Microarray based analysis of gene regulation by mesenchymal stem cells in breast cancer

In Vitro and In Vivo Comparative Analysis of Differentially Expressed Genes and Signaling Pathways in Breast Cancer Cells on Interaction with Mesenchymal Stem Cells

The interaction of breast cancer cells (BCC) with mesenchymal stem cells (MSC) plays a vital role in influencing the gene expression in breast cancer cells and thereby its uncontrolled proliferation, metastasis, and drug resistance. The extent of MSC governing the BCC or the extent of BCC influencing the MSC is a complex process, and the interaction strongly depends upon conditions such as the presence or absence of other cell types and in vivo tumor microenvironment or simple in vitro conditions. Hence, understanding this interaction through gene expression profiling may provide key insights about potential genes which can be targeted for breast cancer treatment. In the current study, in vitro microarray dataset and in vivo RNA-seq dataset of BCC on interaction with the MSC were downloaded from NCBI GEO database and analyzed for differentially expressed genes (DEGs), gene ontology (GO) term enrichment, and Reactome pathway analysis. To target the genes which have similar effect on both in vitro and in vivo, a comparative analysis was performed, 24 genes were commonly upregulated in both in vitro and in vivo datasets, while no common downregulated genes were observed. Out of which, 16 significant genes based upon fold change (logFC > 2) are identified for manipulating the interactions between MSC and BCC. Among them, 6 of the identified genes (FSTL1, LOX, SERPINE1, INHBA, FN1, and VEGFA) have already been reported to be upregulated in BCC on interaction with MSC by various studies. Further experiments need to be conducted to understand the role of remaining 10 identified genes (EFEMP1, IGFBP3, EDIL3, IFITM1, IGFBP4, ITGA5, SLC3A2, HRH1, PPP1R15A, and NNMT) in MSC-BCC interaction. In addition to the reported significant genes and its associated pathways, the expression of long non-coding RNA identified in this study may increase our understanding about the way MSC interacts with BCC and accelerate MSC-based treatment strategies for breast cancer.

Integrated bioinformatics and statistical approaches to explore molecular biomarkers for breast cancer diagnosis, prognosis and therapies

Integrated bioinformatics and statistical approaches are now playing the vital role in identifying potential molecular biomarkers more accurately in presence of huge number of alternatives for disease diagnosis, prognosis and therapies by reducing time and cost compared to the wet-lab based experimental procedures. Breast cancer (BC) is one of the leading causes of cancer related deaths for women worldwide. Several dry-lab and wet-lab based studies have identified different sets of molecular biomarkers for BC. But they did not compare their results to each other so much either computationally or experimentally. In this study, an attempt was made to propose a set of molecular biomarkers that might be more effective for BC diagnosis, prognosis and therapies, by using the integrated bioinformatics and statistical approaches. At first, we identified 190 differentially expressed genes (DEGs) between BC and control samples by using the statistical LIMMA approach. Then we identified 13 DEGs (AKR1C1, IRF9, OAS1, OAS3, SLCO2A1, NT5E, NQO1, ANGPT1, FN1, ATF6B, HPGD, BCL11A, and TP53INP1) as the key genes (KGs) by protein-protein interaction (PPI) network analysis. Then we investigated the pathogenetic processes of DEGs highlighting KGs by GO terms and KEGG pathway enrichment analysis. Moreover, we disclosed the transcriptional and post-transcriptional regulatory factors of KGs by their interaction network analysis with the transcription factors (TFs) and micro-RNAs. Both supervised and unsupervised learning's including multivariate survival analysis results confirmed the strong prognostic power of the proposed KGs. Finally, we suggested KGs-guided computationally more effective seven candidate drugs (NVP-BHG712, Nilotinib, GSK2126458, YM201636, TG-02, CX-5461, AP-24534) compared to other published drugs by cross-validation with the state-of-the-art alternatives top-ranked independent receptor proteins. Thus, our findings might be played a vital role in breast cancer diagnosis, prognosis and therapies.

Fibronectin 1: A Potential Biomarker for Ovarian Cancer

Methods

OVCAR3 and A2780 are the two common cell lines that are used for ovarian cancer studies. The different invasion and migration abilities were observed by scratch tests and transwell experiments in our preliminary study. Gene chip was used to screen the expression gene in these two different cell lines, and then, the differentially expressed genes (at least 2-fold difference, P value < 0.05) were analyzed using KEGG.

Result

Fibronectin 1 (FN1) was found to be the most strongly correlated with the invasion and migration abilities of the OVCAR3 cells. Real-time PCR and FN1 knockout cell line was conducted and confirmed this finding. Based on the Oncomine database analysis, comparing with normal people, ovarian cancer patients exhibited high levels of FN1 expression. Additionally, higher FN1 expression was found in patients with higher FIGO stages of cancer.

Conclusion

FN1 could be a new biomarker for ovarian cancer detection and progress indicator.

Mouse bone marrow mesenchymal stem cells with distinct p53 statuses display differential characteristics

Mesenchymal stem cells (MSCs) affect diverse aspects of tumor progression, such as angiogenesis, tumor growth and metastasis. Bone marrow MSCs (BM‑MSCs) are fibroblast‑like cells with multipotent differentiation ability, that localize to areas of tissue damage, including wounds and solid tumors. The tumor suppressor gene, p53, is functionally involved in cell cycle control, apoptosis and genomic stability, and is mutated and inactivated in most human cancers. The present study aimed to investigate the role of p53 in the biology of BM‑MSCs. In the present study, p53 wild‑type (p53+/+), knockdown (p53+/‑) and knockout (p53‑/‑) mouse BM‑MSCs (mBM‑MSCs) were observed to be similar in appearance and in the expression of cell surface biomarkers, but expressed differential p53 protein levels. The p53+/‑ and p53‑/‑ mBM‑MSCs demonstrated an increased proliferation rate compared with mBM‑MSCs derived from p53+/+ mice. mBM‑MSCs from all three groups, representing distinct p53 statuses, were unable to form tumors over a 3‑month period in vivo. The adipogenic and osteogenic differentiation of mBM‑MSCs was increased in the absence of p53. The colony formation and migratory abilities of p53+/‑ and p53‑/‑ mBM‑MSCs were markedly enhanced, and the expression levels of stem cell‑associated proteins were significantly increased compared with p53+/+. The expression levels of microRNA (miR)‑3152 and miR‑337 were significantly increased in p53+/‑ and p53‑/‑ mBM‑MSCs, whereas the expression levels of miR‑221, miR‑155, miR‑1288 and miR‑4669 were significantly decreased. The expression levels of tumor necrosis factor‑α and interferon‑γ‑inducible protein‑10 were significantly upregulated in the supernatant of p53+/‑ and p53‑/‑ mBM‑MSCs. Ubiquitin protein ligase E3 component n‑recognin 2, RING‑finger protein 31 and matrix metalloproteinase 19 were highly expressed in p53+/‑ and p53‑/‑ mBM‑MSCs. The results of the present study indicated that p53 may serve an important role in the biology of mBM‑MSCs, and may provide novel insights into the role of cells with different p53 statuses in cancer progression.

Bioinformatic analysis identifies potentially key differentially expressed genes in oncogenesis and progression of clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is one of the most common and lethal types of cancer within the urinary system. Great efforts have been made to elucidate the pathogeny. However, the molecular mechanism of ccRCC is still not well understood. The aim of this study is to identify key genes in the carcinogenesis and progression of ccRCC. The mRNA microarray dataset GSE53757 was downloaded from the Gene Expression Omnibus database. The GSE53757 dataset contains tumor and matched paracancerous specimens from 72 ccRCC patients with clinical stage I to IV. The linear model of microarray data (limma) package in R language was used to identify differentially expressed genes (DEGs). The protein–protein interaction (PPI) network of the DEGs was constructed using the search tool for the retrieval of interacting genes (STRING). Subsequently, we visualized molecular interaction networks by Cytoscape software and analyzed modules with MCODE. A total of 1,284, 1,416, 1,610 and 1,185 up-regulated genes, and 932, 1,236, 1,006 and 929 down-regulated genes were identified from clinical stage I to IV ccRCC patients, respectively. The overlapping DEGs among the four clinical stages contain 870 up-regulated and 645 down-regulated genes. The enrichment analysis of DEGs in the top module was carried out with DAVID. The results showed the DEGs of the top module were mainly enriched in microtubule-based movement, mitotic cytokinesis and mitotic chromosome condensation. Eleven up-regulated genes and one down-regulated gene were identified as hub genes. Survival analysis showed the high expression of CENPE, KIF20A, KIF4A, MELK, NCAPG, NDC80, NUF2, TOP2A, TPX2 and UBE2C, and low expression of ACADM gene could be involved in the carcinogenesis, invasion or recurrence of ccRCC. Literature retrieval results showed the hub gene NDC80, CENPE and ACADM might be novel targets for the diagnosis, clinical treatment and prognosis of ccRCC. In conclusion, the findings of present study may help us understand the molecular mechanisms underlying the carcinogenesis and progression of ccRCC, and provide potential diagnostic, therapeutic and prognostic biomarkers.

Molecular prognosticators in clinically and pathologically distinct cohorts of head and neck squamous cell carcinoma-A meta-analysis approach

Head and neck squamous cell carcinomas (HNSCC) includes multiple subsites that exhibit differential treatment outcome, which is in turn reflective of tumor stage/histopathology and molecular profile. This study hypothesized that the molecular profile is an accurate prognostic adjunct in patients triaged based on clinico-pathological characteristics. Towards this effect, publically available micro-array datasets (n = 8), were downloaded, classified based on HPV association (n = 83) and site (tongue n = 88; laryngopharynx n = 53; oropharynx n = 51) and re-analyzed (Genespring; v13.1). The significant genes were validated in respective cohorts in The Cancer Genome Atlas (TCGA) for correlation with clinico-pathological parameters/survival. The gene entities (n = 3258) identified from HPV based analysis, when validated in TCGA identified the subset specifically altered in HPV+ HNSCC (n = 63), with three genes showing survival impact (RPP25, NUDCD2, NOVA1). Site-specific meta-analysis identified respective differentials (tongue: 3508, laryngopharynx: 4893, oropharynx: 2386); validation in TCGA revealed markers with high incidence (altered in >10% of patients) in tongue (n = 331), laryngopharynx (n = 701) and oropharynx (n = 404). Assessment of these genes in clinical sub-cohorts of TCGA indicated that early stage tongue (MTFR1, C8ORF33, OTUD6B) and laryngeal cancers (TWISTNB, KLHL13 and UBE2Q1) were defined by distinct prognosticators. Similarly, correlation with perineural/angiolymophatic invasion, identified discrete marker panels with survival impact (tongue: NUDCD1, PRKC1; laryngopharynx: SLC4A1AP, PIK3CA, AP2M1). Alterations in ANO1, NUDCD1, PIK3CA defined survival in tongue cancer patients with nodal metastasis (node+ECS-), while EPS8 is a significant differential in node+ECS- laryngopharyngeal cancers. In oropharynx, wherein HPV is a major etiological factor, distinct prognosticators were identified in HPV+ (ECHDC2, HERC5, GGT6) and HPV- (GRB10, EMILIN1, FNDC1). Meta-analysis in combination with TCGA validation carried out in this study emphasized on the molecular heterogeneity inherent within HNSCC; the feasibility of leveraging this information for improving prognostic efficacy is also established. Subject to large scale clinical validation, the marker panel identified in this study can prove to be valuable prognostic adjuncts.