Research Project in Pan Cancer Pathogenesis

In order to strengthen your profile so to have better opportunities in your career, i. e, for jobs or for higher studies you need to have good publications under your belt. The only program since 2010 which is fulfilling this need is the Research Project Training Program of BDG LifeSciences which is of novel research projects on the latest technologies of Bioinformatics.

In this program, we implement the current research trend and apply unique ways of teaching plus practical application so to make you learn in the best possible way. As it is done online hence participants have the freedom of choosing the time of training sessions according to their choice and also save a huge amount of money in travel, accommodation, food, etc., As of now we have completed more than 70 research projects and all of them published at International level. This research project can be done as a Major and/or thesis project for the final year or if someone wants to strengthen their profile.

Applications are invited for 5 seats in our 87 novel research project entitled "Decoding Pan-Cancer Pathogenesis: A Multi-Layered Analysis of Prognostic mRNAs, miRNAs, lncRNAs via Co-Expression Networks and PPINs"

FEE- In India Online: 46999 INR, Foreign Online: $ 899 USD

LAST DATE OF REGISTRATION IS 15 SEPTEMBER 2024 AND THE PROJECT STARTS IN OCTOBER 2024

This project aims to elucidate key prognostic mRNAs, miRNAs, and lncRNAs that are commonly implicated in pan-cancer mechanisms. The research will begin with the retrieval and analysis of RNA-Seq datasets relevant to pan-cancer, followed by the identification of hub genes through the establishment of co-expression networks and the application of critical network topology measures. Subsequently, a Protein-Protein Interaction Network (PPIN) will be constructed to pinpoint hub elements, leading to the identification of a robust prognostic signature. Pathway and Gene Ontology (GO) enrichment analyses will be conducted to uncover the associated signaling pathways. Furthermore, feed-forward loop (FFL) analysis will be employed to identify significant miRNAs, transcription factors (TFs), and lncRNAs related to prognostic genes, followed by an investigation of mutational profiles and tumor infiltration patterns.

Primarily, we will acquire mRNA, miRNA, phenotype, survival data corresponding to pan cancer from UCSC Xena browser or via TCGAbiolinks in R. The raw data will be proceeded with pre-processing, batch-correction, gene mapping, and DEGs identification corresponding to a particular threshold. 

Objective 2: Gene co-expression network construction and significant module detection

The screened DEGs will be forwarded to a Gene Co-expression Network construction based on the Pearson/Spearman correlation coefficient for identifying hub communities/modules with overlapping genes among pan-cancer.

Objective 3: Prognostic signature for survival prediction

Protein family members will be listed and overlapped with all the hub genes identified via WGCNA in order to obtain dysregulated proteins which will be forwarded to a PPIN establishment and network topology analysis. OS and RFS analysis of these elements involved in PPIN will be done followed by a risk-score model construction for identifying and validating a prognostic signature..

Objective 4: Pathway and functional enrichment analysis

Pathway and GO term enrichment analysis will assist to filter prognostic DEGs occurring in signaling pathway or terms.

Objective 5: Identification of driver miRNAs, TFs, and lncRNAs from composite FFL

Significant (based on p-value) human TFs, miRNAs, and lncRNAs interacting with our prognostic DEGs will be fetched from highly validated databases. Considering the regulatory relationships between DEGs, TFs, miRNAs, and lncRNAs, a 4-node composite FFL will be constructed. Driver miRNAs, TFs, and lncRNAs will be fetched for our key prognostic DEGs using centrality measures.

Objective 6: Mutational and tumor infiltration analysis

Prognostic genes having significant correlation with mutational and tumor infiltrating cells will be acquired for validating our findings.

DURATION- 2-3 Months- training sessions will be conducted online on ZOOM/GOOGLE MEET/MICROSOFT TEAMS

Novel prognostic genes along with other regulatory elements responsible for the pathogenesis of pan-cancer will be identified via robust multiomics and network-based approach. The hub module genes will be subjected to prognostic, PPIN. enrichment, FFL, mutational and tumor infiltration analyses..

SYSTEM REQUIREMENT: System desktop or laptop with i5, i7 or i9 core processor with internet. Apple M processors will also work.

ELIGIBILITY: Anyone who is a UNDERGRADUATE/GRADUATE or HIGHER DEGREE is the minimum requirement to register for this project. This project is strictly not for HIGH SCHOOL (Class 9 & 10) / COLLEGE (Class 11 & 12) students. For them we have other projects which can be presented in Science Fair.