Accumulating evidence has suggested that the tumor immune microenvironment (TIME) drastically impacts cancer patients’ clinical outcomes, including prognosis and immunotherapy response. However, understanding TIME remains challenging due to its complexity and heterogeneity. In this dissertation, we introduce TimiGP (Tumor Immune Microenvironment Illustration based on Gene Pairs), a computational framework designed to address this challenge. Leveraging single-cell RNA-seq (scRNA-seq) and bulk gene expression data alongside clinical information, TimiGP constructs a cell-cell interaction network that elucidates the relationship between immune cell function and relevant clinical outcomes, such as prognosis and treatment response. With immunological insights, these cell-cell interactions also facilitate the development of interpretable models to predict clinical outcomes. Through network analysis, TimiGP identifies immune cells pivotal in determining clinical outcomes. Harnessing scRNA-seq data, TimiGP offers customizable and high-resolution analysis to characterize the tumor microenvironment across diverse cancer types. In our pan-cancer analysis, TimiGP was applied to study the association of TIME with prognosis (7,938 samples, 23 cancer types) and immunotherapy response (3,410 patients, 7 cancer types). It identified key immune cell types associated with both outcomes, providing insights into the intricate interplay between TIME and cancer progression or treatment response.
","abstract_html":"<p>Accumulating evidence has suggested that the tumor immune microenvironment (TIME) drastically impacts cancer patients’ clinical outcomes, including prognosis and immunotherapy response. However, understanding TIME remains challenging due to its complexity and heterogeneity. In this dissertation, we introduce TimiGP (Tumor Immune Microenvironment Illustration based on Gene Pairs), a computational framework designed to address this challenge. Leveraging single-cell RNA-seq (scRNA-seq) and bulk gene expression data alongside clinical information, TimiGP constructs a cell-cell interaction network that elucidates the relationship between immune cell function and relevant clinical outcomes, such as prognosis and treatment response. With immunological insights, these cell-cell interactions also facilitate the development of interpretable models to predict clinical outcomes. Through network analysis, TimiGP identifies immune cells pivotal in determining clinical outcomes. Harnessing scRNA-seq data, TimiGP offers customizable and high-resolution analysis to characterize the tumor microenvironment across diverse cancer types. In our pan-cancer analysis, TimiGP was applied to study the association of TIME with prognosis (7,938 samples, 23 cancer types) and immunotherapy response (3,410 patients, 7 cancer types). It identified key immune cell types associated with both outcomes, providing insights into the intricate interplay between TIME and cancer progression or treatment response.</p>","abstract_has_math":false,"creators":["Li, Chenyang","https://orcid.org/0000-0001-8109-9388
"],"institution":null,"degree_name":"Doctor of Philosophy (PhD)","degree_level":"Dissertation (PhD)","degree_discipline":null,"degree_department":null,"school":null,"contributors":["Jianjun Zhang, M.D., Ph.D.","Chao Cheng, Ph.D.","Alexandre Reuben, Ph.D."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-12-01T08:00:00Z","date_published":"2024-12-01T08:00:00Z","updated_at":"2026-07-24T05:49:23Z","subjects":["Bioinformatic tools","Computational framework","Multi-omics","Tumor immune microenvironment","Cancer immunotherapy","Pan-cancer immune landscape","Tumor microenvironment","Prognosis","Therapy response","Biomarker","Bioinformatics","Biotechnology","Cancer Biology","Computational Biology","Genomics","Immunity","Immunotherapy","Systems Biology"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.library.tmc.edu/utgsbs_dissertations/1402","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Jianjun Zhang, M.D., Ph.D.","Chao Cheng, Ph.D.","Alexandre Reuben, Ph.D."]},{"key":"dc:creator","label":"Author","values":["Li, Chenyang","https://orcid.org/0000-0001-8109-9388
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"]},{"key":"dc:title","label":"Title","values":["TimiGP: A Computational Framework to Determine the Tumor Immune Microenvironment Associated with Prognosis and Immunotherapy Response"]}]}],"canonical_facts":{"dc:contributor":["Jianjun Zhang, M.D., Ph.D.","Chao Cheng, Ph.D.","Alexandre Reuben, Ph.D."],"dc:creator":["Li, Chenyang","https://orcid.org/0000-0001-8109-9388
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"],"dc:identifier":["https://digitalcommons.library.tmc.edu/utgsbs_dissertations/1402"],"dc:subject":["Bioinformatic tools","Computational framework","Multi-omics","Tumor immune microenvironment","Cancer immunotherapy","Pan-cancer immune landscape","Tumor microenvironment","Prognosis","Therapy response","Biomarker","Bioinformatics","Biotechnology","Cancer Biology","Computational Biology","Genomics","Immunity","Immunotherapy","Systems Biology"],"dc:title":["TimiGP: A Computational Framework to Determine the Tumor Immune Microenvironment Associated with Prognosis and Immunotherapy Response"],"thesis:degree_level":["Dissertation (PhD)"],"thesis:degree_name":["Doctor of Philosophy (PhD)"]},"updated_at":"2026-07-24T05:49:23Z"}