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Expression of Anoikis-Related Genes and Potential Biomarkers in Colon Cancer Based on RNA-seq and scRNA-seq

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Abstract

Colon cancer (CC) is a malignant tumor in the colon. Despite some progress in the early detection and treatment of CC in recent years, some patients still experience recurrence and metastasis. Therefore, it is urgent to better predict the prognosis of CC patients and identify new biomarkers. Recent studies have shown that anoikis-related genes (ARGs) play a significant role in the progression of many tumors. Hence, it is essential to confirm the role of ARGs in the development and treatment of CC by integrating scRNA-seq and transcriptome data. This study integrated transcriptome and single-cell sequencing (scRNA-seq) data from CC samples to evaluate patient stratification, prognosis, and ARG expression in different cell types. Specifically, differential expression of ARGs was identified through consensus clustering to classify CC subtypes. Subsequently, a CC risk model composed of CDKN2A, NOX4, INHBB, CRYAB, TWIST1, CD36, SERPINE1, and MMP3 was constructed using prognosis-related ARGs. Finally, using scRNA-seq data of CC, the expression landscape of prognostic genes in different cell types and the relationship between important immune cells and other cells were explored. Through the above analysis, two CC subtypes were identified, showing significant differences in prognosis and clinical factors. Subsequently, a risk model comprising aforementioned genes successfully categorized all CC samples into two risk groups, which also exhibited significant differences in prognosis, clinical factors, involved pathways, immune landscape, and drug sensitivity. Multiple pathways (cell adhesion molecules (CAMs), and extracellular matrix (ECM) receptor interaction) and immune cells/immune functions (B cell naive, dendritic cell activate, plasma cells, and T cells CD4 memory activated) related to CC were identified. Furthermore, it was found that prognostic genes were highly expressed in various immune cells, and B cells exhibited more and stronger interaction pathways with other cells. The results of this study may provide references for personalized treatment and potential biomarker identification in CC.

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Data Availability

All the data used in this paper are from the TCGA (https://www.cancer.gov/ccg/research/genome-sequencing/tcga) and GEO database (https://www.ncbi.nlm.nih.gov/geo/).

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Acknowledgements

We thank the anonymous reviewers for their constructive comments which have helped improve the manuscript.

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All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by TG and YW. The first draft of the manuscript was written by TG and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Yadong Wang.

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Guo, T., Wang, Y. Expression of Anoikis-Related Genes and Potential Biomarkers in Colon Cancer Based on RNA-seq and scRNA-seq. Appl Biochem Biotechnol (2024). https://doi.org/10.1007/s12010-024-04957-9

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