Elucidating the molecular and immune interplay between head and neck squamous cell carcinoma and diffuse large B-cell lymphoma through bioinformatics and machine learning DOI Open Access

Jing Zheng,

Xinxin Li, Xun Gong

et al.

Translational Cancer Research, Journal Year: 2024, Volume and Issue: 13(11), P. 5725 - 5750

Published: Nov. 1, 2024

Head and neck squamous cell carcinoma (HNSCC) contributes significantly to global health challenges, presenting primarily in the oral cavity, pharynx, nasopharynx, larynx. HNSCC has a high propensity for lymphatic metastasis. Diffuse large B-cell lymphoma (DLBCL), most common subtype of non-Hodgkin lymphoma, exhibits significant heterogeneity aggressive behavior, leading mortality rates. Epstein-Barr virus (EBV) is notably associated with DLBCL certain types HNSCC. The purpose this study elucidate molecular immune interplay between using bioinformatics machine learning (ML) identify shared biomarkers potential therapeutic targets. Differentially expressed genes (DEGs) were identified "limma" package R from dataset Cancer Genome Atlas (TCGA) database, relevant modules selected through weighted gene co-expression network analysis (WGCNA) Gene Expression Omnibus (GEO) database. Based on their intersection genes, functional enrichment analyses conducted Ontology (GO), Disease Ontology, Kyoto Encyclopedia Genes Genomes (KEGG) databases. Protein-protein interaction (PPI) networks ML algorithms employed screen biomarkers. prognostic value these was evaluated Kaplan-Meier (K-M) survival receiver operating characteristic (ROC) curve analyses. Human Protein (HPA) database facilitated examination messenger RNA (mRNA) protein expressions. Further mutations, infiltration, drug predictions, pan-cancer impacts performed. Additionally, single-cell sequencing (scRNA-seq) data at type level provide deeper insights into tumor microenvironment. From 2,040 DEGs 1,983 module-related 85 identified. PPI six proposed 21 prospective followed yielded 16 candidates. Survival ROC pinpointed four hub genes-ACACB, MMP8, PAX5, TNFAIP6-as patient outcomes, demonstrating predictive capabilities. Evaluations mutations coupled prediction comprehensive cancer analysis, highlighted biomarkers' roles response treatment efficacy. scRNA-seq revealed an increased abundance fibroblasts, epithelial cells mononuclear phagocyte system (MPs) tissues compared lymphoid tissues. MMP8 showed higher expression five tissues, while TNFAIP6 PAX5 exhibited specific types. Leveraging ML, pivotal diagnostic capabilities corroborates accuracy, supporting development nomogram assist clinical decision-making.

Language: Английский

Diverse RNA methylation patterns in neutrophils: key drivers in hepatocellular carcinoma DOI
Guangming Xu, Yifan Jiang,

Zhenhua Tu

et al.

Clinical & Translational Oncology, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 2, 2024

Language: Английский

Citations

1
Predictive value of PD-L1 and TMB for short-term efficacy prognosis in non-small cell lung cancer and construction of prediction models DOI Creative Commons

Shuling Shi,

Yingyi Wang, Jingjing Wu

et al.

Frontiers in Oncology, Journal Year: 2024, Volume and Issue: 14

Published: May 2, 2024

To investigate the correlation between programmed death ligand 1(PD-L1), tumor mutation burden (TMB) and short-term efficacy clinical characteristics of anti-PD-1 immune checkpoint inhibitor combination chemotherapy in NSCLC patients. The prediction model was evaluated.

Language: Английский

Citations

0
Heterogeneity in Liver Cancer Immune Microenvironment: Emerging Single-Cell and Spatial Perspectives DOI

Caiyi Cherry Li,

Meng Liu,

Hsin-Pei Lee

et al.

Seminars in Liver Disease, Journal Year: 2024, Volume and Issue: 44(02), P. 133 - 146

Published: May 1, 2024

Primary liver cancer is a solid malignancy with high mortality rate. The success of immunotherapy has shown great promise in improving patient care and highlights crucial need to understand the complexity tumor immune microenvironment (TIME). Recent advances single-cell spatial omics technologies, coupled development systems biology approaches, are rapidly transforming landscape immunology. Here we review cellular TIME from perspectives. We also discuss interaction networks within cell community regulating responses. further highlight challenges opportunities implications for biomarker discovery, stratification, combination immunotherapies.

Language: Английский

Citations

0
Causal effect of thyroid cancer on secondary primary malignancies: findings from the UK Biobank and FinnGen cohorts DOI Creative Commons
Zhengshi Wang, Youlutuziayi Rixiati, Chengyou Jia

et al.

Frontiers in Immunology, Journal Year: 2024, Volume and Issue: 15

Published: Sept. 26, 2024

Background Existing epidemiological data indicated a correlation between thyroid cancer (THCA) and the risk of secondary primary malignancies (SPMs). However, does not always imply causality. Methods The Mendelian randomization (MR) analyses were performed to investigate causal relationships THCA SPMs based on international multicenter data. Odds ratios (ORs) with 95% confidence intervals (95% CIs) calculated. Cancer Genome Atlas (TCGA) was used explore potential mechanisms shared by bladder (BLCA). Results Summary datasets genome-wide association studies (GWAS) 30 types cancers obtained from United Kingdom Biobank (UKB) FinnGen database. Meta-analysis UKB results revealed that significantly positively correlated BLCA (OR = 1.140; CI, 1.072-1.212; P < 0.001). Four genes, including WNT3, FAM171A2, MLLT11, ULBP1, identified as key genes both TCHA BLCA. Correlation analysis may increase through augmentation N2 neutrophil infiltration. Conclusions This study showed causally related It is recommended conduct more rigorous screenings for during follow-up patients.

Language: Английский

Citations

0
The role of neutrophils in osteosarcoma: insights from laboratory to clinic DOI Creative Commons
Ming Xia, Yu Han,

Lihui Sun

et al.

Frontiers in Immunology, Journal Year: 2024, Volume and Issue: 15

Published: Nov. 8, 2024

Osteosarcoma, a highly aggressive malignant bone tumor, is significantly influenced by the intricate interactions within its tumor microenvironment (TME), particularly involving neutrophils. This review delineates multifaceted roles of neutrophils, including tumor-associated neutrophils (TANs) and neutrophil extracellular traps (NETs), in osteosarcoma’s pathogenesis. TANs exhibit both pro- anti-tumor phenotypes, modulating growth immune evasion, while NETs facilitate cell adhesion, migration, immunosuppression. Clinically, neutrophil-related markers such as neutrophil-to-lymphocyte ratio (NLR) predict patient outcomes, highlighting potential for neutrophil-targeted therapies. Unraveling these complex crucial developing novel treatment strategies that harness TME to improve osteosarcoma management.

Language: Английский

Citations

0
Elucidating the molecular and immune interplay between head and neck squamous cell carcinoma and diffuse large B-cell lymphoma through bioinformatics and machine learning DOI Open Access

Jing Zheng,

Xinxin Li, Xun Gong

et al.

Translational Cancer Research, Journal Year: 2024, Volume and Issue: 13(11), P. 5725 - 5750

Published: Nov. 1, 2024

Head and neck squamous cell carcinoma (HNSCC) contributes significantly to global health challenges, presenting primarily in the oral cavity, pharynx, nasopharynx, larynx. HNSCC has a high propensity for lymphatic metastasis. Diffuse large B-cell lymphoma (DLBCL), most common subtype of non-Hodgkin lymphoma, exhibits significant heterogeneity aggressive behavior, leading mortality rates. Epstein-Barr virus (EBV) is notably associated with DLBCL certain types HNSCC. The purpose this study elucidate molecular immune interplay between using bioinformatics machine learning (ML) identify shared biomarkers potential therapeutic targets. Differentially expressed genes (DEGs) were identified "limma" package R from dataset Cancer Genome Atlas (TCGA) database, relevant modules selected through weighted gene co-expression network analysis (WGCNA) Gene Expression Omnibus (GEO) database. Based on their intersection genes, functional enrichment analyses conducted Ontology (GO), Disease Ontology, Kyoto Encyclopedia Genes Genomes (KEGG) databases. Protein-protein interaction (PPI) networks ML algorithms employed screen biomarkers. prognostic value these was evaluated Kaplan-Meier (K-M) survival receiver operating characteristic (ROC) curve analyses. Human Protein (HPA) database facilitated examination messenger RNA (mRNA) protein expressions. Further mutations, infiltration, drug predictions, pan-cancer impacts performed. Additionally, single-cell sequencing (scRNA-seq) data at type level provide deeper insights into tumor microenvironment. From 2,040 DEGs 1,983 module-related 85 identified. PPI six proposed 21 prospective followed yielded 16 candidates. Survival ROC pinpointed four hub genes-ACACB, MMP8, PAX5, TNFAIP6-as patient outcomes, demonstrating predictive capabilities. Evaluations mutations coupled prediction comprehensive cancer analysis, highlighted biomarkers' roles response treatment efficacy. scRNA-seq revealed an increased abundance fibroblasts, epithelial cells mononuclear phagocyte system (MPs) tissues compared lymphoid tissues. MMP8 showed higher expression five tissues, while TNFAIP6 PAX5 exhibited specific types. Leveraging ML, pivotal diagnostic capabilities corroborates accuracy, supporting development nomogram assist clinical decision-making.

Language: Английский

Citations

0