Predictive Risk Factors for Early Recurrence of Stage pIIIA-N2 Non-Small Cell Lung Cancer.

PURPOSE: Inflammatory biomarkers and clinical pathological factors have been reported to predict survival of patients with non-small cell lung cancer (NSCLC). The goal of this study was to identify risk factors for early recurrence in patients with pIIIA-N2 NSCLC who had undergone radial resection. METHODS: A retrospective analysis was conducted on 238 patients with pIIIA-N2 NSCLC who underwent surgical treatment at the First Affiliated Hospital of Wenzhou Medical University between December 2006 and August 2018. The early recurrence (ER) group included patients who recurred within one year of curative resection, while the non-early recurrence (NER) group included patients who did not recurrence or recurrence beyond one year. The univariate and multivariate Cox proportional risk analyses were used to identify prognostic factors associated with early recurrence, while the chi-square test was used for categorical data. Overall survival and recurrence-free survival were assessed by Kaplan-Meier estimates. RESULTS: A total of 69 patients experienced an early recurrence, while the remaining 169 patients did not relapse within one year. ER patients had a much worse prognosis than NER patients, with median survival times of 20.6 and 83.1 months, respectively. Multivariate analysis showed that smoking status, tumor size, metastatic lymph node ratio (LNR) and platelet-to-lymphocyte ratio (PLR) were independent risk factor of early recurrence. Patients with early recurrence were more likely to develop bone metastases. CONCLUSION: Smoking history, large tumour size, and elevated LNR and PLR values in pIIIA-N2 NSCLC patients after complete resection may have a significant risk of early recurrence. Based on these independent risk indicators, this prediction model may successfully predict early recurrence and advise individual treatment.

Construction and Verification of a Hypoxia-Related 4-lncRNA Model for Prediction of Breast Cancer.

INTRODUCTION: Breast cancer is the most common form of cancer worldwide and a serious threat to women. Hypoxia is thought to be associated with poor prognosis of patients with cancer. Long non-coding RNAs are differentially expressed during tumorigenesis and can serve as unambiguous molecular biomarkers for the prognosis of breast cancer. METHODS: Here, we accessed the data from The Cancer Genome Atlas for model construction and performed Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses to identify biological functions. Four prognostic hypoxia-related lncRNAs identified by univariate, LASSO, and multivariate Cox regression analyses were used to develop a prognostic risk-related signature. Kaplan-Meier and receiver operating characteristic curve analyses were performed, and independent prognostic factor analysis and correlation analysis with clinical characteristics were utilized to evaluate the specificity and sensitivity of the signature. Survival analysis and receiver operating characteristic curve analyses of the validation cohort were operated to corroborate the robustness of the model. RESULTS: Our results demonstrate the development of a reliable prognostic gene signature comprising four long non-coding RNAs (AL031316.1, AC004585.1, LINC01235, and ACTA2-AS1). The signature displayed irreplaceable prognostic power for overall survival in patients with breast cancer in both the training and validation cohorts. Furthermore, immune cell infiltration analysis revealed that B cells, CD4 T cells, CD8 T cells, neutrophils, and dendritic cells were significantly different between the high-risk and low-risk groups. The high-risk and low-risk groups could be precisely distinguished using the risk signature to predict patient outcomes. DISCUSSION: In summary, our study proves that hypoxia-related long non-coding RNAs serve as accurate indicators of poor prognosis and short overall survival, and are likely to act as potential targets for future cancer therapy.

The Cancers-Specific Survival of Metastatic Pulmonary Carcinoids and Sites of Distant Metastasis: A Population-Based Study.

BACKGROUND: Lung cancer is the leading cause of cancer-related deaths and pulmonary carcinoids (PCs) account for almost 2% of all pulmonary malignancies. However, few published articles have reported prognosis and related factors of pulmonary carcinoid patients. MATERIAL AND METHOD: The Surveillance, Epidemiology, and End Results (SEER) database was used to collect data of patients diagnosed with metastatic PCs from 2010 to 2016. The prognosis and survival of these patients were compared by employing Cox proportional hazards and the Kaplan-Meier survival analysis. RESULTS: A total of 1763 patients were analyzed. The liver (668, 25.6%) was shown to be the most common metastatic site in the isolated organ metastasis cohort, followed by the lung (636, 24.4%), bone (562, 21.6%), and brain (460, 17.6%). Among the patients, the tumor metastasized to a single distant site included the liver, bone, lung, and brain. Cancer-specific survival (CSS) in metastatic PCs is determined by the site of metastasis and the total number of such sites. Pulmonary carcinoid patients with isolated liver metastasis manifested more favorable survival rates in comparison to patients having isolated metastasis in the lung, brain, or bone. The median CSS was 45, 7, 6, 5 months (P = 0.011). The number of distant metastatic sites and the location of distant metastasis were found to be independent risk factors for CSS. For patients with distant isolated metastasis, liver metastasis (P < 0.0001) had better CSS in comparison to those with bone metastasis. When compared to patients whose carcinoids had metastasized to the bones, patients with a brain (P = 0.273) or lung (P = 0.483) metastasis had the same CSS. CONCLUSION: Cancer-specific survival in metastatic PCs depends on the site of metastasis and the total number of such locations. PC patients with isolated liver metastasis manifested more favorable survival in comparison to patients with isolated metastasis in the lung, brain, or bone.

Identification of EMT-Related Gene Signatures to Predict the Prognosis of Patients With Endometrial Cancer.

BACKGROUND: Endometrial cancer (EC) is one of the most common gynecological cancers. Epithelial-mesenchymal transition (EMT) is believed to be significantly associated with the malignant progression of tumors. However, there is no relevant study on the relationship between EMT-related gene (ERG) signatures and the prognosis of EC patients. METHODS: We extracted the mRNA expression profiles of 543 tumor and 23 normal tissues from The Cancer Genome Atlas database. Then, we selected differentially expressed ERGs (DEERGs) among these mRNAs. Next, univariate and multivariate Cox regression analyses were performed to select the ERGs with predictive ability for the prognosis of EC patients. In addition, risk score models were constructed based on the selected genes to predict patients' overall survival (OS), progression-free survival (PFS), and disease-free survival (DFS). Finally, nomograms were constructed to estimate the OS and PFS of EC patients, and pan-cancer analysis was performed to further analyze the functions of a certain gene. RESULTS: Six OS-, ten PFS-, and five DFS-related ERGs were obtained. By constructing the prognostic risk score model, we found that the OS, PFS, and DFS of the high-risk group were notably poorer. Last, we found that AQP5 appeared in all three gene signatures, and through pan-cancer analysis, it was also found to play an important role in immunity in lower grade glioma (LGG), which may contribute to the poor prognosis of LGG patients. CONCLUSIONS: We constructed ERG signatures to predict the prognosis of EC patients using bioinformatics methods. Our findings provide a thorough understanding of the effect of EMT in patients with EC and provide new targets and ideas for individualized treatment, which has important clinical significance.

Seven-Gene Signature Based on Glycolysis Is Closely Related to the Prognosis and Tumor Immune Infiltration of Patients With Gastric Cancer.

Background: Gastric cancer (GC) is one of the most common malignancies worldwide, exhibiting a high morbidity, and mortality. As the various treatment methods for gastric cancer are limited by disadvantages, many efforts to improve the efficacy of these treatments are being taken. Metabolic recombination is an important characteristic of cancer and has gradually caused a recent upsurge in research. However, systematic analysis of the interaction between glycolysis and GC patient prognosis and its potential associations with immune infiltration is lacking but urgently needed. Methods: We obtained the gene expression data and clinical materials of GC derived from The Cancer Genome Atlas (TCGA) dataset. Univariate and multivariate Cox proportional regression analyses were performed to select the optimal prognosis-related genes for subsequent modeling. We then validated our data in the GEO database and further verified the gene expression using the Oncomine database and PCR experiments. Besides, Gene set variation analysis (GSVA) analysis was employed to further explore the differences in activation status of biological pathways between the high and low risk groups. Furthermore, a nomogram was adopted to predict the individualized survival rate of GC patients. Finally, a violin plot and a TIMMER analysis were performed to analyse the characteristics of immune infiltration in the microenvironment. Results: A seven-gene signature, including STC1, CLDN9, EFNA3, ZBTB7A, NT5E, NUP50, and CXCR4, was established. Based on this seven-gene signature, the patients in the training set and testing sets could be divided into high-risk and low-risk groups. In addition, a nomogram based on risk and age showed good calibration and moderate discrimination. The results proved that the seven-gene signature had a strong capacity to predict the GC patient prognosis. Collectively, the violin plot and TIMMER analysis demonstrated that an immunosuppressive tumor microenvironment caused by hyperglycolysis led to poor prognosis. Conclusion: Taken together, these results established a genetic signature for gastric cancer based on glycolysis, which has reference significance for the in-depth study of the metabolic mechanism of gastric cancer and the exploration of new clinical treatment strategies.

Comprehensive analysis and establishment of a prediction model of alternative splicing events reveal the prognostic predictor and immune microenvironment signatures in triple negative breast cancer.

BACKGROUND: Triple-negative breast cancer (TNBC) is widely concerning because of high malignancy and poor prognosis. There is increasing evidence that alternative splicing (AS) plays an important role in the development of cancer and the formation of the tumour microenvironment. However, comprehensive analysis of AS signalling in TNBC is still lacking and urgently needed. METHODS: Transcriptome and clinical data of 169 TNBC tissues and 15 normal tissues were obtained and integrated from the cancer genome atlas (TCGA), and an overview of AS events was downloaded from the SpliceSeq database. Then, differential comparative analysis was performed to obtain cancer-associated AS events (CAAS). Metascape was used to perform parent gene enrichment analysis based on CAAS. Unsupervised cluster analysis was performed to analyse the characteristics of immune infiltration in the microenvironment. A splicing network was established based on the correlation between CAAS events and splicing factors (SFs). We then constructed prediction models and assessed the accuracy of these models by receiver operating characteristic (ROC) curve and Kaplan-Meier survival analyses. Furthermore, a nomogram was adopted to predict the individualized survival rate of TNBC patients. RESULTS: We identified 1194 cancer-associated AS events (CAAS) and evaluated the enrichment of 981 parent genes. The top 20 parent genes with significant differences were mostly related to cell adhesion, cell component connection and other pathways. Furthermore, immune-related pathways were also enriched. Unsupervised clustering analysis revealed the heterogeneity of the immune microenvironment in TNBC. The splicing network also suggested an obvious correlation between SFs expression and CAAS events in TNBC patients. Univariate and multivariate Cox regression analyses showed that the survival-related AS events were detected, including some significant participants in the carcinogenic process. A nomogram incorporating risk, AJCC and radiotherapy showed good calibration and moderate discrimination. CONCLUSION: Our study revealed AS events related to tumorigenesis and the immune microenvironment, elaborated the potential correlation between SFs and CAAS, established a prognostic model based on survival-related AS events, and created a nomogram to better predict the individual survival rate of TNBC patients, which improved our understanding of the relationship between AS events and TNBC.