Abstract

Lung cancer is a disease of uncontrolled cell growth in tissues of the lung. This growth may lead to metastasis, which is the invasion of adjacent tissue and infiltration beyond the lungs. The vast majority of primary lung cancers are carcinomas of the lung, derived from epithelial cells. Therefore, the present study is aimed at detecting the highly expressed genes, responsible for lung cancer through Serial Analysis of Genome Expression (SAGE) Genie at Cancer Genome Anatomy Project (CGAP). IGL@ (Immunoglobulin lambda locus) gene is predominantly highly expressed in the lung cancer and is considered to be a new target for the Cancerous diseases. The protein was retrieved from the Swissprot/Uniprot KB with accession number Q6GMX3 and was modelled using MODELLER9v7 for predicting the 3D structure of the IGL @ protein which provides an accurate and efficient module to build loops and side chains, found to be identical in sequence. Modelled structure revealed appreciable measures when subjected to structure verification and evaluation using PROCHECK. Ramachandran plot signified the present work undertaken through conformational parameters ᶲ (phi) and ψ (psi) angles calculated from model with 93.4% residues in most favoured region. Further, PROCHECK results confirmed acceptance of model through main and side)chain values. Root mean square distance of planarity was found below 0.02. Hence the model was revealed to have good stereochemistry. Structure of IGL@ Protein can be important tool for future endeavours towards structure based drug designing techniques to impel the search of new efficient inhibitors. Keywords ) SAGE, Lung Cancer, Highly Expressed Genes, Comparative Modeling, PROCHECK

Highlights

  • Cancer is a class of diseases in which a group of cells display uncontrolled growth, invasion, and sometimes metastasis

  • Serial Analysis of Genome Expression (SAGE) Genie is a logistically laid out suite of bioinformatics tools that allow automatic and reliable matches of SAGE tags to known gene transcripts

  • The resulting bioinformatics interface allows automatic tag-to-gene identification, measurement of gene expression normalized to the occurrence of a tag per 200,000 tags collected from a SAGE experiment, and the origins from which a tag is counted

Read more

Summary

Introduction

Cancer is a class of diseases in which a group of cells display uncontrolled growth (division beyond the normal limits), invasion (intrusion on and destruction of adjacent tissues), and sometimes metastasis (spread to other locations in the body via lymph or blood). These three malignant properties of cancers differentiate them from benign tumors, which are self-limited, and do not invade or metastasize. Genes that are over-expressed in the cancerous tissue are of particular interest because overexpression is a trait that we would expect of a gene that is causing the cancer to grow. For example if a gene codes for a protein that usually functions to cause a cell to divide, making more if this protein may lead to uncontrolled

Objectives
Methods
Results
Conclusion
Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call