Literature DB >> 31588830

Autoantibodies as biomarkers for colorectal cancer: A systematic review, meta-analysis, and bioinformatics analysis.

Hejing Wang1, Xiaojin Li1, Donghu Zhou1, Jian Huang1.   

Abstract

Colorectal cancer is a very common cancer worldwide. Serum tumor-associated autoantibodies (TAAbs), especially the anti-p53 autoantibody, may be promising biomarkers to detect early-stage colorectal cancer. This study aimed to identify all known autoantibodies and their value in colorectal cancer diagnosis, as well as exploring the underlying connections and mechanisms through a bioinformatics analysis. Databases were used to select available articles of TAAbs in colorectal cancer. In a meta-analysis of the anti-p53 autoantibody, the diagnostic odds ratio and area under the curve (AUC) of the summary receiver-operating characteristic (SROC) curve were calculated using Stata 12.0 and Meta-Disc 1.4. We identified 73 articles including 199 single autoantibodies and 42 multiple autoantibodies. The maximum value of Youden's index was 0.76, combining c-MYC, p53, cyclin B1, p62, Koc, IMP1, and survivin. The diagnostic odds ratio for anti-p53 autoantibody at all stages was 10.86 (95% CI 8.40, 14.06) with low heterogeneity (I2 = 40.3%) and the AUC of the SROC curve was 0.82. For the anti-p53 autoantibody in early-stage colorectal cancer, the diagnostic odds ratio was 4.82 (95% CI 2.95, 7.87) with heterogeneity (I2 = 7.9%) and the AUC of the SROC curve was 0.72. Eighty-seven autoantibodies were selected for bioinformatics analyses. We found that the most enriched functional terms and protein-protein interactions may relate to the mechanism of autoantibody generation. In summary, our study summarized the diagnostic value of TAAbs in colorectal cancer, either as single molecules or in combination. Bioinformatics analyses may be a new approach to explore the mechanism of autoantibody generation.

Entities:  

Keywords:  Colorectal cancer; autoantibody; bioinformatics analysis; meta-analysis

Mesh:

Substances:

Year:  2019        PMID: 31588830     DOI: 10.1177/1724600819880906

Source DB:  PubMed          Journal:  Int J Biol Markers        ISSN: 0393-6155            Impact factor:   2.659


  5 in total

1.  Loss of CHGA Protein as a Potential Biomarker for Colon Cancer Diagnosis: A Study on Biomarker Discovery by Machine Learning and Confirmation by Immunohistochemistry in Colorectal Cancer Tissue Microarrays.

Authors:  Xueli Zhang; Hong Zhang; Chuanwen Fan; Camilla Hildesjö; Bairong Shen; Xiao-Feng Sun
Journal:  Cancers (Basel)       Date:  2022-05-27       Impact factor: 6.575

2.  Cyclic Peptide Mimotopes for the Detection of Serum Anti-ATIC Autoantibody Biomarker in Hepato-Cellular Carcinoma.

Authors:  Chang-Kyu Heo; Hai-Min Hwang; Won-Hee Lim; Hye-Jung Lee; Jong-Shin Yoo; Kook-Jin Lim; Eun-Wie Cho
Journal:  Int J Mol Sci       Date:  2020-12-19       Impact factor: 5.923

3.  IgM and IgA augmented autoantibody signatures improve early-stage detection of colorectal cancer prior to nodal and distant spread.

Authors:  Md Saiful Islam Roney; Catharine Lanagan; Yong Hua Sheng; Karen Lawler; Christopher Schmidt; Nam-Trung Nguyen; Jakob Begun; Gregor Stefan Kijanka
Journal:  Clin Transl Immunology       Date:  2021-09-26

4.  Biomarkers of non-communicable chronic disease: an update on contemporary methods.

Authors:  Solaiman M Al-Hadlaq; Hanan A Balto; Wail M Hassan; Najat A Marraiki; Afaf K El-Ansary
Journal:  PeerJ       Date:  2022-02-24       Impact factor: 3.061

Review 5.  Antibody Diversity in Cancer: Translational Implications and Beyond.

Authors:  Raghuram Reddy; Joel Mintz; Roei Golan; Fakiha Firdaus; Roxana Ponce; Derek Van Booven; Aysswarya Manoharan; Isabelle Issa; Bonnie B Blomberg; Himanshu Arora
Journal:  Vaccines (Basel)       Date:  2022-07-22
  5 in total

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