Transcriptional Profiles from Paired Normal Samples Offer Complementary Information on Cancer Patient Survival--Evidence from TCGA Pan-Cancer Data

Integrative Analysis of Omics Big Data

The diversity and huge omics data take biology and biomedicine research and application into a big data era, just like that popular in human society a decade ago. They are opening a new challenge from horizontal data ensemble (e.g., the similar types of data collected from different labs or companies) to vertical data ensemble (e.g., the different types of data collected for a group of person with match information), which requires the integrative analysis in biology and biomedicine and also asks for emergent development of data integration to address the great changes from previous population-guided to newly individual-guided investigations.Data integration is an effective concept to solve the complex problem or understand the complicate system. Several benchmark studies have revealed the heterogeneity and trade-off that existed in the analysis of omics data. Integrative analysis can combine and investigate many datasets in a cost-effective reproducible way. Current integration approaches on biological data have two modes: one is "bottom-up integration" mode with follow-up manual integration, and the other one is "top-down integration" mode with follow-up in silico integration.This paper will firstly summarize the combinatory analysis approaches to give candidate protocol on biological experiment design for effectively integrative study on genomics and then survey the data fusion approaches to give helpful instruction on computational model development for biological significance detection, which have also provided newly data resources and analysis tools to support the precision medicine dependent on the big biomedical data. Finally, the problems and future directions are highlighted for integrative analysis of omics big data.

MicroRNA-1 acts as a tumor suppressor microRNA by inhibiting angiogenesis-related growth factors in human gastric cancer

BACKGROUND

We recently reported that miR-1 was one of the most significantly downregulated microRNAs in gastric cancer (GC) patients from The Cancer Genome Atlas microRNA sequencing data. Here we aim to elucidate the role of miR-1 in gastric carcinogenesis.

METHODS

We measured miR-1 expression in human GC cell lines and 90 paired primary GC samples, and analyzed the association of its status with clinicopathological features. The effect of miR-1 on GC cells was evaluated by proliferation and migration assay. To identify the target genes of miR-1, bioinformatic analysis and protein array analysis were performed. Moreover, the regulation mechanism of miR-1 with regard to these predicted targets was investigated by quantitative PCR (qPCR), Western blot, ELISA, and endothelial cell tube formation. The putative binding site of miR-1 on target genes was assessed by a reporter assay.

RESULTS

Expression of miR-1 was obviously decreased in GC cell lines and primary tissues. Patients with low miR-1 expression had significantly shorter overall survival compared with those with high miR-1 expression (P = 0.0027). Overexpression of miR-1 in GC cells inhibited proliferation, migration, and tube formation of endothelial cells by suppressing expression of vascular endothelial growth factor A (VEGF-A) and endothelin 1 (EDN1). Conversely, inhibition of miR-1 with use of antago-miR-1 caused an increase in expression of VEGF-A and EDN1 in nonmalignant GC cells or low-malignancy GC cells.

CONCLUSIONS

MiR-1 acts as a tumor suppressor by inhibiting angiogenesis-related growth factors in human gastric cancer. Downregulated miR-1 not only promotes cellular proliferation and migration of GC cells, but may activates proangiogenesis signaling and stimulates the proliferation and migration of endothelial cells, indicating the possibility of new strategies for GC therapy.

Transcriptional response profiles of paired tumor-normal samples offer novel perspectives in pan-cancer analysis

Both tumor and adjacent normal tissues are valuable in cancer research. Transcriptional response profiles represent the changes of gene expression levels between paired tumor and adjacent normal tissues. In this study, we performed a pan-cancer analysis based on the transcriptional response profiles from 633 samples across 13 cancer types. We obtained two interesting results. Using consensus clustering method, we characterized ten clusters with distinct transcriptional response patterns and enriched pathways. Notably, head and neck squamous cell carcinoma was divided in two subtypes, enriched in cell cycle-related pathways and cell adhesion-related pathways respectively. The other interesting result is that we identified 92 potential pan-cancer genes that were consistently upregulated across multiple cancer types. Knockdown of FAM64A or TROAP inhibited the growth of cancer cells, suggesting that these genes may promote tumor development and are worthy of further validations. Our results suggest that transcriptional response profiles of paired tumor-normal tissues can provide novel perspectives in pan-cancer analysis.

Gene Expression Differences in Prostate Cancers between Young and Old Men

Prostate cancer incidence is increasing in younger men. We investigated whether men diagnosed with Gleason 7 (3+4) T2 prostate cancer at younger ages (≤ 45 years, young cohort) had different mRNA and miRNA expression profiles than men diagnosed at older ages (71–74 years, older cohort). We identified differentially expressed genes (DEGs) related to tumor-normal differences between the cohorts. Subsequent pathway analysis of DEGs revealed that the young cohort had significantly more pronounced inflammatory and immune responses to tumor development compared to the older cohort. Further supporting a role of inflammation-induced immune-suppression in the development of early-onset prostate cancer, we observed significant up-regulation of CTLA4 and IDO1/TDO2 pathways in tumors of the young cohort. Moreover, over-expression of CTLA4 and IDO1 was significantly associated with biochemical recurrence. Our results provide clues on the mechanisms of tumor development and point to potential biomarkers for early detection and treatment for prostate cancer in young men.

The incidence of prostate cancer is increasing in young men, and young men are more likely to develop more aggressive prostate cancers than older men. These findings suggest biological differences between prostate cancers that develop in young men and in older men; yet little data and few studies on men diagnosed under age 50 years exist. In this study, we investigated whether men diagnosed with prostate cancer at young ages (≤ age 45 years) had different gene expression profiles than men diagnosed at older ages (71–74 years). We found that inflammatory and immune-related pathways were up-regulated in the young group as compared to the older group, suggesting fundamental differences in tumor development. Moreover, 21% of the young group, compared to 8% of the older group, had biochemical recurrence of prostate cancer–a surprising result given that both groups were diagnosed in early stages of disease (all T2, Gleason 7 (3+4). The recurrence in the young group was associated with over-expression of two genes involved in immune regulation. After validation in a larger dataset, these may provide clues for potential biomarkers to test for monitoring which young patients are likely to progress.

Unexpected frequency of genomic alterations in histologically normal colonic tissue from colon cancer patients

As shown by genomic studies, colorectal cancer (CRC) is a highly heterogeneous disease, where copy number alterations (CNAs) may greatly vary among different patients. To explore whether CNAs may be present also in histologically normal tissues from patients affected by CRC, we performed CGH + SNP Microarray on 15 paired tumoral and normal samples. Here, we report for the first time the occurrence of CNAs as a common feature of the histologically normal tissue from CRC patients, particularly CNAs affecting different oncogenes and tumor-suppressor genes, including some not previously reported in CRC and others known as being involved in tumor progression. Moreover, from the comparison of normal vs paired tumoral tissue, we were able to identify three groups: samples with an increased number of CNAs in tumoral vs normal tissue, samples with a similar number of CNAs in both tissues, and samples with a decrease of CNAs in tumoral vs normal tissue, which may be likely due to a selection of the cell population within the tumor. In conclusion, our approach allowed us to uncover for the first time an unexpected frequency of genetic alteration in normal tissue, suggesting that tumorigenic genetic lesions are already present in histologically normal colonic tissue and that the use in array comparative genomic hybridization (CGH) studies of normal samples as reference for the paired tumors can lead to misrepresented genomic data, which may be incomplete or limited, especially if used for the research of target molecules for personalized therapy and for the possible correlation with clinical outcome.