The PTPN13 Y2081D (T>G) (rs989902) polymorphism is associated with an increased risk of sporadic colorectal cancer

PTPN13 rs989902 and CHEK2 rs738722 are associated with esophageal cancer

PURPOSE

Individual genetic background can play an essential role in determining the development of esophageal squamous cell carcinoma (ESCC). PTPN13 and CHEK2 play important roles in the pathogenesis of ESCC. This case-control study aimed to analyze the association between gene polymorphisms and ESCC susceptibility.

METHODS

DNA was extracted from the peripheral blood of patients. The Agena MassARRAY platform was used for the genotyping. Statistical analysis was conducted using the chi-squared test or Fisher's exact test, logistic regression analysis, and stratification analysis.

RESULTS

The 'G' allele of rs989902 (PTPN13) and the 'T' allele of rs738722 (CHEK2) were both associated with an increased risk of ESCC (rs989902: OR = 1.23, 95% CI = 1.02-1.47, p = 0.028; rs738722: OR = 1.28, 95% CI = 1.06-1.55, p = 0.011). Stratification analysis showed that SNPs (rs989902 and rs738722) were notably correlated with an increased risk of ESCC after stratification for age, sex, smoking, and drinking status. In addition, rs738722 might be associated with lower stage, while rs989902 had a lower risk of metastasis.

CONCLUSION

Our findings display that PTPN13 rs989902 and CHEK2 rs738722 are associated with an increased risk of ESCC in the Chinese Han population.

miR-200b, ZEB2 and PTPN13 Are Downregulated in Colorectal Carcinoma with Serosal Invasion

Serosal invasion is an independent negative prognostic factor in certain cancers, including CRC. However, the mechanisms behind serosal invasion are poorly understood. We therefore assumed that epithelial-mesenchymal transition (EMT) might be involved. Our study included 34 patients with CRC, 3 stage pT2, 14 stage pT3 and 17 showing serosal invasion (stage pT4a according to TNM staging system). RNA isolated from formalin-fixed paraffin-embedded tissue samples was analysed for expression of the miR-200 family and their target genes CDKN1B, ONECUT2, PTPN13, RND3, SOX2, TGFB2 and ZEB2 using real-time PCR. We found upregulation of miR-200b and ONECUT2 in CRC pT3 and pT4a compared to normal mucosa, and downregulation of CDKN1B in CRC pT3. Moreover, we observed, downregulation of miR-200b, PTPN13 and ZEB2 in CRC with serosal invasion (pT4a) compared to pT3. Our results suggest the involvement of partial EMT in serosal invasion of CRC. In addition, PTPN13 seems to be one of the important regulators involved in serosal invasion, and ONECUT2 in tumour growth.

Dual Role of the PTPN13 Tyrosine Phosphatase in Cancer

In this review article, we present the current knowledge on PTPN13, a class I non-receptor protein tyrosine phosphatase identified in 1994. We focus particularly on its role in cancer, where PTPN13 acts as an oncogenic protein and also a tumor suppressor. To try to understand these apparent contradictory functions, we discuss PTPN13 implication in the FAS and oncogenic tyrosine kinase signaling pathways and in the associated biological activities, as well as its post-transcriptional and epigenetic regulation. Then, we describe PTPN13 clinical significance as a prognostic marker in different cancer types and its impact on anti-cancer treatment sensitivity. Finally, we present future research axes following recent findings on its role in cell junction regulation that implicate PTPN13 in cell death and cell migration, two major hallmarks of tumor formation and progression.

An Eight-CircRNA Assessment Model for Predicting Biochemical Recurrence in Prostate Cancer

Prostate cancer (PCa) is a high morbidity malignancy in males, and biochemical recurrence (BCR) may appear after the surgery. Our study is designed to build up a risk score model using circular RNA sequencing data for PCa. The dataset is from the GEO database, using a cohort of 144 patients in Canada. We removed the low abundance circRNAs (FPKM < 1) and obtained 546 circRNAs for the next step. BCR-related circRNAs were selected by Logistic regression using the “survival” and “survminer” R package. Least absolute shrinkage and selector operation (LASSO) regression with 10-fold cross-validation and penalty was used to construct a risk score model by “glmnet” R software package. In total, eight circRNAs (including circ_30029, circ_117300, circ_176436, circ_112897, circ_112897, circ_178252, circ_115617, circ_14736, and circ_17720) were involved in our risk score model. Further, we employed differentially expressed mRNAs between high and low risk score groups. The following Gene Ontology (GO) analysis were visualized by Omicshare Online tools. As per the GO analysis results, tumor immune microenvironment related pathways are significantly enriched. “CIBERSORT” and “ESTIMATE” R package were used to detect tumor-infiltrating immune cells and compare the level of microenvironment scores between high and low risk score groups. What’s more, we verified two of eight circRNA’s (circ_14736 and circ_17720) circular characteristics and tested their biological function with qPCR and CCK8 in vitro. circ_14736 and circ_17720 were detected in exosomes of PCa patients’ plasma. This is the first bioinformatics study to establish a prognosis model for prostate cancer using circRNA. These circRNAs were associated with CD8⁺ T cell activities and may serve as a circRNA-based liquid biopsy panel for disease prognosis.

Powerful statistical method to detect disease-associated genes using publicly available genome-wide association studies summary data

Genome-wide association studies (GWAS) have thus far achieved substantial success. In the last decade, a large number of common variants underlying complex diseases have been identified through GWAS. In most existing GWAS, the identified common variants are obtained by single marker-based tests, that is, testing one single-nucleotide polymorphism (SNP) at a time. Generally, the basic functional unit of inheritance is a gene, rather than a SNP. Thus, results from gene-level association test can be more readily integrated with downstream functional and pathogenic investigation. In this paper, we propose a general gene-based p-value adaptive combination approach (GPA) which can integrate association evidence of multiple genetic variants using only GWAS summary statistics (either p-value or other test statistics). The proposed method could be used to test genetic association for both continuous and binary traits through not only one study but also multiple studies, which would be helpful to overcome the limitation of existing methods that can only be applied to a specific type of data. We conducted thorough simulation studies to verify that the proposed method controls type I errors well, and performs favorably compared to single-marker analysis and other existing methods. We demonstrated the utility of our proposed method through analysis of GWAS meta-analysis results for fasting glucose and lipids from the international MAGIC consortium and Global Lipids Consortium, respectively. The proposed method identified some novel trait associated genes which can improve our understanding of the mechanisms involved in β -cell function, glucose homeostasis, and lipids traits.