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Decoding Ovarian Cancer: RNA-Seq and Network-Based Discovery of Prognostic Biomarkers

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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 85 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.


CURRENT RESEARCH PROJECT

Our research project training program 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 85 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 so as to have better opportunities in their career, i. e, for jobs or for higher studies.

Applications are invited for ONLY 4 SEATS in our 90 novel research project entitled "RNA-Seq & network-based analysis for the identification of prognostic & diagnostic biomarkers in ovarian cancer tumorigenesis"

INTRODUCTION

Ovarian cancer is a disease that affects women. It is a form of cancer, in which certain cells in the ovary become abnormal and multiply uncontrollably to form a tumor. There are a number of genetic and epigenetic changes that lead to ovarian carcinoma cell transformation. Ovarian carcinoma could originate from any of three potential sites: the surfaces of the ovary, the fallopian tube, or the mesothelium-lined peritoneal cavity. It is also found that any somatic or germline mutations in any of the DNA binding genes will lead to cancers.This can be done if we study gene regulatory networks in detail and select the candidate gene causing ovarian cancer. We need to search and synthesize anticancer compounds. This can be done from drugs that are extracted from the medicinal plants/proteins/peptides followed by In-silico processing such as computer aided drug design approach, virtual screening and molecular dynamics simulation holds infinite possibilities as bioinformatics and biotechnology is revolutionizing the field of medicine by their myriad applications. However, the search for an ovarian cancer inhibitor(s) with anticancer efficacy is a nearly three-decade endeavour. Dire need of the hour is to design a drug showing anticancer activity with least side effects, and it could be established in the sustainable time using computer aided drug design approaches.

Weighted gene co-expression network analysis (WGCNA) is an algorithm widely used to discover co-expressed modules correlated with phenotypes or traits based on expression data [1]. Detection of meaningful densely correlated modules linked to specific clinical traits would be valuable for deducing MODS progression mechanisms and proposing novel hub targets responsible for hampering vital signaling and cellular pathways.

MicroRNAs (miRNAs) are small non-coding RNA molecules that function in RNA silencing and post-transcriptional regulation of gene expression. In contrast, transcription factors (TFs) are protein molecules, excluding RNA polymerase, that regulate the transcription of genes. miRNAs are small non-coding RNA molecules that function in RNA silencing and post-transcriptional regulation of gene expression. In contrast, TFs are protein molecules, excluding RNA polymerase, that regulate the transcription of genes. miRNAs and TFs mutually regulate each other in a tightly coupled manner to form feed-forward loops (FFLs) or feed-back loops (FBLs) where a miRNA represses a TF, or a TF regulates a miRNA and both of them co-regulate a joint target. FFL is a significant network motif in the genome, which might work as the core of the whole gene regulatory network[2]. FFLs can be categorized into 3 types corresponding to their master regulators: miRNA-FFL, TF-FFL, and composite FFL. In a TF-FFL, TF is the master regulator which regulates its partner miRNA and their joint target, while in a miRNA-FFL, miRNA is the master regulator which represses its partner TF and their joint target[3]. TF-FFL and miRNA-FFL merge to form a composite FFL, where TF and miRNA regulate/repress each other along with their joint target. FFLs encompass recurrent network motifs in the mammalian regulatory network[4].Noncoding RNAs (ncRNAs), such as long noncoding RNAs (lncRNAs) and miRNAs, function as key regulators of gene expression, their involvement in various human diseases is being gradually revealed, and the multilayered regulatory networks formed by cross-linked ncRNAs and mRNAs seemly provide new insights into their regulatory mechanism with regards to both physiology and pathology [5]. lncRNAs are conventionally described as transcripts longer than 200 nucleotides with no or little protein-coding capacity. The competitive endogenous RNA (ceRNA) hypothesis, a novel regulatory mechanism that received attention in 2011, indicated that circular RNAs, lncRNAs and pseudogenes can regulate the abundance of miRNAs as molecular sponges.

 

Study Rationale:

In the past many years, remarkable achievements have been obtained in the investigation of prognostic markers for ovarian cancer. For instance, genes (AEBP1, COL11A1, COL5A1, COL6A2, LOX, POSTN, SNAI2, THBS2, TIMP3, and VCAN) has been validated to be associated with poor overall survival in patients with high-grade serous ovarian cancer. The presence of a BRCA1 or BRCA2 mutation is associated with a better prognosis in patients with invasive ovarian cancer. Furthermore, CD73 enhances ovarian tumor cell growth and expression of antiapoptotic BCL-2 family members, indicating a role of CD73 as a prognostic marker of patient survival in high-grade serous ovarian cancer. Although the aforementioned genes have been shown to be correlated with the prognosis in ovarian cancer, their prognostic accuracy may be limited because the development of disease usually involves several genes and the interaction between them to form a complex pathway. Therefore, it is necessary to identify gene networks and pathways including multiple genes and their interactions, which can be achieved by gene regulatory network construction. In the present study, we aimed to construct a gene regulatory network to analyze the miRNA associated with genes that was significantly related to ovarian cancer.

 

Objectives:

  1. Retrieval of mRNA expression profile from TCGA and screening of Differentially Expressed Genes (DEGs) from ovarian cancer cohort.
  2. Significant hub gene module detection using weighted module/community algorithms in ovarian cancer cohort.
  3. Network-centrality based detection of hub genes.
  4. Pathway and functional enrichment analyses for elucidating highly overlapping genes in all cohorts.
  5. Validation using TIMER, GEPIA, cBioPortal, and survival analysis.

 

Methodology:

  • Objective A:

The suitable raw gene expression profile datasets associated with ovarian cancer will be retrieved from TCGA & NCBI-GEO (https://www.ncbi.nlm.nih.gov/geo/) [6]. These datasets will be comprising the mRNA expression profiles for normal and tumor patients. Expression data in the unprocessed CEL files will be preprocessed with background correction, quantile normalization and calculation of gene expression using the robust microarray analysis (RMA) algorithm in affy package and other suitable R packages respectively. The probe IDs will be mapped to official HGNC (HUGO Gene Nomenclature Committee) gene symbol(s) using suitable R packages. Relative expression across genes mapping to more than one probe IDs will be averaged. log2 fold change and p-value computation will be done using appropriate statistical formulations. Meta-analysis of the normalized expression values will be performed using packages like metaMA and limma[7]. The application of combined p-value algorithm based on Fisher’s χ2‑based algorithm will enable to compute single p-value across multiple samples for the same gene. Differentially Expressed Genes (DEGs) will be collected based on statistical threshold involving already computed combined p-values and log2 fold change values. This would be followed by filtering of up and downregulated DEGs.

 

  • Objective B:

The screened DEGs will be further used for Gene Co-expression Network construction (GCN). Pearson/Spearman correlation coefficient will be used to assess the degree of connectedness between any two random DEGs. Nodes of the network will be the DEGs and two nodes are connected if they will possess strong correlation justifying that they are co-expressed across all tissue samples. To know the degree of correlation between genes, we will be using soft threshold power (β). Varierty of community detection algorithms like Weighted Gene Co-expression Network Analysis (WGCNA), igraph, pigengene, CEMiTool, etc. will be used for construction of weighted gene co-expression network based on soft-threshold power and adjacency and other clinical parameters like survival data, age, weight, etc. Highly significant network motifs or hub module(s) from GCN will be screened based on hierarchical clustering (in case of WGCNA, CEMiTool, and pigengene) of strongly correlated DEGs. Structural properties of these modules will be characterized through the behaviors of topological parameters. Whereas, the Leading Eigen Vector method (LEV) will be used (in case of igraph) to detect communities. The LEV method is the most promising one for community detection as it calculates the Eigen value for each link, exemplifying the significance of each link, not nodes. To obtain only motif, we will detect modules from complete network and then sub-modules from the modules at each level of organization. The overlapping genes from all community detection algorithms will be considered as the highly influential ones with respect to each individual cohort.

 

  • Objective C:

Protein-Protein Interaction (PPI) network of up and downregulated DEGs will be constructed for identified hub modules. The interacting protein partners of each DEGwill be extracted from validated databases such as HIPPIE, BioGRID, HPRD, STRING, etc.. The protein partners will be extracted based on some given score or statistical cut-off value. The up and downregulated PPI networks will be constructed and visualized in Cytoscape software[10]. Cytohubba, MCODE, and other plugins available in cytoscape will be used for hub genes identification in each cohort. Top 10-15 DEGs will be ranked based on important centrality measures like bottleneck, degree, stress, closeness, betweenness, radiality, and EPC (using Cytohubba). Similarly important hub module genes will be extracted from MCODE using stringent conditions. The overlapping genes obtained from all these algorithms will be considered as hub genes.

 

  • Objective D:

Pathway and GO term enrichment analyses will be performed using Ingenuity Pathway Analysis (IPA), Broad Institute Gene Set Enrichment Analysis (GSEA), Gene Set Variation Analysis (GSVA), etc. Gene ontology analysis (GO) is a common useful method for annotating genes and gene products and for identifying characteristic biological attributes for high-throughput genome or transcriptomes data. The GO provides us the basic terms subdivided into three important categories namely MF (Molecular functions), BP (Biological process) and CC (Cellular component). We could visualize the core biological processes, molecular functions, cellular components and pathways among those DEGs. Significantly enriched GO terms and pathways will be screened based on significant p-value cut-off. The genes overlapping with the significant pathways and GO terms in all cohorts will be considered as the validated final hub genes.

 

  • Objective E:

Validation of final hub genes will be done using databases such as TIMER, GEPIA, cBioPortal, KM plotter. Etc. followed by validation in an external cohort.

 

Expected Outcomes:

In the proposed work, we will analyze the gene expression profiling of ovarian cancer tumor patients with respect to healthy controls. Robust and advanced microarray approach followed by new generation module detection algorithms will be used to trace down hub module of interest. Our main aim is to identify prognostic biomarkers  associated with our hub genes and are involved in the pathogenesis of ovarian cancer.

 

References: Available upon request


Target Audience

This project is ideal for:

  • Students: Postgraduates, Ph.D. candidates, and undergraduates in bioinformatics, biotechnology, and cancer biology.
  • Academicians: Researchers and faculty exploring cancer genomics and molecular biology.
  • Industry Professionals: Scientists in biotech and pharmaceutical sectors seeking expertise in bioinformatics-driven oncology research.


Methodology

Participants will follow a structured workflow, including RNA-Seq data retrieval, DEG analysis, network construction, and biomarker validation. The project emphasizes hands-on learning using tools like R, Cytoscape, and Ingenuity Pathway Analysis (IPA), ensuring practical exposure to cutting-edge techniques.


Why You Should Join This Project

This project offers an unparalleled opportunity to delve into ovarian cancer research, using advanced computational methods to identify novel biomarkers. By gaining hands-on experience with bioinformatics tools, you’ll develop critical skills to contribute to cancer genomics, precision medicine, and therapeutic innovation.


Why Choose BDG LifeSciences for Research Project Training?

BDG LifeSciences has a proven track record of delivering impactful training programs in bioinformatics and computational biology. Our expert-led research projects are designed to equip participants with practical skills, in-depth knowledge, and real-world problem-solving experience to excel in academic and industrial settings. As of now we have completed more than 85 research projects and all of them are published in Journals, Conferences, etc.


How To Register

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T & C

  • It is recommended to provide GMAIL email address.
  • Once you register relax we will send you the welcome email having all the details after the project registration gets over.
  • Video recording of each session will be provided at the end of the session to give the user a unique learning experience.
  • Interactive training sessions will be conducted on Google Meet/Zoom so to give users a better learning experience.
  • As it is with a LIVE TRAINER hence practical application, i.e., the experiments/tasks to be performed can be done in the best explainable manner.
  • In this program, there will be a LIVE trainer who will solve queries along with training.
  • Make sure you register under the right category. If you register under the wrong category then your registration will be invalid and NO REFUND WILL BE MADE IN THIS CASE. This is your mistake and the company is not responsible for it.
  • To avoid this do read the description of the ticket before selecting and proceeding with payment.
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  • The certificate will be issued as per the details which you provide in the registration form while registering before payment.
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  6. Sucharita Ghosh, B.Tech M.Tech Dual Degree in Biotechnology, KIIT University, Bhubaneswar, Odisha, India.

56. Discovery of new ligands for PPAR Gamma responsible for Diabetes Type 2: A Virtual Screening, Docking & ADMET Study.

  1. Bharti Mittal, PhD, Senior Scientist, Operations, MedGenome Labs Pvt Ltd, Bangalore, India.
  2. Vivek Srivastava, Ph.D., Assistant Professor(Biochemistry), School of Basic Sciences and Research, Sharda University, Greater Noida, UP
  3. Maitili Varma, Advanced Proficiency course in Genetic engineering, Genohelix Biolabs, Jain Group of Institutions, India.
  4. Priyadarshini Sharma, B. Tech, Amity Institute of Biotechnology, Amity University, Noida, India.
  5. Harshita Agarwal, B. Sc, Amity Institute of Biotechnology, Amity University, Noida , India.
  6. Gupta Radha Devi Shivprakash, Masters of Science in Bioinformatics, G.N Khalsa College, Matunga, Mumbai, India
  7. Raisa Dabhilkar, Masters of Science in Bioinformatics, G.N Khalsa College, Matunga, Mumbai, India.

53. Molecular Modelling study of phytoconstituents from medicinal plants of India | Discovery of natural anti-tubercular agents

  1. Taiba Hamed Youssef Gamal El Din, Master in Biotechnology & Life Science, Bani Sueif University, Bani Sueif, Egypt.
  2. Syeda Birjees Misbah, M.Sc., Biochemistry, REVA University, Bangalore, India.
  3. Aindrila Pal, B. Tech, Amity Institute of Biotechnology, Amity University, Noida, India.
  4. Preeti, M.Sc Biotech, Amity Institute of Biotechnology, Amity University, India.
  5. Ngoc Pham, The Applied Chemistry Department of the International University, Ho Chi Minh, Vietnam.
  6. Moumita Ganguly, Research Associate, IIT Mandi, India.
  7. Deeksha Gairola, M.Sc Biotech, Amity Institute of Biotechnology, Amity University, India.
  8. Ketaki Ghatole, B.E. Biotechnology, MS Ramaiah Institute of Technology, Bengaluru, India.

49. Molecular Modeling study of Zika Virus | Virtual Screening, Protein Modeling, Docking, ADMET and MD Simulations Study

  1. Dr. Alam El-Din, Hanaa Mahmoud, Virology and Immnunology Unit, Cancer Biology Department, National Cancer Institute, Cairo University, Cairo, Egypt.
  2. Manjisa Choudhury, VIT University, Vellore, India.
  3. Nada Gamal Ibrahim El-Dawy, Faculty of Biotechnology, October University for Modern Sciences and Arts, Egypt (MSA University).

39. Study of derivatives of Chalcones as new Tyrosinase inhibitors: A Molecular Docking, ADME & Tox Study

  1. Adwaita Das, Department of Botany, The University of Burdwan, Burdwan, West Bengal, India
  2. Shreya John MSc Bioinformatics, St. Aloysius Institute of Management and Technology, Mangalore University.
  3. Lalit R. Samant, Project Assistant, Haffkine Institute for Training Research and Testing, India.
  4. K.C. Haritah Yadav, M. Sc (Medical Pharmacology) from Kamineni Institute of Medical Sciences, Narkrtpally, Nalgonda, Andhra Pradesh.
  5. Dr. N. Hari, School of Chemical & Biotechnology, SASTRA University, Thanjavur, Tamil Nadu.
  6. Dr(Mrs) Jhaumeer Laulloo Sabina, University of Mauritius, Reduit, Mauritius.

34. Study of extracts of Veratrum Dahuricum as potential Anti-tumor molecules: Molecular Docking & Modeling study with Farnesyl Pyrophosphate Synthase (FFPS)

  1. Gomathi Rajendran, ITC Zenith (M) Sdn. Bhd. (Conformity Assessment Body), Malaysia.
  2. Seetha Harilal, Department of Pharmacology, Nehru College of Pharmacy, Kerala University of Health Sciences, Thrissur, Kerala, India.
  3. Akshya Kumar Mailapali, I. M.Sc Bioinformatics , B.J.B. Autonomous College, Utkal University, Bhubaneshwar, India.
  4. Tanya Munjal, M.Sc Biotech, Amity Institute of Biotechnology, Amity University, India.
  5. Sambit Kumar Roy, M.Sc Biotech, Amity Institute of Biotechnology, Amity University, India.
  6. Lucky Gupta, M.Sc Biotech, Amity Institute of Biotechnology, Amity University, India.
  7. Gaurav Dutt, School of Basic and Applied Science, Dayananda Sagar University, Bangalore, India.

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