Comprehensive bioinformatic analysis reveals a cancer-associated fibroblast gene signature as a poor prognostic factor and potential therapeutic target in gastric cancer DOI Creative Commons

Cemre Ucaryilmaz Metin,

Gülnihal Özcan

BMC Cancer, Journal Year: 2022, Volume and Issue: 22(1)

Published: June 23, 2022

Abstract Background Gastric cancer is one of the deadliest cancers, currently available therapies have limited success. Cancer-associated fibroblasts (CAFs) are pivotal cells in stroma gastric tumors posing a great risk for progression and chemoresistance. The poor prognostic signature CAFs not clear cancer, drugs that target lacking clinic. In this study, we aim to identify gene CAFs, targeting which may increase therapeutic success cancer. Methods We analyzed four GEO datasets with network-based approach validated key CAF markers Cancer Genome Atlas (TCGA) Asian Research Group (ACRG) cohorts. implemented stepwise multivariate Cox regression guided by pan-cancer analysis TCGA infiltration Lastly, conducted database search genes. Results Our study revealed COL1A1, COL1A2, COL3A1, COL5A1, FN1 , SPARC as Analysis ACRG cohorts their upregulation significance. elucidated COL1A1 COL5A1 together ITGA4, Emilin1 TSPAN9 genes infiltration. on drug databases collagenase clostridium histolyticum ocriplasmin, halofuginone, natalizumab, firategrast, BIO-1211 potential further investigation. Conclusions demonstrated central role extracellular matrix components secreted remodeled identified carries high predictive tool prognosis patients. Elucidating mechanisms contribute patient outcomes can lead discovery more potent molecular-targeted agents

Language: Английский

Network pharmacology, a promising approach to reveal the pharmacology mechanism of Chinese medicine formula DOI
Li Zhao, Hong Zhang, Ning Li

et al.

Journal of Ethnopharmacology, Journal Year: 2023, Volume and Issue: 309, P. 116306 - 116306

Published: Feb. 27, 2023

Language: Английский

Citations

400
Transcriptome‐level discovery of survival‐associated biomarkers and therapy targets in non‐small‐cell lung cancer DOI Creative Commons
Balázs Győrffy

British Journal of Pharmacology, Journal Year: 2023, Volume and Issue: 181(3), P. 362 - 374

Published: Oct. 3, 2023

Abstract Background and Purpose Survival rate of patients with lung cancer has increased by over 60% in the recent two decades. With longer survival, identification genes associated survival emerged as an issue utmost importance to uncover most promising biomarkers therapeutic targets. Experimental Approach An integrated database was set up combining multiple independent datasets clinical data transcriptome‐level gene expression measurements. Univariate multivariate analyses were performed identify higher levels linked shorter survival. The strongest filtered include only those known druggability. Key Results entire includes 2852 tumour specimens from 17 cohorts. Of these, 2227 have overall 1256 samples progression‐free time. significant MIF , UBC B2M adenocarcinoma ANXA2 CSNK2A2 KRT18 squamous cell carcinoma. We also aimed reveal best druggable targets non‐smokers cancer. three hits this cohort MDK THY1 PADI2 . established added Kaplan–Meier plotter ( https://www.kmplot.com ) enabling validation future expression‐based both present yet unexamined subgroups patients. Conclusions Implications In study, we a comprehensive for can be utilized rank different subtypes

Language: Английский

Citations

143
Epigenomic and transcriptomic analyses define core cell types, genes and targetable mechanisms for kidney disease DOI
Hongbo Liu, Tomohito Doke,

Dong Guo

et al.

Nature Genetics, Journal Year: 2022, Volume and Issue: 54(7), P. 950 - 962

Published: June 16, 2022

Language: Английский

Citations

136
Artificial intelligence for drug discovery: Resources, methods, and applications DOI Creative Commons
Wei Chen, Xuesong Liu, Sanyin Zhang

et al.

Molecular Therapy — Nucleic Acids, Journal Year: 2023, Volume and Issue: 31, P. 691 - 702

Published: Feb. 18, 2023

Conventional wet laboratory testing, validations, and synthetic procedures are costly time-consuming for drug discovery. Advancements in artificial intelligence (AI) techniques have revolutionized their applications to Combined with accessible data resources, AI changing the landscape of In past decades, a series AI-based models been developed various steps These used as complements conventional experiments accelerated discovery process. this review, we first introduced widely resources discovery, such ChEMBL DrugBank, followed by molecular representation schemes that convert into computer-readable formats. Meanwhile, summarized algorithms develop Subsequently, discussed pharmaceutical analysis including predicting toxicity, bioactivity, physicochemical property. Furthermore, de novo design, drug-target structure prediction, interaction, binding affinity prediction. Moreover, also highlighted advanced synergism/antagonism prediction nanomedicine design. Finally, challenges future perspectives on

Language: Английский

Citations

123
Integrated analysis of public datasets for the discovery and validation of survival-associated genes in solid tumors DOI Creative Commons
Balázs Győrffy

The Innovation, Journal Year: 2024, Volume and Issue: 5(3), P. 100625 - 100625

Published: April 9, 2024

Identifying genes with prognostic significance that can act as biomarkers in solid tumors help stratify patients and uncover novel therapy targets. Here, our goal was to expand previous ranking analysis of survival-associated various include colon cancer specimens available transcriptomic clinical data. A Gene Expression Omnibus search performed identify datasets data raw gene expression measurements. combined database set up integrated into Kaplan-Meier plotter, making it possible changes linked altered survival. As a demonstration the utility platform, most powerful overall survival were identified using uni- multivariate Cox regression analysis. The includes 2,137 tumor samples from 17 independent cohorts. significant associated relapse-free false discovery rate below 1% carcinoma

Language: Английский

Citations

116
DGIdb 5.0: rebuilding the drug–gene interaction database for precision medicine and drug discovery platforms DOI Creative Commons
Matthew Cannon, James Stevenson, Kathryn Stahl

et al.

Nucleic Acids Research, Journal Year: 2023, Volume and Issue: 52(D1), P. D1227 - D1235

Published: Nov. 11, 2023

Abstract The Drug–Gene Interaction Database (DGIdb, https://dgidb.org) is a publicly accessible resource that aggregates genes or gene products, drugs and drug–gene interaction records to drive hypothesis generation discovery for clinicians researchers. DGIdb 5.0 the latest release includes substantial architectural functional updates support integration into clinical drug pipelines. service architecture has been split separate client server applications, enabling consistent data access users of both application programming interface (API) web interface. new was developed in ReactJS, dynamic visualizations consistency display user elements. A GraphQL API added customizable queries all drugs, genes, annotations associated data. Updated documentation provides with example detailed usage instructions these features. In addition, six sources have many existing updated. Newly include ChemIDplus, HemOnc, NCIt (National Cancer Institute Thesaurus), Drugs@FDA, HGNC (HUGO Gene Nomenclature Committee) RxNorm. These incorporated provide additional enhance regulatory approval status therapeutics. Methods grouping expanded upon as independent modular normalizers during import. methods resulted an improvement FAIR (findability, accessibility, interoperability reusability) representation DGIdb.

Language: Английский

Citations

97
Spatial transcriptomics reveals distinct and conserved tumor core and edge architectures that predict survival and targeted therapy response DOI Creative Commons
Rohit Arora, Christian Cao, Mehul Kumar

et al.

Nature Communications, Journal Year: 2023, Volume and Issue: 14(1)

Published: Aug. 18, 2023

The spatial organization of the tumor microenvironment has a profound impact on biology and therapy response. Here, we perform an integrative single-cell transcriptomic analysis HPV-negative oral squamous cell carcinoma (OSCC) to comprehensively characterize malignant cells in core (TC) leading edge (LE) transcriptional architectures. We show that TC LE are characterized by unique profiles, neighboring cellular compositions, ligand-receptor interactions. demonstrate gene expression profile associated with is conserved across different cancers while tissue specific, highlighting common mechanisms underlying progression invasion. Additionally, find our signature worse clinical outcomes improved prognosis multiple cancer types. Finally, using silico modeling approach, describe spatially-regulated patterns development OSCC predictably drug Our work provides pan-cancer insights into interactive atlases ( http://www.pboselab.ca/spatial_OSCC/ ; http://www.pboselab.ca/dynamo_OSCC/ ) can be foundational for developing novel targeted therapies.

Language: Английский

Citations

85
Identification of novel protein biomarkers and drug targets for colorectal cancer by integrating human plasma proteome with genome DOI Creative Commons
Jing Sun, Jianhui Zhao, Fangyuan Jiang

et al.

Genome Medicine, Journal Year: 2023, Volume and Issue: 15(1)

Published: Sept. 19, 2023

Abstract Background The proteome is a major source of therapeutic targets. We conducted proteome-wide Mendelian randomization (MR) study to identify candidate protein markers and targets for colorectal cancer (CRC). Methods Protein quantitative trait loci (pQTLs) were derived from seven published genome-wide association studies (GWASs) on plasma proteome, summary-level data extracted 4853 circulating markers. Genetic associations with CRC obtained large-scale GWAS meta-analysis (16,871 cases 26,328 controls), the FinnGen cohort (4957 304,197 UK Biobank (9276 477,069 controls). Colocalization summary-data-based MR (SMR) analyses performed sequentially verify causal role proteins. Single cell-type expression analysis, protein-protein interaction (PPI), druggability evaluation further detect specific cell type enrichment prioritize potential Results Collectively, genetically predicted levels 13 proteins associated risk. Elevated two (GREM1, CHRDL2) decreased 11 an increased risk CRC, among which four CLSTN3, CSF2RA, CD86) prioritized most convincing evidence. These protein-coding genes are mainly expressed in tissue stem cells, epithelial monocytes colon tumor tissue. Two interactive pairs (GREM1 CHRDL2; MMP2 TIMP2) identified be involved osteoclast differentiation tumorigenesis pathways; (POLR2F, CD86, MMP2) have been targeted drug development autoimmune diseases other cancers, potentials being repurposed as CRC. Conclusions This several biomarkers provided new insights into etiology promising screening drugs

Language: Английский

Citations

82
DrugMAP: molecular atlas and pharma-information of all drugs DOI Creative Commons
Fengcheng Li, Jiayi Yin, Mingkun Lu

et al.

Nucleic Acids Research, Journal Year: 2022, Volume and Issue: 51(D1), P. D1288 - D1299

Published: Oct. 16, 2022

Abstract The efficacy and safety of drugs are widely known to be determined by their interactions with multiple molecules pharmacological importance, it is therefore essential systematically depict the molecular atlas pharma-information studied drugs. However, our understanding such information neither comprehensive nor precise, which necessitates construction a new database providing network containing large number interacting molecules. Here, describing (DrugMAP) was constructed. It provides list for >30 000 drugs/drug candidates, gives differential expression patterns >5000 among different disease sites, ADME (absorption, distribution, metabolism excretion)-relevant organs physiological tissues, weaves precise >200 With great efforts made clarify complex mechanism underlying drug pharmacokinetics pharmacodynamics rapidly emerging interests in artificial intelligence (AI)-based analyses, DrugMAP expected become an indispensable supplement existing databases facilitate discovery. now fully freely accessible at: https://idrblab.org/drugmap/

Language: Английский

Citations

74
Revolutionizing Medicinal Chemistry: The Application of Artificial Intelligence (AI) in Early Drug Discovery DOI Creative Commons
Ri Han, Hongryul Yoon, Gahee Kim

et al.

Pharmaceuticals, Journal Year: 2023, Volume and Issue: 16(9), P. 1259 - 1259

Published: Sept. 6, 2023

Artificial intelligence (AI) has permeated various sectors, including the pharmaceutical industry and research, where it been utilized to efficiently identify new chemical entities with desirable properties. The application of AI algorithms drug discovery presents both remarkable opportunities challenges. This review article focuses on transformative role in medicinal chemistry. We delve into applications machine learning deep techniques screening design, discussing their potential expedite early process. In particular, we provide a comprehensive overview use predicting protein structures, drug–target interactions, molecular properties such as toxicity. While accelerated process, data quality issues technological constraints remain Nonetheless, relationships methods have unveiled, demonstrating AI’s expanding understanding interactions For its full be realized, interdisciplinary collaboration is essential. underscores growing influence future trajectory chemistry stresses importance ongoing synergies between computational domain experts.

Language: Английский

Citations

70