J Cancer 2021; 12(19):5967-5976. doi:10.7150/jca.58768 This issue Cite

Research Paper

Use of bioinformatic database analysis and specimen verification to identify novel biomarkers predicting gastric cancer metastasis

Weimin Wang1,2✉*, Ke Min2*, Gaoyang Chen3*, Hui Zhang4, Jianliang Deng1, Mengying Lv2, Zhihong Cao1✉, Yan Zhou1,2✉

1. Department of Oncology, Yixing Hospital Affiliated to Medical College of Yangzhou University, Yangzhou University, Jiangsu, China.
2. Institute of Combining Chinese Traditional and Western Medicine, Medical College, Yangzhou University, Jiangsu, China.
3. Department of Oncology, The second People's Hospital of Taizhou City, Jiangsu, China.
4. Department of Nursing, SuZhou Vocational Health College, Jiangsu, China.
*WW, KM and GC contributed equally to this work.

Citation:
Wang W, Min K, Chen G, Zhang H, Deng J, Lv M, Cao Z, Zhou Y. Use of bioinformatic database analysis and specimen verification to identify novel biomarkers predicting gastric cancer metastasis. J Cancer 2021; 12(19):5967-5976. doi:10.7150/jca.58768. https://www.jcancer.org/v12p5967.htm
Other styles

File import instruction

Abstract

Graphic abstract

Background: Gastric cancer (GC) is a common gastrointestinal tumor, and its metastasis has led to a significant increase in the death rate. The mechanisms of GC metastasis remain unclear.

Methods: The differentially expressed genes (DmRs) and lncRNAs (DlncRs) of GC were selected from The Cancer Genome Atlas (TCGA) database. We applied the weighted gene co-expression network analysis (WGCNA) to construct co-expression modules related with GC metastasis. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) method analyzed the functional regions and signal pathways of genes in vital modules. DmRs-DlncRs co-expression network were drawn for finding out hub nodes. Survival analyses of significant biomarkers were analyzed by Kaplan-Meier (KM) method. Finally, the expressions of selected biomarkers were validated in cell lines and caner tissues by quantitative real-time PCR (qRT-PCR), in GC tissue microarray by Fluorescence in situ hybridization (FISH).

Results: 4776 DmRs and 213 DlncRs were involved the construction of WGCNA network, and MEyellow module was identified to have more significant correlation with GC metastasis. DmRs and DlncRs of MEyellow module were proved to be involved in the processes of cancer pathogenesis by GO and KEGG pathway analysis. Through the DmRs-DlncRs co-expression network, 7 DmRs and 1 DlncRs were considered as hub nodes. Besides, the high expression of TIMD4, CETP, KRT27, PTGDS, FAM30A was worse than low expression in GC patients survival, respectively; However, LRRC26 was opposite trend. FAM30A and TIMD4 were all significant biomarkers of GC survival and hub genes. Simultaneously, TIMD4, CETP, KRT27, PTGDS, FAM30A were increased in GC cell lines and tissues compared with GES-1 and normal tissues, respectively; the expression of LRRC26 was reduced in GC cell lines and tissues.

Conclusion: This study identified 6 genes as new biomarkers affecting the metastasis of GC. Especially, FAM30A and TIMD4 might be an effective marker for predicting the prognosis and a potential-therapeutic target in GC.

Keywords: Gastric cancer, Metastasis, Prognosis, Biomarker, WGCNA


Citation styles

APA
Wang, W., Min, K., Chen, G., Zhang, H., Deng, J., Lv, M., Cao, Z., Zhou, Y. (2021). Use of bioinformatic database analysis and specimen verification to identify novel biomarkers predicting gastric cancer metastasis. Journal of Cancer, 12(19), 5967-5976. https://doi.org/10.7150/jca.58768.

ACS
Wang, W.; Min, K.; Chen, G.; Zhang, H.; Deng, J.; Lv, M.; Cao, Z.; Zhou, Y. Use of bioinformatic database analysis and specimen verification to identify novel biomarkers predicting gastric cancer metastasis. J. Cancer 2021, 12 (19), 5967-5976. DOI: 10.7150/jca.58768.

NLM
Wang W, Min K, Chen G, Zhang H, Deng J, Lv M, Cao Z, Zhou Y. Use of bioinformatic database analysis and specimen verification to identify novel biomarkers predicting gastric cancer metastasis. J Cancer 2021; 12(19):5967-5976. doi:10.7150/jca.58768. https://www.jcancer.org/v12p5967.htm

CSE
Wang W, Min K, Chen G, Zhang H, Deng J, Lv M, Cao Z, Zhou Y. 2021. Use of bioinformatic database analysis and specimen verification to identify novel biomarkers predicting gastric cancer metastasis. J Cancer. 12(19):5967-5976.

This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/). See http://ivyspring.com/terms for full terms and conditions.