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At least 19 recordsLinked to original sources
PubMed2025

ProgModule: A novel computational framework to identify mutation driver modules for predicting cancer prognosis and immunotherapy response.

BACKGROUND: Cancer originates from dysregulated cell proliferation driven by driver gene mutations. Despite numerous algorithms developed to identify genomic mutational signatures, they often suffer from high computational complexity and limited clinical applicability. METHODS: Here, we presented ProgModule, an advanced computational framework designed to identify mutation driver modules for cancer prognosis and immunotherapy response prediction. In ProgModule, we introduced the Prognosis-Related Mutually Exclusive Mutation (PRMEM) score, which optimizes the balance between exclusive mutation coverage and the incorporation of mutation combination mechanisms critical for cancer prognosis. RESULTS: Applying to BLCA and HNSC cohorts, ProgModule successfully identified driver modules that stratify patients into distinct prognostic subgroups, and the combination of these modules could serve as an effective prognostic biomarker. Extending our method to diverse cancers, ProgModule presented robust prognostic performance and stability across model parameters, including stopping criteria and network topology. Moreover, our analysis suggested that driver modules can predict immunotherapeutic benefit more effectively than existing signatures. Further analyses based on published CRISPR data indicated that genes within these modules may serve as potential therapeutic targets. CONCLUSIONS: Altogether, ProgModule emerges as a powerful tool for identifying mutation driver modules as prognostic and immunotherapy response biomarkers, and genes within these modules may be used as potential therapeutic targets for cancer, offering new insights into precision oncology.

Humans↗
PubMed2026

Impact of precision oncology research in pediatric poor prognosis cancer: patient, parent and healthcare provider perspectives.

BACKGROUND: Comprehensive genomic analyses are increasingly accessible to children, adolescents and young adults (AYAs) with poor prognosis cancers. Challenges and successes of pediatric precision oncology studies from the perspectives of AYA patients, parents and healthcare providers (HCPs) are poorly described. METHODS: Between March 2021 and May 2023, we interviewed AYA patients (12-21 years), parents and HCPs who participated in pediatric precision oncology studies for poor prognosis cancers in British Columbia. Interviews followed an investigator-developed semi-structured topic guide. Data were coded inductively and deductively by one qualitative researcher and one trainee, supported by two additional team members. Analytic themes were established using qualitative thematic analysis. RESULTS: We interviewed 9 AYAs, 10 parents, and 17 HCPs. We identified five analytic themes: importance of clear communication of study information between patients, families and multidisciplinary HCPs; a need to support disclosure, understanding and clinical integration of research results; barriers to accessing innovative therapy and mitigation strategies; approaches to managing parent, patient and HCP hopes and expectations; personal challenges and stressors related to participation. CONCLUSIONS: We highlight unmet needs and offer practical considerations for integrating precision oncology into clinical practice. Considerations include educating and supporting oncologists through genomics results disclosure, increasing engagement with multidisciplinary HCPs, streamlining access to study information and results, coordinating efforts to clinically validate results and access therapies, and establishing real-world outcome data to inform clinical decision-making. Implementation of these strategies will optimize care for patients and families who are navigating poor prognosis cancers.

Humans↗
PubMed2026

Deep learning-based multimodal pathogenomics integration for precision cancer prognosis.

BACKGROUND: Recent studies have revealed valuable prognostic insights in haematoxylin and eosin (H&E)-stained histological sections and transcriptomic profiles, suggesting potential applications in machine learning. However, existing methods lack sufficient intra- and inter-modal interactions, and face challenges in clinical validation due to incomplete multimodal data. METHODS: We proposed PathoGems (PathoGenomics-based integrative survival prediction), a weakly-supervised, interpretable multimodal learning framework that integrates histology and genomic profiles for precise cancer prognosis prediction. To evaluate the robustness of PathoGems, we initially curated a dataset of 1965 cases across four cohorts from The Cancer Genome Atlas (TCGA), including breast, colorectal, glioblastoma, and esophageal cancers. For external validation, PathoGems was further evaluated on four independent cohorts, consisting of 76 breast cancer and 41 esophageal squamous cell carcinoma cases from Zhejiang Cancer Hospital, as well as 102 colorectal cancer and 58 glioblastoma cases from the Clinical Proteomic Tumor Analysis Consortium (CPTAC). RESULTS: PathoGems effectively stratified patients into favorable and unfavorable risk groups, revealing significant differences in histological patterns, genomic features, and overall survival (log-rank test, p&#x2009;<&#x2009;0.05). Moreover, the model&#x2019;s predictions are further supported by visualization and transcriptomic analysis, enhancing interpretability and reliability. CONCLUSIONS: By fusing histological and clinicogenomic multimodal models, PathoGems will provide a solid foundation for developing an innovative tool that aids clinicians in making informed decisions and selection personalized treatment strategies for cancer patients.

Humans↗
PubMed2026

Machine learning approaches for cancer prognosis and diagnosis via non-coding RNA: a comprehensive review.

Non-coding RNAs (ncRNAs), once considered genomic dark matter, are now established as key regulators of gene expression with widespread roles in cellular homeostasis and disease. In cancer, ncRNA expression is frequently and systematically dysregulated, and many of these molecules circulate in stable, protected form within biofluids, offering a compelling basis for non-invasive or minimally invasive diagnostic strategies. However, their clinical translation remains substantially hindered to date due to biological complexity, technical noise, and high dimensionality inherent to ncRNA expression datasets. In this context, machine learning (ML) has emerged as a powerful analytical tool to address these challenges, enabling the identification of subtle, reproducible ncRNA signatures predictive of diverse malignancies. This review critically evaluates ML-driven frameworks for cancer diagnosis and prognosis across four ncRNA subclasses, namely miRNAs, lncRNAs, circRNAs, and piRNAs, while also acknowledging the biophysical and thermodynamic models that reinforce ncRNA bioinformatics. Despite substantial methodological progress in ML-based cancer diagnosis and prognosis, key challenges persist, including tumor biological heterogeneity, limited multicenter validation, and the lack of widely adopted standardized protocols for preprocessing, normalization, and reporting workflows. Furthermore, many current ML models lack interpretability in biological or clinical context, constraining their translational utility. By synthesizing recent advances and identifying unresolved barriers, this review charts a roadmap for developing a robust, clinically actionable ncRNA biomarker platform for cancer detection. With global cancer incidence projected to exceed 35 million annual cases by 2050, validated ncRNA-ML-driven frameworks hold potential to revolutionize early-stage detection and personalized therapeutic strategies, thereby reducing the escalating socio-economic burden of cancer worldwide.

Humans↗
PubMed1979

Statistical evaluation of factors influencing prognosis of gastric cancer patients: Predication of prognosis on patient clusters.

We found ten clusters of gastric cancer patients in Imanaga's group under a cancer research project organized by the Ministry of Health and Welfare, and evaluated the prediction of prognosis of those patients in each cluster by using the censored regression of postsurgical survival time on a "prognosic" factor which has been extracted from nine explanatory variables observed mainly at the time of surgery. Consequently, the ten clusters were interpreted and confirmed to be useful for prediction of the patient prognosis by comparison of the failure rates among those clusters and between treated (administration of chemotherapy) group and control group.

Adult↗
PubMed2026

Integrin &#x3b1;3 (ITGA3) expression across breast cancer subtypes: Prognosis and therapeutic relevance.

BACKGROUND: Integrin &#x3b1;3 (ITGA3), which heterodimerizes with integrin &#x3b2;1, has emerged as a potential biomarker and therapeutic target in several epithelial malignancies; however, its clinical relevance in breast cancer remains incompletely characterized. This study evaluated ITGA3 expression across breast cancer molecular subtypes and assessed its prognostic and predictive significance. METHODS: Immunohistochemistry (IHC) was performed on archival breast cancer specimens using tissue microarrays (n = 148) and whole-tissue sections (n = 21). Complete clinicopathologic and outcome data were available for 108 patients, including hormone receptor-positive/human epidermal growth factor receptor 2-negative, HER2-positive, and triple-negative breast cancer (TNBC) subtypes. ITGA3 expression was quantified using H-scores and correlated with clinicopathologic features and survival outcomes. Independent transcriptomic analyses were conducted using the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) and the Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) cohorts to evaluate ITGA3 mRNA expression, co-expressed signaling pathways, and associations with therapeutic response. RESULTS: ITGA3 protein expression was detected in 85.2% of breast cancer specimens and was significantly higher in HR-positive/HER2-negative and HER2-positive tumors compared with TNBC (p < 0.0050). High ITGA3 expression was associated with shorter recurrence-free survival (p < 0.0001). In the METABRIC cohort, tumors with ITGA3 alterations demonstrated significantly worse relapse-free survival (p < 0.0001) and overall survival (p < 0.0500). Transcriptomic analyses revealed that ITGA3 co-expressed with estrogen receptor 1(ESR1), erb-b2 receptor tyrosine kinase 2 (ERBB2), and luminal markers, along with enrichment of estrogen receptor and phosphoinositide 3-kinase-protein kinase B-mechanistic target of rapamycin (PI3K/AKT/mTOR) signaling pathways. ITGA3 expression was not predictive of response to tamoxifen or trastuzumab. CONCLUSION: Elevated ITGA3 expression is associated with breast cancer recurrence and poor clinical outcomes, supporting its potential role as a prognostic biomarker and candidate therapeutic target.

Biomarkers↗
PubMed1979

Leukoerythroblastosis and cancer frequency, prognosis, and physiopathologic significance.

This investigation was carried out on 100 bone marrow biopsies with metastases and 56 autopsies on patients with evidence of cancer. Leukoerythroblastosis was found in 44% of the patients with bone marrow mestastases and was more frequent in prostatic and gastric carcinoma. Moreover, the postmortem study of patients who died with cancer showed that leukoerythroblastosis was always the sign of bone marrow metastasis. A significant correlation was found between these blood changes and bone marrow fibrosis around the metastasis. Furthermore, leukoerythroblastosis seems caused by hepatosplenic extra medullary hematopoiesis.

Anemia, Myelophthisic↗
PubMed2026

High NKAP expression predicts poor prognosis of breast cancer patients.

NF-&#x3ba;B activating protein (NKAP) plays important roles in various cancers, including breast cancer. However, its expression and prognosis value in breast cancer remains uncertain. Gene expression profiling interactive analysis, Human protein atlas database, and University of Alabama at Birmingham Cancer data analysis portal database were used to predict the expression and prognostic value of NKAP in breast cancer. Immunohistochemistry, quantitative real time polymerase chain reaction (qRT-PCR) and western blot were performed to detect NKAP expression. The effects of NKAP on cell proliferation, migration and drug sensitivity were investigated in MDA-MB-231 and SK-BR-3 cells. NKAP protein expression differed in breast cancer tissues and paraneoplastic tissues based on the cancer genome atlas data. The high NKAP expression was significantly correlated with a poor prognosis in breast cancer patients. The results of immunohistochemistry, western blot assay, and qRT-PCR proved that NKAP was highly expressed in breast cancer tissues compared with paraneoplastic tissues. In addition, qRT-PCR results showed that high expression of NKAP was significantly correlated with the larger tumor size and higher TNM stage. Moreover, knockdown of NKAP significantly inhibited the proliferation, migration, and enhanced drug sensitivity of MDA-MB-231 and SK-BR-3 cells. NKAP is highly expressed in breast cancer tissues, and its high expression is closely associated with poor prognosis. NKAP also promotes proliferation, migration, and inhibits drug sensitivity of breast cancer cells.

Humans↗
PubMed2025

Computational Pathology for Accurate Prediction of Breast Cancer Recurrence: Development and Validation of a Deep Learning-Based Tool.

Accurate recurrence risk stratification is crucial for optimizing treatment plans for breast cancer patients. Current prognostic tools like Oncotype DX offer valuable genomic insights into hormone receptor-positive and human epidermal growth factor receptor-negative patients but are limited by cost and accessibility, particularly in underserved populations. In this study, we present Deep-Breast-Cancer-Recurrence (BCR)-Auto, a deep learning-based computational pathology approach that predicts breast cancer recurrence risk from routine hematoxylin and eosin-stained whole slide images. Our methodology was validated on 2 independent cohorts: The Cancer Genome Atlas Program breast cancer data set and an in-house data set from The Ohio State University. Deep-BCR-Auto demonstrated robust performance in stratifying patients into low- and high-recurrence risk categories. On The Cancer Genome Atlas Program breast cancer data set, the model achieved an area under the receiver operating characteristic curve of 0.827, significantly outperforming the existing weakly supervised models (P = .041). In the independent The Ohio State University data set, Deep-BCR-Auto maintained strong generalizability, achieving an area under the receiver operating characteristic curve of 0.832, along with 82.0% accuracy, 85.0% specificity, and 67.7% sensitivity. These findings highlight the potential of computational pathology as a cost-effective alternative for recurrence risk assessment, broadening access to personalized treatment strategies. This study underscores the clinical utility of integrating deep learning-based computational pathology into routine pathological assessment for breast cancer prognosis across diverse clinical settings.

Humans↗
PubMed2025

Liver cancer-specific prognostic model developed using endoplasmic reticulum stress-related LncRNAs and LINC01011 as a potential therapeutic target.

Liver cancer is a serious malignancy worldwide, and long noncoding RNAs (lncRNAs) have been implicated in its prognosis.It remains unclear how lncRNAs related to endoplasmic reticulum stress (ERS) influence liver cancer prognosis. Here, we analyzed RNA and clinical data from the Cancer Genome Atlas and sourced ERS-related genes from the Molecular Signatures Database. Co-expression analysis identified ERS-related lncRNAs, and Cox regression analysis as well as least absolute shrinkage and selection operator regression highlighted three lncRNAs for a prognostic model. Based on median risk scores, we classified patients into two risk groups. The high-risk group displayed poor prognosis, and this finding was validated in the test set. According to consistency clustering, the patients were assigned to two clusters, and tumor microenvironment scores were computed. Patients with a high mutation burden had worse outcomes. Furthermore, immune infiltration analysis indicated more immune cells and mutations in checkpoint molecules among high-risk individuals. Drug sensitivity varied between the risk groups. LINC01011 was selected for functional assays. Colony formation assay and CCK-8 assay revealed that silencing LINC01011 suppressed liver cancer cell proliferation. Transwell and scratch assays indicated that silencing LINC01011 inhibited liver cancer cell migration. Western blotting assay revealed that inhibiting LINC01011 induced apoptosis and simultaneously inhibited epithelial-mesenchymal transition. These findings confirm the validity of the prognostic model and indicate that LINC01011 could serve as a potential research target.

Humans↗
PubMed2026

Prognostic Role of Global DNA Methylation in Renal Cancer Reveals Decitabine Treatment Benefit.

BACKGROUND: Renal cancer presents a significant global health challenge due to its rising incidence and mortality rates. Often undetected in early stages, it complicates diagnosis and treatment. Current therapies face resistance and limited effectiveness, especially in advanced stages. The diverse subtypes of renal cancer highlight the need for new biomarkers and risk assessment tools for targeted treatments. OBJECTIVE: This study aims to assess the prognostic significance of global DNA methylation (GM) levels in renal cancer, identify new biomarkers, and evaluate the therapeutic potential of the DNA methyltransferase inhibitor decitabine. METHODS: Data on RNA sequencing, gene mutations, DNA methylation, and clinical outcomes were collected from TCGA and GEO databases. We calculated global DNA methylation scores (GMS) and categorized patients into high, intermediate, and low GMS groups. Survival analysis and genomic analyses were conducted to explore the relationships between GMS, clinical outcomes, and tumor characteristics. RESULTS: Higher GMS was identified as an independent prognostic factor associated with worse outcomes in renal cancer. Patients with elevated GMS showed increased mutations, copy number variations, and a more aggressive tumor phenotype. Treatment with decitabine was observed to reduce tumor hypermethylation and downregulate cell cycle pathway activity, indicating potential therapeutic benefits. CONCLUSION: Global DNA methylation plays a significant role in renal cancer prognosis. GMS may serve as valuable biomarkers for prognosis and personalized treatment strategies. Decitabine shows potential efficacy for high GMS patients, particularly through its impact on cell cycle regulation, underscoring the importance of personalized approaches in cancer treatment.

Humans↗
PubMed2026

Comprehensive analysis suggests CRIF1 is a potential target in breast cancer associated with prognosis and immune infiltration.

BACKGROUND: CRIF1 is a multifunctional factor that regulates cell biological processes such as the cell cycle, cell proliferation, and energy metabolism, and it is a new molecule that contributes to the poor prognosis of many malignancies. However, its involvement in breast cancer development is not fully known. MATERIALS AND METHODS: To investigate the relationship between CRIF1 expression, prognosis, and clinical characteristics using The Cancer Genome Atlas (TCGA-BRCA). The relationship between CRIF1 expression and the immunological microenvironment was investigated using CIBERSORT, ESTIMATE. Breast tissue and CRIF1 expression were validated by IHC. A tiny interfering plasmid was designed to transiently transfect breast cancer cell lines, and proliferation-related functional tests were carried out. The effect of sh CRIF1 on tumor formation was confirmed using a subcutaneous tumor experiment in naked mice. RESULTS: We discovered that CRIF1 was highly elevated in breast cancer tissues and associated with a poor prognosis. CRIF1 stimulates breast cancer cell proliferation, migration, and invasion. Knockdown decreased PI3K/AKT/mTOR signaling, which boosted autophagy activity. Immune infiltration research revealed that patients with high CRIF1 expression had higher CD8+ T cell expression but reduced macrophage M2 expression. CONCLUSION: Upregulation of CRIF1 in breast cancer cells enhances malignant behavior, which may be mediated by PI3K/AKT/mTOR signaling and is linked to cellular autophagy.

Humans↗