A novel signature predicts prognosis and immunotherapy in lung adenocarcinoma based on cancer-associated fibroblasts

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

Published: May 31, 2023

Background Extensive research has established the significant correlations between cancer-associated fibroblasts (CAFs) and various stages of cancer development, including initiation, angiogenesis, progression, resistance to therapy. In this study, we aimed investigate characteristics CAFs in lung adenocarcinoma (LUAD) develop a risk signature predict prognosis patients with LUAD. Methods We obtained single-cell RNA sequencing (scRNA-seq) bulk RNA-seq data from public database. The Seurat R package was used process scRNA-seq identify CAF clusters based on several biomarkers. CAF-related prognostic genes were further identified using univariate Cox regression analysis. To reduce number genes, Lasso performed, established. A novel nomogram that incorporated clinicopathological features developed clinical applicability model. Additionally, conducted immune landscape immunotherapy responsiveness analyses. Finally, performed vitro experiments verify functions EXO1 Results 5 LUAD data, which 3 significantly associated total 492 found be linked 1731 DEGs construct signature. Moreover, our exploration revealed related scores, its ability confirmed. Furthermore, incorporating showed excellent applicability. verified EXP1 through experiments. Conclusions proven an predictor prognosis, stratifying more appropriately precisely predicting responsiveness. comprehensive characterization can response immunotherapy, thus offering fresh perspectives into management patients. Our study ultimately confirms role facilitating invasion growth tumor cells Nevertheless, validation achieved by conducting vivo

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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Single-cell sequencing analysis related to sphingolipid metabolism guides immunotherapy and prognosis of skin cutaneous melanoma

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

Published: Nov. 23, 2023

Background We explore sphingolipid-related genes (SRGs) in skin melanoma (SKCM) to develop a prognostic indicator for patient outcomes. Dysregulated lipid metabolism is linked aggressive behavior various cancers, including SKCM. However, the exact role and mechanism of sphingolipid remain partially understood. Methods integrated scRNA-seq data from patients sourced GEO database. Through utilization Seurat R package, we successfully identified distinct gene clusters associated with survival data. Key were through single-factor Cox analysis used model using LASSO stepwise regression algorithms. Additionally, evaluated predictive potential these within immune microenvironment their relevance immunotherapy. Finally, validated functional significance high-risk IRX3 vitro experiments. Results Analysis expression patterns 4 specific diverse cell subpopulations. Re-clustering cells based on increased SRG revealed 7 subgroups significant implications. Using marker genes, lasso, regression, selected 11 construct risk signature. This signature demonstrated strong correlation infiltration stromal scores, highlighting its tumor microenvironment. Functional studies involving knockdown A375 WM-115 showed reductions viability, proliferation, invasiveness. Conclusion SRG-based holds promise precise prognosis. An in-depth exploration characteristics offers insights into immunotherapy response. Therapeutic targeting may benefit patients.

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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A fibroblast-associated signature predicts prognosis and immunotherapy in esophageal squamous cell cancer

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

Published: May 29, 2023

Background Current paradigms of anti-tumor therapies are not qualified to evacuate the malignancy ascribing cancer stroma’s functions in accelerating tumor relapse and therapeutic resistance. Cancer-associated fibroblasts (CAFs) has been identified significantly correlated with progression therapy Thus, we aimed probe into CAFs characteristics esophageal squamous (ESCC) construct a risk signature based on predict prognosis ESCC patients. Methods The GEO database provided single-cell RNA sequencing (scRNA-seq) data. TCGA databases were used obtain bulk RNA-seq data microarray ESCC, respectively. CAF clusters from scRNA-seq using Seurat R package. CAF-related prognostic genes subsequently univariate Cox regression analysis. A was constructed Lasso regression. Then, nomogram model clinicopathological developed. Consensus clustering conducted explore heterogeneity ESCC. Finally, PCR utilized validate that hub play Results Six data, three which had associations. total 642 found be pool 17080 DEGs, 9 selected generate signature, mainly involved 10 pathways such as NRF1, MYC, TGF-Beta. stromal immune scores, well some cells. Multivariate analysis demonstrated an independent factor for its potential predicting immunotherapeutic outcomes confirmed. novel integrating CAF-based clinical stage developed, exhibited favorable predictability reliability prediction. consensus further confirmed Conclusion can effectively predicted by signatures, comprehensive characterization may aid interpreting response immunotherapy offer new strategies treatment.

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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Using ultrasound and microbubble to enhance the effects of conventional cancer therapies in clinical settings

Cancer and Metastasis Reviews, Journal Year: 2025, Volume and Issue: 44(1)

Published: March 1, 2025

Language: Английский

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Molecular characteristics and therapeutic implications of Toll-like receptor signaling pathway in melanoma

Scientific Reports, Journal Year: 2023, Volume and Issue: 13(1)

Published: Sept. 4, 2023

Melanoma is a malignant tumor of melanocytes and often considered immunogenic cancer. Toll-like receptor-related genes are expressed differently in most types cancer, depending on the immune microenvironment inside key function receptors (TLRs) for melanoma has not been fully elucidated. Based multi-omics data from TCGA GEO databases, we first performed pan-cancer analysis TLR, including CNV, SNV, mRNA changes TLR-related multiple human cancers, as well patient prognosis characterization. Then, divided patients into three subgroups (clusters 1, 2, 3) according to expression TLR pathway, explored correlation between pathway prognosis, infiltration, metabolic reprogramming, oncogene characteristics. Finally, through univariate Cox regression LASSO algorithm, selected six construct survival prognostic model, training set, internal validation set external validations, discussed model clinical features patients. In conclusion, constructed based that precisely independently demonstrated potential assess traits patients, which critical patients' survival.

Language: Английский

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Low-density lipoprotein receptor promotes crosstalk between cell stemness and tumor immune microenvironment in breast cancer: a large data-based multi-omics study

Journal of Translational Medicine, Journal Year: 2023, Volume and Issue: 21(1)

Published: Nov. 30, 2023

Abstract Background Tumor cells with stemness in breast cancer might facilitate the immune microenvironment’s suppression process and led to anti-tumor effects. The primary objective of this study was identify potential targets disrupt communication between cell microenvironment. Methods In study, we initially isolated tumor varying degrees using a spheroid formation assay. Subsequently, employed RNA-seq proteomic analyses genes associated through gene trend analysis. These stemness-related were then subjected pan-cancer analysis elucidate their functional roles broader spectrum types. data 3132 patients clinical obtained from public databases. Using identified genes, constructed two distinct subtypes, denoted as C1 C2. We subsequently conducted comprehensive differences these subtypes pathway enrichment methodology infiltration algorithms. Furthermore, key immune-related by employing lasso regression Cox survival model. vitro experiments ascertain regulatory impact on stemness. Additionally, utilized delineate functions attributed gene. Lastly, single-cell RNA sequencing (scRNA-seq) conduct more examination gene’s role within Results our set 65 displaying capabilities. analysis, pinpointed 41 that held prognostic significance. observed C2 subtype exhibited higher capacity compared displayed aggressive malignancy profile. Further Lasso-Cox algorithm LDLR pivotal It became evident played crucial shaping demonstrated regulated cancer. Immune determined inhibited proliferation promote progression. scRNA-seq discovered associations marker tissues. Moreover, expression levels cells, further emphasizing its relevance context Conclusion is an important regulates enhances crosstalk

Language: Английский

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Single cell sequencing revealed the mechanism of CRYAB in glioma and its diagnostic and prognostic value

Frontiers in Immunology, Journal Year: 2024, Volume and Issue: 14

Published: Jan. 11, 2024

Background We explored the characteristics of single-cell differentiation data in glioblastoma and established prognostic markers based on CRYAB to predict prognosis patients. Aberrant expression is associated with invasive behavior various tumors, including glioblastoma. However, specific role mechanisms are still unclear. Methods assessed RNA-seq microarray from TCGA GEO databases, combined scRNA-seq glioma patients GEO. Utilizing Seurat R package, we identified distinct survival-related gene clusters data. Prognostic pivotal genes were discovered through single-factor Cox analysis, a model was using LASSO stepwise regression algorithms. Moreover, investigated predictive potential these immune microenvironment their applicability immunotherapy. Finally, vitro experiments confirmed functional significance high-risk CRYAB. Results By analyzing ScRNA-seq data, 28 cell representing seven types. After dimensionality reduction clustering obtained four subpopulations within oligodendrocyte lineage trajectory. Using as marker for terminal-stage subpopulation, found that its poor prognosis. In demonstrated knocking out U87 LN229 cells reduced viability, proliferation, invasiveness. Conclusion The risk holds promise accurately predicting A comprehensive study would contribute understanding response Targeting may be beneficial

Language: Английский

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