Cancer Informatics最新文献

Background and objectives: Next-generation sequencing (NGS) is transforming clinical diagnostics by enabling the detection of genetic variation with unprecedented precision. However, successful implementation of NGS workflows necessitates stringent quality control. This study introduces Molecular Genetics Dashboard (MGDB), a novel bioinformatics tool designed to enhance quality control in clinical NGS workflows.

Methods: Using the Python dash framework for visualizations and MySQL databases, we have developed a novel tool for variant-level monitoring of clinical NGS sequencing runs. MGDB uses a docker-compose containerization for improved portability and can flexibly include or exclude samples from accumulated statistics with notes from interpreters.

Results: MGDB facilitates variant-level run-to-run monitoring, ensuring the consistency of variant detection across sequencing cycles. The tool provides an interactive platform for visualizing and assessing variant data, identifying potential inconsistencies or outliers and improving data management and interpretation compared to traditional methods. MGDB was tested using samples sequenced with Oncomine Focus/Comprehensive Plus assays on S5 sequencers and analyzed via IonReporter software.

Conclusions: MGDB offers a robust and user-friendly solution for enhancing quality control in clinical NGS workflows, contributing to greater accuracy and reliability in variant detection. The tool is freely available on GitHub: https://github.com/acri-nb/GeneticVariantsDB.

Objectives: Cancer stratification is essential for accurate prognosis and personalized treatment selection. While many existing approaches integrate multiple omics data types to identify cancer subtypes, it remains unclear how clustering results from individual omics layers compare in their ability to capture survival-related patient clusters. This study aims to examine patient clusters separately defined by different omics data types and to explore the consistency of these clusters as well as their associations with survival outcomes.

Methods: In this study, we conducted clustering analysis on miRNA expression, gene expression, and DNA methylation data across 20 cancer types in TCGA. We employed a standard clustering pipeline similar to the widely used Seurat clustering pipeline in scRNA-seq analysis. We performed survival analysis to assess whether the resulting patient clusters exhibit significantly different survival outcomes.

Results: We observed significant survival differences among patient clusters in 11 cancer types. Notably, in 6 of these 11 cancer types, the survival differences among patient clusters were significant in multiple omics data types. For each of these 6 cancer types, we compared the consistency of patient clusters across different omics data types. Interestingly, in each cancer type, we noticed one set of patients who consistently clustered together irrespective of the omics data type, and these patients exhibited either the most favorable or the most unfavorable survival outcomes. This observation suggested that those patients with the most prominent survival outcomes show distinct expression patterns in multiple genomics aspects and could be captured by clustering analysis in multiple omics data types. To interpret these findings, we identified differentially expressed molecular features. Using established miRNA-target relationships, gene-gene interactions, as well as gene-CpG relationships, we constructed networks specific to each cancer type based on the differentially expressed features. These networks revealed several molecular modules associated with patient survival outcomes, such as the miR-200c-3p/ZEB2 axis in bladder cancer, the regulatory role of miR-98 in breast cancer, as well as the association of miR-21 with target genes APC in kidney renal cell carcinoma.

Conclusion: These findings suggest that omics-specific clustering can identify robust survival-related patient clusters and uncover molecular features that may contribute to differential survival outcomes.

Objectives: With the increasing application of high-throughput transcriptomic data in cancer research, identifying reliable cancer biomarkers in high-dimensional settings remains a major challenge. This study aims to systematically evaluate various regularized conditional logistic regression (CLR) methods under a matched case-control (MCC) design, focusing on their performance in variable selection, parameter estimation, and predictive accuracy. Special emphasis is placed on the importance of the matching design in reducing confounding effects and improving model interpretability.

Methods: We utilize RNA-seq data from The Cancer Genome Atlas (TCGA), specifically datasets for liver, thyroid, and lung cancers, which include paired tumor and adjacent normal tissue samples. In our analysis, we apply 4 regularized CLR methods implemented in R packages-namely "clogitL1," "pclogit," "clogitLasso," and "penalizedclr"-to analyze over 20 000 gene expression features. We evaluate the comparative performance of these methods based on metrics such as gene selection stability, predictive accuracy, and interpretability. Additionally, we employ a bootstrap resampling framework to estimate gene selection probabilities, which serve as a measure of gene importance.

Results: Our results show that incorporating the MCC design significantly enhances feature selection performance by mitigating confounding noise. The regularized CLR models successfully identify several well-established cancer-related genes with high selection consistency and statistical significance. In contrast, models that ignore the matched design tend to miss critical biomarkers or produce excessive false positives, leading to potentially misleading interpretations.

Conclusions: This study highlights the value of integrating a matched case-control design with regularized CLR methods for the analysis of high-dimensional transcriptomic data. The proposed analytical framework offers improved accuracy, robustness, and biological relevance, providing a practical and scalable approach for cancer genomics research. It also supports the advancement of precision medicine and translational applications.

Background: Post-mastectomy PTSD is a serious mental health issue, but it has not been studied enough, particularly in low-resource settings like Bangladesh. This study aimed to predict PTSD among breast cancer survivors using machine learning (ML) models and identify significant predictors through the Boruta algorithm, a feature selection tool, offering scalable solutions for early detection and intervention.

Methods: A cross-sectional study of 138 post-mastectomy breast cancer patients was conducted across 3 hospitals in Bangladesh. Data on sociodemographic, health history, social experience, and treatment were collected using validated tools, including the PTSD Checklist for DSM-5 (PCL-5). The Boruta algorithm identified key predictors, and 10 ML models were evaluated for PTSD prediction using metrics such as accuracy, sensitivity, specificity, and AUC.

Results: Random Forest (RF) outperformed other models (accuracy: 88.9%, AUC: 0.914). Significant predictors included education, monthly income, and changes in family behaviour. Factors like marital status, having chronic diseases, and hormone therapy were not statistically significant. PTSD prevalence was 34.1%, with urban residents and younger patients facing higher risks.

Conclusion: ML models, particularly RF, demonstrated strong predictive performance and identified critical PTSD predictors. These findings highlight the potential for cost-effective PTSD screening in resource-constrained settings. Future research should focus on broader validation and longitudinal studies to refine predictive models.

Objectives: The aim of this study is to investigate the role of epithelial cell transforming sequence 2 (ECT2) as a pan-cancer biomarker and to assess its potential as an immune-related target for cancer immunotherapy.

Methods: We conducted a comprehensive analysis of ECT2 expression across 44 tumor types using large-scale transcriptomic datasets from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) project. Pan-cancer Cox regression analyses were performed to evaluate the correlation between ECT2 expression and patient survival outcomes. Functional assays, including ECT2 knockdown via shRNA in the HepG2 hepatocellular carcinoma (HCC) cell line, were employed to investigate its mechanistic role. Transcriptomic profiling and pathway analyses were also conducted to explore the impact of ECT2 on cell proliferation and the tumor immune microenvironment.

Results: ECT2 was found to be significantly upregulated in 31 tumor types. Elevated ECT2 expression was consistently associated with worse overall survival (OS), disease-specific survival (DSS), disease-free interval (DFI), and progression-free interval (PFI) across multiple cancer subtypes. Functional assays revealed that ECT2 knockdown significantly reduced HepG2 cell viability and impaired cell cycle progression, with downregulation of Cyclin D1. Transcriptomic analysis of ECT2-depleted cells indicated enriched gene sets related to cell proliferation and mitotic regulation. Additionally, ECT2 expression was significantly correlated with immune features, including immune cell infiltration, immune checkpoint gene expression, tumor mutational burden (TMB), and microsatellite instability (MSI).

Conclusion: ECT2 is identified as a potential pan-cancer prognostic biomarker with dual roles in tumor initiation and progression, as well as in modulating the tumor immune microenvironment. Our findings suggest that ECT2 may serve as a promising therapeutic target in cancer immunotherapy, warranting further investigation into its immune-regulatory and oncogenic functions.

Background: Breast cancer remains a predominant malignancy and a leading cause of oncologic mortality among women globally. The discovery of novel biomarkers is crucial for improving therapeutic outcomes.

Methods: We conducted a comprehensive analysis of the immunological and prognostic significance of hepatitis A virus cellular receptor 1 (HAVCR1) in breast cancer using publicly available datasets.

Results: HAVCR1 expression was markedly downregulated in breast cancer tissues. Significantly, lower expression levels of HAVCR1 in pre-treatment tumor samples were associated with poorer prognosis among pan-cancer patients undergoing immunotherapy, and a higher incidence of metastasis was observed in the breast cancer subgroup. Subtype-specific DEG analyses further indicated that distinct patterns of immune infiltration may underlie this association. Moreover, gene set enrichment analysis (GSEA) highlighted the immunological relevance of HAVCR1, particularly its involvement in T cell activation within the TNBC subtype. Clinically, elevated levels of HAVCR1 expression in pre-treatment T cells were indicative of a more favorable response to PD-1 blockade therapy compared to those with diminished expression.

Conclusion: The expression of HAVCR1 exhibits a strong correlation with immune infiltration and holds potential as a prognostic biomarker for breast cancer, offering predictive insight into the efficacy of immunotherapeutic interventions.

Objective: Breast cancer is one of the most prominent and deadly diseases in the world, and its prognosis varies widely based on the expression of certain genes. Identification of these genes is important for developing and interpreting clinical prognostic tests as well as furthering our understanding of breast cancer biology. We expand on prior efforts in the field toward identifying prognostic genes, by integrating powerful statistical methods.

Methods: To this end, we use an unsupervised random forest model, which allows for robust learning of non-linear gene expression/survival relationships and the ability to identify the most important genes affecting both positive and negative breast cancer prognosis. In total, 1,518 participants were considered from the METABRIC dataset, using 20,387 mRNA expression level variables and 23 clinical variables including HER2 mutation status. The top 250 & bottom 250 expressing genes and 6 clinical features were selected for the unsupervised random forest model.

Results: Our research corroborates previous discoveries of 27 important prognostic genes while also identifying 3 genes as potentially novel prognostic factors. Based on gene ontology analysis, we additionally show that these genes have plausible connections to breast cancer biology that should be experimentally investigated.

Conclusions: Here, we demonstrate the utility of the unsupervised random forest model over K-means clustering for identifying important genes in breast cancer.

Objectives: This study aimed to identify transcript isoforms of protein-coding genes with potential relevance to the malignant transformation of gut mucosa.

Methods: Colon cancer cell lines (HCT116, DLD1, SW620) and immortalized cells derived from healthy gut epithelium (HCEC-1CT) were cultured as spheroids and subjected to RNA sequencing to profile both canonical and non-canonical transcripts. The resulting data were compared with prior bioinformatics study findings that analyzed RNA-seq datasets from 473 patient-derived tumor and 417 non-tumor colon tissue samples.

Results: Among 375 transcripts previously reported as significantly dysregulated in colon (39 up-regulated and 336 down-regulated), 32 transcripts displayed expression patterns in colon cell lines consistent with those observed in patient tissues (4 up-regulated and 28 down-regulated). In silico characterization of these molecules revealed that all of them exhibited at least 1 feature commonly associated with RNAs possessing regulatory functions, such as coding truncated protein isoform, exosomal localization, or enrichment in repetitive elements. The most prominently dysregulated transcripts with consistent expression profiles across both datasets were NTMT1-204 (up-regulated in cancer) and BLOC1S6-218 and DCTN1-205 (both down-regulated in cancer). The remaining 343 transcripts did not show consistent expression patterns in the cell lines, suggesting their dysregulation in patient-derived tissues may be due to the stromal or microenvironmental factors absent in vitro.

Conclusion: In summary, this comparative transcriptomic analysis identified 32 transcript isoforms, comprising 2 canonical and 30 non-canonical transcripts, that may play regulatory roles in colon carcinogenesis and warrant further investigation in the context of gut epithelial cell biology.

Background: Patients with Human Immunodeficiency Virus (HIV)have an atypical imaging pattern of lymphoma. There is paucity of literature on differences in tumor volume or burden of disease amongst HIV positive patients compared with HIV negative patients and how this correlates with clinicopathological parameters of aggressiveness and prognosis.

Methods: This was a retrospective cross-sectional study of patients with non-Hodgkin lymphoma which were categorized into HIV positive and HIV negative. The tumor burden, disease sites, international prognostic score and Ki-67 index were recorded. Continuous variables were analyzed using the Kruskal Wallis test and categorical variables with Fisher's Exact test.

Results: Out of the 92 patients with non-Hodgkin lymphoma, 51.1% were HIV positive with a median age of 45.0 years. The median sum of product diameters used to measure tumor burden was 102.6 [IQR: 51.7, 173.1] with no significant difference seen between the 2 groups. The extranodal disease was significantly higher in the HIV positive group (85.1%) while exclusive nodal disease was seen predominantly in the non-HIV group (66.7%) (P < .001). Complete treatment response was higher in the non-HIV group 54.5% compared to 20.9% for the HIV group (P < .001). More HIV positive patients succumbed, 37.2% compared to the 4.5% for non-HIV patients (P < .001).

Conclusion: HIV-related lymphoma remains a poorly understood subset. Although there was no significant difference in overall tumor burden between HIV positive and negative patients, extranodal disease was significantly higher in the HIV positive patients. Furthermore, the clinical prognostication score and Ki-67 which apply well for HIV-negative patients may not apply for HIV-related lymphoma.