Crosstalk of disulfidptosis-related subtypes, establishment of a prognostic signature and immune infiltration characteristics in bladder cancer based on a machine learning survival framework

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

Published: April 19, 2023

Background Bladder cancer (BLCA) is the most common malignancy of urinary tract. On other hand, disulfidptosis, a mechanism disulfide stress-induced cell death, closely associated with tumorigenesis and progression. Here, we investigated impact disulfidptosis-related genes (DRGs) on prognosis BLCA, identified various DRG clusters, developed risk model to assess patient prognosis, immunological profile, treatment response. Methods The expression mutational characteristics four DRGs were first analyzed in bulk RNA-Seq single-cell RNA sequencing data, IHC staining role BLCA progression, two clusters by consensus clustering. Using differentially expressed (DEGs) from these transformed ten machine learning algorithms into more than 80 combinations finally selected best algorithm construct prognostic signature (DRPS). We based this selection mean C-index three cohorts. Furthermore, explored differences clinical characteristics, landscape, immune infiltration, predicted efficacy immunotherapy between high low-risk groups. To visually depict value DRPS, employed nomograms. Additionally, verified whether DRPS predicts response patients utilizing Tumour Immune Dysfunction Rejection (TIDE) IMvigor 210 Results In integrated cohort, several gene that differed significantly overall survival (OS) tumor microenvironment. After integration clinicopathological features, showed robust predictive power. Based median score divided (LR) high-risk (HR) groups, LR group having better higher load being sensitive chemotherapy. Conclusion Our study, therefore, provides valuable tool further guide management tailor patients, offering new insights individualized treatment.

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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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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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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Multi‑omics identification of a signature based on malignant cell-associated ligand–receptor genes for lung adenocarcinoma

BMC Cancer, Journal Year: 2024, Volume and Issue: 24(1)

Published: Sept. 12, 2024

Lung adenocarcinoma (LUAD) significantly contributes to cancer-related mortality worldwide. The heterogeneity of the tumor immune microenvironment in LUAD results varied prognoses and responses immunotherapy among patients. Consequently, a clinical stratification algorithm is necessary inevitable effectively differentiate molecular features microenvironments, facilitating personalized treatment approaches.

Language: Английский

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Cuproptosis: unveiling a new frontier in cancer biology and therapeutics

Cell Communication and Signaling, Journal Year: 2024, Volume and Issue: 22(1)

Published: May 1, 2024

Copper plays vital roles in numerous cellular processes and its imbalance can lead to oxidative stress dysfunction. Recent research has unveiled a unique form of copper-induced cell death, termed cuproptosis, which differs from known death mechanisms. This process involves the interaction copper with lipoylated tricarboxylic acid cycle enzymes, causing protein aggregation death. Recently, growing number studies have explored link between cuproptosis cancer development. review comprehensively examines systemic metabolism copper, including tumor-related signaling pathways influenced by copper. It delves into discovery mechanisms connection various cancers. Additionally, suggests potential treatments using ionophores that induce combination small molecule drugs, for precision therapy specific types.

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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A novel T-cell exhaustion-related feature can accurately predict the prognosis of OC patients

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

Published: May 22, 2023

The phenomenon of T Cell exhaustion (TEX) entails a progressive deterioration in the functionality cells within immune system during prolonged conflicts with chronic infections or tumors. In context ovarian cancer immunotherapy, development, and outcome treatment are closely linked to T-cell exhaustion. Hence, gaining an in-depth understanding features TEX microenvironment is paramount importance for management OC patients. To this end, we leveraged single-cell RNA data from perform clustering identify marker genes utilizing Unified Modal Approximation Projection (UMAP) approach. Through GSVA WGCNA bulk RNA-seq data, identified 185 TEX-related (TEXRGs). Subsequently, transformed ten machine learning algorithms into 80 combinations selected most optimal one construct prognostic (TEXRPS) based on mean C-index three cohorts. addition, explored disparities clinicopathological features, mutational status, cell infiltration, immunotherapy efficacy between high-risk (HR) low-risk (LR) groups. Upon integration TEXRPS displayed robust predictive power. Notably, patients LR group exhibited superior prognosis, higher tumor load (TMB), greater infiltration abundance, enhanced sensitivity immunotherapy. Lastly, verified differential expression model gene CD44 using qRT-PCR. conclusion, our study offers valuable tool guide clinical targeted therapy OC.

Language: Английский

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PANoptosis-related molecular subtype and prognostic model associated with the immune microenvironment and individualized therapy in pancreatic cancer

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

Published: July 14, 2023

Background PANoptosis is an inflammatory type of programmed cell death regulated by PANopotosome. Mounting evidence has shown that could be involved in cancer pathogenesis and the tumor immune microenvironment. Nevertheless, there have been no studies on mechanism pancreatic (PC) pathogenesis. Methods We downloaded data transcriptomic clinical features PC patients from Cancer Genome Atlas (TCGA) Gene Expression Omnibus databases. Additionally, copy number variation (CNV), methylation somatic mutations genes 33 types cancers were obtained TCGA. Next, we identified PANoptosis-related molecular subtype using consensus clustering analysis, constructed validated prognostic model LASSO Cox regression analyses. Moreover, RT-qPCR was performed to determine expression model. Results 66 (PANRGs) published studies. Of these, 24 PC-specific prognosis-related identified. Pan-cancer analysis revealed complex genetic changes, including CNV, methylation, mutation PANRGs various cancers. By classified into two patterns: PANcluster A B. In A, patient prognosis significantly worse compared The CIBERSORT algorithm showed a significant increase infiltration CD8 + T cells, monocytes, naïve B macrophages, activated mast dendritic cells higher A. Patients more sensitive erlotinib, selumetinib trametinib, whereas highly irinotecan, oxaliplatin sorafenib. predict patient’s survival. Finally, GEPIA Human Protein databases analyzed, performed. Compared normal tissues, CXCL10 ITGB6 (associated with model) observed tissues. Conclusion first subtypes established for predicting survival PC. These results would aid exploring mechanisms

Language: Английский

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