Correlation analysis between immune-related genes and cell infiltration revealed prostate cancer immunotherapy biomarkers linked to T cells gamma delta

Niu, Wenkang; Zhang, Tingting; Ma, Lei

doi:10.1038/s41598-023-28475-6

Cited by 5 publications

(3 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In osteosarcoma, analysis of immune-related gene expression identified key genes associated with disease progression and prognosis [30]. Similarly, in prostate cancer, gene expression significantly correlated with immune cell infiltration levels, particularly T-cells γδ, with important implications for understanding disease progression and personalized treatment strategies [31]. These studies highlight the critical interaction between gene expression and immune cell infiltration in understanding the immune microenvironment and improving patient prognosis.…”

Section: Discussionmentioning

confidence: 93%

Thyroid cancer prognostic biomarker ARL4A and its relationship with immune infiltration

Han

2024

Int J Clin Exp Pathol

View full text Add to dashboard Cite

Background: Thyroid cancer (THCA) is a prevalent form of cancer with high rates of morbidity and mortality. The small GTPase ADP-ribosylation factor-like 4A (ARL4A) is integral to various cellular processes, including cytoskeletal restructuring, vesicular transport, cell migration, and neuronal development. However, the role of ARL4A as a clinical predictor, particularly its relation to immune cell infiltration in THCA, remains unclear. Methods: A combination of experimental studies and analysis of online databases was employed to investigate ARL4A expression in THCA. Clinical and pathological data from THCA patients were compiled for a comprehensive subgroup analysis. The Kaplan-Meier and Cox regression methods were utilized to evaluate the prognostic significance of ARL4A in THCA patients. Finally, the "Cancer Genome Atlas" was analyzed to explore the correlation between immune cell infiltration, ARL4A expression, and their joint impact on prognosis. Results: ARL4A exhibited low expression in THCA. An elevated ARL4A was associated with poor prognosis. Moreover, the expression of ARL4A was correlated with the age, gender, and pathological stage of THCA patients. Finally, ARL4A expression was found to be negatively correlated with immune cell infiltration and influenced the prognosis of patients through changes in the immune environment. Conclusion: ARL4A may serve as a potential biomarker for the diagnosis and treatment of THCA, impacting the prognosis of patients through the modulation of the immune microenvironment.

show abstract

Section: Discussionmentioning

confidence: 93%

Thyroid cancer prognostic biomarker ARL4A and its relationship with immune infiltration

Han

2024

Int J Clin Exp Pathol

View full text Add to dashboard Cite

show abstract

“…On a further literature study of the topic, B. Ozer and U. Sezerman also remarked that "lowest significance and fold changes were observed at the prostate cancer dataset" compared to colon, breast, and thyroid cancers [28]. In addition, another recent study by W. Niu, T. Zhang, and L. Ma also identified fewer upregulated genes compared to downregulated genes in prostate cancer [22]. The most significantly reduced levels of gene expression were noticed for ANGPT1, a gene coding for angiopoietin 1, and APOBEC3C, a gene coding for an isoform of apolipoprotein B.…”

Section: Discussionmentioning

confidence: 98%

“…Their bioinformatics data combined with cell culture analysis focused on two genes, steroid 5 alpha-reductase 2 (SRD5A2) and integrin subunit alpha 11 (ITGA11), as markers for cell migration and proliferation [21]. Besides the communicated gene signatures associated with PCa progression, Niu and co-workers dedicated an entire study to identifying biomarkers related to the tumor microenvironment and immune cells [22]. After analyzing samples from patients with primary PCa, xenograft mouse models, transgenic adenocarcinoma mouse prostate (TRAMP) mouse models, and data from the TCGA and MSKCC databases, Gu and co-workers showed that downregulation of the IQGAP1 motif belonging to small G proteins was strongly connected to castration-resistant and advanced PCa.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Gene Networks and Pathways Correlated with Tissue Differentiation in Prostate Cancer

Filippi,

Aurelian,

Mocanu

2024

IJMS

View full text Add to dashboard Cite

Prostate cancer (PCa) is the most prevalent non-cutaneous cancer in men. Early PCa detection has been made possible by the adoption of screening methods based on the serum prostate-specific antigen and Gleason score (GS). The aim of this study was to correlate gene expression with the differentiation level of prostate adenocarcinomas, as indicated by GS. We used data from The Cancer Genome Atlas (TCGA) and included 497 prostate cancer patients, 52 of which also had normal tissue sample sequencing data. Gene ontology analysis revealed that higher GSs were associated with greater responses to DNA damage, telomere lengthening, and cell division. Positive correlation was found with transcription factor activator of the adenovirus gene E2 (E2F) and avian myelocytomatosis viral homolog (MYC) targets, G2M checkpoints, DNA repair, and mitotic spindles. Immune cell deconvolution revealed high M0 macrophage counts and an increase in M2 macrophages dependent on the GS. The molecular pathways most correlated with GSs were cell cycle, RNA transport, and calcium signaling (depleted). A combinatorial approach identified a set of eight genes able to differentiate by k-Nearest Neighbors (kNN) between normal tissues, low-Gleason tissues, and high-Gleason tissues with high accuracy. In conclusion, our study could be a step forward to better understanding the link between gene expression and PCa progression and aggressiveness.

show abstract

Machine learning-based integration develops a mitophagy-related lncRNA signature for predicting the progression of prostate cancer: a bioinformatic analysis

Dai,

Zeng,

Zhang

et al. 2024

Discov Onc

View full text Add to dashboard Cite

Prostate cancer remains a complex and challenging disease, necessitating innovative approaches for prognosis and therapeutic guidance. This study integrates machine learning techniques to develop a novel mitophagy-related long non-coding RNA (lncRNA) signature for predicting the progression of prostate cancer. Leveraging the TCGA-PRAD dataset, we identify a set of four key lncRNAs and formulate a riskscore, revealing its potential as a prognostic indicator. Subsequent analyses unravel the intricate connections between riskscore, immune cell infiltration, mutational landscapes, and treatment outcomes. Notably, the pan-cancer exploration of YEATS2-AS1 highlights its pervasive impact, demonstrating elevated expression across various malignancies. Furthermore, drug sensitivity predictions based on riskscore guide personalized chemotherapy strategies, with drugs like Carmustine and Entinostat showing distinct suitability for high and low-risk group patients. Regression analysis exposes significant correlations between the mitophagy-related lncRNAs, riskscore, and key mitophagy-related genes. Molecular docking analyses reveal promising interactions between Cyclophosphamide and proteins encoded by these genes, suggesting potential therapeutic avenues. This comprehensive study not only introduces a robust prognostic tool but also provides valuable insights into the molecular intricacies and potential therapeutic interventions in prostate cancer, paving the way for more personalized and effective clinical approaches.

show abstract

Correlation analysis between immune-related genes and cell infiltration revealed prostate cancer immunotherapy biomarkers linked to T cells gamma delta

Cited by 5 publications

References 53 publications

Thyroid cancer prognostic biomarker ARL4A and its relationship with immune infiltration

Thyroid cancer prognostic biomarker ARL4A and its relationship with immune infiltration

Analysis of the Gene Networks and Pathways Correlated with Tissue Differentiation in Prostate Cancer

Machine learning-based integration develops a mitophagy-related lncRNA signature for predicting the progression of prostate cancer: a bioinformatic analysis

Contact Info

Product

Resources

About