Insights to aging prediction with AI based epigenetic clocks DOI
Joshua Levy, Alos Diallo,

Marietta K. Saldias Montivero

et al.

Epigenomics, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 9

Published: Nov. 25, 2024

Over the past century, human lifespan has increased remarkably, yet inevitability of aging persists. The disparity between biological age, which reflects pathological deterioration and disease, chronological indicative normal aging, driven prior research focused on identifying mechanisms that could inform interventions to reverse excessive age-related reduce morbidity mortality. DNA methylation emerged as an important predictor leading development epigenetic clocks quantify extent beyond what is typically expected for a given age. Machine learning technologies offer promising avenues enhance our understanding governing by further elucidating gap ages. This perspective article examines current algorithmic approaches clocks, explores use machine age estimation from methylation, discusses how refining interpretation ML methods tailoring their inferences specific patient populations cell types can amplify utility these in prediction. By harnessing insights learning, we are well-positioned effectively adapt, customize personalize aimed at aging.

Language: Английский

Emerging strategies to investigate the biology of early cancer DOI
Ran Zhou, Xiwen Tang, Yuan Wang

et al.

Nature reviews. Cancer, Journal Year: 2024, Volume and Issue: 24(12), P. 850 - 866

Published: Oct. 21, 2024

Language: Английский

Citations

5
Exposomal determinants of non-genetic plasticity in tumor initiation DOI

Davide Carra,

Silvana C. E. Maas, José A. Seoane

et al.

Trends in cancer, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 1, 2025

Language: Английский

Citations

0
Haploinsufficient phenotypes promote selection of PTEN and ARID1A-deficient clones in human colon DOI Creative Commons
Nefeli Skoufou-Papoutsaki,

Sam Adler,

Shenay Mehmed

et al.

EMBO Reports, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 7, 2025

Cancer driver mutations are defined by their high prevalence in cancers and presumed rarity normal tissues. However, recent studies show that positive selection epithelia can increase the of some cancer drivers. To determine true cancer-driving potential, it is essential to evaluate how frequent these tissues what phenotypes. Here, we explore bioavailability somatic variants quantifying age-related mutational burdens human colonic epithelium using immunodetection FFPE samples (N = 181 patients). Positive tumour suppressor genes PTEN ARID1A associates with monoallelic gene loss as confirmed CRISPR/Cas9 mutagenesis changes downstream effectors. Comparison burden tissue colorectal allows quantification potency based on relative representation. Additionally, immune exclusion, a hallmark feature, observed within ARID1A-deficient clones histologically tissue. The behaviour resulting from haploinsufficiency demonstrates mosaicism suppressors arises predispose initiation.

Language: Английский

Citations

0
Advances and global trends of precancerous lesions of gastric cancer: A bibliometric analysis DOI

Yuan-Ping Jia,

D. Liu,

Ting-Lan Cao

et al.

World Journal of Gastrointestinal Oncology, Journal Year: 2025, Volume and Issue: 17(3)

Published: Feb. 13, 2025

BACKGROUND Precancerous lesions of gastric cancer (PLGC) represent a critical pathological stage in the development intestinal cancer. Early detection and diagnosis are key to reducing incidence Substantial advancements have been made PLGC research recent years, making it necessary provide updated reviews using bibliometric methods. We hypothesize that this review will identify emerging trends, areas, gaps research, providing insights could guide future studies enhance prevention strategies. AIM To comprehensively current state on PLGC, examining trends hotspots. METHODS conducted analysis PLGC-related published between 2004 2023 Web Science Core Collection database. employed Software, including VOSviewer, CiteSpace, R software, SCImago Graphica, map scientific networks visualize knowledge terms publication volume, countries/regions, institutions, journals, authors, keywords. RESULTS A total 4097 articles were included, overall volume showed an increasing trend. Over past two decades, China most articles, followed by United States, Japan, South Korea, Italy. Among top 10 contributors, States ranked highest citations demonstrated strongest international collaboration. Research keywords field clustered into three main categories: Risk factors, pathogenesis, treatment. Pathogenesis molecular biomarkers remain areas focus. Future should explore mechanisms gut microbiota, immune microenvironment, metabolic reprogramming, epigenetics. Advanced technologies, single-cell sequencing, spatially resolved analysis, multi-omics approaches, artificial intelligence, machine learning, likely accelerate in-depth investigations PLGC. CONCLUSION has rapidly developed gaining considerable attention. This reveals over 20 for studies.

Language: Английский

Citations

0
Multimodal Spatial Profiling Reveals Immune Suppression and Microenvironment Remodeling in Fallopian Tube Precursors to High-Grade Serous Ovarian Carcinoma DOI Creative Commons
Tanjina Kader, Jia‐Ren Lin, Clemens B. Hug

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 27, 2024

High-Grade Serous Ovarian Cancer (HGSOC) originates from fallopian tube (FT) precursors. However, the molecular changes that occur as precancerous lesions progress to HGSOC are not well understood. To address this, we integrated high-plex imaging and spatial transcriptomics analyze human tissue samples at different stages of development, including p53 signatures, serous tubal intraepithelial carcinomas (STIC), invasive HGSOC. Our findings reveal immune modulating mechanisms within precursor epithelium, characterized by chromosomal instability, persistent interferon (IFN) signaling, dysregulated innate adaptive immunity. FT precursors display elevated expression MHC-class I, HLA-E, IFN-stimulated genes, typically linked later-stage tumorigenesis. These alterations coincide with progressive shifts in tumor microenvironment, transitioning surveillance early STICs suppression advanced cancer. insights identify potential biomarkers therapeutic targets for interception clarify transitions precancer

Language: Английский

Citations

1
Enhancing disease risk gene discovery by integrating transcription factor-linked trans-variants into transcriptome-wide association analyses DOI Creative Commons
Jingni He, Deshan Perera, Wanqing Wen

et al.

Nucleic Acids Research, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 13, 2024

Transcriptome-wide association studies (TWAS) have been successful in identifying disease susceptibility genes by integrating cis-variants predicted gene expression with genome-wide (GWAS) data. However, trans-variants for predicting remain largely unexplored. Here, we introduce transTF-TWAS, which incorporates transcription factor (TF)-linked to enhance model building TF downstream target genes. Using data from the Genotype-Tissue Expression project, predict and alternative splicing applied these prediction models large GWAS datasets breast, prostate, lung cancers other diseases. We demonstrate that transTF-TWAS outperforms existing TWAS approaches both constructing disease-associated genes, as shown simulations real analysis. Our approach significantly contributes discovery of risk Findings this study shed new light on several genetically driven key regulators their associated TF-gene regulatory networks underlying susceptibility.

Language: Английский

Citations

0
Benefits and Harms of Interception and Early Detection of Cancer DOI
Giovanni Parmigiani

Hematology/Oncology Clinics of North America, Journal Year: 2024, Volume and Issue: 38(4), P. 731 - 741

Published: May 23, 2024

Language: Английский

Citations

0
An AI-Powered tissue-agnostic cellular morphometrics biomarker for risk assessment in patients with pan-gastrointestinal precancerous lesions and cancers DOI

Pin Wang,

Chengfei Jiang,

Aiqin Mao

et al.

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 15, 2024

Abstract PURPOSE Tissue-agnostic biomarkers that capture the commonality in cancer biology, may provide a new avenue for treatment development and optimization across types. Here, we aimed to evaluate validate clinical value of tissue-agnostic cellular morphometrics biomarker (CMB) signature, which was discovered by artificial intelligence (AI) from H&E-stained whole-slide images (WSI) diagnostic slides colon cancers, pan-gastrointestinal (pan-GI) pre-cancer lesions cancers. METHODS We CMBs WSI using our well-established CMB-ML pipeline established CMB risk score (CMBRS) multivariate regression models. Based on CMBRS, assigned individual patients The Cancer Genome Atlas Colon Adenocarcinoma Cohort (TCGA-COAD) (n=430) groups (CMBRG). then extensively evaluated CMBRS CMBRG multi-cohorts with different types GI (n=2,219) assessment precancerous (n=1,016). unraveled each CMB-related biological function bulk RNA-sequencing, single-cell RNA-sequencing (scRNA-seq) opal multiplex immunohistochemistry (IHC) techniques. RESULTS From TCGA-COAD cohort, developed 13-CMB signature constructed CMBRS/CMBRG predict prognosis patients. Importantly, this proved prognostic predictive values TCGA rectal, gastric esophageal independent traditional factors. These findings were independently validated multiple cohorts Drum Tower Hospital. Moreover, exhibited power stratification adenoma early neoplastic lesion predicting progression. In addition, demonstrated impacts gene signatures significant increase integration Correlations between expression levels revealed association functions including cell proliferation, epithelial-to-mesenchymal transition immune microenvironment. microenvironment prospectively scRNA-seq further confirmed Opal IHC staining cancer. CONCLUSION This study demonstrates AI-empowered defined functions, can be used settings assess risk, diagnose disease, guide interventions. potentially rapid, robust cost-effective cross-cancer prediction is essential developing common strategy

Language: Английский

Citations

0
Insights to aging prediction with AI based epigenetic clocks DOI
Joshua Levy, Alos Diallo,

Marietta K. Saldias Montivero

et al.

Epigenomics, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 9

Published: Nov. 25, 2024

Over the past century, human lifespan has increased remarkably, yet inevitability of aging persists. The disparity between biological age, which reflects pathological deterioration and disease, chronological indicative normal aging, driven prior research focused on identifying mechanisms that could inform interventions to reverse excessive age-related reduce morbidity mortality. DNA methylation emerged as an important predictor leading development epigenetic clocks quantify extent beyond what is typically expected for a given age. Machine learning technologies offer promising avenues enhance our understanding governing by further elucidating gap ages. This perspective article examines current algorithmic approaches clocks, explores use machine age estimation from methylation, discusses how refining interpretation ML methods tailoring their inferences specific patient populations cell types can amplify utility these in prediction. By harnessing insights learning, we are well-positioned effectively adapt, customize personalize aimed at aging.

Language: Английский

Citations

0