mRNA Expression Information Research Articles

Tumor hypoxia in immune infiltration and prognosis of bladder cancer.

Bladder cancer (BC) is the sixth most common cancer and the ninth leading cause of cancer death among men in the world. Previous studies have shown that tumor hypoxia plays an important role in the occurrence and development of BC, but the role of tumor hypoxia in the prognosis and immune infiltration of BC remains unclear. Our aim was to perform a bioinformatics analysis combined with a clinical analysis to explore the roles of hypoxia in BC. We acquired datasets (GSE13507, GSE5287, and GSE1827) containing mRNA expression information from BC cohorts from the Gene Expression Omnibus (GEO) and measured the Hypoxia score using the Gene Set Variation Analysis (GSVA). Then we used X-tile method and log-rank test and Pearson's correlation test to analyze the relation among the Hypoxia score and the clinicopathological and immunological characteristics of BC and used stepwise Cox regression analysis to establish a Prognostic model. Hypoxia was found to be closely associated with tumor grade, pathological type, invasion, and prognosis of BC in our study. Moreover, we determined that hypoxia was closely related to the infiltration abundance of multiple immune cells through a correlation analysis, and the tumor immune cell infiltration was further found to be significantly associated with the tumor grade and tumor type of BC. Furthermore, we constructed several models based on the Hypoxia score and tumor immune infiltration with C-indexes ranging from 0.703 and 0.888, which showed good performance in predicting the prognosis of BC. Our study showed that hypoxia plays an important role in the progression, prognosis, and tumor immune infiltration of BC. Our models based on hypoxia and tumor immune infiltration play a guiding role in the prognosis and treatment of BC patients.

Function and prognostic value of basement membrane -related genes in lung adenocarcinoma.

Background: Lung adenocarcinoma (LUAD) has become a common cause of cancer-related death. Many studies have shown that the basement membrane (BM) is associated with the development of cancer. However, BM-related gene expression and its relationship to LUAD prognosis remains unclear. Methods: BM-related genes from previous studies were used. Clinical and mRNA expression information were obtained from TCGA database. Cox, minimum absolute contraction, and selection operator regression were applied to analyze the selected genes affecting LUAD prognosis. A prognostic-risk model was then established. Furthermore, this study applied Kaplan-Meier analysis to assess the outcomes of high- and low-risk groups, then explored their differences in drug sensitivity. The DSigDB database was used to screen for therapeutic small-molecule drugs. Results: Fourteen prognostic models based on BM-related genes were successfully constructed and validated in patients with LUAD. We also found that independence was a prognostic factor in all 14 BM-based models. Functional analysis showed that the enrichment of BM-related genes mainly originated from signaling pathways related to cancer. The BM-based model also suggested that immune cell infiltration is associated with checkpoints. The low-risk patients may benefit from cyclopamine and docetaxel treatments. Conclusion: This study identified a reliable biomarker to predict survival in patients with LUAD and offered new insights into the function of BM-related genes in LUAD.

A new ferroptosis-related gene model for prognostic prediction of papillary thyroid carcinoma

ABSTRACT Papillary thyroid carcinoma (PTC) is a highly heterogeneous malignancy with diverse prognoses. Ferroptosis is a new type of cell death dependent on iron. Nevertheless, the predictive ability of ferroptosis-related genes for PTC is unclear. Based on the mRNA expression information from The Cancer Genome Atlas, we compared tumor and normal tissues in terms of the gene expression, for identifying differentially expressed genes (DEGs). Then, the risk score of a 5-gene signature was calculated and a prognostic model was established to test the predictive value of this gene signature by virtue of the LASSO Cox regression. The 5 genes were validated in PTC tissues by RT-qPCR.At last, functional analysis was implemented to investigate the underlying mechanisms. We found a total of 45 ferroptosis-related genes expressed differentially between tumor and normal tissues. 6 DEGs exhibited a significant relevance to the overall survival (OS) (P< 0.05). We classified patients into group with high risk and group with low risk based on the median risk score of a 5-gene signature. Patients in the group with low risk presented a remarkably higher OS relative to the group with high risk (P< 0.01). The Cox regression analysis displayed the independent predictive ability of the risk score. The receiver operating characteristic analysis helped to validate the predictive power owned by the gene signature. After validation, the 5 genes were abnormally expressed between PTC and normal tissues. Functional analysis showed two groups had different immune status. A new ferroptosis-related gene signature can predict the outcomes of PTC patients.

Integrating data and knowledge to identify functional modules of genes: a multilayer approach

BackgroundCharacterizing the modular structure of cellular network is an important way to identify novel genes for targeted therapeutics. This is made possible by the rising of high-throughput technology. Unfortunately, computational methods to identify functional modules were limited by the data quality issues of high-throughput techniques. This study aims to integrate knowledge extracted from literature to further improve the accuracy of functional module identification.ResultsOur new model and algorithm were applied to both yeast and human interactomes. Predicted functional modules have covered over 90% of the proteins in both organisms, while maintaining a comparable overall accuracy. We found that the combination of both mRNA expression information and biomedical knowledge greatly improved the performance of functional module identification, which is better than those only using protein interaction network weighted with transcriptomic data, literature knowledge, or simply unweighted protein interaction network. Our new algorithm also achieved better performance when comparing with some other well-known methods, especially in terms of the positive predictive value (PPV), which indicated the confidence of novel discovery.ConclusionHigher PPV with the multiplex approach suggested that information from both sources has been effectively integrated to reduce false positive. With protein coverage higher than 90%, our algorithm is able to generate more novel biological hypothesis with higher confidence.

In silico discovery and experimental validation of new protein–protein interactions

We introduce a framework for predicting novel protein-protein interactions (PPIs), based on Fisher's method for combining probabilities of predictions that are based on different data sources, such as the biomedical literature, protein domain and mRNA expression information. Our method compares favorably to our previous method based on text-mining alone and other methods such as STRING. We evaluated our algorithms through the prediction of experimentally found protein interactions underlying Muscular Dystrophy, Huntington's Disease and Polycystic Kidney Disease, which had not yet been recorded in protein-protein interaction databases. We found a 1.74-fold increase in the mean average prediction precision for dysferlin and a 3.09-fold for huntingtin when compared to STRING. The top 10 of predicted interaction partners of huntingtin were analysed in depth. Five were identified previously, and the other five were new potential interaction partners. The full matrix of human protein pairs and their prediction scores are available for download. Our framework can be extended to predict other types of relationships such as proteins in a complex, pathway or related disease mechanisms.