Serum anti-GAPDH autoantibody levels reflect the severity of cervical lesions: A potential serum biomarker for cervical cancer screening

A Five Autophagy-Related Long Non-Coding RNA Prognostic Model for Patients with Lung Adenocarcinoma

Purpose

Lung adenocarcinoma is the most common pathological type among non-small cell lung cancer. Although huge progress has been made in terms of early diagnosis and precision treatment in recent years, the overall 5-year survival rate of a patient remains low. In our study, we try to construct an autophagy-related lncRNA prognostic signature that may guide clinical practice.

Methods

The mRNA and lncRNA expression matrix of lung adenocarcinoma patients were retrieved from the TCGA database. Next, we constructed a co-expression network of lncRNAs and autophagy-related genes. Lasso regression and multivariate Cox regression were then applied to establish a prognostic risk model. Subsequently, a risk score was generated to differentiate the high and low risk groups and a ROC curve and nomogram to visualize the predictive ability of the current signature. Finally, gene ontology and pathway enrichment analysis were executed via GSEA.

Results

A total of 1,703 autophagy-related lncRNAs were screened and five autophagy-related lncRNAs (LINC01137, AL691432.2, LINC01116, AL606489.1, and HLA-DQB1-AS1) were finally included in our signature. Judging from univariate (HR=1.075, 95% CI=1.046–1.104) and multivariate (HR=1.088, 95% CI=1.057−1.120) Cox regression analysis, the risk score is an independent factor for LUAD patients. Further, the AUC value based on the risk score for 1-year, 3-years, and 5-years, was 0.735, 0.672, and 0.662, respectively, indicating a reliable model. Drug sensitivity analysis revealed low risk patients were more sensitive to Gemcitabine and Gefitinib, while high risk patients had a better response to Paclitaxel and Erlotinib. Moreover, the lncRNAs included in our signature were primarily enriched in the autophagy process, metabolism, p53 pathway, and JAK/STAT pathway. Finally, a multi-omics analysis of correlated genes showed CFLAR overexpressed in the tumor sample, while GAPDH and MLST8 had a slightly higher expression in the normal sample.

Conclusion

Overall, our study indicated that the prognostic model we generated had certain predictability for LUAD patients’ prognosis and the related genes might be potential biomarkers and therapeutic targets.

Anti-Cancer Auto-Antibodies: Roles, Applications and Open Issues

Auto-antibodies are classically associated with autoimmune diseases, where they are an integral part of diagnostic panels. However, recent evidence is accumulating on the presence of auto-antibodies against single or selected panels of auto-antigens in many types of cancer. Auto-antibodies might initially represent an epiphenomenon derived from the inflammatory environment induced by the tumor. However, their effect on tumor evolution can be crucial, as is discussed in this paper. It has been demonstrated that some of these auto-antibodies can be used for early detection and cancer staging, as well as for monitoring of cancer regression during treatment and follow up. Interestingly, certain auto-antibodies were found to promote cancer progression and metastasis, while others contribute to the body's defense against it. Moreover, auto-antibodies are of a polyclonal nature, which means that often several antibodies are involved in the response to a single tumor antigen. Dissection of these antibody specificities is now possible, allowing their identification at the genetic, structural, and epitope levels. In this review, we report the evidence available on the presence of auto-antibodies in the main cancer types and discuss some of the open issues that still need to be addressed by the research community.

Aberrant Methylation and Differential Expression of SLC2A1, TNS4, GAPDH, ATP8A2, and CASZ1 Are Associated with the Prognosis of Lung Adenocarcinoma

Lung cancer is one of the leading triggers for cancer death worldwide. In this study, the relationship of the aberrantly methylated and differentially expressed genes in lung adenocarcinoma (LUAD) with cancer prognosis was investigated, and 5 feature genes were identified eventually. Specifically, we firstly downloaded the LUAD-related mRNA expression profile (including 57 normal tissue samples and 464 LUAD tissue samples) and Methy450 expression data (including 32 normal tissue samples and 373 LUAD tissue samples) from the TCGA database. The package "limma" was used to screen differentially expressed genes and aberrantly methylated genes, which were intersected for identifying the hypermethylated downregulated genes (DGs Hyper) and the hypomethylated upregulated genes (UGs Hypo). GO annotation and KEGG pathway enrichment analysis were further performed, and it was found that these DGs Hyper and UGs Hypo were predominantly activated in the biological processes and signaling pathways such as the regulation of vasculature development, DNA-binding transcription activator activity, and Ras signaling pathway, indicating that these genes play a vital role in the initiation and progression of LUAD. Additionally, univariate and multivariate Cox regression analyses were conducted to find the genes significantly associated with LUAD prognosis. Five genes including SLC2A1, TNS4, GAPDH, ATP8A2, and CASZ1 were identified, with the former three highly expressed and the latter two poorly expressed in LUAD, indicating poor prognosis of LUAD patients as judged by survival analysis.