Association of PPP1R13L and CD3EAP polymorphisms with risk and survival of non-small cell lung cancer in Chinese non-smoking females

Colon cancer transcriptome

Over the last four decades, methodological innovations have continuously changed transcriptome profiling. It is now feasible to sequence and quantify the transcriptional outputs of individual cells or thousands of samples using RNA sequencing (RNA-seq). These transcriptomes serve as a connection between cellular behaviors and their underlying molecular mechanisms, such as mutations. This relationship, in the context of cancer, provides a chance to unravel tumor complexity and heterogeneity and uncover novel biomarkers or treatment options. Since colon cancer is one of the most frequent malignancies, its prognosis and diagnosis seem to be critical. The transcriptome technology is developing for an earlier and more accurate diagnosis of cancer which can provide better protectivity and prognostic utility to medical teams and patients. A transcriptome is a whole set of expressed coding and non-coding RNAs in an individual or cell population. The cancer transcriptome includes RNA-based changes. The combined genome and transcriptome of a patient may provide a comprehensive picture of their cancer, and this information is beginning to affect treatment decision-making in real-time. A full assessment of the transcriptome of colon (colorectal) cancer has been assessed in this review paper based on risk factors such as age, obesity, gender, alcohol use, race, and also different stages of cancer, as well as non-coding RNAs like circRNAs, miRNAs, lncRNAs, and siRNAs. Similarly, they have been examined independently in the transcriptome study of colon cancer.

Integrated Analysis of Nine Prognostic RNA-Binding Proteins in Soft Tissue Sarcoma

RNA-binding proteins (RBPs) have been shown to be dysregulated in cancer transcription and translation, but few studies have investigated their mechanism of action in soft tissue sarcoma (STS). Here, The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases were used to identify differentially expressed RBPs in STS and normal tissues. Through a series of biological information analyses, 329 differentially expressed RBPs were identified. Functional enrichment analysis showed that differentially expressed RBPs were mainly involved in RNA transport, RNA splicing, mRNA monitoring pathways, ribosome biogenesis and translation regulation. Through Cox regression analyses, 9 RBPs (BYSL, IGF2BP3, DNMT3B, TERT, CD3EAP, SRSF12, TLR7, TRIM21 and MEX3A) were all up-regulated in STS as prognosis-related genes, and a prognostic model was established. The model calculated a risk score based on the expression of 9 hub RBPs. The risk score could be used for risk stratification of patients and had a high prognostic value based on the receiver operating characteristic (ROC) curve. We also established a nomogram containing risk scores and 9 key RBPs to predict the 1-year, 3-year, and 5-year survival rates of patients in STS. Afterwards, methylation analysis showed significant changes in the methylation degree of BYSL, CD3EAP and MEX2A. Furthermore, the expression of 9 hub RBPs was closely related to immune infiltration rather than tumor purity. Based on the above studies, these findings may provide new insights into the pathogenesis of STS and will provide candidate biomarkers for the prognosis of STS.

Determination of Endothelial Nitric Oxide Synthase Gene Polymorphism and Plasma Asymmetric Dimethyl Arginine Concentrations in Patients with Lung Cancer

Objective

Genetic factors can contribute to both the occurrence and development of lung cancer. This study aimed to investigate endothelial nitric oxide synthase (eNOS) G894T and T-786C polymorphisms and plasma asymmetric dimethylarginine (ADMA) levels of lung cancer patients in comparison with healthy subjects.

Materials and Methods

A total of 200 subjects, 100 patients with lung cancer and 100 healthy volunteers were included in this study. To determine eNOS gene polymorphisms, we collected and analyzed blood samples with polymerase chain reaction (PCR). Plasma ADMA levels were evaluated by high-performance liquid chromatography (HPLC).

Results

The difference in gene polymorphisms between lung cancer patients and healthy controls were insignificant. However, lung cancer patients had statistically significantly higher plasma ADMA levels than healthy controls. The patients and control groups with CC polymorphisms and TT polymorphisms on eNOS T-786C and G894T gene regions had higher plasma ADMA levels. The CC polymorphisms and plasma ADMA levels were higher in patients with small-cell lung cancer compared to those in patients with non-small-cell lung cancer.

Conclusion

Although eNOS gene polymorphisms had no significant difference between lung cancer patients and healthy controls, plasma ADMA levels were higher in lung cancer patients compared to healthy controls. Our study suggests that CC genotypes and elevated plasma ADMA levels might be associated with small-cell lung cancer.