Prognostic signatures of sphingolipids: Understanding the immune landscape and predictive role in immunotherapy response and outcomes of hepatocellular carcinoma

Frontiers in Immunology, Journal Year: 2023, Volume and Issue: 14

Published: March 17, 2023

Hepatocellular carcinoma (HCC) is a complex disease with poor outlook for patients in advanced stages. Immune cells play an important role the progression of HCC. The metabolism sphingolipids functions both tumor growth and immune infiltration. However, little research has focused on using sphingolipid factors to predict HCC prognosis. This study aimed identify key genes (SPGs) develop reliable prognostic model based these genes.The TCGA, GEO, ICGC datasets were grouped SPGs obtained from InnateDB portal. A gene signature was created by applying LASSO-Cox analysis evaluating it Cox regression. validity verified GEO datasets. microenvironment (TME) examined ESTIMATE CIBERSORT, potential therapeutic targets identified through machine learning. Single-cell sequencing used examine distribution within TME. Cell viability migration tested confirm SPGs.We 28 that have impact survival. Using clinicopathological features 6 genes, we developed nomogram high- low-risk groups found distinct characteristics response drugs. Unlike CD8 T cells, M0 M2 macrophages be highly infiltrated TME high-risk subgroup. High levels good indicator immunotherapy. In cell function experiments, SMPD2 CSTA enhance survival Huh7 while silencing increased sensitivity lapatinib.The presents six-gene can aid clinicians choosing personalized treatments patients. Furthermore, uncovers connection between sphingolipid-related microenvironment, offering novel approach By focusing crucial like CSTA, efficacy anti-tumor therapy cells.

Language: Английский

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FAM family gene prediction model reveals heterogeneity, stemness and immune microenvironment of UCEC

Frontiers in Molecular Biosciences, Journal Year: 2023, Volume and Issue: 10

Published: May 19, 2023

Background: Endometrial cancer (UCEC) is a highly heterogeneous gynecologic malignancy that exhibits variable prognostic outcomes and responses to immunotherapy. The Familial sequence similarity (FAM) gene family known contribute the pathogenesis of various malignancies, but extent their involvement in UCEC has not been systematically studied. This investigation aimed develop robust risk profile based on FAM genes (FFGs) predict prognosis suitability for immunotherapy patients. Methods: Using TCGA-UCEC cohort from Cancer Genome Atlas (TCGA) database, we obtained expression profiles FFGs 552 35 normal samples, analyzed patterns relevance 363 genes. samples were randomly divided into training test sets (1:1), univariate Cox regression analysis Lasso conducted identify differentially expressed (FAM13C, FAM110B, FAM72A) significantly associated with prognosis. A scoring system was constructed these three characteristics using multivariate proportional regression. clinical potential immune status CiberSort, SSGSEA, tumor dysfunction rejection (TIDE) algorithms. qRT-PCR IHC detecting levels 3-FFGs. Results: Three FFGs, namely, FAM13C, FAM72A, identified as strongly effective predictors Multivariate demonstrated developed model an independent predictor UCEC, patients low-risk group had better overall survival than those high-risk group. nomogram scores exhibited good power. Patients higher mutational load (TMB) more likely benefit Conclusion: study successfully validated novel biomarkers predicting can accurately assess facilitate identification specific subgroups who may personalized treatment chemotherapy.

Language: Английский

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Integrating multiple machine learning methods to construct glutamine metabolism-related signatures in lung adenocarcinoma

Frontiers in Endocrinology, Journal Year: 2023, Volume and Issue: 14

Published: May 17, 2023

Background Glutamine metabolism (GM) is known to play a critical role in cancer development, including lung adenocarcinoma (LUAD), although the exact contribution of GM LUAD remains incompletely understood. In this study, we aimed discover new targets for treatment patients by using machine learning algorithms establish prognostic models based on GM-related genes (GMRGs). Methods We used AUCell and WGCNA algorithms, along with single-cell bulk RNA-seq data, identify most prominent GMRGs associated LUAD. Multiple were employed develop risk optimal predictive performance. validated our multiple external datasets investigated disparities tumor microenvironment (TME), mutation landscape, enriched pathways, response immunotherapy across various groups. Additionally, conducted vitro vivo experiments confirm LGALS3 Results identified 173 strongly activity selected Random Survival Forest (RSF) Supervised Principal Components (SuperPC) methods model. Our model’s performance was datasets. analysis revealed that low-risk group had higher immune cell infiltration increased expression checkpoints, indicating may be more receptive immunotherapy. Moreover, experimental results confirmed promoted proliferation, invasion, migration cells. Conclusion study established model can predict effectiveness provide novel approaches findings also suggest potential therapeutic target

Language: Английский

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Construction of a diagnostic model for hepatitis B-related hepatocellular carcinoma using machine learning and artificial neural networks and revealing the correlation by immunoassay

Tumour Virus Research, Journal Year: 2023, Volume and Issue: 16, P. 200271 - 200271

Published: Sept. 27, 2023

HBV infection profoundly escalates hepatocellular carcinoma (HCC) susceptibility, responsible for a majority of HCC cases. HBV-driven immune-mediated hepatocyte impairment significantly fuels progression. Regrettably, inconspicuous early symptoms often culminate in belated diagnoses. Nevertheless, surgically treated early-stage patients relish augmented five-year survival rates. In contrast, advanced exhibits feeble responses to conventional interventions like radiotherapy, chemotherapy, and surgery, leading diminished This investigation endeavors unearth diagnostic hallmark genes HBV-HCC leveraging bioinformatics framework, thus refining detection. Candidate were sieved via differential analysis Weighted Gene Co-Expression Network Analysis (WGCNA). Employing three distinct machine learning algorithms unearthed feature (HHIP, CXCL14, CDHR2). Melding these yielded an innovative Artificial Neural (ANN) blueprint, portending alleviate patient encumbrance elevate life quality. Immunoassay scrutiny unveiled accentuated immune damage relative solitary HCC. Through consensus clustering, was stratified into two subtypes (C1 C2), the latter potentially indicating milder impairment. The model grounded showcased robust transferrable prognostic potentialities, introducing novel outlook diagnosis. exhaustive immunological odyssey stands poised expedite immunotherapeutic curatives' emergence HBV-HCC.

Language: Английский

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Informing immunotherapy with multi-omics driven machine learning

npj Digital Medicine, Journal Year: 2024, Volume and Issue: 7(1)

Published: March 14, 2024

Abstract Progress in sequencing technologies and clinical experiments has revolutionized immunotherapy on solid hematologic malignancies. However, the benefits of are limited to specific patient subsets, posing challenges for broader application. To improve its effectiveness, identifying biomarkers that can predict response is crucial. Machine learning (ML) play a pivotal role harnessing multi-omic cancer datasets unlocking new insights into immunotherapy. This review provides an overview cutting-edge ML models applied omics data analysis, including prediction immunotherapy-relevant tumor microenvironment identification. We elucidate how leverages diverse types identify significant biomarkers, enhance our understanding mechanisms, optimize decision-making process. Additionally, we discuss current limitations this rapidly evolving field. Finally, outline future directions aimed at overcoming these barriers improving efficiency research.

Language: Английский

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Proposing new early detection indicators for pancreatic cancer: Combining machine learning and neural networks for serum miRNA-based diagnostic model

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

Published: Aug. 3, 2023

Background Pancreatic cancer (PC) is a lethal malignancy that ranks seventh in terms of global cancer-related mortality. Despite advancements treatment, the five-year survival rate remains low, emphasizing urgent need for reliable early detection methods. MicroRNAs (miRNAs), group non-coding RNAs involved critical gene regulatory mechanisms, have garnered significant attention as potential diagnostic and prognostic biomarkers pancreatic (PC). Their suitability stems from their accessibility stability blood, making them particularly appealing clinical applications. Methods In this study, we analyzed serum miRNA expression profiles three independent PC datasets obtained Gene Expression Omnibus (GEO) database. To identify miRNAs associated with incidence, employed machine learning algorithms: Support Vector Machine-Recursive Feature Elimination (SVM-RFE), Least Absolute Shrinkage Selection Operator (LASSO), Random Forest. We developed an artificial neural network model to assess accuracy identified PC-related (PCRSMs) create nomogram. These findings were further validated through qPCR experiments. Additionally, patient samples classified using consensus clustering method. Results Our analysis revealed PCRSMs, namely hsa-miR-4648, hsa-miR-125b-1-3p, hsa-miR-3201, algorithms. The demonstrated high distinguishing between normal samples, verification training groups exhibiting AUC values 0.935 0.926, respectively. also utilized method classify into two optimal subtypes. Furthermore, our investigation PCRSMs unveiled negative correlation hsa-miR-125b-1-3p age. Conclusion study introduces novel diagnosis cancer, carrying implications. provide valuable insights pathogenesis offer avenues drug screening, personalized immunotherapy against disease.

Language: Английский

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The integrated single-cell analysis developed an immunogenic cell death signature to predict lung adenocarcinoma prognosis and immunotherapy

Aging, Journal Year: 2023, Volume and Issue: 15(19), P. 10305 - 10329

Published: Oct. 4, 2023

Background: Research on immunogenic cell death (ICD) in lung adenocarcinoma (LUAD) has been relatively limited. This study aims to create ICD-related signatures for accurate survival prognosis prediction LUAD patients, addressing the challenge of lacking reliable early prognostic indicators this type cancer. Methods: Using single-cell RNA sequencing (scRNA-seq) analysis, ICD activity cells was calculated by AUCell algorithm, divided into high- and low-ICD groups according median values, key regulatory genes were identified through differential these integrated TCGA data construct using LASSO COX regression multi-dimensional analysis terms prognosis, immunotherapy, tumor microenvironment (TME), mutational landscape. Results: The constructed signature reveals a pronounced disparity between low-risk patients. statistical discrepancies times among patients from both GEO databases further corroborate observation. Additionally, heightened levels immune infiltration expression are evidenced group, suggesting potential benefit immunotherapeutic interventions pivotal risk-associated tissue samples assessed utilizing qRT-PCR, thereby unveiling PITX3 as plausible therapeutic target context LUAD. Conclusions: Our provide help predicting immunotherapy some extent guide clinical treatment

Language: Английский

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Advancements in targeted and immunotherapy strategies for glioma: toward precision treatment

Frontiers in Immunology, Journal Year: 2025, Volume and Issue: 15

Published: Jan. 14, 2025

In recent years, significant breakthroughs have been made in cancer therapy, particularly with the development of molecular targeted therapies and immunotherapies, owing to advances tumor biology immunology. High-grade gliomas (HGGs), characterized by their high malignancy, remain challenging treat despite standard treatment regimens, including surgery, radiotherapy, chemotherapy, treating fields (TTF). These provide limited efficacy, highlighting need for novel strategies. Molecular immunotherapy emerged as promising avenues improving outcomes high-grade gliomas. This review explores current status advancements immunotherapeutic approaches

Language: Английский

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Revealing the role of regulatory T cells in the tumor microenvironment of lung adenocarcinoma: a novel prognostic and immunotherapeutic signature

Frontiers in Immunology, Journal Year: 2023, Volume and Issue: 14

Published: Aug. 21, 2023

Regulatory T cells (Tregs), are a key class of cell types in the immune system. In tumor microenvironment (TME), presence Tregs has important implications for response and development. Relatively little is known about role lung adenocarcinoma (LUAD).Tregs were identified using but single-cell RNA sequencing (scRNA-seq) analysis interactions between other TME investigated. Next, we used multiple bulk RNA-seq datasets to construct risk models based on marker genes explored differences prognosis, mutational landscape, infiltration immunotherapy high- low-risk groups, finally, qRT-PCR function experiments performed validate model genes.The cellchat showed that MIF-(CD74+CXCR4) pairs play interaction with subpopulations, Tregs-associated signatures (TRAS) could well classify LUAD cohorts into groups. Immunotherapy may offer greater potential benefits group, as indicated by their superior survival, increased cells, heightened expression checkpoints. Finally, experiment verified LTB PTTG1 relatively highly expressed cancer tissues, while PTPRC was paracancerous tissues. Colony Formation assay confirmed knockdown reduced proliferation ability cells.TRAS constructed scRNA-seq distinguish patient subgroups, which provide assistance clinical management patients.

Language: Английский

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Uncovering the immune microenvironment and molecular subtypes of hepatitis B-related liver cirrhosis and developing stable a diagnostic differential model by machine learning and artificial neural networks

Frontiers in Molecular Biosciences, Journal Year: 2023, Volume and Issue: 10

Published: Sept. 22, 2023

Background: Hepatitis B-related liver cirrhosis (HBV-LC) is a common clinical disease that evolves from chronic hepatitis B (CHB). The development of can be suppressed by pharmacological treatment. When CHB progresses to HBV-LC, the patient's quality life decreases dramatically and drug therapy ineffective. Liver transplantation most effective treatment, but lack donor required for transplantation, high cost procedure post-transplant rejection make this method unsuitable patients. Methods: aim study was find potential diagnostic biomarkers associated with HBV-LC bioinformatics analysis classify into specific subtypes consensus clustering. This will provide new perspective early diagnosis, treatment prevention HCC in Two study-relevant datasets, GSE114783 GSE84044, were retrieved GEO database. We screened feature genes using differential analysis, weighted gene co-expression network (WGCNA), three machine learning algorithms including least absolute shrinkage selection operator (LASSO), support vector recursive elimination (SVM-RFE), random forest (RF) total five methods. After that, we constructed an artificial neural (ANN) model. A cohort consisting GSE123932, GSE121248 GSE119322 used external validation. To better predict risk development, also built nomogram And multiple enrichment analyses samples performed understand biological processes which they significantly enriched. different analyzed Immune infiltration approach. Results: Using data downloaded GEO, developed ANN model based on six genes. clustering classified them two subtypes, C1 C2, it hypothesized patients subtype C2 might have milder symptoms immune analysis. Conclusion: column line graphs showed excellent predictive power, providing diagnosis possible HBV-LC. delineation facilitate future

Language: Английский

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