Identification of miRNA based therapeutic compounds against Nonsmall Cell Lung Carcinoma (NSCLC) by integrative bioinformatics analysis

Project Title

Identification of miRNA based therapeutic compounds against Nonsmall Cell Lung Carcinoma (NSCLC) by integrative bioinformatics analysis

Project Area of Specialization Biomedical EngineeringProject Summary

Problem Statement:

Non-small Cell Lung Carcinoma (NSCLC) is the most common and aggressive type of lung cancer. NSCLC constitutes an alarming burden worldwide as it accounts for 80% of all lung cancers and it remains a challenging medical task in public health worldwide (Figure 1). Different conventional treatment methods such as surgery, radiotherapy, chemotherapy, and drugs have made a notable impact on the lives of patients but unfortunately treatment outcomes for NSCLC patient still remain disappointing. In this era of coronavirus, researchers investigated that COVID-19 share symptoms with NSCLC, however no clear study has made on it yet. It has been pointed out that NSCLC patients had almost twice the risk of COVID-19. The bell is ringing slightly, there is an urgent need to elucidate the underlying molecular mechanisms associated with pathogenesis of NSCLC to support the search for novel NSCLC therapies.

Proposed Solution:

Recently, bioinformatics analysis and microarray technology enables researchers to identify the miRNA-associated genes involved in the pathogenesis of NSCLC. Various bioinformatics related researches had proven reliable and persuasive so it implies that integrative bioinformatics analysis can contribute for the evaluation of underlying complex molecular mechanism of NSCLC. This project will attempt to establish a thorough and understandable solution by providing more enthralling and promising treatment option for NSCLC and hope that it will serve as a reference for clinicians who treat patients with NSCLC. Proposed study will emphasize on in silico analysis to untangle the intricate molecular mechanism underlying the pathogenesis of NSCLC and to uncover molecular candidates with effective diagnostic and prognostic value

Plan of work:

This project will cover an integrative bioinformatics analysis to predict putative differential expressed miRNAs (DEMs) of NSCLC. This will be of high interest, thereby the secondary aim of this project will be screening of DEGs to predict the potential therapeutic chemicals against NSCLC. Moreover, molecular docking approach will be adopted to validate the drug-target interaction

Prospects:

This project will yield a new perspective in context of understanding the pathogenesis of NSCLC. In future, therapeutic compounds would be capable of improving the therapeutics inventions for NSCLC and lessen the burden and provide “Good Health and Well-being” to people all over the world.

Figure 1: Percentage of cases by Non-small Cell Lung Carcinoma subtype

Project Objectives

1. To elucidate molecular mechanisms to better understand the pathogenesis of Non-small cell lung carcinoma (NSCLC)
2. To evaluate the epigenetic evaluation in NSCLC using miRNA associated genes network
3. To identify the drugs with potential therapeutic efficacy against NSCLC.

Project Implementation Method

1- Dataset collection

 Gene expression dataset and miRNA-associated microarray dataset of NSCLC will be retrieved from the publicly available database, Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/).  The datasets should meet the following criteria: (1) tissue samples must be from human NSCLC tissues and paired non-tumor tissues; (2) each dataset must involve more than three samples.

2- Differentially expressed genes (DEGs) and miRNAs (DEMs) identification in NSCLC

A Bioconductor package ‘Limma’ will apply to screen the DEGs and DEMs from NSCLC and adjacent non-tumor tissues. Target genes associated with DEMs will be retrieved from miRwalk database (miRWalk, http://mirwalk.umm.uni-heidelberg.de/). Overlapping genes between those selected by the miRWalk and DEGs from expression dataset analysis will identify using the FunRich tool.

3- Gene Ontology (GO) and Pathway enrichment analysis

GO functional annotations will be performed to determine the potential molecular mechanisms employed by the miRNA-associated DEGs. GO terms analysis of selected DEGs will be performed using the DAVID database (https://david.ncifcrf.gov/; version: 6.8). The FunRich tool (version: 3.0) will be mainly used for analyzing the functional enrichment and interaction networks of genes and proteins [18]. In proposed study, FunRich will be used to analyze the biological pathways of DEGs.

4- Construction of a protein-protein interaction (PPI) network

A PPI network will construct using STRING (https://string-db.org), a database that provides functional interactions among the proteins.

5- Drug repurposing of NSCLC using CMap

miRNA associated upregulated and downregulated genes will be uploaded to CMap tool (CMap, https://www.broadinstitute.org/CMap/) to predict the potential drug candidates for NSCLC.

6- Molecular docking analysis to validate the drug-target interaction

Molecular docking among protein encoded miRNA associated genes and drug compound will be performed using Schrodinger tool to validate the drug-gene interaction. Interaction between the drug-gene interactions would be visualized by PyMOL software.

Figure 2: Schematic diagram representing the methodology used for the proposed study

Benefits of the Project

The economic burden of lung cancer is substantial in all over the world. The proposed study could be beneficial in terms to provide the Biotechnology, and Biotechnology companies with predicted biomarkers for the diagnosis of NSCLC. The financial costs of cancer are high for both the person with cancer and for society as a whole. As cancer treatment costs increase, early detection efforts can be more cost-effective, and potentially cost-saving. Therefore, the proposed study will help to not only lessen the economic burden but also have impact on society. Proposed study could provide the promising therapeutic targets and drugs that will be used for personalized therapy.

Technical Details of Final Deliverable

1. Potential biomarkers will be proposed by present study, which help to diagnose the NSCLC at early stages. Such kind of biomarkers will also help to understand the prognosis of cancer which help doctors to Develop a treatment plan. Moreover, the biomarker could also be used for prediction of response to different treatments of NSCLC in order to determine the most appropriate treatment option for a patient and the ability to monitor disease response and disease recurrence during or after treatment.
2. The importance of epigenetic regulation in NSCLC will be revealed by miRNA–associated DEGs network. Understanding the epigenetics could help to design epigenetic drug used in the laboratory study stops the ability of cancer cells to hide from the immune system and makes the tumor vulnerable.
3. Promising and novel therapeutic targets and drugs will be found for the personalized therapy.

Final Deliverable of the Project Software SystemCore Industry HealthOther IndustriesCore Technology Big DataOther TechnologiesSustainable Development Goals Good Health and Well-Being for PeopleRequired Resources

Item Name	Type	No. of Units	Per Unit Cost (in Rs)	Total (in Rs)
			Total in (Rs)	80000
PyMOL	Equipment	1	50000	50000
Schodinger	Equipment	1	20000	20000
Printer paper Rim	Miscellaneous	1	500	500
Stapler	Miscellaneous	1	2500	2500
Marker	Miscellaneous	4	150	600
File folder	Miscellaneous	2	600	1200
USB flash drive 32GB	Miscellaneous	2	1100	2200
Evo cloud device	Miscellaneous	1	3000	3000