Association of Genetic Polymorphisms in TNFRSF11 with the Progression of Genetic Susceptibility to Gastric Cancer

Using machine learning approach for screening metastatic biomarkers in colorectal cancer and predictive modeling with experimental validation

Colorectal cancer (CRC) liver metastasis accounts for the majority of fatalities associated with CRC. Early detection of metastasis is crucial for improving patient outcomes but can be delayed due to a lack of symptoms. In this research, we aimed to investigate CRC metastasis-related biomarkers by employing a machine learning (ML) approach and experimental validation. The gene expression profile of CRC patients with liver metastasis was obtained using the GSE41568 dataset, and the differentially expressed genes between primary and metastatic samples were screened. Subsequently, we carried out feature selection to identify the most relevant DEGs using LASSO and Penalized-SVM methods. DEGs commonly selected by these methods were selected for further analysis. Finally, the experimental validation was done through qRT-PCR. 11 genes were commonly selected by LASSO and P-SVM algorithms, among which seven had prognostic value in colorectal cancer. It was found that the expression of the MMP3 gene decreases in stage IV of colorectal cancer compared to other stages (P value < 0.01). Also, the expression level of the WNT11 gene was observed to increase significantly in this stage (P value < 0.001). It was also found that the expression of WNT5a, TNFSF11, and MMP3 is significantly lower, and the expression level of WNT11 is significantly higher in liver metastasis samples compared to primary tumors. In summary, this study has identified a set of potential biomarkers for CRC metastasis using ML algorithms. The findings of this research may provide new insights into identifying biomarkers for CRC metastasis and may potentially lay the groundwork for innovative therapeutic strategies for treatment of this disease.

Case-control study on CYP4B1 gene polymorphism and susceptibility to gastric cancer in the chinese Han population

Background:

In China, gastric cancer (GC) is one of the most common malignant tumors. This study aimed to explore the relationship of rs2297810, rs4646491 and rs2297809 polymorphisms of CYP4B1 with susceptibility to GC in the Chinese Han population.

Methods:

A case-control study including 707 GC cases and 707 normal controls was conducted. Three single nucleotide polymorphisms (SNPs) were genotyped by Agena MassARRAY system. Logistic regression analysis was utilized to assess the effects of SNPs on GC risk. Furthermore, multifactor dimensionality reduction (MDR) approach was used to analyze the SNP-SNP interactions.

Results:

No significant relationships were found between rs2297810 and rs2297809 and GC risk under all genetic models. For rs4646491, people with TC genotype had a 1.40-fold higher risk of GC than those with CC genotype (OR = 1.40; 95% CI = 1.13–1.74; p = 0.002), and people with TT-TC genotype had a 1.30-fold higher risk of GC than those with CC genotype (OR = 1.30; 95% CI = 1.06–1.61; p = 0.014). Stratification results showed that GC risk in people carrying TC genotype was higher than that in people with CC genotype, males (OR = 1.36; 95% CI = 1.06–1.75; p = 0.015), non-smokers (OR = 1.52; 95% CI = 1.11–2.07; p = 0.009) and non-drinkers (OR = 1.50; 95% CI = 1.10–2.04; p = 0.010). Additionally, the study also revealed that GC risk in people carrying TT-TC genotype was higher than that in people with CC genotype, males (OR = 1.29; 95% CI = 1.01–1.64; p = 0.040), non-smokers (OR = 1.40; 95% CI = 1.04–1.89; p = 0.027) and non-drinkers (OR = 1.39; 95% CI = 1.03–1.87; p = 0.030).

Conclusion:

This study firstly found that CYP4B1-rs4646491 was significantly correlated with GC risk, and it might be a risk factor for GC.

Genetic interactions effects for cancer disease identification using computational models: a review

Genome-wide association studies (GWAS) provide clear insight into understanding genetic variations and environmental influences responsible for various human diseases. Cancer identification through genetic interactions (epistasis) is one of the significant ongoing researches in GWAS. The growth of the cancer cell emerges from multi-locus as well as complex genetic interaction. It is impractical for the physician to detect cancer via manual examination of SNPs interaction. Due to its importance, several computational approaches have been modeled to infer epistasis effects. This article includes a comprehensive and multifaceted review of all relevant genetic studies published between 2001 and 2020. In this contemporary review, various computational methods are as follows: multifactor dimensionality reduction-based approaches, statistical strategies, machine learning, and optimization-based techniques are carefully reviewed and presented with their evaluation results. Moreover, these computational approaches' strengths and limitations are described. The issues behind the computational methods for identifying the cancer disease through genetic interactions and the various evaluation parameters used by researchers have been analyzed. This review is highly beneficial for researchers and medical professionals to learn techniques adapted to discover the epistasis and aids to design novel automatic epistasis detection systems with strong robustness and maximum efficiency to address the different research problems in finding practical solutions effectively.