Lung Cancer Bioinformatics Platform

Data Integration and Management

Multi-omics Data Integration

• Integrate multiple levels of data such as genomics, transcriptomics, proteomics, and metabolomics to build a comprehensive molecular map of lung cancer.

Clinical Data Integration

• Integrate patient clinical data (such as pathological classification, stage, treatment response, and survival) and correlate them with molecular data.

Database Management

• Build and maintain lung cancer-related databases, such as mutation database, drug response database, and biomarker database.

Genomics Analysis

Mutation Analysis

• Identify driver mutations (EGFR, KRAS, ALK, TP53) and copy number variants (CNVs) and detect gene fusion events (such as EML4-ALK) and insertion/deletion mutations.

Whole Genome Sequencing (WGS) and Exon Sequencing

• Analyze the whole genome or exome of the tumor to identify potential cancer-causing mutations and targets.

Single-cell Sequencing

• Study tumor heterogeneity and analyze genomic characteristics of individual tumor cells.

Transcriptomics Analysis

Gene Expression Profile Analysis

• Analysis of tumor gene expression patterns by RNA sequencing (RNA-SEQ) or microarray technology.
• Identify differentially expressed genes (DEGs) and key signaling pathways.

Non-coding RNA Analysis

• To study the regulatory role of miRNAs, lncRNA, and circRNAs in lung cancer.

Variable Splicing Analysis

• Detect variable splicing events of genes to explore their role in lung cancer.

Proteomics Analysis

Protein Expression and Modification

• Identification of protein expression profiles and post-translational modifications (e.g., phosphorylation, ubiquitination) in tumors by mass spectrometry.

Protein Interaction Network

• Construct protein-protein interaction (PPI) networks and identify key regulatory nodes.

Biomarker Discovery

• Screening for potential diagnostic, prognostic, and therapeutic target protein markers.

Metabolomics Analysis

Metabolite Identification

• Analysis of tumor metabolites by mass spectrometry or nuclear magnetic resonance (NMR) techniques.

Metabolic Pathway Analysis

• Study tumor metabolic reprogramming and identify key metabolic pathways (e.g., glycolysis, glutamine metabolism).

Metabolic Markers Found

• Screening for metabolic markers associated with the development, progression, and treatment response of lung cancer.

Biomarker Discovery and Validation

Diagnostic Markers

• Identify molecular markers that can be used for early diagnosis (e.g. ctDNA, miRNA).

Prognostic Markers

• Identify markers associated with patient survival and treatment response.

Therapeutic Target Validation

• Validate the expression and function of potential therapeutic targets to support drug development.

Drug Target Prediction and Virtual Screening

Prediction of Drug-Target Interaction

• Use computational models to predict the binding affinity of a drug to a target.

Virtual Filtering

• Screening potential drug candidates from a compound library through molecular docking and machine learning algorithms.

Drug Relocation

• Analyze new targets and new indications for existing drugs to accelerate drug development.

Visualization and Report Generation

Data Visualization Tools

• Provide a variety of visualization tools such as heat maps, network maps, and survival curves to help researchers visualize the data.

Automated Report Generation

• Automatically generate detailed lab reports based on analysis results, including methods, results, and conclusions.