Association of CHEK2 polymorphisms with the efficacy of platinum-based chemotherapy for advanced non-small-cell lung cancer in Chinese never-smoking women

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.

Identifying General Tumor and Specific Lung Cancer Biomarkers by Transcriptomic Analysis

The bioinformatic pipeline previously developed in our research laboratory is used to identify potential general and specific deregulated tumor genes and transcription factors related to the establishment and progression of tumoral diseases, now comparing lung cancer with other two types of cancer. Twenty microarray datasets were selected and analyzed separately to identify hub differentiated expressed genes and compared to identify all the deregulated genes and transcription factors in common between the three types of cancer and those unique to lung cancer. The winning DEGs analysis allowed to identify an important number of TFs deregulated in the majority of microarray datasets, which can become key biomarkers of general tumors and specific to lung cancer. A coexpression network was constructed for every dataset with all deregulated genes associated with lung cancer, according to DAVID’s tool enrichment analysis, and transcription factors capable of regulating them, according to oPOSSUM´s tool. Several genes and transcription factors are coexpressed in the networks, suggesting that they could be related to the establishment or progression of the tumoral pathology in any tissue and specifically in the lung. The comparison of the coexpression networks of lung cancer and other types of cancer allowed the identification of common connectivity patterns with deregulated genes and transcription factors correlated to important tumoral processes and signaling pathways that have not been studied yet to experimentally validate their role in lung cancer. The Kaplan–Meier estimator determined the association of thirteen deregulated top winning transcription factors with the survival of lung cancer patients. The coregulatory analysis identified two top winning transcription factors networks related to the regulatory control of gene expression in lung and breast cancer. Our transcriptomic analysis suggests that cancer has an important coregulatory network of transcription factors related to the acquisition of the hallmarks of cancer. Moreover, lung cancer has a group of genes and transcription factors unique to pulmonary tissue that are coexpressed during tumorigenesis and must be studied experimentally to fully understand their role in the pathogenesis within its very complex transcriptomic scenario. Therefore, the downstream bioinformatic analysis developed was able to identify a coregulatory metafirm of cancer in general and specific to lung cancer taking into account the great heterogeneity of the tumoral process at cellular and population levels.

The DDR-related gene signature with cell cycle checkpoint function predicts prognosis, immune activity, and chemoradiotherapy response in lung adenocarcinoma

BACKGROUND

As a DNA surveillance mechanism, cell cycle checkpoint has recently been discovered to be closely associated with lung adenocarcinoma (LUAD) prognosis. It is also an essential link in the process of DNA damage repair (DDR) that confers resistance to radiotherapy. Whether genes that have both functions play a more crucial role in LUAD prognosis remains unclear.

METHODS

In this study, DDR-related genes with cell cycle checkpoint function (DCGs) were selected to investigate their effects on the prognosis of LUAD. The TCGA-LUAD cohort and two GEO external validation cohorts (GSE31210 and GSE42171) were performed to construct a prognosis model based on the least absolute shrinkage and selection operator (LASSO) regression. Patients were divided into high-risk and low-risk groups based on the model. Subsequently, the multivariate COX regression was used to construct a prognostic nomogram. The ssGSEA, CIBERSORT algorithm, TIMER tool, CMap database, and IC50 of chemotherapeutic agents were used to analyze immune activity and responsiveness to chemoradiotherapy.

RESULTS

4 DCGs were selected as prognostic signatures, and patients in the high-risk group had a lower overall survival (OS). The lower infiltration levels of immune cells and the higher expression levels of immune checkpoints appeared in the high-risk group. The damage repair pathways were upregulated, and chemotherapeutic agent sensitivity was poor in the high-risk group.

CONCLUSIONS

The 4-DCGs signature prognosis model we constructed could predict the survival rate, immune activity, and chemoradiotherapy responsiveness of LUAD patients.

Construction of a prognostic model for non-small-cell lung cancer based on ferroptosis-related genes

We wished to construct a prognostic model based on ferroptosis-related genes and to simultaneously evaluate the performance of the prognostic model and analyze differences between high-risk and low-risk groups at all levels. The gene-expression profiles and relevant clinical data of patients with non-small-cell lung cancer (NSCLC) were downloaded from public databases. Differentially expressed genes (DEGs) were obtained by analyzing differences between cancer tissues and paracancerous tissues, and common genes between DEGs and ferroptosis-related genes were identified as candidate ferroptosis-related genes. Next, a risk-score model was constructed using univariate Cox analysis and least absolute shrinkage and selection operator (Lasso) analysis. According to the median risk score, samples were divided into high-risk and low-risk groups, and a series of bioinformatics analyses were conducted to verify the predictive ability of the model. Single-sample gene set enrichment analysis (ssGSEA) was used to investigate differences in immune status between high-risk and low-risk groups, and differences in gene mutations between the two groups were investigated. A risk-score model was constructed based on 21 ferroptosis-related genes. A Kaplan-Meier curve and receiver operating characteristic curve showed that the model had good prediction ability. Univariate and multivariate Cox analyses revealed that ferroptosis-related genes associated with the prognosis may be used as independent prognostic factors for the overall survival time of NSCLC patients. The pathways enriched with DEGs in low-risk and high-risk groups were analyzed, and the enriched pathways were correlated significantly with immunosuppressive status.

Impact of genetic factors on platinum-induced gastrointestinal toxicity

Severe gastrointestinal (GI) toxicity is a common side effect after platinum-based chemotherapy. The incidence and severity of GI toxicity vary among patients with the same chemotherapy. Genetic factors involved in platinum transport, metabolism, detoxification, DNA repair, cell cycle control, and apoptosis pathways may account for the interindividual difference in GI toxicity. The influence of gene polymorphisms in the platinum pathway on GI toxicity has been extensively analyzed. Variations in study sample size, ethnicity, design, treatment schedule, dosing, endpoint definition, and assessment of toxicity make it difficult to precisely interpret the results. Hence, we conducted a review to summarize the most recent pharmacogenomics studies of GI toxicity in platinum-based chemotherapy and identify the most promising avenues for further research.