
|
Weng Y, Ning P. Construction of a prognostic prediction model for renal clear cell carcinoma combining clinical traits. Sci Rep 2023; 13:3358. [PMID: 36849551 PMCID: PMC9970964 DOI: 10.1038/s41598-023-30020-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 02/14/2023] [Indexed: 03/01/2023] Open
Abstract
Kidney renal clear cell carcinoma (KIRC) is one of the common malignant tumors of the urinary system. Patients with different risk levels are other in terms of disease progression patterns and disease regression. The poorer prognosis for high-risk patients compared to low-risk patients. Therefore, it is essential to accurately high-risk screen patients and gives accurate and timely treatment. Differential gene analysis, weighted correlation network analysis, Protein-protein interaction network, and univariate Cox analysis were performed sequentially on the train set. Next, the KIRC prognostic model was constructed using the least absolute shrinkage and selection operator (LASSO), and the Cancer Genome Atlas (TCGA) test set and the Gene Expression Omnibus dataset verified the model's validity. Finally, the constructed models were analyzed; including gene set enrichment analysis (GSEA) and immune analysis. The differences in pathways and immune functions between the high-risk and low-risk groups were observed to provide a reference for clinical treatment and diagnosis. A four-step key gene screen resulted in 17 key factors associated with disease prognosis, including 14 genes and 3 clinical features. The LASSO regression algorithm selected the seven most critical key factors to construct the model: age, grade, stage, GDF3, CASR, CLDN10, and COL9A2. In the training set, the accuracy of the model in predicting 1-, 2- and 3-year survival rates was 0.883, 0.819, and 0.830, respectively. The accuracy of the TCGA dataset was 0.831, 0.801, and 0.791, and the accuracy of the GSE29609 dataset was 0.812, 0.809, and 0.851 in the test set. Model scoring divided the sample into a high-risk group and a low-risk group. There were significant differences in disease progression and risk scores between the two groups. GSEA analysis revealed that the enriched pathways in the high-risk group mainly included proteasome and primary immunodeficiency. Immunological analysis showed that CD8 (+) T cells, M1 macrophages, PDCD1, and CTLA4 were upregulated in the high-risk group. In contrast, antigen-presenting cell stimulation and T-cell co-suppression were more active in the high-risk group. This study added clinical characteristics to constructing the KIRC prognostic model to improve prediction accuracy. It provides help to assess the risk of patients more accurately. The differences in pathways and immunity between high and low-risk groups were also analyzed to provide ideas for treating KIRC patients.
Collapse
Affiliation(s)
- Yujie Weng
- grid.410612.00000 0004 0604 6392College of Computer and Information, Inner Mongolia Medical University, Hohhot, 010110 Inner Mongolia Autonomous Region China
| | - Pengfei Ning
- College of Computer and Information, Inner Mongolia Medical University, Hohhot, 010110, Inner Mongolia Autonomous Region, China.
| |
Collapse
|
11
|
Liu L, Zhuang M, Tu XH, Li CC, Liu HH, Wang J. Bioinformatics analysis of markers based on m 6 A related to prognosis combined with immune invasion of renal clear cell carcinoma. Cell Biol Int 2023; 47:260-272. [PMID: 36200528 DOI: 10.1002/cbin.11929] [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: 02/24/2022] [Revised: 09/13/2022] [Accepted: 09/23/2022] [Indexed: 01/19/2023]
Abstract
The incidence rate of renal cell carcinoma (RCC) is about 3% of all adult cancers. Of these, the Kidney clear cell renal cell carcinoma (KIRC) is the most common type, accounting for about 70%-75% of RCC. KIRC is difficult to be detected in time clinically. KIRC still has no effective treatment at this stage. We combined high-throughput bioinformatics analysis to obtained the structural sequence transcriptome data, relevant clinical information, and m6 A gene map of KIRC patients from genomics TCGA database. Pearson's correlation analysis was used to explore m6 A related gene long noncoding RNAs (lncRNAs), and then univariate Cox regression analysis was performed to screen the prognostic role of KIRC patients. Lasso-Cox regression was performed to establish the lncRNAs risk model associated with m6 A.LINC02154 and AC016773.2, Z98200.2, AL161782.1, EMX2OS, AC021483.2, CD27-AS1, AC006213.3 were iidentif. Compared with the low-risk group, the overall survival of patients in the high-risk group was significantly worse. Analyzing whether there are differences in immune cells between high-risk and low-risk subgroups. There were CD4 memory resting, Monocytes, Macrophages M1, Dendritic cells activated, Mast cells resting, which had higher infiltrations in the low-risk group. We performed Go enrichment analysis, Kyoto Encyclopedia of Genes and Genomes enrichment analysis and gene set enrichment analysis enrichment analysis. Overall, our results suggest that the component of m6A-related lncRNAs in the prognostic signal may be a key mediator in the immune microenvironment of KIRC, which represents a promising therapeutic effect.
Collapse
Affiliation(s)
- Lian Liu
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China
| | - Meng Zhuang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China
| | - Xin-Hua Tu
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China
| | - Cheng-Cheng Li
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China
| | - Hong-Hui Liu
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China
| | - Jing Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China.,Medical Data Processing Center of School of Public Health of Anhui Medical University, Anhui Medical University, Hefei, China
| |
Collapse
|
12
|
Mo L, Su Y, Yuan J, Xiao Z, Zhang Z, Lan X, Huang D. Comparisons of Forecasting for Survival Outcome for Head and Neck Squamous Cell Carcinoma by using Machine Learning Models based on Multi-omics. Curr Genomics 2022; 23:94-108. [PMID: 36778975 PMCID: PMC9878835 DOI: 10.2174/1389202923666220204153744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 01/13/2022] [Accepted: 01/19/2022] [Indexed: 11/22/2022] Open
Abstract
Background: Machine learning methods showed excellent predictive ability in a wide range of fields. For the survival of head and neck squamous cell carcinoma (HNSC), its multi-omics influence is crucial. This study attempts to establish a variety of machine learning multi-omics models to predict the survival of HNSC and find the most suitable machine learning prediction method. Methods: The HNSC clinical data and multi-omics data were downloaded from the TCGA database. The important variables were screened by the LASSO algorithm. We used a total of 12 supervised machine learning models to predict the outcome of HNSC survival and compared the results. In vitro qPCR was performed to verify core genes predicted by the random forest algorithm. Results: For omics of HNSC, the results of the twelve models showed that the performance of multi-omics was better than each single-omic alone. Results were presented, which showed that the Bayesian network(BN) model (area under the curve [AUC] 0.8250, F1 score=0.7917) and random forest(RF) model (area under the curve [AUC] 0.8002,F1 score=0.7839) played good prediction performance in HNSC multi-omics data. The results of in vitro qPCR were consistent with the RF algorithm. Conclusion: Machine learning methods could better forecast the survival outcome of HNSC. Meanwhile, this study found that the BN model and the RF model were the most superior. Moreover, the forecast result of multi-omics was better than single-omic alone in HNSC.
Collapse
Affiliation(s)
- Liying Mo
- School of Basic Medical Sciences, Guangxi Medical University, Nanning, Guangxi, China;,These authors contributed equally to this work
| | - Yuangang Su
- School of Basic Medical Sciences, Guangxi Medical University, Nanning, Guangxi, China;,Research Centre for Regenerative Medicine, Guangxi Key Laboratory of Regenerative Medicine, Guangxi Medical University, Nanning, Guangxi, China;,These authors contributed equally to this work
| | - Jianhui Yuan
- School of Basic Medical Sciences, Guangxi Medical University, Nanning, Guangxi, China;,The Laboratory of Biomedical Photonics and Engineering, Guangxi Medical University, Nanning, China
| | - Zhiwei Xiao
- School of Information and Management, Guangxi Medical University, Nanning, Guangxi, China
| | - Ziyan Zhang
- Life Sciences Institute, Guangxi Medical University, Nanning, Guangxi, China
| | - Xiuwan Lan
- School of Basic Medical Sciences, Guangxi Medical University, Nanning, Guangxi, China;,These authors contributed equally to this work
| | - Daizheng Huang
- School of Basic Medical Sciences, Guangxi Medical University, Nanning, Guangxi, China;,The Laboratory of Biomedical Photonics and Engineering, Guangxi Medical University, Nanning, China;,Address correspondence to this author at the School of Basic Medical Sciences, Guangxi Medical University, Nanning, Guangxi, China; The Laboratory of Biomedical Photonics and Engineering, Guangxi Medical University, Nanning, China; Tel: +867715358270; E-mail:
| |
Collapse
|
13
|
Yao H, Jiang X, Fu H, Yang Y, Jin Q, Zhang W, Cao W, Gao W, Wang S, Zhu Y, Ying J, Tian L, Chen G, Tong Z, Qi J, Zhou S. Exploration of the Immune-Related Long Noncoding RNA Prognostic Signature and Inflammatory Microenvironment for Cervical Cancer. Front Pharmacol 2022; 13:870221. [PMID: 35662687 PMCID: PMC9161697 DOI: 10.3389/fphar.2022.870221] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2022] [Accepted: 03/22/2022] [Indexed: 12/18/2022] Open
Abstract
Purpose: Our research developed immune-related long noncoding RNAs (lncRNAs) for risk stratification in cervical cancer (CC) and explored factors of prognosis, inflammatory microenvironment infiltrates, and chemotherapeutic therapies. Methods: The RNA-seq data and clinical information of CC were collected from the TCGA TARGET GTEx database and the TCGA database. lncRNAs and immune-related signatures were obtained from the GENCODE database and the ImPort database, respectively. We screened out immune-related lncRNA signatures through univariate Cox, LASSO, and multivariate Cox regression methods. We established an immune-related risk model of hub immune-related lncRNAs to evaluate whether the risk score was an independent prognostic predictor. The xCell and CIBERSORTx algorithms were employed to appraise the value of risk scores which are in competition with tumor-infiltrating immune cell abundances. The estimation of tumor immunotherapy response through the TIDE algorithm and prediction of innovative recommended medications on the target to immune-related risk model were also performed on the basis of the IC50 predictor. Results: We successfully established six immune-related lncRNAs (AC006126.4, EGFR-AS1, RP4-647J21.1, LINC00925, EMX2OS, and BZRAP1-AS1) to carry out prognostic prediction of CC. The immune-related risk model was constructed in which we observed that high-risk groups were strongly linked with poor survival outcomes. Risk scores varied with clinicopathological parameters and the tumor stage and were an independent hazard factor that affect prognosis of CC. The xCell algorithm revealed that hub immune-related signatures were relevant to immune cells, especially mast cells, DCs, megakaryocytes, memory B cells, NK cells, and Th1 cells. The CIBERSORTx algorithm revealed an inflammatory microenvironment where naive B cells (p < 0.01), activated dendritic cells (p < 0.05), activated mast cells (p < 0.0001), CD8+ T cells (p < 0.001), and regulatory T cells (p < 0.01) were significantly lower in the high-risk group, while macrophages M0 (p < 0.001), macrophages M2 (p < 0.05), resting mast cells (p < 0.0001), and neutrophils (p < 0.01) were highly conferred. The result of TIDE indicated that the number of immunotherapy responders in the low-risk group (124/137) increased significantly (p = 0.00000022) compared to the high-risk group (94/137), suggesting that the immunotherapy response of CC patients was completely negatively correlated with the risk scores. Last, we compared differential IC50 predictive values in high- and low-risk groups, and 12 compounds were identified as future treatments for CC patients. Conclusion: In this study, six immune-related lncRNAs were suggested to predict the outcome of CC, which is beneficial to the formulation of immunotherapy.
Collapse
Affiliation(s)
- Hui Yao
- Department of Gynecology, Anhui Medical University Affiliated Maternity and Child Healthcare Hospital, Hefei, China.,Department of Gynecology, Anhui Province Maternity and Child Healthcare Hospital, Hefei, China
| | - Xiya Jiang
- Department of Gynecology, Anhui Medical University Affiliated Maternity and Child Healthcare Hospital, Hefei, China.,Department of Gynecology, Anhui Province Maternity and Child Healthcare Hospital, Hefei, China
| | - Hengtao Fu
- Department of Pharmacy, North China University of Science and Technology, Tangshan, China
| | - Yinting Yang
- Department of Gynecology, Anhui Medical University Affiliated Maternity and Child Healthcare Hospital, Hefei, China.,Department of Gynecology, Anhui Province Maternity and Child Healthcare Hospital, Hefei, China
| | - Qinqin Jin
- Department of Gynecology, Anhui Medical University Affiliated Maternity and Child Healthcare Hospital, Hefei, China.,Department of Gynecology, Anhui Province Maternity and Child Healthcare Hospital, Hefei, China
| | - Weiyu Zhang
- Department of Gynecology, Anhui Medical University Affiliated Maternity and Child Healthcare Hospital, Hefei, China.,Department of Gynecology, Anhui Province Maternity and Child Healthcare Hospital, Hefei, China
| | - Wujun Cao
- Department of Clinical Laboratory, Anhui Medical University Affiliated Maternity and Child Healthcare Hospital, Anhui Province Maternity and Child Healthcare Hospital, Hefei, China
| | - Wei Gao
- Department of Gynecology, Anhui Medical University Affiliated Maternity and Child Healthcare Hospital, Hefei, China.,Department of Gynecology, Anhui Province Maternity and Child Healthcare Hospital, Hefei, China
| | - Senlin Wang
- Department of Clinical Laboratory, Anhui Medical University Affiliated Maternity and Child Healthcare Hospital, Anhui Province Maternity and Child Healthcare Hospital, Hefei, China
| | - Yuting Zhu
- Department of Gynecology, Anhui Medical University Affiliated Maternity and Child Healthcare Hospital, Hefei, China.,Department of Gynecology, Anhui Province Maternity and Child Healthcare Hospital, Hefei, China
| | - Jie Ying
- Department of Gynecology, Anhui Medical University Affiliated Maternity and Child Healthcare Hospital, Hefei, China.,Department of Gynecology, Anhui Province Maternity and Child Healthcare Hospital, Hefei, China
| | - Lu Tian
- Department of Gynecology, Anhui Medical University Affiliated Maternity and Child Healthcare Hospital, Hefei, China.,Department of Gynecology, Anhui Province Maternity and Child Healthcare Hospital, Hefei, China
| | - Guo Chen
- Department of Gynecology, Anhui Medical University Affiliated Maternity and Child Healthcare Hospital, Hefei, China.,Department of Gynecology, Anhui Province Maternity and Child Healthcare Hospital, Hefei, China
| | - Zhuting Tong
- Department of Radiation Oncology, the First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Jian Qi
- Anhui Province Key Laboratory of Medical Physics and Technology, Center of Medical Physics and Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China
| | - Shuguang Zhou
- Department of Gynecology, Anhui Medical University Affiliated Maternity and Child Healthcare Hospital, Hefei, China.,Department of Gynecology, Anhui Province Maternity and Child Healthcare Hospital, Hefei, China
| |
Collapse
|
14
|
A Novel Machine Learning 13-Gene Signature: Improving Risk Analysis and Survival Prediction for Clear Cell Renal Cell Carcinoma Patients. Cancers (Basel) 2022; 14:cancers14092111. [PMID: 35565241 PMCID: PMC9103317 DOI: 10.3390/cancers14092111] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 04/11/2022] [Accepted: 04/12/2022] [Indexed: 02/05/2023] Open
Abstract
Simple Summary Clear cell renal cell carcinoma is a type of kidney cancer which comprises the majority of all renal cell carcinomas. Many efforts have been made to identify biomarkers which could help healthcare professionals better treat this kind of cancer. With extensive public data available, we conducted a machine learning study to determine a gene signature that could indicate patient survival with high accuracy. Through the min-Redundancy and Max-Relevance algorithm we generated a signature of 13 genes highly correlated with patient outcomes. These findings reveal potential strategies for personalized medicine in the clinical practice. Abstract Patients with clear cell renal cell carcinoma (ccRCC) have poor survival outcomes, especially if it has metastasized. It is of paramount importance to identify biomarkers in genomic data that could help predict the aggressiveness of ccRCC and its resistance to drugs. Thus, we conducted a study with the aims of evaluating gene signatures and proposing a novel one with higher predictive power and generalization in comparison to the former signatures. Using ccRCC cohorts of the Cancer Genome Atlas (TCGA-KIRC) and International Cancer Genome Consortium (ICGC-RECA), we evaluated linear survival models of Cox regression with 14 signatures and six methods of feature selection, and performed functional analysis and differential gene expression approaches. In this study, we established a 13-gene signature (AR, AL353637.1, DPP6, FOXJ1, GNB3, HHLA2, IL4, LIMCH1, LINC01732, OTX1, SAA1, SEMA3G, ZIC2) whose expression levels are able to predict distinct outcomes of patients with ccRCC. Moreover, we performed a comparison between our signature and others from the literature. The best-performing gene signature was achieved using the ensemble method Min-Redundancy and Max-Relevance (mRMR). This signature comprises unique features in comparison to the others, such as generalization through different cohorts and being functionally enriched in significant pathways: Urothelial Carcinoma, Chronic Kidney disease, and Transitional cell carcinoma, Nephrolithiasis. From the 13 genes in our signature, eight are known to be correlated with ccRCC patient survival and four are immune-related. Our model showed a performance of 0.82 using the Receiver Operator Characteristic (ROC) Area Under Curve (AUC) metric and it generalized well between the cohorts. Our findings revealed two clusters of genes with high expression (SAA1, OTX1, ZIC2, LINC01732, GNB3 and IL4) and low expression (AL353637.1, AR, HHLA2, LIMCH1, SEMA3G, DPP6, and FOXJ1) which are both correlated with poor prognosis. This signature can potentially be used in clinical practice to support patient treatment care and follow-up.
Collapse
|
15
|
Jiang H, Xu A, Li M, Han R, Wang E, Wu D, Fei G, Zhou S, Wang R. Seven autophagy-related lncRNAs are associated with the tumor immune microenvironment in predicting survival risk of nonsmall cell lung cancer. Brief Funct Genomics 2021; 21:177-187. [PMID: 34849558 DOI: 10.1093/bfgp/elab043] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Revised: 10/26/2021] [Accepted: 11/01/2021] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Nonsmall cell lung cancer (NSCLC) ranks first among global cancer-related deaths. Despite the emergence of various immunological and targeted therapies, immune tolerance remains a barrier to treatment. METHODS It has been found that this obstacle can be overcome by targeting autophagy-related genes (ATGs). ATGs were screened by coexpression analysis and the genes related to the prognosis of lung cancer were screened using Kaplan-Meier (K-M) survival analysis, univariate Cox regression and multivariate Cox regression. The prognostic risk model of ATGs was constructed and verified using K-M survival analysis and receiver operating characteristic (ROC) curve analysis. RESULTS The prognostic risk model of ATGs was constructed. Gene set enrichment analysis (GSEA) showed that the function and pathway of ATG enrichment were closely related to immune cell function. CIBERSORT, LM22 matrix and Pearson correlation analysis showed that risk signals were significantly correlated with immune cell infiltration and immune checkpoint genes. CONCLUSIONS We identified and independently verified the ATG (AL691432.2, MMP2-AS1, AC124067.2, CRNDE, ABALON, AL161431.1, NKILA) in NSCLC patients and found that immune regulation in the tumor microenvironment is closely related to this gene.
Collapse
|
16
|
Che X, Su W, Li X, Liu N, Wang Q, Wu G. Angiogenesis Pathway in Kidney Renal Clear Cell Carcinoma and Its Prognostic Value for Cancer Risk Prediction. Front Med (Lausanne) 2021; 8:731214. [PMID: 34778292 PMCID: PMC8581140 DOI: 10.3389/fmed.2021.731214] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2021] [Accepted: 09/28/2021] [Indexed: 12/11/2022] Open
Abstract
Angiogenesis, a process highly regulated by pro-angiogenic and anti-angiogenic factors, is disrupted and dysregulated in cancer. Despite the increased clinical use of angiogenesis inhibitors in cancer therapy, most molecularly targeted drugs have been less effective than expected. Therefore, an in-depth exploration of the angiogenesis pathway is warranted. In this study, the expression of angiogenesis-related genes in various cancers was explored using The Cancer Genome Atlas datasets, whereupon it was found that most of them were protective genes in the patients with kidney renal clear cell carcinoma (KIRC). We divided the samples from the KIRC dataset into three clusters according to the mRNA expression levels of these genes, with the enrichment scores being in the order of Cluster 2 (upregulated expression) > Cluster 3 (normal expression) > Cluster 1 (downregulated expression). The survival curves plotted for the three clusters revealed that the patients in Cluster 2 had the highest overall survival rates. Via a sensitivity analysis of the drugs listed on the Genomics of Drug Sensitivity in Cancer database, we generated IC50 estimates for 12 commonly used molecularly targeted drugs for KIRC in the three clusters, which can provide a more personalized treatment plan for the patients according to angiogenesis-related gene expression. Subsequently, we investigated the correlation between the angiogenesis pathway and classical cancer-related genes as well as that between the angiogenesis score and immune cell infiltration. Finally, we used the least absolute shrinkage and selection operator (LASSO)-Cox regression analysis to construct a risk score model for predicting the survival of patients with KIRC. According to the areas under the receiver operating characteristic (ROC) curves, this new survival model based on the angiogenesis-related genes had high prognostic prediction value. Our results should provide new avenues for the clinical diagnosis and treatment of patients with KIRC.
Collapse
Affiliation(s)
- Xiangyu Che
- Department of Urology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Wenyan Su
- Department of Nephrology, Cheeloo College of Medicine, Shandong Provincial Hospital, Shandong University, Jinan, China
| | - Xiaowei Li
- Department of Urology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Nana Liu
- Department of Breast Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Qifei Wang
- Department of Urology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Guangzhen Wu
- Department of Urology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| |
Collapse
|
17
|
Development and validation of ferroptosis-related lncRNAs prognosis signatures in kidney renal clear cell carcinoma. Cancer Cell Int 2021; 21:591. [PMID: 34736453 PMCID: PMC8567554 DOI: 10.1186/s12935-021-02284-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Accepted: 10/20/2021] [Indexed: 12/14/2022] Open
Abstract
Background Ferroptosis is a recently recognised new type of cell death which may be a potential target for cancer therapy. In the present study, we aimed to screen ferroptosis-related differentially expressed long non-coding RNAs as biomarkers to predict the outcome of kidney renal clear cell carcinoma. Methods RNAseq count data and corresponding clinical information were obtained from the Cancer Genome Atlas database. Lists of ferroptosis-related genes and long non-coding RNAs were obtained from the FerrDb and GENCODE databases, respectively. The candidate prognostic signatures were screened by Cox regression analyses and least absolute shrinkage and selection operator analyses. Results Three ferroptosis-related long non-coding RNAs (DUXAP8, LINC02609, and LUCAT1) were significantly correlated with the overall survival of kidney renal clear cell carcinoma independently. Kidney renal clear cell carcinoma patients with high-risk values displayed worse OS. Meanwhile, the expression of these three ferroptosis-related long non-coding RNAs and their risk scores were significantly correlated with clinicopathological features. Principal component analyses showed that patients with kidney renal clear cell carcinoma have differential risk values were well distinguished by the three ferroptosis-related long non-coding RNAs. Conclusions The present study suggests that the risk assessment model constructed by these three ferroptosis-related long non-coding RNAs could accurately predict the outcome of kidney renal clear cell carcinoma. We also provide a novel perspective for cancer prognosis screening. Supplementary Information The online version contains supplementary material available at 10.1186/s12935-021-02284-1.
Collapse
|
18
|
Li H, Mo Z. Prognostic Value of Metabolism-Related Genes and Immune Infiltration in Clear Cell Renal Cell Carcinoma. Int J Gen Med 2021; 14:6885-6898. [PMID: 34703293 PMCID: PMC8536843 DOI: 10.2147/ijgm.s328109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 10/05/2021] [Indexed: 11/29/2022] Open
Abstract
Background Clear cell renal cell carcinoma (ccRCC) is one of the most prevalent cancers. Thus, it is warranted to detect the status of metabolism-related genes (MRGs) and infiltrating immune cells in ccRCC progression for the prognosis of ccRCC. This research was designed to establish and verify the prognostic signature of ccRCC using MRGs. In addition, we investigated the potential link between the relative proportion of tumor infiltrated immune cells (TIICs) and ccRCC prognosis. Methods Sequencing data of metabolism-related gene sets in ccRCC cases were obtained from The Cancer Genome Atlas database (TCGA) and Gene Expression Omnibus Database (GEO). The R Programming Language software packages were applied for differential analysis of MRGs. First, a univariate Cox regression model was applied to determine the MRGs linked with overall survival (OS). Then, the multivariate Cox regression model was applied to establish the prognostic signature. Finally, the CIBERSORT algorithm was used to determine the proportion of TIICs. Results Overall, 286 differentially expressed MRGs were identified in the TCGA dataset. Univariate and multivariate Cox regression models were applied to develop a prognostic signature with six MRGs. The predictive capability of the prognostic signature was further verified by TCGA and GEO database. In addition, RS positively correlated with memory B cells, plasma cells, activated memory CD4+ T cells, follicular helper T cells, regulatory T cells, CD8+ T cells, and M0 macrophages, and were negatively associated with resting memory CD4+ T cells, resting dendritic cells, activated dendritic cells, M2 macrophages, monocytes, resting mast cells, and eosinophils. Conclusion Herein, a prognostic signature was developed using MRGs for ccRCC prognosis. The proportion of 22 TIICs in ccRCC and the association between TIICs and clinical outcomes were also determined. The identified genes and cells could guide future targeted therapy and immunotherapy.
Collapse
Affiliation(s)
- Hanwen Li
- Department of Urology, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, Guangxi, People's Republic of China.,Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, 530021, Guangxi, People's Republic of China.,Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Nanning, 530021, Guangxi, People's Republic of China.,Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Key Laboratory of Colleges and Universities, Nanning, 530021, Guangxi, People's Republic of China
| | - Zengnan Mo
- Department of Urology, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, Guangxi, People's Republic of China.,Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, 530021, Guangxi, People's Republic of China.,Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Nanning, 530021, Guangxi, People's Republic of China.,Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Key Laboratory of Colleges and Universities, Nanning, 530021, Guangxi, People's Republic of China
| |
Collapse
|
19
|
Wei J, Wang B, Gao X, Sun D. Prognostic Value of a Novel Signature With Nine Hepatitis C Virus-Induced Genes in Hepatic Cancer by Mining GEO and TCGA Databases. Front Cell Dev Biol 2021; 9:648279. [PMID: 34336819 PMCID: PMC8322788 DOI: 10.3389/fcell.2021.648279] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Accepted: 05/25/2021] [Indexed: 01/29/2023] Open
Abstract
Background Hepatitis C virus-induced genes (HCVIGs) play a critical role in regulating tumor development in hepatic cancer. The role of HCVIGs in hepatic cancer remains unknown. This study aimed to construct a prognostic signature and assess the value of the risk model for predicting the prognosis of hepatic cancer. Methods Differentially expressed HCVIGs were identified in hepatic cancer data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases using the library (“limma”) package of R software. The protein–protein interaction (PPI) network was constructed using the Cytoscape software. Functional enrichment analysis was performed using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Univariate and multivariate Cox proportional hazard regression analyses were applied to screen for prognostic HCVIGs. The signature of HCVIGs was constructed. Gene Set Enrichment Analysis (GSEA) compared the low-risk and high-risk groups. Finally, the International Cancer Genome Consortium (ICGC) database was used to validate this prognostic signature. Polymerase chain reaction (PCR) was performed to validate the expression of nine HCVIGs in the hepatic cancer cell lines. Results A total of 143 differentially expressed HCVIGs were identified in TCGA hepatic cancer dataset. Functional enrichment analysis showed that DNA replication was associated with the development of hepatic cancer. The risk score signature was constructed based on the expression of ZIC2, SLC7A11, PSRC1, TMEM106C, TRAIP, DTYMK, FAM72D, TRIP13, and CENPM. In this study, the risk score was an independent prognostic factor in the multivariate Cox regression analysis [hazard ratio (HR) = 1.433, 95% CI = 1.280–1.605, P < 0.001]. The overall survival curve revealed that the high-risk group had a poor prognosis. The Kaplan–Meier Plotter online database showed that the survival time of hepatic cancer patients with overexpression of HCVIGs in this signature was significantly shorter. The prognostic signature-associated GO and KEGG pathways were significantly enriched in the risk group. This prognostic signature was validated using external data from the ICGC databases. The expression of nine prognostic genes was validated in HepG2 and LO-2. Conclusion This study evaluates a potential prognostic signature and provides a way to explore the mechanism of HCVIGs in hepatic cancer.
Collapse
Affiliation(s)
- Jianming Wei
- Department of General Surgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Bo Wang
- Department of Paediatric Surgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Xibo Gao
- Department of Dermatology, Tianjin Children's Hospital, Tianjin, China
| | - Daqing Sun
- Department of Paediatric Surgery, Tianjin Medical University General Hospital, Tianjin, China
| |
Collapse
|
20
|
Hass MR, Brissette D, Parameswaran S, Pujato M, Donmez O, Kottyan LC, Weirauch MT, Kopan R. Runx1 shapes the chromatin landscape via a cascade of direct and indirect targets. PLoS Genet 2021; 17:e1009574. [PMID: 34111109 PMCID: PMC8219162 DOI: 10.1371/journal.pgen.1009574] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Revised: 06/22/2021] [Accepted: 05/03/2021] [Indexed: 11/18/2022] Open
Abstract
Runt-related transcription factor 1 (Runx1) can act as both an activator and a repressor. Here we show that CRISPR-mediated deletion of Runx1 in mouse metanephric mesenchyme-derived mK4 cells results in large-scale genome-wide changes to chromatin accessibility and gene expression. Open chromatin regions near down-regulated loci enriched for Runx sites in mK4 cells lose chromatin accessibility in Runx1 knockout cells, despite remaining Runx2-bound. Unexpectedly, regions near upregulated genes are depleted of Runx sites and are instead enriched for Zeb transcription factor binding sites. Re-expressing Zeb2 in Runx1 knockout cells restores suppression, and CRISPR mediated deletion of Zeb1 and Zeb2 phenocopies the gained expression and chromatin accessibility changes seen in Runx1KO due in part to subsequent activation of factors like Grhl2. These data confirm that Runx1 activity is uniquely needed to maintain open chromatin at many loci, and demonstrate that Zeb proteins are required and sufficient to maintain Runx1-dependent genome-scale repression. Runt-related transcription factor (Runx) 1 & 2 impact development and disease by activating or repressing transcription. In this manuscript we used genome editing tools to remove Runx1, and as expected, observed widespread changes in chromatin accessibility. Newly closed areas contained Runx1 binding sites and were enriched near genes whose expression depended on Runx1. Interestingly, this occurred despite continued binding of Runx2 to the same regions of DNA, which suggests that Runx2 is insufficient to maintain open chromatin and expression of Runx1 target genes in this cellular context. By contrast, newly opened chromatin regions, many near genes that were upregulated in Runx1 knockout cells, did not enrich for Runx1 binding sites. Instead, these regions were enriched for sites for the repressor Zeb proteins. We found that the loss of Zeb 1 & 2 expression, direct transcriptional targets of Runx1, resulted in the opening of chromatin and upregulation of genes residing near the newly open sites in Runx1 knockout cells. The same sites were also open and nearby genes expressed in edited Zeb1 and Zeb2 knockout cells. Among them were transcription factors, such as the Grhl2 gene, which in turn bind to and upregulate their target genes. Thus, the loss of a single transcription factor initiates a cascade of direct and indirect ramifications with likely negative effects on development and health.
Collapse
Affiliation(s)
- Matthew R. Hass
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States of America
- Division of Developmental Biology, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
| | - Daniel Brissette
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States of America
- Division of Developmental Biology, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
| | - Sreeja Parameswaran
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States of America
- Center for Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
| | - Mario Pujato
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States of America
- Center for Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
| | - Omer Donmez
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States of America
- Center for Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
| | - Leah C. Kottyan
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States of America
- Division of Developmental Biology, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
- Center for Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
| | - Matthew T. Weirauch
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States of America
- Division of Developmental Biology, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
- Center for Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
- * E-mail: (MTW); (RK)
| | - Raphael Kopan
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States of America
- Division of Developmental Biology, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
- * E-mail: (MTW); (RK)
| |
Collapse
|
21
|
Sun G, Ge Y, Zhang Y, Yan L, Wu X, Ouyang W, Wang Z, Ding B, Zhang Y, Long G, Liu M, Shi R, Zhou H, Chen Z, Ye Z. Transcription Factors BARX1 and DLX4 Contribute to Progression of Clear Cell Renal Cell Carcinoma via Promoting Proliferation and Epithelial-Mesenchymal Transition. Front Mol Biosci 2021; 8:626328. [PMID: 34124141 PMCID: PMC8188704 DOI: 10.3389/fmolb.2021.626328] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2020] [Accepted: 05/06/2021] [Indexed: 11/29/2022] Open
Abstract
Dysregulation of transcription factors contributes to the carcinogenesis and progression of cancers. However, their roles in clear cell renal cell carcinoma remain largely unknown. This study aimed to evaluate the clinical significance of TFs and investigate their potential molecular mechanisms in ccRCC. Data were accessed from the cancer genome atlas kidney clear cell carcinoma cohort. Bioinformatics algorithm was used in copy number alterations mutations, and differentially expressed TFs’ analysis. Univariate and multivariate Cox regression analyses were performed to identify clinically significant TFs and construct a six-TF prognostic panel. TFs’ expression was validated in human tissues. Gene set enrichment analysis (GSEA) was utilized to find enriched cancer hallmark pathways. Functional experiments were conducted to verify the cancer-promoting effect of BARX homeobox 1 (BARX1) and distal-less homeobox 4 (DLX4) in ccRCC, and Western blot was performed to explore their downstream pathways. As for results, many CNAs and mutations were identified in transcription factor genes. TFs were differentially expressed in ccRCC. An applicable predictive panel of six-TF genes was constructed to predict the overall survival for ccRCC patients, and its diagnostic efficiency was evaluated by the area under the curve (AUC). BARX1 and DLX4 were associated with poor prognosis, and they could promote the proliferation and migration of ccRCC. In conclusion, the six-TF panel can be used as a prognostic biomarker for ccRCC patients. BARX1 and DLX4 play oncogenic roles in ccRCC via promoting proliferation and epithelial–mesenchymal transition. They have the potential to be novel therapeutic targets for ccRCC.
Collapse
Affiliation(s)
- Guoliang Sun
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Hubei Institute of Urology, Wuhan, China.,Department of Urology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Yue Ge
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Hubei Institute of Urology, Wuhan, China
| | - Yangjun Zhang
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Hubei Institute of Urology, Wuhan, China
| | - Libin Yan
- Department of Urology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Xiaoliang Wu
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Hubei Institute of Urology, Wuhan, China
| | - Wei Ouyang
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Hubei Institute of Urology, Wuhan, China
| | - Zhize Wang
- Department of Urology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Beichen Ding
- Department of Urology, First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Yucong Zhang
- Department of Geriatric, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Gongwei Long
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Hubei Institute of Urology, Wuhan, China
| | - Man Liu
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Hubei Institute of Urology, Wuhan, China
| | - Runlin Shi
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Hui Zhou
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Hubei Institute of Urology, Wuhan, China
| | - Zhiqiang Chen
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Hubei Institute of Urology, Wuhan, China
| | - Zhangqun Ye
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Hubei Institute of Urology, Wuhan, China
| |
Collapse
|
22
|
The Role of Epigenetics in the Progression of Clear Cell Renal Cell Carcinoma and the Basis for Future Epigenetic Treatments. Cancers (Basel) 2021; 13:cancers13092071. [PMID: 33922974 PMCID: PMC8123355 DOI: 10.3390/cancers13092071] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 04/22/2021] [Accepted: 04/23/2021] [Indexed: 12/13/2022] Open
Abstract
Simple Summary The accumulated evidence on the role of epigenetic markers of prognosis in clear cell renal cell carcinoma (ccRCC) is reviewed, as well as state of the art on epigenetic treatments for this malignancy. Several epigenetic markers are likely candidates for clinical use, but still have not passed the test of prospective validation. Development of epigenetic therapies, either alone or in combination with tyrosine-kinase inhibitors of immune-checkpoint inhibitors, are still in their infancy. Abstract Clear cell renal cell carcinoma (ccRCC) is curable when diagnosed at an early stage, but when disease is non-confined it is the urologic cancer with worst prognosis. Antiangiogenic treatment and immune checkpoint inhibition therapy constitute a very promising combined therapy for advanced and metastatic disease. Many exploratory studies have identified epigenetic markers based on DNA methylation, histone modification, and ncRNA expression that epigenetically regulate gene expression in ccRCC. Additionally, epigenetic modifiers genes have been proposed as promising biomarkers for ccRCC. We review and discuss the current understanding of how epigenetic changes determine the main molecular pathways of ccRCC initiation and progression, and also its clinical implications. Despite the extensive research performed, candidate epigenetic biomarkers are not used in clinical practice for several reasons. However, the accumulated body of evidence of developing epigenetically-based biomarkers will likely allow the identification of ccRCC at a higher risk of progression. That will facilitate the establishment of firmer therapeutic decisions in a changing landscape and also monitor active surveillance in the aging population. What is more, a better knowledge of the activities of chromatin modifiers may serve to develop new therapeutic opportunities. Interesting clinical trials on epigenetic treatments for ccRCC associated with well established antiangiogenic treatments and immune checkpoint inhibitors are revisited.
Collapse
|
23
|
Novel prognostic prediction model constructed through machine learning on the basis of methylation-driven genes in kidney renal clear cell carcinoma. Biosci Rep 2021; 40:225719. [PMID: 32633782 PMCID: PMC7374278 DOI: 10.1042/bsr20201604] [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: 05/11/2020] [Revised: 06/24/2020] [Accepted: 07/06/2020] [Indexed: 02/06/2023] Open
Abstract
Kidney renal clear cell carcinoma (KIRC) is a common tumor with poor prognosis and is closely related to many aberrant gene expressions. DNA methylation is an important epigenetic modification mechanism and a novel research target. Thus, exploring the relationship between methylation-driven genes and KIRC prognosis is important. The methylation profile, methylation-driven genes, and methylation characteristics in KIRC was revealed through the integration of KIRC methylation, RNA-seq, and clinical information data from The Cancer Genome Atlas. The Lasso regression was used to establish a prognosis model on the basis of methylation-driven genes. Then, a trans-omics prognostic nomogram was constructed and evaluated by combining clinical information and methylated prognosis model. A total of 242 methylation-driven genes were identified. The Gene Ontology terms of these methylation-driven genes mainly clustered in the activation, adhesion, and proliferation of immune cells. The methylation prognosis prediction model that was established using the Lasso regression included four genes in the methylation data, namely, FOXI2, USP44, EVI2A, and TRIP13. The areas under the receiver operating characteristic curve of 1-, 3-, and 5-year survival rates were 0.810, 0.824, and 0.799, respectively, in the training group and 0.794, 0.752, and 0.731, respectively, in the testing group. An easy trans-omics nomogram was successfully established. The C-indices of the nomogram in the training and the testing groups were 0.8015 and 0.8389, respectively. The present study revealed the overall perspective of methylation-driven genes in KIRC and can help in the evaluation of the prognosis of KIRC patients and provide new clues for further study.
Collapse
|
24
|
MicroRNA related prognosis biomarkers from high throughput sequencing data of kidney renal clear cell carcinoma. BMC Med Genomics 2021; 14:72. [PMID: 33750388 PMCID: PMC7941961 DOI: 10.1186/s12920-021-00932-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Accepted: 03/04/2021] [Indexed: 12/21/2022] Open
Abstract
Background Kidney renal clear cell carcinoma (KIRC) is the most common type of kidney cell carcinoma which has the worst overall survival rate. Almost 30% of patients with localized cancers eventually develop to metastases despite of early surgical treatment carried out. MicroRNAs (miRNAs) play a critical role in human cancer initiation, progression, and prognosis. The aim of our study was to identify potential prognosis biomarkers to predict overall survival of KIRC. Methods All data were downloaded from an open access database The Cancer Genome Atlas. DESeq2 package in R was used to screening the differential expression miRNAs (DEMs) and genes (DEGs). RegParallel and Survival packages in R was used to analysis their relationships with the KIRC patients. David version 6.8 and STRING version 11 were used to take the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. Results We found 2 DEGs (TIMP3 and HMGCS1) and 3 DEMs (hsa-miR-21-5p, hsa-miR-223-3p, and hsa-miR-365a-3p) could be prognosis biomarkers for the prediction of KIRC patients. The constructed prognostic model based on those 2 DEGs could effectively predict the survival status of KIRC. And the constructed prognostic model based on those 3 DEMs could effectively predict the survival status of KIRC in 3-year and 5-year. Conclusion The current study provided novel insights into the miRNA related mRNA network in KIRC and those 2 DEGs biomarkers and 3 DEMs biomarkers may be independent prognostic signatures in predicting the survival of KIRC patients. Supplementary Information The online version contains supplementary material available at 10.1186/s12920-021-00932-z.
Collapse
|
25
|
Zhang L, Zhang K, Liu S, Zhang R, Yang Y, Wang Q, Zhao S, Yang L, Zhang Y, Wang J. Identification of a ceRNA Network in Lung Adenocarcinoma Based on Integration Analysis of Tumor-Associated Macrophage Signature Genes. Front Cell Dev Biol 2021; 9:629941. [PMID: 33738286 PMCID: PMC7960670 DOI: 10.3389/fcell.2021.629941] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Accepted: 02/11/2021] [Indexed: 12/11/2022] Open
Abstract
As research into tumor-immune interactions progresses, immunotherapy is becoming the most promising treatment against cancers. The tumor microenvironment (TME) plays the key role influencing the efficacy of anti-tumor immunotherapy, in which tumor-associated macrophages (TAMs) are the most important component. Although evidences have emerged revealing that competing endogenous RNAs (ceRNAs) were involved in infiltration, differentiation and function of immune cells by regulating interactions among different varieties of RNAs, limited comprehensive investigation focused on the regulatory mechanism between ceRNA networks and TAMs. In this study, we aimed to utilize bioinformatic approaches to explore how TAMs potentially influence the prognosis and immunotherapy of lung adenocarcinoma (LUAD) patients. Firstly, according to TAM signature genes, we constructed a TAM prognostic risk model by the least absolute shrinkage and selection operator (LASSO) cox regression in LUAD patients. Then, differential gene expression was analyzed between high- and low-risk patients. Weighted gene correlation network analysis (WGCNA) was utilized to identify relevant gene modules correlated with clinical characteristics and prognostic risk score. Moreover, ceRNA networks were built up based on predicting regulatory pairs in differentially expressed genes. Ultimately, by synthesizing information of protein-protein interactions (PPI) analysis and survival analysis, we have successfully identified a core regulatory axis: LINC00324/miR-9-5p (miR-33b-5p)/GAB3 (IKZF1) which may play a pivotal role in regulating TAM risk and prognosis in LUAD patients. The present study contributes to a better understanding of TAMs associated immunosuppression in the TME and provides novel targets and regulatory pathway for anti-tumor immunotherapy.
Collapse
Affiliation(s)
- Lei Zhang
- Department of Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Biotherapy Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Kai Zhang
- Biotherapy Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Shasha Liu
- Biotherapy Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Ruizhe Zhang
- Reproductive Medicine Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yang Yang
- Department of Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Qi Wang
- Department of Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Song Zhao
- Department of Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Li Yang
- Biotherapy Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yi Zhang
- Biotherapy Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jiaxiang Wang
- Department of Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| |
Collapse
|
26
|
Nazari M, Shiri I, Zaidi H. Radiomics-based machine learning model to predict risk of death within 5-years in clear cell renal cell carcinoma patients. Comput Biol Med 2020; 129:104135. [PMID: 33254045 DOI: 10.1016/j.compbiomed.2020.104135] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2020] [Revised: 10/21/2020] [Accepted: 11/11/2020] [Indexed: 12/13/2022]
Abstract
PURPOSE The aim of this study was to develop radiomics-based machine learning models based on extracted radiomic features and clinical information to predict the risk of death within 5 years for prognosis of clear cell renal cell carcinoma (ccRCC) patients. METHODS According to image quality and clinical data availability, we eventually selected 70 ccRCC patients that underwent CT scans. Manual volume-of-interest (VOI) segmentation of each image was performed by an experienced radiologist using the 3D slicer software package. Prior to feature extraction, image pre-processing was performed on CT images to extract different image features, including wavelet, Laplacian of Gaussian, and resampling of the intensity values to 32, 64 and 128 bin levels. Overall, 2544 3D radiomics features were extracted from each VOI for each patient. Minimum Redundancy Maximum Relevance (MRMR) algorithm was used as feature selector. Four classification algorithms were used, including Generalized Linear Model (GLM), Support Vector Machine (SVM), K-nearest Neighbor (KNN) and XGBoost. We used the Bootstrap resampling method to create validation sets. Area under the receiver operating characteristic (ROC) curve (AUROC), accuracy, sensitivity, and specificity were used to assess the performance of the classification models. RESULTS The best single performance among 8 different models was achieved by the XGBoost model using a combination of radiomic features and clinical information (AUROC, accuracy, sensitivity, and specificity with 95% confidence interval were 0.95-0.98, 0.93-0.98, 0.93-0.96 and ~1.0, respectively). CONCLUSIONS We developed a robust radiomics-based classifier that is capable of accurately predicting overall survival of RCC patients for prognosis of ccRCC patients. This signature may help identifying high-risk patients who require additional treatment and follow up regimens.
Collapse
Affiliation(s)
- Mostafa Nazari
- Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland; Geneva University Neurocenter, Geneva University, Geneva, Switzerland; Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands; Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark.
| |
Collapse
|
27
|
Establishment and Validation of a Prognostic Risk Model for Autophagy-Related Genes in Clear Cell Renal Cell Carcinoma. DISEASE MARKERS 2020; 2020:8841859. [PMID: 33224313 PMCID: PMC7676277 DOI: 10.1155/2020/8841859] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/16/2020] [Revised: 09/30/2020] [Accepted: 10/17/2020] [Indexed: 12/26/2022]
Abstract
Background Autophagy plays an essential role in tumorigenesis. At present, due to the unclear role of autophagy in renal clear cell carcinoma, we studied the potential value of autophagy-related genes (ARGs) in renal clear cell carcinoma (ccRCC). Methods We obtained all ccRCC data from The Cancer Genome Atlas (TCGA). We extracted the expression data of ARGs for difference analysis and carried out biological function analysis on the different results. The autophagy risk model was constructed. The 5-year survival rate was assessed using the model, and the predictive power of the model was evaluated from multiple perspectives. Cox regression analysis was use to assess whether the model could be an independent prognostic factor. Finally, the correlation between the model and clinical indicators is analyzed. Results The patients were divided into the high-risk group and low-risk group according to the median of autophagy risk score, and the results showed that the prognosis of the low-risk group was better than that of a high-risk group. The validation results of external data sets show that our model has good predictive value for ccRCC patients. The model can be an independent prognostic factor. Finally, the results show that our model has a stable predictive ability. Conclusion The autophagy gene model we constructed can be used as an excellent prognostic indicator for ccRCC. Our study provides the possibility of individualized and precise treatment for ccRCC patients.
Collapse
|
28
|
Yao H, Zhang N, Zhang R, Duan M, Xie T, Pan J, Peng E, Huang J, Zhang Y, Xu X, Xu H, Zhou F, Wang G. Severity Detection for the Coronavirus Disease 2019 (COVID-19) Patients Using a Machine Learning Model Based on the Blood and Urine Tests. Front Cell Dev Biol 2020; 8:683. [PMID: 32850809 PMCID: PMC7411005 DOI: 10.3389/fcell.2020.00683] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 07/06/2020] [Indexed: 01/08/2023] Open
Abstract
The recent outbreak of the coronavirus disease-2019 (COVID-19) caused serious challenges to the human society in China and across the world. COVID-19 induced pneumonia in human hosts and carried a highly inter-person contagiousness. The COVID-19 patients may carry severe symptoms, and some of them may even die of major organ failures. This study utilized the machine learning algorithms to build the COVID-19 severeness detection model. Support vector machine (SVM) demonstrated a promising detection accuracy after 32 features were detected to be significantly associated with the COVID-19 severeness. These 32 features were further screened for inter-feature redundancies. The final SVM model was trained using 28 features and achieved the overall accuracy 0.8148. This work may facilitate the risk estimation of whether the COVID-19 patients would develop the severe symptoms. The 28 COVID-19 severeness associated biomarkers may also be investigated for their underlining mechanisms how they were involved in the COVID-19 infections.
Collapse
Affiliation(s)
- Haochen Yao
- Department of Pathogenobiology, The Key Laboratory of Zoonosis, Chinese Ministry of Education, College of Basic Medical Science, Jilin University, Changchun, China
| | - Nan Zhang
- The First Hospital of Jilin University, Jilin University, Changchun, China
| | - Ruochi Zhang
- BioKnow Health Informatics Lab, College of Software, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China
| | - Meiyu Duan
- BioKnow Health Informatics Lab, College of Software, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China
| | - Tianqi Xie
- School of Computing and Information, University of Pittsburgh, Pittsburgh, PA, United States
| | - Jiahui Pan
- Department of Pathogenobiology, The Key Laboratory of Zoonosis, Chinese Ministry of Education, College of Basic Medical Science, Jilin University, Changchun, China
| | - Ejun Peng
- Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Juanjuan Huang
- Department of Pathogenobiology, The Key Laboratory of Zoonosis, Chinese Ministry of Education, College of Basic Medical Science, Jilin University, Changchun, China
| | - Yingli Zhang
- The First Hospital of Jilin University, Jilin University, Changchun, China
| | - Xiaoming Xu
- The First Hospital of Jilin University, Jilin University, Changchun, China
| | - Hong Xu
- The First Hospital of Jilin University, Jilin University, Changchun, China
| | - Fengfeng Zhou
- BioKnow Health Informatics Lab, College of Software, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China
| | - Guoqing Wang
- Department of Pathogenobiology, The Key Laboratory of Zoonosis, Chinese Ministry of Education, College of Basic Medical Science, Jilin University, Changchun, China
| |
Collapse
|
29
|
Raimondo F, Pitto M. Prognostic significance of proteomics and multi-omics studies in renal carcinoma. Expert Rev Proteomics 2020; 17:323-334. [PMID: 32428425 DOI: 10.1080/14789450.2020.1772058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
INTRODUCTION Renal carcinoma, and in particular its most common variant, the clear cell subtype, is often diagnosed incidentally through abdominal imaging and frequently, the tumor is discovered at an early stage. However, 20% to 40% of patients undergoing nephrectomy for clinically localized renal cancer, even after accurate histological and clinical classification, will develop metastasis or recurrence, justifying the associated mortality rate. Therefore, even if renal carcinoma is not among the most frequent nor deadly cancers, a better prognostication is needed. AREAS COVERED Recently proteomics or other omics combinations have been applied to both cancer tissues, on the neoplasia itself and surrounding microenvironment, cultured cells, and biological fluids (so-called liquid biopsy) generating a list of prognostic molecular tools that will be reviewed in the present paper. EXPERT OPINION Although promising, none of the approaches listed above has been yet translated in clinics. This is likely due to the peculiar genetic and phenotypic heterogeneity of this cancer, which makes nearly each tumor different from all the others. Attempts to overcome this issue will be also revised. In particular, we will discuss how the application of omics-integrated approaches could provide the determinants of response to the different targeted drugs.
Collapse
Affiliation(s)
- Francesca Raimondo
- Clinical Proteomics and Metabolomics Unit, School of Medicine and Surgery, University of Milano - Bicocca , Vedano al Lambro, Italy
| | - Marina Pitto
- Clinical Proteomics and Metabolomics Unit, School of Medicine and Surgery, University of Milano - Bicocca , Vedano al Lambro, Italy
| |
Collapse
|
30
|
Jiang H, Chen H, Chen N. Construction and validation of a seven-gene signature for predicting overall survival in patients with kidney renal clear cell carcinoma via an integrated bioinformatics analysis. Anim Cells Syst (Seoul) 2020; 24:160-170. [PMID: 33209196 PMCID: PMC7651852 DOI: 10.1080/19768354.2020.1760932] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Revised: 04/09/2020] [Accepted: 04/16/2020] [Indexed: 02/05/2023] Open
Abstract
Kidney renal clear cell carcinoma (KIRC) remains a significant challenge worldwide because of its poor prognosis and high mortality rate, and accurate prognostic gene signatures are urgently required for individual therapy. This study aimed to construct and validate a seven-gene signature for predicting overall survival (OS) in patients with KIRC. The mRNA expression profile and clinical data of patients with KIRC were obtained from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC). Prognosis-associated genes were identified, and a prognostic gene signature was constructed. Then, the prognostic efficiency of the gene signature was assessed. The results obtained using data from the TCGA were validated using those from the ICGC and other online databases. Gene set enrichment analyses (GSEA) were performed to explore potential molecular mechanisms. A seven-gene signature (PODXL, SLC16A12, ZIC2, ATP2B3, KRT75, C20orf141, and CHGA) was constructed, and it was found to be effective in classifying KIRC patients into high- and low-risk groups, with significantly different survival based on the TCGA and ICGC validation data set. Cox regression analysis revealed that the seven-gene signature had an independent prognostic value. Then, we established a nomogram, including the seven-gene signature, which had a significant clinical net benefit. Interestingly, the seven-gene signature had a good performance in distinguishing KIRC from normal tissues. GSEA revealed that several oncological signatures and GO terms were enriched. This study developed a novel seven-gene signature and nomogram for predicting the OS of patients with KIRC, which may be helpful for clinicians in establishing individualized treatments.
Collapse
Affiliation(s)
- Huiming Jiang
- Department of Urology, Meizhou People’s Hospital (Huangtang Hospital), Guangdong Provincial Key Laboratory of Precision Medicine and Clinical Translational Research of Hakka Population, Meizhou, People’s Republic of China
| | - Haibin Chen
- Department of Histology and Embryology, Shantou University Medical College, Shantou, People’s Republic of China
| | - Nanhui Chen
- Department of Urology, Meizhou People’s Hospital (Huangtang Hospital), Guangdong Provincial Key Laboratory of Precision Medicine and Clinical Translational Research of Hakka Population, Meizhou, People’s Republic of China
- Nanhui Chen Department of Urology, Meizhou People’s Hospital (Huangtang Hospital), Guangdong Provincial Key Laboratory of Precision Medicine and Clinical Translational Research of Hakka Population, No. 63, Huang Tang Road, Meizhou, Guangdong Province514031, P.R. China
| |
Collapse
|
31
|
Zhang M, Sun L, Ru Y, Zhang S, Miao J, Guo P, Lv J, Guo F, Liu B. A risk score system based on DNA methylation levels and a nomogram survival model for lung squamous cell carcinoma. Int J Mol Med 2020; 46:252-264. [PMID: 32377703 PMCID: PMC7255475 DOI: 10.3892/ijmm.2020.4590] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2019] [Accepted: 01/30/2020] [Indexed: 12/20/2022] Open
Abstract
Lung squamous cell carcinoma (LSCC) is one of the primary types of non-small cell lung carcinoma, and patients with recurrent LSCC usually have a poor prognosis. The present study was conducted to build a risk score (RS) system for LSCC. Methylation data on LSCC (training set) and on head and neck squamous cell carcinoma (validation set 2) were obtained from The Cancer Genome Atlas database, and GSE39279 (validation set 1) was retrieved from the Gene Expression Omnibus database. Differentially methylated protein-coding genes (DMGs)/long non-coding RNAs (DM-lncRNAs) between recurrence-associated samples and nonrecurrence samples were screened out using the limma package, and their correlation analysis was conducted using the cor.test() function. Following identification of the optimal combinations of DMGs or DM-lncRNAs using the penalized package in R, RS systems were built, and the system with optimal performance was selected. Using the rms package, a nomogram survival model was then constructed. For the differentially expressed genes (DEGs) between the high- and low-risk groups, pathway enrichment analysis was performed by Gene Set Enrichment Analysis. There were 335 DMGs and DM-lncRNAs in total. Following screening out of the top 10 genes (aldehyde dehydrogenase 7 family member A1, chromosome 8 open reading frame 48, cytokine-like 1, heat shock protein 90 alpha family class A member 1, isovaleryl-CoA dehydrogenase, phosphodiesterase 3A, PNMA family member 2, SAM domain, SH3 domain and nuclear localization signals 1, thyroid hormone receptor interactor 13 and zinc finger protein 878) and 6 top lncRNAs, RS systems were constructed. According to Kaplan-Meier analysis, the DNA methylation level-based RS system exhibited the best performance. In combination with independent clinical prognostic factors, a nomogram survival model was built and successfully predicted patient survival. Furthermore, 820 DEGs between the high- and low-risk groups were identified, and 3 pathways were identified to be enriched in this gene set. The 10-DMG methylation level-based RS system and the nomogram survival model may be applied for predicting the outcomes of patients with LSCC.
Collapse
Affiliation(s)
- Ming Zhang
- Department of Oncology, The Affiliated Suzhou Municipal Hospital of Nanjing Medical University, Suzhou, Jiangsu 215001, P.R. China
| | - Libing Sun
- Department of Oncology, The Affiliated Suzhou Municipal Hospital of Nanjing Medical University, Suzhou, Jiangsu 215001, P.R. China
| | - Yi Ru
- Department of Oncology, The Affiliated Suzhou Municipal Hospital of Nanjing Medical University, Suzhou, Jiangsu 215001, P.R. China
| | - Shasha Zhang
- Department of Oncology, The Affiliated Suzhou Municipal Hospital of Nanjing Medical University, Suzhou, Jiangsu 215001, P.R. China
| | - Junjun Miao
- Department of Oncology, The Affiliated Suzhou Municipal Hospital of Nanjing Medical University, Suzhou, Jiangsu 215001, P.R. China
| | - Pengda Guo
- Department of Oncology, The Affiliated Suzhou Municipal Hospital of Nanjing Medical University, Suzhou, Jiangsu 215001, P.R. China
| | - Jinghuan Lv
- Department of Oncology, The Affiliated Suzhou Municipal Hospital of Nanjing Medical University, Suzhou, Jiangsu 215001, P.R. China
| | - Feng Guo
- Department of Oncology, The Affiliated Suzhou Municipal Hospital of Nanjing Medical University, Suzhou, Jiangsu 215001, P.R. China
| | - Biao Liu
- Department of Oncology, The Affiliated Suzhou Municipal Hospital of Nanjing Medical University, Suzhou, Jiangsu 215001, P.R. China
| |
Collapse
|
32
|
Zhang M, Wang X, Chen X, Zhang Q, Hong J. Novel Immune-Related Gene Signature for Risk Stratification and Prognosis of Survival in Lower-Grade Glioma. Front Genet 2020; 11:363. [PMID: 32351547 PMCID: PMC7174786 DOI: 10.3389/fgene.2020.00363] [Citation(s) in RCA: 107] [Impact Index Per Article: 26.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2019] [Accepted: 03/25/2020] [Indexed: 01/08/2023] Open
Abstract
Objective Despite several clinicopathological factors being integrated as prognostic biomarkers, the individual variants and risk stratification have not been fully elucidated in lower grade glioma (LGG). With the prevalence of gene expression profiling in LGG, and based on the critical role of the immune microenvironment, the aim of our study was to develop an immune-related signature for risk stratification and prognosis prediction in LGG. Methods RNA-sequencing data from The Cancer Genome Atlas (TCGA), Genome Tissue Expression (GTEx), and Chinese Glioma Genome Atlas (CGGA) were used. Immune-related genes were obtained from the Immunology Database and Analysis Portal (ImmPort). Univariate, multivariate cox regression, and Lasso regression were employed to identify differentially expressed immune-related genes (DEGs) and establish the signature. A nomogram was constructed, and its performance was evaluated by Harrell’s concordance index (C-index), receiver operating characteristic (ROC), and calibration curves. Relationships between the risk score and tumor-infiltrating immune cell abundances were evaluated using CIBERSORTx and TIMER. Results Noted, 277 immune-related DEGs were identified. Consecutively, 6 immune genes (CANX, HSPA1B, KLRC2, PSMC6, RFXAP, and TAP1) were identified as risk signature and Kaplan–Meier curve, ROC curve, and risk plot verified its performance in TCGA and CGGA datasets. Univariate and multivariate Cox regression indicated that the risk group was an independent predictor in primary LGG. The prognostic signature showed fair accuracy for 3- and 5-year overall survival in both internal (TCGA) and external (CGGA) validation cohorts. However, predictive performance was poor in the recurrent LGG cohort. The CIBERSORTx algorithm revealed that naïve CD4+ T cells were significant higher in low-risk group. Conversely, the infiltration levels of M1-type macrophages, M2-type macrophages, and CD8+T cells were significant higher in high-risk group in both TCGA and CGGA cohorts. Conclusion The present study constructed a robust six immune-related gene signature and established a prognostic nomogram effective in risk stratification and prediction of overall survival in primary LGG.
Collapse
Affiliation(s)
- Mingwei Zhang
- Department of Radiation Oncology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.,Institute of Immunotherapy, Fujian Medical University, Fuzhou, China.,Key Laboratory of Radiation Biology (Fujian Medical University), Fujian Province University, Fuzhou, China.,Fujian Key Laboratory of Individualized Active Immunotherapy, Fuzhou, China.,Fujian Medical University Union Hospital, Fuzhou, China
| | - Xuezhen Wang
- Department of Radiation Oncology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Xiaoping Chen
- Department of Statistics, College of Mathematics and Informatics & FJKLMAA, Fujian Normal University, Fuzhou, China
| | - Qiuyu Zhang
- Institute of Immunotherapy, Fujian Medical University, Fuzhou, China
| | - Jinsheng Hong
- Department of Radiation Oncology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.,Key Laboratory of Radiation Biology (Fujian Medical University), Fujian Province University, Fuzhou, China.,Fujian Key Laboratory of Individualized Active Immunotherapy, Fuzhou, China
| |
Collapse
|
33
|
Yao H, Zhang N, Zhang R, Duan M, Xie T, Pan J, Peng E, Huang J, Zhang Y, Xu X, Xu H, Zhou F, Wang G. Severity Detection for the Coronavirus Disease 2019 (COVID-19) Patients Using a Machine Learning Model Based on the Blood and Urine Tests. Front Cell Dev Biol 2020. [PMID: 32850809 DOI: 10.2139/ssrn.3564426] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/30/2023] Open
Abstract
The recent outbreak of the coronavirus disease-2019 (COVID-19) caused serious challenges to the human society in China and across the world. COVID-19 induced pneumonia in human hosts and carried a highly inter-person contagiousness. The COVID-19 patients may carry severe symptoms, and some of them may even die of major organ failures. This study utilized the machine learning algorithms to build the COVID-19 severeness detection model. Support vector machine (SVM) demonstrated a promising detection accuracy after 32 features were detected to be significantly associated with the COVID-19 severeness. These 32 features were further screened for inter-feature redundancies. The final SVM model was trained using 28 features and achieved the overall accuracy 0.8148. This work may facilitate the risk estimation of whether the COVID-19 patients would develop the severe symptoms. The 28 COVID-19 severeness associated biomarkers may also be investigated for their underlining mechanisms how they were involved in the COVID-19 infections.
Collapse
Affiliation(s)
- Haochen Yao
- Department of Pathogenobiology, The Key Laboratory of Zoonosis, Chinese Ministry of Education, College of Basic Medical Science, Jilin University, Changchun, China
| | - Nan Zhang
- The First Hospital of Jilin University, Jilin University, Changchun, China
| | - Ruochi Zhang
- BioKnow Health Informatics Lab, College of Software, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China
| | - Meiyu Duan
- BioKnow Health Informatics Lab, College of Software, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China
| | - Tianqi Xie
- School of Computing and Information, University of Pittsburgh, Pittsburgh, PA, United States
| | - Jiahui Pan
- Department of Pathogenobiology, The Key Laboratory of Zoonosis, Chinese Ministry of Education, College of Basic Medical Science, Jilin University, Changchun, China
| | - Ejun Peng
- Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Juanjuan Huang
- Department of Pathogenobiology, The Key Laboratory of Zoonosis, Chinese Ministry of Education, College of Basic Medical Science, Jilin University, Changchun, China
| | - Yingli Zhang
- The First Hospital of Jilin University, Jilin University, Changchun, China
| | - Xiaoming Xu
- The First Hospital of Jilin University, Jilin University, Changchun, China
| | - Hong Xu
- The First Hospital of Jilin University, Jilin University, Changchun, China
| | - Fengfeng Zhou
- BioKnow Health Informatics Lab, College of Software, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China
| | - Guoqing Wang
- Department of Pathogenobiology, The Key Laboratory of Zoonosis, Chinese Ministry of Education, College of Basic Medical Science, Jilin University, Changchun, China
| |
Collapse
|