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Hoseini SH, Enayati P, Nazari M, Babakhanzadeh E, Rastgoo M, Sohrabi NB. Biomarker Profile of Colorectal Cancer: Current Findings and Future Perspective. J Gastrointest Cancer 2024; 55:497-510. [PMID: 38168859 DOI: 10.1007/s12029-023-00990-9] [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] [Accepted: 11/19/2023] [Indexed: 01/05/2024]
Abstract
OBJECTIVE Breakthroughs in omics technology have led to a deeper understanding of the fundamental molecular changes that play a critical role in the development and progression of cancer. This review delves into the hidden molecular drivers of colorectal cancer (CRC), offering potential for clinical translation through novel biomarkers and personalized therapies. METHODS We summarizes recent studies utilizing various omics approaches, including genomics, transcriptomics, proteomics, epigenomics, metabolomics and data integration with computational algorithms, to investigate CRC. RESULTS Integrating multi-omics data in colorectal cancer research unlocks hidden biological insights, revealing new pathways and mechanisms. This powerful approach not only identifies potential biomarkers for personalized prognosis, diagnosis, and treatment, but also predicts patient response to specific therapies, while computational tools illuminate the landscape by deciphering complex datasets. CONCLUSIONS Future research should prioritize validating promising biomarkers and seamlessly translating them into clinical practice, ultimately propelling personalized CRC management to new heights.
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Affiliation(s)
| | - Parisa Enayati
- Biological Sciences Department, Northern Illinois University, DeKalb, IL, USA
| | - Majid Nazari
- Department of Medical Genetics, Shahid Sadoughi University of Medical Sciences, Yazd, Iran.
- , P.O. Box, Tehran, 64155-65117, Iran.
| | - Emad Babakhanzadeh
- Department of Medical Genetics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Maryam Rastgoo
- Department of Microbiology, Shiraz Islamic Azad University, Shiraz, Iran
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Wang Z, Shi P, Huang P, Xu C, He Y, Lei W. Identification of secretory pathway-related genes based on Random Forest algorithm to predict the prognosis and immunotherapy response of hepatocellular carcinoma. J Gene Med 2024; 26:e3593. [PMID: 37730948 DOI: 10.1002/jgm.3593] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 07/31/2023] [Accepted: 08/25/2023] [Indexed: 09/22/2023] Open
Abstract
BACKGROUND The dysfunction of secretory pathways may represent biomarkers or therapeutic targets of cancer. The hepatocellular carcinoma (HCC) phenotype was studied in relation to the genes in the secretory pathway and to screen for a combination of genes that may be a viable therapeutic target for HCC and connected to the pathophysiological features of the tumor. METHODS Using the HCC information from The Cancer Genome Atlas, somatic mutation and prognostic association analysis were performed on the secretory pathway genes. Based on prognostic genes in the secretory pathway, the samples were consensus clustered, and a Random Forest model was built. The clinical characteristics, tumor mutation burden, functional status and potential responses to immunotherapy and tumor suppressor medications of various subtypes and risk groups were discussed. RESULTS Of the 84 genes for secretory pathway, 32 were prognostic genes related to HCC, which divided HCC into two categories: C1 and C2. By comparing the two types of HCC samples, it was found that the survival outcome of C1 was inferior, with stronger adaptive and innate immunity, but less sensitive to immunotherapy than C2. The constructed prognostic signature included seven of the 32 prognostic genes in the secretory pathway, which showed significant correlation with the prognosis, somatic mutation, biological pathway status, potential response to immunotherapy and sensitivity of 72 tumor suppressor drugs from different HCC cohorts, and had a feasible prognostic effect for 31 types of cancer and immunotherapy cohorts. CONCLUSIONS In this study, HCC was divided into two molecular subtypes according to prognostic genes in the secretory pathway, and seven of them were combined into one signature, which produced significant results in evaluating the prognosis of different HCC cohorts, pan-cancer cohorts and immunotherapy cohorts, and had potential guiding significance for prophylactic immunotherapy in patients with HCC.
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Affiliation(s)
- Zanzhi Wang
- Department of Emergency Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Pengwei Shi
- Department of Emergency Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Peng Huang
- Department of Emergency Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Chun Xu
- Department of Emergency Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Yaoquan He
- Department of Emergency Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Wenxiong Lei
- Department of Emergency Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, China
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Sun S, Cai X, Shao J, Zhang G, Liu S, Wang H. Machine learning-based approach for efficient prediction of diagnosis, prognosis and lymph node metastasis of papillary thyroid carcinoma using adhesion signature selection. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:20599-20623. [PMID: 38124567 DOI: 10.3934/mbe.2023911] [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: 12/23/2023]
Abstract
The association between adhesion function and papillary thyroid carcinoma (PTC) is increasingly recognized; however, the precise role of adhesion function in the pathogenesis and prognosis of PTC remains unclear. In this study, we employed the robust rank aggregation algorithm to identify 64 stable adhesion-related differentially expressed genes (ARDGs). Subsequently, using univariate Cox regression analysis, we identified 16 prognostic ARDGs. To construct PTC survival risk scoring models, we employed Lasso Cox and multivariate + stepwise Cox regression methods. Comparative analysis of these models revealed that the Lasso Cox regression model (LPSRSM) displayed superior performance. Further analyses identified age and LPSRSM as independent prognostic factors for PTC. Notably, patients classified as low-risk by LPSRSM exhibited significantly better prognosis, as demonstrated by Kaplan-Meier survival analyses. Additionally, we investigated the potential impact of adhesion feature on energy metabolism and inflammatory responses. Furthermore, leveraging the CMAP database, we screened 10 drugs that may improve prognosis. Finally, using Lasso regression analysis, we identified four genes for a diagnostic model of lymph node metastasis and three genes for a diagnostic model of tumor. These gene models hold promise for prognosis and disease diagnosis in PTC.
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Affiliation(s)
- Shuo Sun
- Department of Hepatobiliary and Pancreatic Surgery, Affiliated Hospital of Beihua University, Beihua University, Jilin 132013, China
| | - Xiaoni Cai
- Department of General Surgery, Shangyu People's Hospital of Shaoxing, the Second Affiliated Hospital of Zhejiang University Medical College Hospital, Shaoxing 312399, China
| | - Jinhai Shao
- Department of General Surgery, Shangyu People's Hospital of Shaoxing, the Second Affiliated Hospital of Zhejiang University Medical College Hospital, Shaoxing 312399, China
| | - Guimei Zhang
- Department of Neurology and Neuroscience Center, The First Hospital of Jilin University, Jilin University, Changchun 130061, China
| | - Shan Liu
- Department of Nuclear Medicine, The Second Hospital of Jilin University, Jilin University, Changchun 130041, China
| | - Hongsheng Wang
- Department of Hepatobiliary and Pancreatic Surgery, Affiliated Hospital of Beihua University, Beihua University, Jilin 132013, China
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Gholami N, Haghparast A, Alipourfard I, Nazari M. Prostate cancer in omics era. Cancer Cell Int 2022; 22:274. [PMID: 36064406 PMCID: PMC9442907 DOI: 10.1186/s12935-022-02691-y] [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: 05/25/2022] [Accepted: 08/22/2022] [Indexed: 11/18/2022] Open
Abstract
Recent advances in omics technology have prompted extraordinary attempts to define the molecular changes underlying the onset and progression of a variety of complex human diseases, including cancer. Since the advent of sequencing technology, cancer biology has become increasingly reliant on the generation and integration of data generated at these levels. The availability of multi-omic data has transformed medicine and biology by enabling integrated systems-level approaches. Multivariate signatures are expected to play a role in cancer detection, screening, patient classification, assessment of treatment response, and biomarker identification. This review reports current findings and highlights a number of studies that are both novel and groundbreaking in their application of multi Omics to prostate cancer.
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Affiliation(s)
- Nasrin Gholami
- Hematology and Oncology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | | | - Iraj Alipourfard
- Institutitue of Biology, Biotechnology and Environmental Protection, Faculty of Natural Sciences, University of Silesia, Katowice, Poland
| | - Majid Nazari
- Department of Medical Genetics, Faculty of Medicine, Shahid Sadoughi University of Medical Sciences, Yazd, Iran.
- , P.O. Box 14155-6117, Shiraz, Iran.
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Keshavarz-Rahaghi F, Pleasance E, Kolisnik T, Jones SJM. A p53 transcriptional signature in primary and metastatic cancers derived using machine learning. Front Genet 2022; 13:987238. [PMID: 36134028 PMCID: PMC9483853 DOI: 10.3389/fgene.2022.987238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Accepted: 08/01/2022] [Indexed: 11/13/2022] Open
Abstract
The tumor suppressor gene, TP53, has the highest rate of mutation among all genes in human cancer. This transcription factor plays an essential role in the regulation of many cellular processes. Mutations in TP53 result in loss of wild-type p53 function in a dominant negative manner. Although TP53 is a well-studied gene, the transcriptome modifications caused by the mutations in this gene have not yet been explored in a pan-cancer study using both primary and metastatic samples. In this work, we used a random forest model to stratify tumor samples based on TP53 mutational status and detected a p53 transcriptional signature. We hypothesize that the existence of this transcriptional signature is due to the loss of wild-type p53 function and is universal across primary and metastatic tumors as well as different tumor types. Additionally, we showed that the algorithm successfully detected this signature in samples with apparent silent mutations that affect correct mRNA splicing. Furthermore, we observed that most of the highly ranked genes contributing to the classification extracted from the random forest have known associations with p53 within the literature. We suggest that other genes found in this list including GPSM2, OR4N2, CTSL2, SPERT, and RPE65 protein coding genes have yet undiscovered linkages to p53 function. Our analysis of time on different therapies also revealed that this signature is more effective than the recorded TP53 status in detecting patients who can benefit from platinum therapies and taxanes. Our findings delineate a p53 transcriptional signature, expand the knowledge of p53 biology and further identify genes important in p53 related pathways.
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Affiliation(s)
- Faeze Keshavarz-Rahaghi
- Canada’s Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada
- Department of Bioinformatics, University of British Columbia, Vancouver, BC, Canada
| | - Erin Pleasance
- Canada’s Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada
| | - Tyler Kolisnik
- Canada’s Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada
- School of Natural and Computational Sciences, Massey University, Auckland, New Zealand
| | - Steven J. M. Jones
- Canada’s Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
- Department of Molecular Biology and Biochemistry, Simon Fraser University, Vancouver, BC, Canada
- *Correspondence: Steven J. M. Jones,
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Mi J, Ma S, Chen W, Kang M, Xu M, Liu C, Li B, Wu F, Liu F, Zhang Y, Wang R, Jiang L. Integrative Pan-Cancer Analysis of KIF15 Reveals Its Diagnosis and Prognosis Value in Nasopharyngeal Carcinoma. Front Oncol 2022; 12:772816. [PMID: 35359374 PMCID: PMC8963360 DOI: 10.3389/fonc.2022.772816] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 02/18/2022] [Indexed: 12/19/2022] Open
Abstract
BackgroundKIF15 plays a vital role in many biological processes and has been reported to influence the occurrence and development of certain human cancers. However, there are few systematic evaluations on the role of KIF15 in human cancers, and the role of KIF15 in the diagnosis and prognosis of nasopharyngeal carcinoma (NPC) also remains unexplored. Therefore, this study aimed to conduct a pan-cancer analysis of KIF15 and evaluate its diagnostic and prognostic potential in NPC.MethodsThe expression pattern, prognostic value, molecular function, tumor mutation burden, microsatellite instability, and immune cell infiltration of KIF15 were examined based on public databases. Next, the diagnostic value of KIF15 in NPC was analyzed using the Gene Expression Omnibus (GEO) database and immunohistochemistry (IHC). Kaplan–Meier curves, Cox regression analyses, and nomograms were used to evaluate the effects of KIF15 expression on NPC prognosis. Finally, the effect of KIF15 on NPC was explored by in vitro experiments.ResultsThe expression of KIF15 was significantly upregulated in 20 out of 33 cancer types compared to adjacent normal tissue. Kyoto Encyclopedia of Genes and Genomes enrichment (KEGG) analysis showed that KIF15 could participate in several cancer-related pathways. The increased expression level of KIF15 was correlated with worse clinical outcomes in many types of human cancers. Additionally, KIF15 expression was related to cancer infiltration of immune cells, tumor mutation burden, and microsatellite instability. In the analysis of NPC, KIF15 was significantly upregulated based on the GEO database and immunohistochemistry. A high expression of KIF15 was negatively associated with the prognosis of patients with NPC. A nomogram model integrating clinical characteristics and KIF15 expression was established, and it showed good predictive ability with an area under the curve value of 0.73. KIF15 knockdown significantly inhibited NPC cell proliferation and migration.ConclusionsOur findings revealed the important and functional role of KIF15 as an oncogene in pan-cancer. Moreover, high expression of KIF15 was found in NPC tissues, and was correlated with poor prognosis in NPC. KIF15 may serve as a potential therapeutic target in NPC treatment.
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Affiliation(s)
- Jinglin Mi
- Department of Radiation Oncology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Shanshan Ma
- Department of Radiation Oncology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Wei Chen
- Department of Radiation Oncology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
- Department of Oncology, Yunfu People’s Hospital, Yunfu, China
| | - Min Kang
- Department of Radiation Oncology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
- Key Laboratory of High-Incidence-Tumor Prevention & Treatment (Guangxi Medical University), Ministry of Education, Nanning, China
| | - Meng Xu
- Department of Radiation Oncology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Chang Liu
- Department of Radiation Oncology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Bo Li
- Department of Radiation Oncology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Fang Wu
- Department of Radiation Oncology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
- Key Laboratory of High-Incidence-Tumor Prevention & Treatment (Guangxi Medical University), Ministry of Education, Nanning, China
| | - Fengju Liu
- Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Yong Zhang
- Department of Radiation Oncology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Rensheng Wang
- Department of Radiation Oncology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
- Key Laboratory of High-Incidence-Tumor Prevention & Treatment (Guangxi Medical University), Ministry of Education, Nanning, China
- *Correspondence: Li Jiang, ; Rensheng Wang,
| | - Li Jiang
- Department of Radiation Oncology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
- *Correspondence: Li Jiang, ; Rensheng Wang,
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