Mining gene link information for survival pathway hunting

Area of Science:

• Bioinformatics
• Computational Biology
• Cancer Research

Background:

• Identifying pathways associated with patient survival is crucial for cancer research.
• Existing methods may not fully capture complex gene interactions relevant to survival outcomes.

Purpose of the Study:

• To develop a novel gene link-based method for survival time-related pathway hunting.
• To incorporate gene link information into survival analysis for improved pathway identification.

Main Methods:

• A gene link-based Cox proportional hazard model (Link-Cox) was established, considering pairs of linked genes.
• A pathway survival score (PSS) was formulated by aggregating survival significance across gene links within a pathway.
• A permutation test was devised to assess the statistical significance of the PSS.

Main Results:

• The proposed Link-Cox model effectively estimates the association between gene links and cancer patient survival time.
• The pathway survival score (PSS) accurately measures pathway associations with survival.
• The method demonstrated effectiveness and efficiency on simulation and real-world gene expression datasets.

Conclusions:

• The proposed gene link-based method offers an effective and efficient approach for survival pathway hunting.
• Incorporating gene link information enhances the ability to identify pathways relevant to cancer patient survival.
• This method provides a valuable tool for uncovering novel survival-associated pathways in cancer research.