Genetic variants in GHR and PLCE1 genes are associated with susceptibility to esophageal cancer

Single Nucleotide Polymorphisms (SNPs) in the Shadows: Uncovering their Function in Non-Coding Region of Esophageal Cancer

Esophageal cancer is a complex disease influenced by genetic and environmental factors. Single nucleotide polymorphisms (SNPs) in non-coding regions of the genome have emerged as crucial contributors to esophageal cancer susceptibility. This review provides a comprehensive overview of the role of SNPs in non-coding regions and their association with esophageal cancer. The accumulation of SNPs in the genome has been implicated in esophageal cancer risk. Various studies have identified specific locations in the genome where SNPs are more likely to occur, suggesting a location-specific response. Chromatin conformational studies have shed light on the localization of SNPs and their impact on gene transcription, posttranscriptional modifications, gene expression regulation, and histone modification. Furthermore, miRNA-related SNPs have been found to play a significant role in esophageal squamous cell carcinoma (ESCC). These SNPs can affect miRNA binding sites, thereby altering target gene regulation and contributing to ESCC development. Additionally, the risk of ESCC has been linked to base excision repair, suggesting that SNPs in this pathway may influence disease susceptibility. Somatic DNA segment alterations and modified expression quantitative trait loci (eQTL) have also been associated with ESCC. These alterations can lead to disrupted gene expression and cellular processes, ultimately contributing to cancer development and progression. Moreover, SNPs have been found to be associated with the long non-coding RNA HOTAIR, which plays a crucial role in ESCC pathogenesis. This review concludes with a discussion of the current and future perspectives in the field of SNPs in non-coding regions and their relevance to esophageal cancer. Understanding the functional implications of these SNPs may lead to the identification of novel therapeutic targets and the development of personalized approaches for esophageal cancer prevention and treatment.

Integrated bioinformatical analysis, machine learning and in vitro experiment-identified m6A subtype, and predictive drug target signatures for diagnosing renal fibrosis

Renal biopsy is the gold standard for defining renal fibrosis which causes calcium deposits in the kidneys. Persistent calcium deposition leads to kidney inflammation, cell necrosis, and is related to serious kidney diseases. However, it is invasive and involves the risk of complications such as bleeding, especially in patients with end-stage renal diseases. Therefore, it is necessary to identify specific diagnostic biomarkers for renal fibrosis. This study aimed to develop a predictive drug target signature to diagnose renal fibrosis based on m6A subtypes. We then performed an unsupervised consensus clustering analysis to identify three different m6A subtypes of renal fibrosis based on the expressions of 21 m6A regulators. We evaluated the immune infiltration characteristics and expression of canonical immune checkpoints and immune-related genes with distinct m6A modification patterns. Subsequently, we performed the WGCNA analysis using the expression data of 1,611 drug targets to identify 474 genes associated with the m6A modification. 92 overlapping drug targets between WGCNA and DEGs (renal fibrosis vs. normal samples) were defined as key drug targets. A five target gene predictive model was developed through the combination of LASSO regression and stepwise logistic regression (LASSO-SLR) to diagnose renal fibrosis. We further performed drug sensitivity analysis and extracellular matrix analysis on model genes. The ROC curve showed that the risk score (AUC = 0.863) performed well in diagnosing renal fibrosis in the training dataset. In addition, the external validation dataset further confirmed the outstanding predictive performance of the risk score (AUC = 0.755). These results indicate that the risk model has an excellent predictive performance for diagnosing the disease. Furthermore, our results show that this 5-target gene model is significantly associated with many drugs and extracellular matrix activities. Finally, the expression levels of both predictive signature genes EGR1 and PLA2G4A were validated in renal fibrosis and adjacent normal tissues by using qRT-PCR and Western blot method.

Genetic variants in GHR and PLCE1 genes are associated with susceptibility to esophageal cancer

Background

Esophageal cancer (EC) is the leading cause of cancer‐related mortality worldwide. The underlying genetic risk factors remain unclear. The association between gene growth hormone receptor (GHR) and phospholipase C epsilon 1 (PLCE1) polymorphisms and the EC risk were identified in this study.

Methods

A total of 506 EC cases and 507 controls were included in this research. Two SNPs (rs6898743 of GHR and rs2274223 of PLCE1) were selected and genotyped. The associations between gene polymorphisms and the EC risk were assessed by logistic regression analysis. The databases RegulomeDB, GTEx, and UALCAN were used for functional annotations.

Results

In the allelic frequencies analysis, the rs6898743 of GHR was associated with decreased susceptibility of EC (OR = 0.83, 95% CI: 0.70–1.00, p = 0.049), while rs2274223 of PLCE1 was associated with increased 0.25‐fold EC risk (OR = 1.25, 95% CI: 1.02–1.53, p = 0.037). The “GC” genotype of rs6898743 was associated with a 0.24‐fold decreased risk of EC under co‐dominant model (OR = 0.76, 95% CI: 0.58–0.99, p = 0.046), and the “GA” genotype of rs2274223 was associated with increased EC risk under co‐dominant model (OR = 1.36, 95% CI: 1.04–1.77, p = 0.023). Using GTEx database, rs2274223 was found to be significant associated with increased PLCE1 expression (p = 4.1 × 10⁻⁷) in esophagus muscularis. The UALCAN database demonstrated that the GHR gene was under‐expressed in esophageal cancer tissues (p = 0.017).

Conclusion

The gene GHR and PLCE1 polymorphisms are associated with EC in the general population and the results need to be verified in future.