Biology subjects

Hossain, M. A.

Publications and source records attributed to Hossain, M. A..

57 records · Page 4Linked to original sources

bioRxiv2018

Network-based genetic profiling, and therapeutic target identification of Thyroid Cancer

Pathogenic mechanisms that underlie malignant follicular thyroid carcinoma (FTC) development are poorly understood. To identify key genes and pathways driving malignant behaviour we employed a system biology-based integrative analyses comparing FTC transcriptomes with a similar but benign lesion, follicular thyroid adenoma (FTA). We identified differentially expressed genes (DEGs) in microarray gene expression datasets (n=52) of FTCs and FTA tissues. Pathway analyses of DEGs using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) resources revealed significant pathways, and pathway hub genes using protein-protein interactions (PPI). We identified 598 DEGs (relative to FTAs) in FTCs and 12 significant pathways with altered expression in FTC. 10 GO groups were significantly connected with FTC-high expression DEGs and 80 with low-FTC expression. PPI analysis identified 12 potential hub genes based on degree and betweenness centrality. Moreover, 10 transcription factors (TFs) were identified that may underlie DEG expression as well as a number of microRNA (miRNAs). Thus, we identified DEGs, pathways, TFs and miRNAs that reflect molecular mechanisms differing between FTC and benign FTA. These may constitute biomarkers that distinguish these lesions and, given the similarities and common origin of the lesions, they may also be indicators of malignant progression potential.

bioinformatics↗

bioRxiv2018

Identification of Ovarian Cancer Gene Expression Patterns Associated with Disease Progression and Mortality

Ovarian cancer (OC) is a common cause of death from cancer among women worldwide, so there is a pressing need to identify factors influencing mortality. Much OC patient clinical data is now publically accessible (including patient age, cancer site stage and subtype), as are large datasets of OC gene transcription profiles. These have enabled studies correlating OC patient survival with clinical variables and with gene expression but it is not well understood how these two aspects interact to influence mortality. To study this we integrated clinical and tissue transcriptome data from the same patients available from the Broad Institute Cancer Genome Atlas (TCGA) portal. We investigated OC mRNA expression levels (relative to normal patient tissue) of 26 genes already strongly implicated in OC, assessed how their expression in OC tissue predicts patient survival then employed Cox Proportional Hazard regression models to analyse both clinical factors and transcriptomic information to determine relative risk of death associated with each factor. Multivariate analysis of combined data (clinical and gene mRNA expression) found age, ovary tumour site and cancer stage IB significantly correlated with patient survival. Univariate analysis also confirmed significant differences in patient survival time when altered transcription levels of KLK6, CD36, MEF2C and SCGB2A1 were evident, while multivariate analysis that considered the 26 genes simultaneously revealed a significant relationship of mortality with KLK6, CD36 and E2F1 genes. However, analysis that considered all 26 genes with clinical variables together identified WFDC2, E2F1, BRCA1, KLK6, SCGB2A1 and SLPI genes as independently related to mortality in OC. This indicated that the latter genes affect OC patient survival, i.e., provided mechanistic and predictive information in addition to that of the clinical traits and provide strong evidence that these genes are critical markers of processes that underlie OC progression and mortality.

bioinformatics↗

bioRxiv2018

The geography of malaria elimination in Bangladesh: combining data layers to estimate the spatial spread of parasites

Malaria control programs face difficult resource allocation decisions. Of particular concern for countries aiming for malaria elimination, the regular movement of individuals to and from endemic areas undermines local interventions by reintroducing infections and sustaining local transmission. Quantifying this movement of malaria parasites around a country has become a priority for national control programs, but remains methodologically challenging, particularly in areas with highly mobile populations. Here, we combined multiple data sources to measure the geographical spread of malaria parasites, including epidemiological surveillance data, travel surveys, parasite genetic data, and anonymized mobile phone data. We collected parasite genetic barcodes and travel surveys from 2,090 patients residing in 176 unions in southeast Bangladesh. We developed a genetic mixing index to quantify the likelihood of samples being local or imported. We then inferred the direction and intensity of parasite flow between locations using an epidemiological model, and estimated the proportion of imported cases assuming mobility patterns parameterized using the travel survey and mobile phone calling data. Our results show that each data source provided related but different information about the patterns of geographic spread of parasites. We identify a consistent north/south separation of the Chittagong Hill Tracts region in Bangladesh, and found that in addition to imported infections from forested regions, frequent mixing also occurs in low transmission but highly populated areas in the southwest. Thus, unlike risk maps generated from incidence alone, our maps provide evidence that elimination programs must address ongoing movement of parasites around the lower transmission areas in the southwest.

epidemiology↗