Single-cell sequencing reveals PHLDA1-positive smooth muscle cells promote local invasion in head and neck squamous cell carcinoma

Translational Oncology, Journal Year: 2025, Volume and Issue: 55, P. 102301 - 102301

Published: March 25, 2025

Language: Английский

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From single-cell to spatial transcriptomics: decoding the glioma stem cell niche and its clinical implications

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

Published: Sept. 17, 2024

Background Gliomas are aggressive brain tumors associated with a poor prognosis. Cancer stem cells (CSCs) play significant role in tumor recurrence and resistance to therapy. This study aimed identify characterize glioma (GSCs), analyze their interactions various cell types, develop prognostic signature. Methods Single-cell RNA sequencing data from 44 primary samples were analyzed GSC populations. Spatial transcriptomics gene regulatory network analyses performed investigate localization transcription factor activity. CellChat analysis was conducted infer cell-cell communication patterns. A signature (GSCS) developed using machine learning algorithms applied bulk multiple cohorts. In vitro vivo experiments validate the of TUBA1C, key within Results distinct population identified, characterized by high proliferative potential an enrichment E2F1, E2F2, E2F7, BRCA1 regulons. GSCs exhibited spatial proximity myeloid-derived suppressor (MDSCs). revealed active MIF signaling pathway between MDSCs. 26-gene GSCS demonstrated superior performance compared existing models. Knockdown TUBA1C significantly inhibited migration, invasion , reduced growth . Conclusion offers comprehensive characterization MDSCs, while presenting robust GSCS. The findings offer new insights into biology therapeutic targets, particularly at improving patient outcomes.

Language: Английский

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Unravelling tumour cell diversity and prognostic signatures in cutaneous melanoma through machine learning analysis

Journal of Cellular and Molecular Medicine, Journal Year: 2024, Volume and Issue: 28(14)

Published: July 1, 2024

Abstract Melanoma, a highly malignant tumour, presents significant challenges due to its cellular heterogeneity, yet research on this aspect in cutaneous melanoma remains limited. In study, we utilized single‐cell data from 92,521 cells explore the tumour cell landscape. Through clustering analysis, identified six distinct clusters and investigated their differentiation metabolic heterogeneity using multi‐omics approaches. Notably, cytotrace analysis pseudotime trajectories revealed stages of differentiation, which have implications for patient survival. By leveraging markers these clusters, developed cell‐specific machine learning model (TCM). This not only predicts outcomes responses immunotherapy, but also distinguishes between genomically stable unstable tumours identifies inflamed (‘hot’) versus non‐inflamed (‘cold’) tumours. Intriguingly, TCM score showed strong association with TOMM40, experimentally validated as an oncogene promoting proliferation, invasion migration. Overall, our findings introduce novel biomarker that aids selecting patients improved prognoses targeted thereby guiding clinical treatment decisions.

Language: Английский

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Integrating necroptosis into pan-cancer immunotherapy: a new era of personalized treatment

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

Published: Dec. 9, 2024

Introduction Necroptosis has emerged as a promising biomarker for predicting immunotherapy responses across various cancer types. Its role in modulating immune activation and therapeutic outcomes offers potential precision oncology. Methods A comprehensive pan-cancer analysis was performed using bulk RNA sequencing data to develop necroptosis-related gene signature, termed Necroptosis.Sig. Multi-omics approaches were employed identify critical pathways key regulators of necroptosis, including HMGB1. Functional validation experiments conducted A549 lung cells evaluate the effects HMGB1 knockdown on tumor proliferation malignancy. Results The Necroptosis.Sig signature effectively predicted checkpoint inhibitors (ICIs). analyses highlighted modulator with enhance efficacy. demonstrated that significantly suppressed malignancy, reinforcing targeting necroptosis. Discussion These findings underscore utility necroptosis guide personalized strategies. By advancing oncology, provides novel avenue improving treatment outcomes.

Language: Английский

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Leveraging single-cell and multi-omics approaches to identify MTOR-centered deubiquitination signatures in esophageal cancer therapy

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

Published: Dec. 17, 2024

Esophageal squamous cell carcinoma (ESCC) remains a significant challenge in oncology due to its aggressive nature and heterogeneity. As one of the deadliest malignancies, ESCC research lags behind other cancer types. The balance between ubiquitination deubiquitination processes plays crucial role cellular functions, with disruption linked various diseases, including cancer.

Language: Английский

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Occurrence and Prognosis of Mixed Subtype Adenocarcinoma and Adeno-Squamous Carcinoma in Esophageal Cancer

Journal of Cancer, Journal Year: 2024, Volume and Issue: 15(5), P. 1442 - 1461

Published: Jan. 1, 2024

Purpose: To gain a deeper understanding of the incidence and survival rates rare esophageal mixed adenoacanthoma (EAM) adeno-squamous carcinoma (EASC) to promote more comprehensive these two subtypes.Background: EAM EASC are subtypes cancer with limited literature available.Extensive research has been conducted on clinical pathological characteristics gastric colorectal adenoacanthomas, but there is relatively little adenoacanthomas.Therefore, this study aims investigate in depth.Methods: Patients diagnosed between 2000 2019 were selected from SEER database for study.Joinpoint software was used calculate AM ASC patients, differences overall (OS) cancer-specific (CSS) based Kaplan-Meier curves compared.Multivariate Cox regression analysis employed identify independent prognostic factors OS CSS, model established validated accuracy.Results: The found that increased until 2014, followed by decline, while decreased 2017, an increase.Both common male patients those over age 65.For preoperative chemoradiotherapy associated better rates, radiotherapy combined adjuvant chemotherapy improved survival.Finally, we constructed nomograms predicting incorporating identified risk factors, which demonstrated good sensitivity specificity.Conclusion: cancer, in-depth exploration their provides valuable data insights subtypes.This information can assist decision-making healthcare professionals.

Language: Английский

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To develop a prognostic model for neoadjuvant immunochemotherapy efficacy in esophageal squamous cell carcinoma by analyzing the immune microenvironment

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

Published: April 25, 2024

Objective The choice of neoadjuvant therapy for esophageal squamous cell carcinoma (ESCC) is controversial. This study aims to provide a basis clinical treatment selection by establishing predictive model the efficacy immunochemotherapy (NICT). Methods A retrospective analysis 30 patients was conducted, divided into Response and Non-response groups based on whether they achieved major pathological remission (MPR). Differences in genes immune microenvironment between two were analyzed through next-generation sequencing (NGS) multiplex immunofluorescence (mIF). Variables most closely related therapeutic selected LASSO regression ROC curves establish model. An additional 48 prospectively collected as validation set verify model’s effectiveness. Results NGS suggested seven differential (ATM, ATR, BIVM-ERCC5, MAP3K1, PRG, RBM10, TSHR) (P < 0.05). mIF indicated significant differences quantity location CD3+, PD-L1+, CD3+PD-L1+, CD4+PD-1+, CD4+LAG-3+, CD8+LAG-3+, LAG-3+ before Dynamic also that CD8+, CD20+ all increased after both groups, with more increase CD8+ group 0.05), decrease PD-L1+ three variables curves: Tumor area (AUC= 0.881), CD3+PD-L1+ 0.833), CD3+ 0.826), established. showed high performance training 0.938) 0.832). Compared traditional CPS scoring criteria, improvements accuracy (83.3% vs 70.8%), sensitivity (0.625 0.312), specificity (0.937 0.906). Conclusion NICT may exert anti-tumor effects enriching cells activating exhausted T cells. are efficacy. containing these can accurately predict outcomes, providing reliable plans.

Language: Английский

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Investigating cellular similarities and differences between upper tract urothelial carcinoma and bladder urothelial carcinoma using single-cell sequencing

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

Published: June 6, 2024

Background Upper tract urothelial carcinoma (UTUC) and bladder (BLCA) both originate from uroepithelial tissue, sharing remarkably similar clinical manifestations therapeutic modalities. However, emerging evidence suggests that identical treatment regimens may lead to less favorable outcomes in UTUC compared BLCA. Therefore, it is imperative explore molecular processes of identify biological differences between Methods In this study, we performed a comprehensive analysis using single-cell RNA sequencing (scRNA-seq) on three cases four normal ureteral tissues. These data were combined with publicly available datasets previous BLCA studies (RNA-seq) for cancer types. This pooled allowed us delineate the transcriptional among distinct cell subsets within microenvironment, thus identifying critical factors contributing progression phenotypic Results scRNA-seq revealed seemingly but transcriptionally cellular identities ecosystems. Notably, observed striking acquired immunological landscapes varied functional phenotypes these two cancers. addition, uncovered immunomodulatory functions vein endothelial cells (ECs) UTUC, intercellular network demonstrated fibroblasts play important roles microenvironment. Further intersection showed MARCKS promote progression, immunohistochemistry (IHC) staining diverse expression patterns ureter Conclusion study expands our multidimensional understanding similarities distinctions Our findings lay foundation further investigations develop diagnostic targets UTUC.

Language: Английский

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Advancing lung adenocarcinoma prognosis and immunotherapy prediction with a multi‐omics consensus machine learning approach

Journal of Cellular and Molecular Medicine, Journal Year: 2024, Volume and Issue: 28(13)

Published: July 1, 2024

Lung adenocarcinoma (LUAD) is a tumour characterized by high heterogeneity. Although there are numerous prognostic and immunotherapeutic options available for LUAD, dearth of precise, individualized treatment plans. We integrated mRNA, lncRNA, microRNA, methylation mutation data from the TCGA database LUAD. Utilizing ten clustering algorithms, we identified stable multi-omics consensus clusters (MOCs). These were then amalgamated with machine learning approaches to develop robust model capable reliably identifying patient prognosis predicting immunotherapy outcomes. Through two prognostically relevant MOCs identified, MOC2 showing more favourable subsequently constructed MOCs-associated (MOCM) based on eight MOCs-specific hub genes. Patients lower MOCM score exhibited better overall survival responses immunotherapy. findings consistent across multiple datasets, compared many previously published LUAD biomarkers, our demonstrated superior predictive performance. Notably, low group was inclined towards 'hot' tumours, higher levels immune cell infiltration. Intriguingly, significant positive correlation between GJB3 (R = 0.77, p < 0.01) discovered. Further experiments confirmed that significantly enhances proliferation, invasion migration, indicating its potential as key target treatment. Our developed accurately predicts patients identifies beneficiaries immunotherapy, offering broad clinical applicability.

Language: Английский

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BNIP3 + cancer-associated fibroblasts and their associated genes are accelerators of pancreatic cancer

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: July 30, 2024

Background Pancreatic cancer is one of the most malignant gastrointestinal tumors. Due to difficulty early diagnosis and limited treatment, prognosis pancreatic patients very poor. characterized by high interstitial fibrosis, in which activation cancer-associated fibroblasts (CAFs) plays a key role. CAFs abundant cell tumor microenvironment, with degree plasticity, participates various processes development through crosstalk cells other microenvironment. Elucidate heterogeneity its mechanism action, helps find new effective treatment for cancer. Methods We used single-cell RNA sequencing (scRNA-seq) transcriptomics analyze from patient specimens. This approach was able identify subpopulations elucidate their contribution progression. Subsequently, we established prediction model using Cox regression LASSO algorithm conducted experiments verify it. Results Our study identified BNIP3 + tumor-associated fibroblast this cell-associated gene construct prognostic cancer, feature that effectively divided PDAC into high-risk low-risk groups outperformed traditional clinicopathological features predicting survival outcomes patients. In vitro co-culture showed could have more effects on cells. Conclusion screened C1 fibroblasts, advanced our knowledge understanding heterogeneity. The constructed can predict response

Language: Английский

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