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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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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Mast cell marker gene signature: prognosis and immunotherapy response prediction in lung adenocarcinoma through integrated scRNA-seq and bulk RNA-seq

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

Published: May 15, 2023

Mast cells, comprising a crucial component of the tumor immune milieu, modulate neoplastic progression by secreting an array pro- and antitumorigenic factors. Numerous extant studies have produced conflicting conclusions regarding impact mast cells on prognosis patients afflicted with lung adenocarcinoma (LUAD).Employing single-cell RNA sequencing (scRNA-seq) analysis, cell-specific marker genes in LUAD were ascertained. Subsequently, cell-related (MRGs) signature was devised to stratify into high- low-risk cohorts based median risk value. Further investigations conducted assess influence distinct categories microenvironment. The prognostic import capacity prognosticate immunotherapy benefits MRGs corroborated using four external cohorts. Ultimately, functional roles SYAP1 validated through vitro experimentation.After scRNA-seq bulk RNA-seq data we established consisting nine MRGs. This profile effectively distinguished favorable survival outcomes both training validation In addition, identified group as population more effective for immunotherapy. cellular experiments, found that silencing significantly reduced proliferation, invasion migratory while increasing apoptosis.Our offers valuable insights involvement determining may prove instrumental navigational aid selection, well predictor response patients.

Language: Английский

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Unraveling the role of disulfidptosis-related LncRNAs in colon cancer: a prognostic indicator for immunotherapy response, chemotherapy sensitivity, and insights into cell death mechanisms

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

Published: Oct. 17, 2023

Background: Colon cancer, a prevalent and deadly malignancy worldwide, ranks as the third leading cause of cancer-related mortality. Disulfidptosis stress triggers unique form programmed cell death known disulfidoptosis, characterized by excessive intracellular cystine accumulation. This study aimed to establish reliable bioindicators based on long non-coding RNAs (LncRNAs) associated with disulfidptosis-induced death, providing novel insights into immunotherapeutic response prognostic assessment in patients colon adenocarcinoma (COAD). Methods: Univariate Cox proportional hazard analysis Lasso regression were performed identify differentially expressed genes strongly prognosis. Subsequently, multifactorial model for risk was developed using multiple regression. Furthermore, we conducted comprehensive evaluations characteristics disulfidptosis response-related LncRNAs, considering clinicopathological features, tumor microenvironment, chemotherapy sensitivity. The expression levels prognosis-related COAD validated quantitative real-time fluorescence PCR (qRT-PCR). Additionally, role ZEB1-SA1 cancer investigated through CCK8 assays, wound healing experiment transwell experiments. Results: LncRNAs identified robust predictors Multifactorial revealed that score derived from these served an independent factor COAD. Patients low-risk group exhibited superior overall survival (OS) compared those high-risk group. Accordingly, our Nomogram prediction model, integrating clinical scores, demonstrated excellent efficacy. In vitro experiments promoted proliferation migration cells. Conclusion: Leveraging medical big data artificial intelligence, constructed TCGA-COAD cohort, enabling accurate patients. implementation this practice can facilitate precise classification patients, identification specific subgroups more likely respond favorably immunotherapy chemotherapy, inform development personalized treatment strategies scientific evidence.

Language: Английский

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Heterogeneity and molecular landscape of melanoma: implications for targeted therapy

Molecular Biomedicine, Journal Year: 2024, Volume and Issue: 5(1)

Published: May 10, 2024

Abstract Uveal cancer (UM) offers a complex molecular landscape characterized by substantial heterogeneity, both on the genetic and epigenetic levels. This heterogeneity plays critical position in shaping behavior response to therapy for this uncommon ocular malignancy. Targeted treatments with gene-specific therapeutic molecules may prove useful overcoming radiation resistance, however, diverse makeups of UM call patient-specific approach procedures. We need understand intricate develop targeted customized each patient's specific mutations. One promising approaches is using liquid biopsies, such as circulating tumor cells (CTCs) DNA (ctDNA), detecting monitoring disease at early stages. These non-invasive methods can help us identify most effective treatment strategies patient. Single-cellular brand-new analysis platform that gives treasured insights into diagnosis, prognosis, remedy. The incorporation data known clinical genomics information will give better understanding complicated mechanisms diseases exploit. In review, we focused panorama UM, achieve goal, authors conducted an exhaustive literature evaluation spanning 1998 2023, keywords like "uveal melanoma, “heterogeneity”. “Targeted therapies”," "CTCs," "single-cellular analysis".

Language: Английский

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Glycogen metabolism-mediated intercellular communication in the tumor microenvironment influences liver cancer prognosis

Oncology Research Featuring Preclinical and Clinical Cancer Therapeutics, Journal Year: 2024, Volume and Issue: 32(3), P. 563 - 576

Published: Jan. 1, 2024

Glycogen metabolism plays a key role in the development of hepatocellular carcinoma (HCC), but function glycogen genes tumor microenvironment (TME) is still to be elucidated. Single-cell RNA-seq data were obtained from ten HCC samples totaling 64,545 cells, and 65 analyzed by nonnegative matrix factorization (NMF). The prognosis immune response new TME cell clusters predicted using immunotherapy cohorts public databases. single-cell analysis was divided into fibroblasts, NT T macrophages, endothelial B which separately gene annotation. Pseudo-temporal trajectory demonstrated temporal differentiation different subtype clusters. Cellular communication revealed extensive interactions between cells with metabolizing cell-related subtypes SCENIC transcription factors upstream metabolism. In addition, found enriched expression CAF subtypes, CD8 depleted, M1, M2 types. Bulk-seq showed prognostic significance metabolism-mediated HCC, while significant cohort patients treated checkpoint blockade (ICB), especially for CAFs, macrophages. summary, our study reveals first time that mediates intercellular elucidating anti-tumor mechanisms responses

Language: Английский

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Single-Cell Sequencing: Genomic and Transcriptomic Approaches in Cancer Cell Biology

International Journal of Molecular Sciences, Journal Year: 2025, Volume and Issue: 26(5), P. 2074 - 2074

Published: Feb. 27, 2025

This article reviews the impact of single-cell sequencing (SCS) on cancer biology research. SCS has revolutionized our understanding and tumor heterogeneity, clonal evolution, complex interplay between cells microenvironment. provides high-resolution profiling individual in genomic, transcriptomic, epigenomic landscapes, facilitating detection rare mutations, characterization cellular diversity, integration molecular data with phenotypic traits. The multi-omics provided a multidimensional view states regulatory mechanisms cancer, uncovering novel therapeutic targets. Advances computational tools, artificial intelligence (AI), machine learning have been crucial interpreting vast amounts generated, leading to identification new biomarkers development predictive models for patient stratification. Furthermore, there emerging technologies such as spatial transcriptomics situ sequencing, which promise further enhance microenvironment organization interactions. As its related continue advance, they are expected drive significant advances personalized diagnostics, prognosis, therapy, ultimately improving outcomes era precision oncology.

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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Clinical Applications of Machine Learning in the Management of Intraocular Cancers: A Narrative Review

Investigative Ophthalmology & Visual Science, Journal Year: 2023, Volume and Issue: 64(10), P. 29 - 29

Published: July 21, 2023

Purpose: There is great promise in use of machine learning (ML) for the diagnosis, prognosis, and treatment various medical conditions ophthalmology beyond. Applications ML ocular neoplasms are early development this review synthesizes current state oncology. Methods: We queried PubMed Web Science evaluated 804 publications, excluding nonhuman studies. Metrics on algorithm performance were collected Prediction model study Risk Of Bias ASsessment Tool was used to evaluate bias. report results 63 unique Results: Research regarding applications intraocular cancers has leveraged multiple algorithms data sources. Convolutional neural networks (CNNs) one most commonly work focused uveal melanoma retinoblastoma. The majority models discussed here developed diagnosis prognosis. Algorithms primarily imaging (e.g., optical coherence tomography) as inputs, whereas those prognosis combinations gene expression, tumor characteristics, patient demographics. Conclusions: potential improve management cancers. Published perform well, but occasionally limited by small sample sizes owing low prevalence This could be overcome with synthetic enhancement low-shot techniques. CNNs can integrated into existing diagnostic workflows, while non-neural well determining

Language: Английский

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