An autophagy-related model of 4 key genes for predicting prognosis of patients with laryngeal cancer

NLR, PLR, LMR and MWR as diagnostic and prognostic markers for laryngeal carcinoma

OBJECTIVE

To evaluate whether neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte to monocyte ratio (LMR) and monocyte-to-white blood cell ratio (MWR) can be used as diagnostic and prognostic markers for laryngeal carcinoma (LC).

METHODS

In this retrospective study, 50 patients with LC treated in the Department of Otolaryngology, Head and Neck Surgery of Beijing Tongren Hospital from August 2014 to August 2015 were enrolled in research group. In addition, 40 healthy volunteers from the same period were selected as control group. The counts of white blood cells, neutrophils, lymphocytes, monocytes and platelets in the peripheral blood of participants were measured with a blood counting instrument (Sysmex XE-2100, Sysmex Corporation, Japan), and the NLR, PLR, LMR and MWR were calculated. After that, the survival rate of patients was observed through a 5-year follow-up. The prognostic value of the above four indexes and their combination was discussed in patients with different clinical characteristics.

RESULTS

Compared with the control group, the NLR, PLR and MWR were higher and the LMR was lower in the research group. In terms of survival, patients with higher NLR, PLR and MWR and lower LMR showed a higher 5-year mortality than those with lower NLR, PLR and MWR and higher LMR, indicating that NLR, PLR and MWR were higher and LMR was lower in the survival group than in the death group. Subsequent analysis identified that NLR, PLR, LMR and MWR were closely correlated with age, alcohol drinking, smoking, clinical staging and T-staging. Clinical staging, T-staging, NLR, PLR, LMR, and MWR were confirmed as influencing factors for LC.

CONCLUSIONS

NLR, PLR, LMR, and MWR can be used as diagnostic and prognostic markers for LC and their combination has a superior diagnostic performance.

Development of a model to predict the prognosis of esophageal carcinoma based on autophagy-related genes

Aim: To identify a potential prognostic signature of esophageal carcinoma based on autophagy-related genes (ARGs). Methods: RNA sequencing and clinical data were downloaded from the Cancer Genome Atlas. Significantly different ARGs were identified by Wilcoxon signed-rank test. A prognostic model was established employing Cox regression analysis. The model was evaluated by receiver operating characteristic and Kaplan-Meier curve. Results: A total of 28 significantly different ARGs were identified. Seven ARGs were screened to construct the prognostic model. The efficacy of the model was verified. A nomogram also validated the role of risk score in predicting prognosis. Enrichment analyses showed the possible underlying mechanisms. Conclusion: The seven-ARGs prognostic model was validated to be promising for predicting the prognosis of patients with esophageal carcinoma.

Construction and Comprehensive Prognostic Analysis of a Novel Immune-Related lncRNA Signature and Immune Landscape in Gastric Cancer

Gastric cancer (GC) is a malignant tumor with high mortality and poor prognosis. Immunotherapies, especially immune checkpoint inhibitors (ICI), are widely used in various tumors, but patients with GC do not benefit much from immunotherapies. Therefore, effective predictive biomarkers are urgently needed for GC patients to realize the benefits of immunotherapy. Recent studies have indicated that long noncoding RNAs (lncRNAs) could be used as biomarkers in the immune landscape of multiple tumors. In this study, we constructed a novel immune-related lncRNA (irlncRNA) risk model to predict the survival and immune landscape of GC patients. First, we identified differentially expressed irlncRNAs (DEirlncRNAs) from RNA-Seq data of The Cancer Genome Atlas (TCGA). By using various algorithms, we constructed a risk model with 11 DEirlncRNA pairs. We then tested the accuracy of the risk model, demonstrating that the risk model has good efficiency in predicting the prognosis of GC patients. Inner validation sets were further used to confirm the effectiveness of the risk model. In addition, our risk model has a preferable performance in predicting the immune infiltration status of tumors, immune checkpoint status of the patients, and immunotherapy score. In conclusion, our risk model may provide insights into the prognosis of and immunotherapy strategy for GC.

Identification of autophagy-related gene and lncRNA signatures in the prognosis of HNSCC

OBJECTIVE

The aim of this study was to identify prognostic autophagy-related genes and lncRNAs to predict clinical outcomes in head and neck squamous cell carcinoma (HNSCC).

SUBJECTS AND METHODS

Differentially expressed autophagy-related genes and autophagy-related lncRNAs were identified by comparing pare-carcinoma and carcinoma samples of HNSCC. And then, we constructed an ARG and an AR-lncRNA signature risk score. Receiver operating characteristic (ROC) curve analyses were performed to assess the prognostic prediction capacity. Gene Set Enrichment Analysis (GSEA) and Gene Ontology (GO) functional annotation were used to analysis the functions of ARGs and AR-lncRNAs.

RESULTS

Six ARGs and thirteen AR-lncRNAs were identified in the ARG and AR-lncRNA signatures, and overall survival (OS) in the high-risk group was significantly shorter than the low-risk group. ROC analysis showed the ARG and AR-lncRNA signatures have excellent ability of predicting the total OS of patients with HNSCC. What's more, GSEA and GO functional annotation proved that autophagy-related pathways are mainly enriched in the high-risk group.

CONCLUSIONS

These findings indicated that our ARG signature and AR-lncRNA signature could be considered to predict the prognosis of patients with HNSCC and provide a deep understanding of the biological mechanisms of autophagy in HNSCC.

Prediction of Overall Survival Rate in Patients With Hepatocellular Carcinoma Using an Integrated Model Based on Autophagy Gene Marker

The autophagy cell, which can inhibit the formation of tumor in the early stage and can promote the development of tumor in the late stage, plays an important role in the development of tumor. Therefore, it has potential significance to explore the influence of autophagy-related genes (AAGs) on the prognosis of hepatocellular carcinoma (HCC). The differentially expressed AAGs are selected from HCC gene expression profile data and clinical data downloaded from the TCGA database, and human autophagy database (HADB). The role of AAGs in HCC is elucidated by GO functional annotation and KEGG pathway enrichment analysis. Combining with clinical data, we selected age, gender, grade, stage, T state, M state, and N state as Cox model indexes to construct the multivariate Cox model and survival curve of Kaplan Meier (KM) was drawn to estimate patients’ survival between high- and low-risk groups. Through an ROC curve drawn by univariate and multivariate Cox regression analysis, we found that seven genes with high expression levels, including HSP90AB1, SQSTM1, RHEB, HDAC1, ATIC, HSPB8, and BIRC5 were associated with poor prognosis of HCC patients. Then the ICGC database is used to verify the reliability and robustness of the model. Therefore, the prognosis model of HCC constructed by autophagy genes might effectively predict the overall survival rate and help to find the best personalized targeted therapy of patients with HCC, which can provide better prognosis for patients.

The role of autophagy in escaping therapy-induced polyploidy/senescence

Autophagy is an evolutionarily conserved process necessary to maintain cell homeostasis in response to various forms of stress such as nutrient deprivation and hypoxia as well as functioning to remove damaged molecules and organelles. The role of autophagy in cancer varies depending on the stage of cancer. Cancer therapeutics can also simultaneously evoke cancer cell senescence and ploidy increase. Both cancer cell senescence and polyploidization are reversible by depolyploidization giving rise to the progeny. Autophagy activation may be indispensable for cancer cell escape from senescence/polyploidy. As cancer cell polyploidy is proposed to be involved in cancer origin, the role of autophagy in polyploidization/depolyploidization of senescent cancer cells seems to be crucial. Accordingly, this review is an attempt to understand the complicated interrelationships between reversible cell senescence/polyploidy and autophagy.

Development and validation of a novel survival model for head and neck squamous cell carcinoma based on autophagy-related genes

BACKGROUND

In view of the critical role of autophagy-related genes (ARGs) in the pathogenesis of various diseases including cancer, this study aims to identify and evaluate the potential value of ARGs in head and neck squamous cell carcinoma (HNSCC).

METHODS

RNA sequencing and clinical data in The Cancer Genome Atlas (TCGA) were analyzed by univariate Cox regression analysis and Lasso Cox regression analysis model established a novel 13- autophagy related prognostic genes, which were used to build a prognostic risk model. A multivariate Cox proportional regression model and the survival analysis were used to evaluate the prognostic risk model. Moreover, the efficiency of prognostic risk model was tested by receiver operating characteristic (ROC) curve analysis based on data from TCGA database and Gene Expression Omnibus (GEO). Besides, the other independent datasets from Human Protein Atlas dataset (HPA) also applied.

RESULTS

13 ARGs (GABARAPL1, ITGA3, USP10, ST13, MAPK9, PRKN, FADD, IKBKB, ITPR1, TP73, MAP2K7, CDKN2A, and EEF2K) with prognostic value were identified in HNSCC patients. Subsequently, a prognostic risk model was established based on 13 ARGs, and significantly stratified HNSCC patients into high- and low-risk groups in terms of overall survival (OS) (HR = 0.379，95% CI: 0.289-0.495, p < 0.0001). The multivariate Cox analysis revealed that this model was an independent prognostic factor (HR = 1.506, 95% CI = 1.330-1.706, P < 0.001). The areas under the ROC curves (AUC) were significant for both the TCGA and GEO, with AUC of 0.685 and 0.928 respectively. Functional annotation revealed that model significantly enriched in many critical pathways correlated with tumorigenesis, including the p53 pathway, IL2 STAT5 signaling, TGF beta signaling, PI3K Ak mTOR signaling by gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA). In addition, we developed a nomogram shown some clinical net could be used as a reference for clinical decision-making.

CONCLUSIONS

Collectively, we developed and validated a novel robust 13-gene signatures for HNSCC prognosis prediction. The 13 ARGs could serve as an independent and reliable prognostic biomarkers and therapeutic targets for the HNSCC patients.