Unraveling the Expression Patterns of Immune Checkpoints Identifies New Subtypes and Emerging Therapeutic Indicators in Lung Adenocarcinoma

Zhang, Jinguo; Han, Xinghua; Lin, Lin; Chen, Jian; Wang, Rui; Ding, Qi; Li, Hao; Wang, Lingyu; Wei, Jie; Wang, Yong; Pan, Yueyin

doi:10.1155/2022/3583985

Cited by 13 publications

(13 citation statements)

References 47 publications

(47 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Significant differential expression of ICGs was observed in tumors ( Figure 5C ), and these ICGs had a distinct cancer-specific profile compared to normal controls ( Figure 5D ). We found that the expression of most ICGs was up-regulated in KIRC, KIRP, ESCA, SKCM, and HNSC compared to para-cancerous tissues, suggesting a potential response to immunotherapy in the corresponding tumors ( Zhang et al, 2022 ).…”

Section: Resultsmentioning

confidence: 91%

Identification of Potential Biomarkers for Pan-Cancer Diagnosis and Prognosis Through the Integration of Large-Scale Transcriptomic Data

Zhu

Xing

et al. 2022

Front. Pharmacol.

View full text Add to dashboard Cite

Cancer is one of the leading causes of death worldwide, bringing a significant burden to human health and society. Accurate cancer diagnosis and biomarkers that can be used as robust therapeutic targets are of great importance as they facilitate early and effective therapies. Shared etiology among cancers suggests the existence of pan-cancer biomarkers, performance of which could benefit from the large sample size and the heterogeneity of the studied patients. In this study, we conducted a systematic RNA-seq study of 9,213 tumors and 723 para-cancerous tissue samples of 28 solid tumors from the Cancer Genome Atlas (TCGA) database, and 7,008 normal tissue samples from the Genotype-Tissue Expression (GTEx) database. By differential gene expression analysis, we identified 214 up-regulated and 186 downregulated differentially expressed genes (DEGs) in more than 80% of the studied tumors, respectively, and obtained 20 highly linked up- and downregulated hub genes from them. These markers have rarely been reported in multiple tumors simultaneously. We further constructed pan-cancer diagnostic models to classify tumors and para-cancerous tissues using 10 up-regulated hub genes with an AUC of 0.894. Survival analysis revealed that these hub genes were significantly associated with the overall survival of cancer patients. In addition, drug sensitivity predictions for these hub genes in a variety of tumors obtained several broad-spectrum anti-cancer drugs targeting pan-cancer. Furthermore, we predicted immunotherapy sensitivity for cancers based on tumor mutational burden (TMB) and the expression of immune checkpoint genes (ICGs), providing a theoretical basis for the treatment of tumors. In summary, we identified a set of biomarkers that were differentially expressed in multiple types of cancers, and these biomarkers can be potentially used for diagnosis and used as therapeutic targets.

show abstract

Section: Resultsmentioning

confidence: 91%

Identification of Potential Biomarkers for Pan-Cancer Diagnosis and Prognosis Through the Integration of Large-Scale Transcriptomic Data

Zhu

Xing

et al. 2022

Front. Pharmacol.

View full text Add to dashboard Cite

show abstract

“…A list of the top 20 enriched pathways was selected with adjusted p < 0:05. To estimate the abundance of 23 immune cell infiltrations in TNBC, the single-sample gene-set enrichment analysis (ssGSEA) algorithm was performed as described before [ 21 ]. Gene sets for each immune cell type that we used were reported, previously [ 22 ].…”

Section: Methodsmentioning

confidence: 99%

Combination of Immune-Related Network and Molecular Typing Analysis Defines a Three-Gene Signature for Predicting Prognosis of Triple-Negative Breast Cancer

et al. 2022

Self Cite

View full text Add to dashboard Cite

Recent breakthroughs in immune checkpoint inhibitors (ICIs) have shown promise in triple-negative breast cancer (TNBC). Due to the intrinsic heterogeneity among TNBC, clinical response to ICIs varies greatly among individuals. Thus, discovering rational biomarkers to select susceptible patients for ICIs treatment is warranted. A total of 422 TNBC patients derived from The Cancer Genome Atlas (TCGA) database and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset were included in this study. High immunogenic gene modules were identified using weighted gene co-expression network analysis (WGCNA). Immune-related genes (IRGs) expression patterns were generated by consensus clustering. We developed a three-gene signature named immune-related gene panel (IRGP) by Cox regression method. Afterward, the associations of IRGP with survival outcomes, infiltration of immune cells, drug sensitivity, and the response to ICIs therapy were further explored. We found five high immunogenic gene modules. Two distinct IRGclusters and IRG-related genomic clusters were identified. The IRGP was constructed based on TAPBPL, FBP1, and GPRC5C genes. TNBC patients were then subdivided into high- and low-IRGriskscore subgroups. TNBC patients with low IRGriskscore had a better survival outcome, higher infiltration of immune cells, lower TP53 mutation rate, and more benefit from ICIs treatment than high IRGriskscore patients. These findings offer novel insights into molecular subtype of TNBC and provided potential indicators for guiding ICIs treatment.

show abstract

“…A list of the top 20 enriched pathways was selected with adjusted P < 0:05. To estimate the abundance of 23 immune cell in ltrations in TNBC TIME, the single-sample gene-set enrichment analysis (ssGSEA) algorithm was performed as described before [21]. Gene sets for each immune cell type that we used were reported, previously [22].…”

Section: Consensus Molecular Clustering Of Irgs and Gene Set Variatio...mentioning

confidence: 99%

Combination of Immune-Related Network and Subtype-Specific Analysis Define a Three- Gene Panel for Predicting Prognosis of Triple-Negative Breast Cancer

Zhang

Pan

Jin

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Background: Recent breakthroughs in immune checkpoint inhibitors (ICIs) have shown promise in triple-negative breast cancer (TNBC). Due to the intrinsic heterogeneity among TNBC, clinical response to ICIs varies greatly among individuals. Thus, discovering rational biomarkers to select susceptible patients for ICIs treatment is warranted.Methods: A total of 422 TNBC patients derived from The Cancer Genome Atlas (TCGA) database and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset were included in this study. High immunogenic gene modules were identified using weighted gene coexpression network analysis (WGCNA). Immune-related genes (IRGs) expression patterns were generated by consensus clustering. We developed a three-gene signature named immune-related gene panel (IRGP) by Cox regression method. Afterward, the associations of IRGP with survival outcomes, infiltration of immune cells, drug sensitivity and the response to ICIs therapy were further explored.Results: We found five high immunogenic gene modules. Two distinct IRGclusters and IRG-related genomic clusters were identified. The IRGP was constructed based on TAPBPL, FBP1, and GPRC5C genes. TNBC patients were subdivided into high- and low-IRGriskscore subgroups. TNBC patients with low IRGriskscore had a better survival outcome, higher infiltration of immune cells, lower TP53 mutation rate and more benefit from ICIs treatment than high IRGriskscore patients.Conclusions: These findings offered novel insights into molecular subtype of TNBC and provided potential indicators for guiding ICIs treatment.

show abstract

Unraveling the Expression Patterns of Immune Checkpoints Identifies New Subtypes and Emerging Therapeutic Indicators in Lung Adenocarcinoma

Cited by 13 publications

References 47 publications

Identification of Potential Biomarkers for Pan-Cancer Diagnosis and Prognosis Through the Integration of Large-Scale Transcriptomic Data

Identification of Potential Biomarkers for Pan-Cancer Diagnosis and Prognosis Through the Integration of Large-Scale Transcriptomic Data

Combination of Immune-Related Network and Molecular Typing Analysis Defines a Three-Gene Signature for Predicting Prognosis of Triple-Negative Breast Cancer

Combination of Immune-Related Network and Subtype-Specific Analysis Define a Three- Gene Panel for Predicting Prognosis of Triple-Negative Breast Cancer

Contact Info

Product

Resources

About