Integrative analysis of lung development-cancer expression associations reveals the roles of signatures with inverse expression patterns

Identification of a robust functional subpathway signature for pancreatic ductal adenocarcinoma by comprehensive and integrated analyses

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy and its mortality continues to rise globally. Because of its high heterogeneity and complex molecular landscapes, published gene signatures have demonstrated low specificity and robustness. Functional signatures containing a group of genes involved in similar biological functions may display a more robust performance.

METHODS

The present study was designed to excavate potential functional signatures for PDAC by analyzing maximal number of datasets extracted from available databases with a recently developed method of FAIME (Functional Analysis of Individual Microarray Expression) in a comprehensive and integrated way.

RESULTS

Eleven PDAC datasets were extracted from GEO, ICGC and TCGA databases. By systemically analyzing these datasets, we identified a robust functional signature of subpathway (path:00982_1), which belongs to the drug metabolism-cytochrome P450 pathway. The signature has displayed a more powerful and robust capacity in predicting prognosis, drug response and chemotherapeutic efficacy for PDAC, particularly for the classical subtype, in comparison with published gene signatures and clinically used TNM staging system. This signature was verified by meta-analyses and validated in available cell line and clinical datasets with chemotherapeutic efficacy.

CONCLUSION

The present study has identified a novel functional PDAC signature, which has the potential to improve the current systems for predicting the prognosis and monitoring drug response, and to serve a linkage to therapeutic options for combating PDAC. However, the involvement of path:00982_1 subpathway in the metabolism of anti-PDAC chemotherapeutic drugs, particularly its biological interpretation, requires a further investigation. Video Abstract.

Integrative analysis of the inverse expression patterns in pancreas development and cancer progression

BACKGROUND

As the malignant tumor, pancreatic cancer with a meager 5-years survival rate has been widely concerning. However, the molecular mechanisms that result in malignant transformation of pancreatic cells remain elusive.

AIM

To investigate the gene expression profiles in normal or malignant transformed pancreas development.

METHODS

MaSigPro and ANOVA were performed on two pancreas development datasets downloaded from the Gene Expression Omnibus database. Six pancreatic cancer datasets collected from TCGA database were used to establish differentially expressed genes related to pancreas development and pancreatic cancer. Moreover, gene clusters with highly similar interpretation patterns between pancreas development and pancreatic cancer progression were established by self-organizing map and singular value decomposition. Additionally, the hypergeometric test was performed to compare the corresponding interpretation patterns. Abnormal regions of metabolic pathway were analyzed using the Sub-pathway-GM method.

RESULTS

This study established the continuously upregulated and downregulated genes at different stages in pancreas development and progression of pancreatic cancer. Through analysis of the differentially expressed genes, we established the inverse and consistent direction development-cancer pattern associations. Based on the application of the Subpathway-GM analysis, we established 17 significant metabolic sub-pathways that were closely associated with pancreatic cancer. Of note, the most significant metabolites sub-pathway was related to glycerophospholipid metabolism.

CONCLUSION

The inverse and consistent direction development-cancer pattern associations were established. There was a significant correlation in the inverse patterns, but not consistent direction patterns.

Integrating gene and lncRNA expression to infer subpathway activity for tumor analyses

LncRNAs acting as miRNA sponges to indirectly regulate mRNAs is a novel layer of gene regulation, therefore, it is necessary to integrate lncRNA and gene levels for interpreting tumor biological mechanism. In this study, we developed a lncRNA-gene integrated strategy to infer functional activities for tumor analyses at the subpathway level. In this strategy, we reconstructed subpathway graphs by embedding lncRNA components and considered the expression levels of both genes and lncRNAs to infer subpathway activities for each tumor sample. And the activities were applied to three aspects of tumor analyses; First, the subpathway activities across tumor samples of five tumor types were analyzed, and it was observed that the samples with consistent subpathway activities were derived from the same or similar tumor types. Also, the subpathway activities could stratify samples into several subtypes which has different clinical characterization, e.g. survival status. Second, the subpathway activities between tumor and normal samples were analyzed, and the comparative results showed that subpathway activities displayed more specificities than entire pathway activities. Finally, based on the subpathway activities, we identified prognostic subpathways for lung cancer. Our subpathway-based signatures shared significant overlap with enrichment analysis results and displayed predictive power in the independent testing sets. In conclusion, our integrated strategy provided a framework to infer subpathway activities for tumor analyses and identify subpathway signatures for clinical use.

sPAGM: inferring subpathway activity by integrating gene and miRNA expression-robust functional signature identification for melanoma prognoses

MicroRNAs (miRNAs) regulate biological pathways by inhibiting gene expression. However, most current analytical methods fail to consider miRNAs, when inferring functional or pathway activities. In this study, we developed a model called sPAGM to infer subpathway activities by integrating gene and miRNA expressions. In this model, we reconstructed subpathway graphs by embedding miRNA components, and characterized subpathway activity (sPA) scores by simultaneously considering the expression levels of miRNAs and genes. The results showed that the sPA scores could distinguish different samples across tumor types, as well as samples between tumor and normal conditions. Moreover, the sPAGM model displayed more specificities than the entire pathway-based analyses. This model was applied to melanoma tumors to perform a prognosis analysis, which identified a robust 55-subpathway signature. By using The Cancer Genome Atlas and independently verified data sets, the subpathway-based signature significantly predicted the patients’ prognoses, which were independent of clinical variables. In the prognostic performance comparison, the sPAGM model was superior to the gene-only and miRNA-only methods. Finally, we dissected the functional roles and interactions of components within the subpathway signature. Taken together, the sPAGM model provided a framework for inferring subpathway activities and identifying functional signatures for clinical applications.

Cell cycle related genes up-regulated in human colorectal development predict the overall survival of late-stage colorectal cancer patients

A tumor can be perceived as a special "organ" that undergoes aberrant and poorly regulated organogenesis. Embryonic development and carcinogenesis share striking similarities in their cellular behavior and underlying molecular mechanisms. This intimate association makes embryonic development a viable reference model for studying cancer thereby circumventing the potentially misleading complexity of tumor heterogeneity. Therefore, on the basis of global expression profile, the genes simultaneously activated (up-regulated in terms of expression profile) or suppressed (down-regulated) in both the embryonic development and cancer stage, probably contain profound information on the molecular mechanism of cancer. In this study, the Affymetrix expression profile of 1593 colorectal cancer samples was downloaded from Gene Expression Omnibus. The 1396 differentially expressed probes were robustly obtained using 660 colorectal normal and cancer samples, the expression pattern of which was analyzed using our human colorectal developmental data. All of these 1396 probes were classified into 27 distinct patterns based on their expression patterns during the developmental process. By means of gene set enrichment analysis, we collected 393 V probes simultaneously up-regulated in both development and carcinogenesis and 207 A probes down-regulated in both. Functional enrichment analysis indicated that the V probes were significantly related to cell cycle regulation. Notably, 28 cell-cycle related probes within the V probe group were found to be significantly associated with an overall survival of Stage III/IV patients (GSE17536 cross validation, n = 96, p = 5.70 × 10(-3); GSE29621, n = 36, p = 1.70 × 10(-3); GSE39084, n = 38, p = 0.05; GSE39582, n = 264, p = 0.047; GSE17537, n = 36, p = 5.90 × 10(-3)).