Prognosis Of Lung Squamous Cell Carcinoma Patients Research Articles

Prognostic role of extracellular vesicles in squamous cell carcinoma of the lung.

BackgroundResearch on diagnosing recurrent non‐small cell lung cancer (NSCLC) and applying target gene treatment using exosomes in a less invasive way is very important. Recently, however, it has been argued that exosomes do not contain double‐stranded DNA (dsDNA) or histones. In this study, we describe the expression of extracellular vesicle (EV) markers in specimens from squamous cell carcinoma (SCC) of the lung and analyze their relationship with the prognosis of patients.MethodsClinical and pathological data were obtained from 96 patients who had undergone surgery for SCC of the lung. Tissue microarray blocks were made using representative paraffin blocks of samples from patients with SCC of the lung. Two pathologists graded the intensity of CD63, CD9, LC3A/B, P62, and ANXA1 expression as high or low expression. In addition, the authors designated the combined expression of these five independent markers as “positive EV expression” in this article.ResultsSCCs with low CD63 and SCCs with low EV expression showed unfavorable disease‐free survival (DFS) (P‐value = 0.037 and 0.006, respectively) in the survival analysis. The Kaplan‐Meier survival curve confirmed that the low EV expression showed a statistically significant relationship with unfavorable DFS (P‐value = 0.004). There were no statistically significant differences in DFS and disease‐specific survival in each low and high expression group for CD9, LC3A/B, ANXA1, and P62 in the Cox regression analysis.ConclusionsAs EV expression was related to the prognosis of lung SCC patients, a broader approach using different extracellular vesicles rather than a conventional exosome‐dependent one is needed.

Identification of lncRNAs associated with lung squamous cell carcinoma prognosis in the competitive endogenous RNA network.

BackgroundLong noncoding RNAs (lncRNAs) play a role in the formation, development, and prognosis of various cancers. Our study aimed to identify prognostic-related lncRNAs in lung squamous cell carcinoma (LUSC), which may provide new perspectives for individualized treatment of patients.Materials and MethodsThe RNA sequencing (lncRNA, microRNA (miRNA), mRNA) data and clinical information related to LUSC were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed RNA sequences were used to construct the competitive endogenous RNA (ceRNA) network. In present study, we mainly used two prognostic verification methods, Cox analysis and survival analysis, to identify the prognostic relevance of specific lncRNAs and construct prognostic model of lncRNA.ResultsDatasets on 551 samples of lncRNA and mRNA and 523 miRNA samples were retrieved from the TCGA database. Analysis of the normal and LUSC samples identified 170 DElncRNAs, 331 DEmiRNAs, and 417 DEmRNAs differentially expressed RNAs. The ceRNA network contained 27 lncRNAs, 43 miRNAs, and 11 mRNAs. Furthermore, we identified seven specific lncRNAs (ERVH48-1, HCG9, SEC62-AS1, AC022148.1, LINC00460, C5orf17, LINC00261) as potential prognostic factors after correlation analysis, and five of the seven lncRNAs (AC022148.1, HCG9, LINC00460, C5orf17, LINC00261) constructed a prognostic model of LUSC.ConclusionIn present study, we identified seven lncRNAs in the ceRNA network that are associated with potential prognosis in LUSC patients, and constructed a prognostic model of LUSC which can be used to assess the prognosis risk of clinical patients. Further biological experiments are needed to elucidate the specific molecular mechanisms underlying them.

Identification of seven-gene signature for prediction of lung squamous cell carcinoma.

Background and aim: Lung squamous cell carcinoma (LUSC), is a pathological subtype of lung cancer, accounting for 30% of the lung cancers. A reliable model was constructed, based on the whole gene expression profiles, to predict the prognosis of patients with LUSC.Methods: The RNA-Seq data of LUSC was downloaded from the TCGA database, and differentially expressed genes (p<0.05, |log2fold change| >1) were screened out. By univariate and multivariate Cox regression analysis, we identified seven prognosis-related genes. Then, we established a risk score staging system to predict the prognosis of patients with LUSC. Compared with other clinical parameters, the risk score was an independent prognostic factor and had a better performance in predicting prognosis. Finally, GSEA analysis was carried out to determine the enrichment pathway significantly. The risk score models were established by Cox proportional hazard regression analysis; the ROC curve was applied to test the performance of risk score model. All the statistical analysis was accomplished by R packages.Results: In this study, a model was constructed to predict prognosis, which contains seven genes: CSRNP1, CLEC18B, MIR27A, AC130456.4, DEFA6, ARL14EPL, and ZFP42. Based on the model, the risk score of each patient was calculated with LUSC (hazard ratio [HR]=2.673, 95% CI=1.871–3.525). It was found that the risk score can distinguish high-risk and low-risk groups in prognosis of LUSC patients, independently. Furthermore, the model was validated by ROC curves in the testing dataset and the whole dataset. Lastly, by gene set enrichment analysis (GSEA), we showed the main enrichment pathways were DNA damage stimulus, DNA repair, and DNA replication. It was suggested that the risk score may provide a new and reliable method for prognosis prediction.Conclusion: The results of this study suggested that the risk score based on seven-genes could indicate a promising and independent prognostic biomarker for LUSC patients.

Expression of miR-590 in lung cancer and its correlation with prognosis

The study aim was to evaluate the association of the expression of serum microribonucleic acid-590 (miR-590) with the risk of lung squamous cell carcinoma (LUSC), clinicopathological staging and prognosis. A total of 237 patients with LUSC and 100 healthy volunteers (control group) were included in the study. Total RNA was extracted from the peripheral blood serum of the subjects, and the expression level of miR-590 was detected by reverse transcription real-time quantitative polymerase chain reaction. The baseline clinicopathological information of LUSC patients was evaluated, and the patients were followed up with the median follow-up of 47 months. Compared with that in the control group, the expression level of serum miR-590 in LUSC patients was significantly decreased [0.532 (0.367- 0.821) vs. 1.63 (0.893–1.347), P<0.001]. The receiver operating characteristic (ROC) curve showed that the value of predicting LUSC risk using miR-590 was high, the area under curve (AUC) was 0.883, and 95% confidence interval (CI) was 0.829–0.934. In addition, the expression level of serum miR-590 was correlated with pathological staging (P=0.022), lymph node metastasis (P=0.012), distant metastasis (P<0.001) and tumor, node and metastasis (TNM) staging (P=0.044). The overall survival (OS) of patients in the serum miR-590 low expression group was significantly lower than that of the serum miR-590 high expression group (P=0.012), and the low expression of miR-590 was an independent risk factor for the prognosis of patients [hazard ratio (HR)=2.152, 95% CI=1.285–3.233, P=0.004]. The results suggested that the expression level of miR-590 can be used as a biomarker for the risk of disease, disease staging and prognosis of LUSC patients.

Long noncoding RNA SFTA1P promoted apoptosis and increased cisplatin chemosensitivity via regulating the hnRNP-U-GADD45A axis in lung squamous cell carcinoma.

Chemotherapeutic insensitivity remains one of the major obstacles in clinical treatment of lung squamous cell carcinoma (LSCC). Recently, increasing evidence has suggested that long non-coding RNAs (lncRNAs) promote tumorigenesis in many cancer types. However, the potential biological roles and regulatory mechanisms of lncRNAs in response to cisplatin treatment are poorly understood. Here, we found that lncRNA SFTA1P (surfactant associated 1, pseudogene), highly expressed in lung, was down-regulated in LSCC tissues and could be induced upon cisplatin treatment in LSCC cells. Elevated SFTA1P induced apoptosis and enhanced the sensitivity to cisplatin of LSCC cells. We further identified that hnRNP-U (heterogeneous nuclear ribonucleoprotein U) was down-regulated in LSCCs and positively correlated with patients’ poor prognosis as well as SFTA1P. Mechanistic studies revealed that SFTA1P could up-regulate hnRNP-U expression. In addition, we identified that hnRNP-U enhanced cisplatin-induced apoptosis through up-regulation of GADD45A, high expression of which was correlated with good prognosis in LSCC patients. Our findings demonstrated that SFTA1P might serve as a useful biomarker for LSCC diagnosis and a predictor for cisplatin chemotherapy response in patients with LSCC.

The expression of PD-L1 protein as a prognostic factor in lung squamous cell carcinoma

BackgroundProgrammed cell death-1 (PD-1)/programmed cell death-ligand 1 (PD-L1) pathway-targeted immunotherapy has become the standard option of care in the management of lung cancer. The expression of the PD-L1 protein in lung cancer is expected to be a prognostic factor or to predict the response to PD-1-blocking antibodies. However, the association between PD-L1 positivity and the clinicopathological features and patient outcomes in lung squamous cell carcinoma (SCC) remains unclear because the definitive cut-off value for the expression of PD-L1 protein remains to be established. Materials and methodsThe expression of PD-L1 protein in 205 surgically resected primary lung SCC patients was evaluated by immunohistochemistry with the antibody clone SP142. We generated a histogram to show the proportion of PD-L1-positive carcinoma cells, and set the cut-off values as 1%, 5%, 10% and 50%. Moreover, we examined the proliferative capacity of these tumors using Ki-67 immunohistochemistry. ResultsThe samples from 106 (51.7%), 72 (35.1%), 61 (29.7%) and 37 (18.0%) patients were positive for the expression of PD-L1 protein at cut-off values of 1%, 5%, 10% and 50%, respectively. Fisher’s exact test showed that, for almost all of the factors, PD-L1 positivity was not associated with the clinicopathological features with any of the four cut-off values. Univariate and multivariate survival analyses revealed that the PD-L1-positive patients only had a poorer prognosis than the PD-L1-negative patients at the 1% cut-off value. The Ki-67 labeling index in the PD-L1-positive patients was higher than that in the PD-L1-negative patients. ConclusionsThe expression of PD-L1 protein was associated with a poor prognosis in lung SCC patients. The 1% cut-off value for PD-L1 might become a better predictive marker than the other cut-off values.