Exploring potential key genes and pathways associatedwith hepatocellular carcinoma prognosis through bioinformatics analysis, followed by experimental validation DOI
Xi Chen, Jianhua Zhao,

Jiaming Shu

et al.

American Journal of Translational Research, Journal Year: 2024, Volume and Issue: 16(12), P. 7286 - 7302

Published: Jan. 1, 2024

Liver Hepatocellular Carcinoma (LIHC) is a prevalent and aggressive liver cancer with limited therapeutic options. Identifying key genes involved in LIHC can enhance our understanding of its molecular mechanisms aid the development targeted therapies. This study aims to identify differentially expressed (DEGs) hub using bioinformatics approaches experimental validation. We analyzed two LIHC-related datasets, GSE84598 GSE19665, from Gene Expression Omnibus (GEO) database DEGs. Differential expression analysis was performed limma package R DEGs between cancerous non-cancerous tissues. A Protein-Protein Interaction (PPI) network constructed STRING determine genes. Further validation these conducted through UALCAN, OncoDB, Human Protein Atlas (HPA) databases for mRNA protein levels. Promoter methylation mutational analyses were cBioPortal. Kaplan-Meier survival assessed impact gene on patient survival. Correlations immune cell abundance drug sensitivity explored GSCA. Finally, AURKA knocked down HepG2 cells, proliferation, colony formation, wound healing assays performed. Analysis identified 180 DEGs, four genes, including AURKA, BUB1B, CCNA2, PTTG1 showing significant overexpression hypomethylation knockdown cells led decreased reduced impaired healing, confirming role progression. These also hypomethylated their elevated correlated poor overall are crucial pathogenesis may serve as potential biomarkers or targets. Our findings provide new insights into suggest promising avenues future research development.

Language: Английский

Advances in the study of disulfidptosis in digestive tract tumors DOI Creative Commons
Yue Chen, Dachuan Zhang, Huijuan Yang

et al.

Discover Oncology, Journal Year: 2025, Volume and Issue: 16(1)

Published: Feb. 15, 2025

Disulfidptosis, a recently identified cell death mechanism, plays pivotal role in the development, progression, and treatment of digestive tract tumors, including gastric cancer, hepatocellular esophageal colorectal pancreatic cholangiocarcinoma, neuroendocrine which have high global incidence mortality rates. Analyzing expression disulfidptosis-related gene within tumor microenvironment enhances our understanding biology facilitates novel diagnostic therapeutic strategies. Research on immune infiltration checkpoints can identify targets linked to disulfidptosis, thereby improving immunotherapy efficacy. Targeting genes such as SLC7A11, are essential for maintaining glutathione levels regulating oxidative stress, may overcome chemoresistance enhance existing treatments. Disulfidptosis could complement current therapies it induces cytoskeletal collapse selective death, especially chemoresistant cancers. Additionally, like RPN1, NCKAP1 cancer correlate with poor prognosis, highlighting their potential prognostic biomarkers. Personalized medicine approaches utilizing biomarkers patients who would benefit from targeting stress regulation, leading more precise treatments improved outcomes. This review summarizes disulfidptosis mechanisms, advancements cancers, related response evaluation, targeted therapies, providing perspectives diagnosis personalized treatment.

Language: Английский

Citations

1
Constructing a disulfidptosis-related prognostic signature of hepatocellular carcinoma based on single-cell sequencing and weighted co-expression network analysis DOI
Zelin Tian,

Junbo Song,

Jiang She

et al.

APOPTOSIS, Journal Year: 2024, Volume and Issue: 29(9-10), P. 1632 - 1647

Published: May 17, 2024

Language: Английский

Citations

5
SLC7A11, a disulfidptosis-related gene, correlates with multi-omics prognostic analysis in hepatocellular carcinoma DOI Creative Commons
Shizhe Li, Xiaotong Wang, Jie Xiao

et al.

European journal of medical research, Journal Year: 2025, Volume and Issue: 30(1)

Published: March 12, 2025

This study sought to establish a risk score signature based on disulfidptosis-related genes (DRGs) predict the prognosis of hepatocellular carcinoma (HCC) patients. The expression data DRGs from Cancer Genome Atlas (TCGA) and International Consortium (ICGC) was analyzed develop validate DRG prognostic (DRGPS). In vitro, experiments were conducted explore expressions roles in HCC tissues cell lines. tissue microarrays employed analyze SLC7A11 its association with clinicopathological characteristics. DRGPS consisted 5 (SLC7A11, MATN3, CLEC3B, CCNJL, PON1). survival rate patients high-risk group significantly lower than that low-risk group. also associated modulation tumor microenvironment (TME), mutation burden (TMB), stemness chemosensitivity. Furthermore, pan-cancer analysis suggested immune infiltration multiple cancers. Moreover, our had potential for predicting treatment efficacy Finally, we confirmed downregulation SLC7A11, DRG, inhibited proliferation migration cells, while high correlated advanced TNM clinical stage larger size. systematically describes novel constructed prognosis, providing new approach stratification options. It investigates function contributing further exploration molecular mechanism underlying disulfidptosis HCC, as well therapeutic implications.

Language: Английский

Citations

0
Establishment of a prognostic signature of disulfidptosis-related lncRNAs for predicting survival and immune landscape in clear cell renal cell carcinoma DOI Creative Commons
Jinhui Liu, Zhou Zhang, Lei Xiao

et al.

ONCOLOGIE, Journal Year: 2024, Volume and Issue: 26(4), P. 603 - 618

Published: June 1, 2024

Abstract Objectives A novel cell death pathway, disulfidptosis, marked by intracellular disulfide build-up, is a recently identified form of death. This study developed dependable model using disulfidptosis-associated lncRNAs to predict outcomes and immune interactions in clear renal carcinoma (ccRCC) patients. Methods Data from ccRCC patients, including genomic clinicopathological details, were sourced The Cancer Genome Atlas database. We employed the least absolute shrinkage selection operator (LASSO) along with regression analyses construct prognostic consisting 12 disulfidptosis-related (DRLs). model’s validity was tested RECA-EU GSE29609 datasets. Results model, incorporating DRLs – LINC01671 , DOCK9-DT AL078581.2 SPINT1-AS1 ZNF503-AS1 AL391883.1 AC002070.1 AP001372.2 AC068338.3 AC026401.3 AL355835.1 AL162377.1 distinguished high-risk patients diminished survival rates both training validation cohorts. Further through Cox confirmed this risk independent capability regarding overall (OS). Functional enrichment analysis indicated significant involvement differentially expressed genes response mediator production. nomogram, integrating clinical features, showed strong predictive accuracy as receiver operating characteristic curves. Additionally, assessments functionality tumor mutation burden varied across categories microenvironment, highlighting potential targets for anticancer drugs. Conclusions findings suggest signature potent indicator may serve forecast responses immunotherapy

Language: Английский

Citations

1
Precision prognostication in breast cancer: unveiling a long non-coding RNA-based model linked to disulfidptosis for tailored immunotherapeutic strategies DOI Creative Commons
Cheng‐Lu Jiang,

Shengke Zhang,

Lai Jiang

et al.

Aging, Journal Year: 2024, Volume and Issue: unknown

Published: June 18, 2024

Background: Breast cancer, comprising 15% of newly diagnosed malignancies, poses a formidable global oncological challenge for women. The severity this malady stems from tumor infiltration, metastasis, and elevated mortality rates. Disulfidptosis, an emerging cellular demise mechanism, presents promising avenue precision therapy. Our aim was to construct prognostic framework centered on long non-coding RNAs (lncRNAs) associated with disulfidptosis, aiming guide the strategic use clinical drugs, enhance precision, advance immunotherapy prognosis assessment. Methods: We systematically analyzed TCGA-BRCA dataset identify disulfidptosis-linked lncRNAs. Employing co-expression analysis, we discerned significant relationships between disulfidptosis-associated genes Identified lncRNAs underwent univariate Cox regression validation through LASSO regression, culminating in identification eight signature using multivariate proportional risk model. Then, utilized selected build prediction models. Results: DAL model exhibited outstanding efficacy, establishing itself as autonomous determinant breast cancer prognosis. It adeptly differentiated low high-risk patient cohorts, individuals experiencing significantly abbreviated survival durations. Notably, these cohorts displayed marked discrepancies markers microenvironment attributes. Conclusions: has performed well assessment by combining it other traditional indicators Nomogram plots gene expression data calculate patients' disease scores. This approach provides new ideas decision support personalized treatment decisions patients different levels.

Language: Английский

Citations

1
Identification and Verification of a Novel Disulfidptosis-Related lncRNAs Prognostic Signature to Predict the Prognosis and Immune Activity of Head and Neck Squamous Carcinoma DOI Creative Commons
Zi Yin, Jue Wang, Changqing Zhu

et al.

Iranian Journal of Public Health, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 19, 2024

Background: We aimed to explore the prediction value of disulfidptosis-related long noncoding RNAs (lncRNAs) on prognosis and immunotherapy efficiency patients with head neck squamous carcinoma (HNSCC). Methods: Clinical RNA-seq information were collected from The Cancer Genome Atlas (TCGA) Data Sharing (GDC) portal. Pearson correlation analysis, univariate COX regression least absolute shrinkage selection operator (LASSO) employed construct lncRNAs (DRLs) prognostic model. Kaplan-Meier survival curve, principal component analysis (PCA), receiver operating characteristic (ROC) curves areas under (AUCs) used examine accuracy ssGSEA, mutation functional gene set enrichment was performed quantify immune cell infiltration, function enrichments. Finally, mRNA expression DRLs verified by real‑time PCR (RT-PCR) in HNSCC cells. Results: A new model (AC083967.1, AC106820.5, AC245041.2, AL590617.2, AP002478.1, VPS9D1-AS1) an independent successfully identified. In addition, related signature drug therapy response. Meanwhile, level 6 detected RT-PCR consistent results bioinformatic analysis. Conclusion: developed a HNSCC, which could effectively predicate response provide insights into personalized therapeutics.

Language: Английский

Citations

1
Multiple machine learning algorithms, validation of external clinical cohort and assessments of model gain effects will better serve cancer research on bioinformatic models DOI Creative Commons
Fangshi Xu,

Zongyu Li,

Hao Guan

et al.

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

Published: Dec. 23, 2024

Bioinformatics models greatly contribute to individualized assessments of cancer patients. However, considerable research neglected some critical technological points, including comparisons multiple modeling algorithms, evaluating gain effects constructed model, comprehensive bioinformatics analyses and validation clinical cohort. These issues are worthy emphasizing, which will better serve future research.

Language: Английский

Citations

1
The role of disulfidptosis-associated LncRNA-LINC01137 in Osteosarcoma Biology and its regulatory effects on macrophage polarization DOI
Ning Tang, Yifan Chen, Su Yang

et al.

Functional & Integrative Genomics, Journal Year: 2024, Volume and Issue: 24(6)

Published: Nov. 22, 2024

Language: Английский

Citations

0
Exploring potential key genes and pathways associatedwith hepatocellular carcinoma prognosis through bioinformatics analysis, followed by experimental validation DOI
Xi Chen, Jianhua Zhao,

Jiaming Shu

et al.

American Journal of Translational Research, Journal Year: 2024, Volume and Issue: 16(12), P. 7286 - 7302

Published: Jan. 1, 2024

Liver Hepatocellular Carcinoma (LIHC) is a prevalent and aggressive liver cancer with limited therapeutic options. Identifying key genes involved in LIHC can enhance our understanding of its molecular mechanisms aid the development targeted therapies. This study aims to identify differentially expressed (DEGs) hub using bioinformatics approaches experimental validation. We analyzed two LIHC-related datasets, GSE84598 GSE19665, from Gene Expression Omnibus (GEO) database DEGs. Differential expression analysis was performed limma package R DEGs between cancerous non-cancerous tissues. A Protein-Protein Interaction (PPI) network constructed STRING determine genes. Further validation these conducted through UALCAN, OncoDB, Human Protein Atlas (HPA) databases for mRNA protein levels. Promoter methylation mutational analyses were cBioPortal. Kaplan-Meier survival assessed impact gene on patient survival. Correlations immune cell abundance drug sensitivity explored GSCA. Finally, AURKA knocked down HepG2 cells, proliferation, colony formation, wound healing assays performed. Analysis identified 180 DEGs, four genes, including AURKA, BUB1B, CCNA2, PTTG1 showing significant overexpression hypomethylation knockdown cells led decreased reduced impaired healing, confirming role progression. These also hypomethylated their elevated correlated poor overall are crucial pathogenesis may serve as potential biomarkers or targets. Our findings provide new insights into suggest promising avenues future research development.

Language: Английский

Citations

0