In recent years, the incidence and mortality of GC have decreased with the improvement of treatment methods (15). However, the diagnosis and treatment of GC still face great challenges, especially the survival of patients. We cannot accurately predict the survival time only with the current staging system (16). The development of tumors depends on mutations in multiple genes, which means that models composed of multiple genes may be better predictors of patient prognosis than single indicators.6
Random errors often occur in the process of DNA replication, and the mismatched DNA will be repaired in time by the body's repair system. When this dynamic balance is broken, gene mutations will often occur. Such replication errors are also affected by external environment, such as radiation, smoking, diet, etc. When the mutated genes accumulate gradually or the key parts of the genes have missense mutations, the growth of cells will begin to be not regulated by the body normally, and the division and proliferation of cells will change, which means the beginning of cancer. CHDs gene family is a family of genes closely related to DNA duplex unwinding, DNA geometric change, DNA conformation change, DNA helicase activity and other biological functions. Studies have found that the occurrence of GC is often accompanied by changes in CHDs gene family. In other types of cancer have similar reports, such as high expression of CHD4 will promote colorectal cancer cell proliferation, invasion and metastasis (17), which were also determined for regulating the proliferation and migration of breast cancer oncogene (18, 19).There also have studies reported mutations of CHD4 through TGF-β signal pathway in promoting endometrial cancer (20). The loss of CHD1 increases the risk of postoperative metastasis of prostate cancer (21), and the expression of CHD5 and CHD9 may be independent biomarkers for prognosis of colorectal cancer (22, 23). Another study reported that patients with high expression of CHD9 had a worse prognosis compared with patients with low expression of CHD9 (24).This attracts us to use TCGA and other public databases for further analysis of CHDs gene, so as to provide new reference for future clinical treatment.
Tumor mutation load refers to the number of somatic mutations removed from the germ line of the tumor genome. Theoretically, higher TMB would also produce more neoantigens, and targeted immunotherapy might be more effective (25).Defective DNA mismatch repair will lead to increased mutation load, and the occurrence of GC and drug resistance are related to this(26, 27). Genomic instability is closely associated with defective repair of DNA damage, and CHD4, which is involved in chromatin relaxation, may affect DNA repair when mutated(28). CDH1 is also involved in chromatin repair, and the loss of CHD1 leads to chromatin dysregulation (29). In addition, CHD1 has been reported to promote DNA damage repair in prostate epithelial cells (30).We analyzed the relationship between CHDs genes and tumor mutation load and showed that mutations in CHDs directly contributed to the increase in TMB, which may be related to the involvement of CHDs genes in DNA duplex unwinding and other biological functions.
With the development of gene research, it has been found that the regulation of protein synthesis is not only the unique function of coding genes. In addition to the role of genetic information carrier, RNA also plays a variety of regulatory functions. LncRNA also plays a complex and precise regulatory role in body development, gene expression, transcriptional activation, transcriptional interference, nuclear transport and other regulatory processes, which have attracted extensive attention. With the further study of lncRNA, the changes of lncRNA transcriptome are expected to become a new indicator in tumor diagnosis and become a new diagnostic and therapeutic tool (12). In this study, CHDs gene-related lncRNAs were studied. Firstly, OS-related lncRNAs were extracted, and a prognostic model based on these lncRNAs was established by Lasso regression method to predict the survival of GC patients. Secondly, ROC curve was used to represent the reliability of the model. Risk curve and survival curve were used to show the survival status of GC patients with different risks scores. Univariate and multivariate Cox regressions analysis were used to test whether risk score could be used as an independent prognostic indicator to predict patient survival. Next, we divided the patients into different groups and used the remaining TCGA data to test the correctness of the model. The results showed good performance, with the increase of risk score, the mortality rate of the patients also increased.
However, our current study still has several limitations to consider. First, our patient data comes from the TCGA database, and the sample size is relatively small with regional distribution. Therefore, we need more data to verify the reliability of the model. Secondly, we need more and larger research centers to verify the value of CHDs-related lncRNA model. Thirdly, we have not yet studied the mechanism of these lncRNAs, and more experiments are needed to clarify the molecular mechanism.