Predictive value of an eight-mRNA signature in colon adenocarcinoma prognosis

Abstract

Colorectal cancer is a prevalent malignancy, with colon adenocarcinoma as the most common type. Early diagnosis biomarkers and effective risk stratification are crucial for optimal treatment. In this study, gene expression data from the Cancer Genome Atlas and Gene Expression Omnibus (GEO) were analyzed to identify relevant genes for colon adenocarcinoma. These datasets were standardized and subjected to weighted gene co-expression network analysis and differentially expressed gene analysis. Univariate Cox regression and least absolute shrinkage and selection operator Cox regression analyses were performed to generate a risk profile and identify prognosis-related genes. Receiver operating characteristic (ROC) analysis, Kaplan-Meier (KM) curve, and Cox analyses validated the risk signature. Immune cell infiltration patterns and immunological activities in high- and low-risk groups were assessed using single-sample gene set enrichment analysis (ssGSEA). GSEA was used to investigate the signaling pathways associated with low-risk and high-risk groups, whereas ssGSEA was used to analyze those associated with high-risk groups. A line graph was created to predict the overall survival (OS) of patients. Quantitative real-time polymerase chain reaction confirmed differential gene expression between normal and cancerous colon tissues. The eight genes identified - ACOX1, ATP8B1, CHGA, NAT2, PKIB, SLC39A8, TINAG, and VEGFA - correlated with tumor immunity and clinical outcomes. This eight-gene risk profile can accurately stratify risk and predict OS based on KM curves, ROC analysis, and regression models. GSEA analysis revealed calcium ion metabolism as the top pathway in the GEO dataset. These findings provide a foundation for prognostic evaluation and may guide therapeutic decision-making in colon adenocarcinoma.

Keywords

Colon adenocarcinoma / Weighted gene co-expression network analysis / Gene set enrichment analysis / Prognosis

Funding

This study was supported by the Tianjin Municipal Health Commission Chinese Medicine and Western Medicine Research Project (2023193), the Tianjin Municipal Health Commission Chinese Medicine and Western Medicine Research Project (2023222), and the Tianjin Municipal Education Commission Subjects (2022KJ187).

Conflict of interest

The authors declare no competing interest.

References

[1] Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021; 71(3):209-249. doi: 10.3322/caac.21660

[2] Wang H, Liu J, Li J, et al. Identification of gene modules and hub genes in colon adenocarcinoma associated with pathological stage based on WGCNA analysis. Cancer Genet. 2020; 242:1-7. doi: 10.1016/j.cancergen.2020.01.05

[3] Aaltonen LA. The multistep process of colon carcinogenesis. Cytokines Mol Ther. 1996; 2(2):111-114.

[4] Miller KD, Nogueira L, Mariotto AB, et al. Cancer treatment and survivorship statistics, 2019. CA Cancer J Clin. 2019; 69(5):363-385. doi: 10.3322/caac.21565

[5] Arnold M, Abnet CC, Neale RE, et al. Global burden of 5 major types of gastrointestinal cancer. Gastroenterology. 2020; 159(1):335-349.e15. doi: 10.1053/j.gastro.2020.02.068

[6] Oberndorfer F, Moling S, Hagelkruys LA, et al. Risk reclassification of patients with endometrial cancer based on tumor molecular profiling: First real world data. J Pers Med. 2021; 11(1):48. doi: 10.3390/jpm11010048

[7] Langfelder P, Horvath S. WGCNA: An R package for weighted correlation network analysis. BMC Bioinform. 2008; 9:559. doi: 10.1186/1471-2105-9-559

[8] Tu J, Kuang Z, Xie X, et al. Prognostic and predictive value of a mRNA signature in peripheral T-cell lymphomas: A mRNA expression analysis. J Cell Mol Med. 2021; 25(1):84-95. doi: 10.1111/jcmm.15851

[9] Marisa L, De Reyniès A, Duval A, et al. Gene expression classification of colon cancer into molecular subtypes: Characterization, validation, and prognostic value. PLoS Med. 2013; 10(5):e1001453. doi: 10.1371/journal.pmed.1001453

[10] Clough E, Barrett T. The gene expression omnibus database. Methods Mol Biol. 2016; 1418:93-110. doi: 10.1007/978-1-4939-3578-9-5

[11] Flicek P, Amode MR, Barrell D, et al. Ensembl 2011. Nucleic Acids Res. 2011; 39(S 1):D800-D806. doi: 10.1093/nar/gkq1064

[12] Yuan Y, Chen J, Wang J, et al. Development and clinical validation of a novel 4-gene prognostic signature predicting survival in colorectal cancer. Front Oncol. 2020; 10:595. doi: 10.3389/fonc.2020.00595

[13] Mo X, Huang X, Feng Y, et al. Immune infiltration and immune gene signature predict the response to fluoropyrimidine-based chemotherapy in colorectal cancer patients. Oncoimmunology. 2020; 9(1):1832347. doi: 10.1080/2162402X.2020

[14] Miao YD, Wang JT, Yang Y, et al. Identification of prognosis-associated immune genes and exploration of immune. Biomark Med. 2020; 14(14):1353-1369. doi: 10.2217/bmm-2020-0024

[15] Cucchiara V, Cooperberg MR, Dall’Era M, et al. Genomic Markers in Prostate Cancer Decision Making. Eur Urol. 2018; 73(4):572-582. doi: 10.1016/j.eururo.2017.10.036

[16] Chen XF, Tian MX, Sun RQ, et al. SIRT5 inhibits peroxisomal ACOX1 to prevent oxidative damage and is downregulated in liver cancer. EMBO Rep. 2018; 19(5):e45124. doi: 10.15252/embr.201745124

[17] Sun LN, Zhi Z, Chen LY, et al. SIRT1 suppresses colorectal cancer metastasis by transcriptional repression of mir- 15b-5p. Cancer Lett. 2017; 409:104-115. doi: 10.1016/j.canlet.2017.09.001

[18] Deng L, Niu GM, Ren J, Ke CW. Identification of ATP8B1 as a tumor suppressor gene for colorectal cancer and its involvement in phospholipid homeostasis. Biomed Res Int. 2020; 2020:2015648. doi: 10.1155/2020/2015648

[19] Liu L, Yang J, Wang C. Analysis of the prognostic significance of solute carrier (SLC) family 39 genes in breast cancer. Biosci Rep. 2020; 40(8):BSR20200764. doi: 10.1042/BSR20200764

[20] Shao Y, Jia H, Huang L, et al. An original ferroptosis-related gene signature effectively predicts the prognosis and clinical status for colorectal cancer patients. Front Oncol. 2021; 11:711776. doi: 10.3389/fonc.2021.711776

[21] Wang R, Ma Y, Zhan S, et al. B7-H3 promotes colorectal cancer angiogenesis through activating the NF-Κb pathway to induce VEGFA expression. Cell Death Dis. 2020; 11(1):55. doi: 10.1038/s41419-020-2252-3

[22] Han J, Zhang X, Liu Y, Jing L, Liu YB, Feng L. CLCA4 and MS4A12 as the significant gene biomarkers of primary colorectal cancer. Biosci Rep. 2020; 40(8):BSR20200963. doi: 10.1042/BSR20200963

[23] Wang HW, Duan ZJ, Hu SS, Wang S. Expression of cAMP-dependent protein kinase inhibitor beta in colorectal carcinoma and its clinical significance. Nan Fang Yi Ke Da Xue Xue Bao. 2017; 37(6):744-749. doi: 10.3969/j.issn.1673-4254.2017.06.05

[24] Fei L, Wang Y. Microrna-495 reduces visceral sensitivity in mice with diarrhea-predominant irritable bowel syndrome through suppression of the PI3K/AKT signaling pathway via PKIB. IUBMB Life. 2020; 72(7):1468-1480. doi: 10.1002/iub.2270

[25] Bootman MD, Bultynck G. Fundamentals of cellular calcium signaling: A primer. Cold Spring Harb Perspect Biol. 2020; 12(1):a038802. doi: 10.1101/cshperspect.a038802

[26] Marchi S, Giorgi C, Galluzzi L, Pinton P. Ca2+ fluxes and cancer. Mol Cell. 2020; 78(6):1055-1069. doi: 10.1016/j.molcel.2020.04.017

[27] Tsoi H, Lam KC, Dong Y, et al. Pre-45s rRNA promotes colon cancer and is associated with poor survival of CRC patients. Oncogene. 2017; 36(44):6109-6118. doi: 10.1038/onc.2017.86

[28] Pan K, Xie Y. LncRNA FOXC2-AS1 enhances FOXC2 mRNA stability to promote colorectal cancer progression via activation of Ca2+-FAK signal pathway. Cell Death Dis. 2020; 11(6):434. doi: 10.1038/s41419-020-2633-7.2041-488