HHLA2 immune-regulatory roles in cancer

Biomedicine & Pharmacotherapy, Journal Year: 2023, Volume and Issue: 162, P. 114639 - 114639

Published: April 1, 2023

Human endogenous retrovirus H long terminal repeat-associating protein 2 (HHLA2 or B7-H7) is a newly discovered B7 family member. HHLA2 aberrantly expressed in solid tumors and exerts co-stimulatory co-inhibitory activities dependent on interaction with counter receptors. represents effects upon transmembrane immunoglobulin domain containing (TMIGD2, also called CD28H), but its killer cell Ig-like receptor, three Ig domains cytoplasmic tail 3 (KIR3DL3) renders effects. TMIGD2 mainly resting naïve T cells, whereas expression of KIR3DL3 occurs activated cells. HHLA2/KIR3DL3 attenuates responses from both innate adaptive anti-tumor immunity, the activity within this axis regarded as biomarker weak prognosis cancer patients. promotes CD8+ exhaustion induces macrophage polarity toward pro-tumor M2 phenotype. diverse profile tumor stroma. Tumoral presumably higher compared programmed death-ligand 1 (PD-L1), co-expression PD-L1 indicative more severe outcomes. A suggested strategy patients HHLA2high to use monoclonal antibodies for specifically suppressing inhibitory receptor KIR3DL3, not ligand. can be target development agonistic bispecific hampering resistance death-1 (PD-1)/PD-L1 blockade therapy.

Language: Английский

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Single-cell RNA sequencing integrated with bulk RNA sequencing analysis identifies a tumor immune microenvironment-related lncRNA signature in lung adenocarcinoma

BMC Biology, Journal Year: 2024, Volume and Issue: 22(1)

Published: March 22, 2024

Abstract Background Recently, long non-coding RNAs (lncRNAs) have been demonstrated as essential roles in tumor immune microenvironments (TIME). Nevertheless, researches on the clinical significance of TIME-related lncRNAs are limited lung adenocarcinoma (LUAD). Methods Single-cell RNA sequencing and bulk data integrated to identify lncRNAs. A total 1368 LUAD patients enrolled from 6 independent datasets. An integrative machine learning framework is introduced develop a lncRNA signature (TRLS). Results This study identified analysis single‑cell data. According these lncRNAs, was developed validated an procedure six cohorts. TRLS exhibited robust reliable performance predicting overall survival. Superior prediction barged forefront comparison with general features, molecular characters, published signatures. Moreover, low displayed abundant cell infiltration active lipid metabolism, while high harbored significant genomic alterations, PD-L1 expression, elevated DNA damage repair (DDR) relevance. Notably, subclass mapping nine immunotherapeutic cohorts that were more sensitive immunotherapy. Conclusions promising tool based which might contribute tailored treatment prognosis management patients.

Language: Английский

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An integrated machine learning framework for developing and validating a diagnostic model of major depressive disorder based on interstitial cystitis-related genes

Journal of Affective Disorders, Journal Year: 2024, Volume and Issue: 359, P. 22 - 32

Published: May 14, 2024

Major depressive disorder (MDD) and interstitial cystitis (IC) are two highly debilitating conditions that often coexist with reciprocal effect, significantly exacerbating patients' suffering. However, the molecular underpinnings linking these disorders remain poorly understood.

Language: Английский

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Machine learning for catalysing the integration of noncoding RNA in research and clinical practice

EBioMedicine, Journal Year: 2024, Volume and Issue: 106, P. 105247 - 105247

Published: July 18, 2024

The human transcriptome predominantly consists of noncoding RNAs (ncRNAs), transcripts that do not encode proteins. governs a multitude pathophysiological processes, offering rich source next-generation biomarkers. Toward achieving holistic view disease, the integration these with clinical records and additional data from omic technologies ("multiomic" strategies) has motivated adoption artificial intelligence (AI) approaches. Given their intricate biological complexity, machine learning (ML) techniques are becoming key component ncRNA-based research. This article presents an overview potential challenges associated employing AI/ML-driven approaches to identify clinically relevant ncRNA biomarkers decipher ncRNA-associated pathogenetic mechanisms. Methodological conceptual constraints discussed, along exploration ethical considerations inherent AI applications for healthcare ultimate goal is provide comprehensive examination multifaceted landscape this innovative field its implications.

Language: Английский

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PI3K/AKT/mTOR pathway-derived risk score exhibits correlation with immune infiltration in uveal melanoma patients

Frontiers in Oncology, Journal Year: 2023, Volume and Issue: 13

Published: April 20, 2023

Uveal melanoma (UVM) is a rare but highly aggressive intraocular tumor with poor prognosis and limited therapeutic options. Recent studies have implicated the PI3K/AKT/mTOR pathway in pathogenesis progression of UVM. Here, we aimed to explore potential mechanism pathway-related genes (PRGs) UVM develop novel prognostic-related risk model. Using unsupervised clustering on 14 PRGs profiles, identified three distinct subtypes varying immune characteristics. Subtype A demonstrated worst overall survival showed higher expression human leukocyte antigen, checkpoints, cell infiltration. Further enrichment analysis revealed that subtype mainly functioned inflammatory response, apoptosis, angiogenesis, signaling pathway. Differential between different 56 differentially expressed (DEGs), major these DEGs associated PI3K/AKT/mTOR. Based DEGs, developed consensus machine learning-derived signature (RSF model) exhibited best power for predicting among 76 algorithm combinations. The excellent robustness predictive ability patients. Moreover, observed patients classified by scores had distinguishable status mutation. In conclusion, our study as biomarker prognostic prediction Our findings suggest this correlated infiltration may serve valuable tool personalized therapy clinical setting.

Language: Английский

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Integrating PANoptosis insights to enhance breast cancer prognosis and therapeutic decision-making

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

Published: March 5, 2024

Background Despite advancements, breast cancer outcomes remain stagnant, highlighting the need for precise biomarkers in precision medicine. Traditional TNM staging is insufficient identifying patients who will respond well to treatment. Methods Our study involved over 6,900 from 14 datasets, including in-house clinical data and single-cell 8 (37,451 cells). We integrated 10 machine learning algorithms 55 combinations analyzed 100 existing signatures. IHC assays were conducted validation, potential immunotherapies chemotherapies explored. Results pinpointed six stable Panoptosis-related genes multi-center cohorts, leading a robust Panoptosis-model. This model outperformed molecular features predicting recurrence mortality risks, with high-risk showing worse outcomes. validation 30 confirmed our findings, indicating model’s broader applicability. Additionally, suggested that low-risk benefit more immunotherapy, while are sensitive specific like BI-2536 ispinesib. Conclusion The Panoptosis-model represents major advancement prognosis treatment personalization, offering significant insights effectively managing wide range of patients.

Language: Английский

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Integration analysis of cell division cycle-associated family genes revealed potential mechanisms of gliomagenesis and constructed an artificial intelligence-driven prognostic signature

Cellular Signalling, Journal Year: 2024, Volume and Issue: 119, P. 111168 - 111168

Published: April 9, 2024

Language: Английский

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Unveiling the Potential of Migrasomes: A Machine-Learning-Driven Signature for Diagnosing Acute Myocardial Infarction

Biomedicines, Journal Year: 2024, Volume and Issue: 12(7), P. 1626 - 1626

Published: July 22, 2024

Recent studies have demonstrated that the migrasome, a newly functional extracellular vesicle, is potentially significant in occurrence, progression, and diagnosis of cardiovascular diseases. Nonetheless, its diagnostic significance biological mechanism acute myocardial infarction (AMI) yet to be fully explored.

Language: Английский

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Identification of cuprotosis-mediated subtypes, the development of a prognosis model, and influence immune microenvironment in hepatocellular carcinoma

Frontiers in Oncology, Journal Year: 2022, Volume and Issue: 12

Published: Aug. 30, 2022

Cuprotosis is a newly discovered form of non-apoptotic regulated cell death and characterized by copper-dependent associated with mitochondrial respiration. However, the prognostic significance function cuprotosis-related genes (CRGs) in hepatocellular carcinoma (HCC) are unknown. This study aims to develop cuprotosis-mediated patterns-related gene (CMPRG) prediction models for prognosis patients HCC, exploring functional underlying CRGs on influence tumor microenvironment (TME) features.This obtained transcriptome profiling corresponding clinical information from TCGA GEO databases. Besides, Cox regression model LASSO was implemented build multi-gene signature, which then validated an internal validation set two external sets through Kaplan-Meier, DCA, ROC analyses.According analysis, we screened out pattern 5-gene combination (including PBK; MMP1; GNAZ; GPC1 AKR1D1). A nomogram constructed presentation final model. The curve assessed model's predictive ability, resulted area under (AUC) values ranging 0.604 0.787 underwent sets. Meanwhile, risk score divided into groups high low risk, survival rate high-risk significantly lower than that low-risk (P<0.01). could be independent factor multifactorial analysis Functional revealed immune status, mutational loads, drug sensitivity differed between groups.In summary, identified three patterns HCC. And CMPRGs promising candidate biomarker HCC early detection, owing their strong performance predicting therapy. Quantifying individual samples may help improve understanding multiomic characteristics guide development targeted therapy

Language: Английский

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Artificial intelligence‐driven consensus gene signatures for improving bladder cancer clinical outcomes identified by multi‐center integration analysis

Molecular Oncology, Journal Year: 2022, Volume and Issue: 16(22), P. 4023 - 4042

Published: Sept. 9, 2022

To accurately predict the prognosis and further improve clinical outcomes of bladder cancer (BLCA), we leveraged large-scale data to develop validate a robust signature consisting small gene sets. Ten machine-learning algorithms were enrolled subsequently transformed into 76 combinations, which performed on eight independent cohorts (n = 1218). We ultimately determined consensus artificial intelligence-derived (AIGS) with best performance among model types. In this model, patients high AIGS showed higher risk mortality, recurrence, disease progression. is not only traditional traits [(e.g., American Joint Committee Cancer (AJCC) stage)] molecular features (e.g., TP53 mutation) but also demonstrated superior these variables. Comparisons 58 published signatures indicated that possessed performance. Additionally, combination AJCC stage could achieve better Patients low scores sensitive immunotherapy, whereas might benefit from seven potential therapeutics: BRD-K45681478, 1S,3R-RSL-3, RITA, U-0126, temsirolimus, MRS-1220, LY2784544. some mutations (TP53 RB1), copy number variations (7p11.2), methylation-driven target characterized by AIGS-related multi-omics alterations. Overall, provides an attractive platform optimize decision-making surveillance protocol for individual BLCA patients.

Language: Английский

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