TRIM59 as a novel molecular biomarker to predict the prognosis of patients with NSCLC

Autophagy-related tumor subtypes associated with significant gene expression profiles and immune cell infiltration signatures to reveal the prognosis of non-small cell lung cancer

Autophagy plays an important role in non-small cell lung cancer (NSCLC). We aimed to establish novel autophagy-related tumor subtypes to distinguish the prognosis of NSCLC. In this study, gene expression profiles, mutation data and clinical information obtained from the Cancer Genome Atlas. Kaplan Meier-plotter could evaluate prognostic value of autophagy-related genes. Consensus clustering revealed autophagy-related tumor subtypes. Gene expression profiles, mutation data and immune infiltration signatures were identified, oncogenic pathways and gene-drug interactions were performed according to the clusters. Finally, a total of 23 prognostic genes were screened and consensus clustering analysis divided the NSCLC into 2 clusters. The mutation signature showed that 6 genes are special. Immune infiltration signatures showed that higher fraction of immune cells was associated with cluster 1. The oncogenic pathways and gene-drug interactions also showed different patterns. In conclusion, autophagy-related tumor subtypes have different prognosis. Understanding the subtypes of NSCLC are helpful to accurately identify the NSCLC and personalized treatment.

The significance of the crosstalk between ubiquitination or deubiquitination and ncRNAs in non-small cell lung cancer

Lung cancer (LC) remains the leading cause of cancer-related deaths worldwide, with extremely high morbidity and mortality rates. Non-small cell lung cancer (NSCLC) is the most critical type of LC. It seriously threatens the life and health of patients because of its early metastasis, late clinical symptoms, limited early screening methods, and poor treatment outcomes. Non-coding RNAs (ncRNAs), including microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs), participate in cell proliferation, metastasis, and chemoresistance. Several previous studies have proven that ncRNAs are vital regulators of tumorigenesis. Ubiquitination plays the most crucial role in protein post-translational modification (PTM). Deubiquitination and ubiquitination form a homeostasis. In summary, ubiquitination and deubiquitination play essential roles in mediating the degradation or overexpression of a range of crucial proteins in various cancers. A growing number of researchers have found that interactions between ncRNAs and ubiquitination (or deubiquitination) play a crucial role in NSCLC. This review presents several typical examples of the important effects of ncRNAs and ubiquitination (or deubiquitination) in NSCLC, aiming to provide more creative ideas for exploring the diagnosis and treatment of NSCLC.

A glycolysis-based three-gene signature predicts survival in patients with lung squamous cell carcinoma

BACKGROUND

Lung cancer is one of the most lethal and most prevalent malignant tumors worldwide, and lung squamous cell carcinoma (LUSC) is one of the major histological subtypes. Although numerous biomarkers have been found to be associated with prognosis in LUSC, the prediction effect of a single gene biomarker is insufficient, especially for glycolysis-related genes. Therefore, we aimed to develop a novel glycolysis-related gene signature to predict survival in patients with LUSC.

METHODS

The mRNA expression files and LUSC clinical information were obtained from The Cancer Genome Atlas (TCGA) dataset.

RESULTS

Based on Gene Set Enrichment Analysis (GSEA), we found 5 glycolysis-related gene sets that were significantly enriched in LUSC tissues. Univariate and multivariate Cox proportional regression models were performed to choose prognostic-related gene signatures. Based on a Cox proportional regression model, a risk score for a three-gene signature (HKDC1, ALDH7A1, and MDH1) was established to divide patients into high-risk and low-risk subgroups. Multivariate Cox regression analysis indicated that the risk score for this three-gene signature can be used as an independent prognostic indicator in LUSC. Additionally, based on the cBioPortal database, the rate of genomic alterations in the HKDC1, ALDH7A1, and MDH1 genes were 1.9, 1.1, and 5% in LUSC patients, respectively.

CONCLUSION

A glycolysis-based three-gene signature could serve as a novel biomarker in predicting the prognosis of patients with LUSC and it also provides additional gene targets that can be used to cure LUSC patients.

Construction and Validation of a Protein Prognostic Model for Lung Squamous Cell Carcinoma

Lung squamous cell carcinoma (LUSCC), as the major type of lung cancer, has high morbidity and mortality rates. The prognostic markers for LUSCC are much fewer than lung adenocarcinoma. Besides, protein biomarkers have advantages of economy, accuracy and stability. The aim of this study was to construct a protein prognostic model for LUSCC. The protein expression data of LUSCC were downloaded from The Cancer Protein Atlas (TCPA) database. Clinical data of LUSCC patients were downloaded from The Cancer Genome Atlas (TCGA) database. A total of 237 proteins were identified from 325 cases of LUSCC patients based on the TCPA and TCGA database. According to Kaplan-Meier survival analysis, univariate and multivariate Cox analysis, a prognostic prediction model was established which was consisted of 6 proteins (CHK1_pS345, CHK2, IRS1, PAXILLIN, BRCA2 and BRAF_pS445). After calculating the risk values of each patient according to the coefficient of each protein in the risk model, the LUSCC patients were divided into high risk group and low risk group. The survival analysis demonstrated that there was significant difference between these two groups (p= 4.877e-05). The area under the curve (AUC) value of the receiver operating characteristic (ROC) curve was 0.699, which suggesting that the prognostic risk model could effectively predict the survival of LUSCC patients. Univariate and multivariate analysis indicated that this prognostic model could be used as independent prognosis factors for LUSCC patients. Proteins co-expression analysis showed that there were 21 proteins co-expressed with the proteins in the risk model. In conclusion, our study constructed a protein prognostic model, which could effectively predict the prognosis of LUSCC patients.