Cancer Informatics最新文献

Background: Gastric cancer is aggressive with poor prognosis due to high invasion and metastasis rates, a hallmark of cancer. The Snail family (SNAI1 and SNAI2) drives EMT, enabling epithelial cells to gain migratory and invasive traits.

Methods: We used "limma" package to identify genes with differential expression between high and low levels of SNAI1/SNAI2 in TCGA stomach adenocarcinoma dataset, intersecting these with cancer invasion and metastasis genes obtained from 5 databases. Using Cox regression analysis, we developed a risk score model and created a nomogram incorporating clinical data. The model's prognostic accuracy was validated with survival and ROC analyses in both TCGA and GEO datasets. Additionally, we performed WGCNA and constructed a ceRNA network to investigate gene interactions, and used CIBERSORT analysis to evaluate immune cell composition in the tumor microenvironment.

Results: We developed 5 and 9 risk signatures and nomograms incorporating clinical data. Survival analysis showed high-risk patients had worse overall survival than low-risk patients. WGCNA identified a lightyellow module associated with SNAI1 and SNAI2 expressions, emphasizing extracellular matrix organization. CeRNA network analyses found 6 common hub genes linked to SNAI1 and SNAI2. Immune profiling showed that SNAI1 expression was related to 8 types of immune cells, while SNAI2 was connected to 6, indicating their roles in influencing the tumor microenvironment.

Conclusion: This study highlights the significant prognostic impact of SNAI1 and SNAI2 in stomach adenocarcinoma, linking their high expression to poorer survival and aggressive tumor behavior, while also identifying potential therapeutic targets through comprehensive computational analysis.

Objectives: Early skin cancer detection in primary care settings is crucial for prognosis, yet clinicians often lack relevant training. Machine learning (ML) methods may offer a potential solution for this dilemma. This study aimed to develop a neural network for the binary classification of skin lesions into malignant and benign categories using smartphone images and clinical data via a multimodal and transfer learning-based approach.

Methods: We used the PAD-UFES-20 dataset, which included 2298 sets of lesion images. Three neural network models were developed: (1) a clinical data-based network, (2) an image-based network using a pre-trained DenseNet-121 and (3) a multimodal network combining clinical and image data. Models were tuned using Bayesian Optimisation HyperBand across 5-fold cross-validation. Model performance was evaluated using AUC-ROC, average precision, Brier score, calibration curve metrics, Matthews correlation coefficient (MCC), sensitivity and specificity. Model explainability was explored using permutation importance and Grad-CAM.

Results: During cross-validation, the multimodal network achieved an AUC-ROC of 0.91 (95% confidence interval [CI] 0.88-0.93) and a Brier score of 0.15 (95% CI 0.11-0.19). During internal validation, it retained an AUC-ROC of 0.91 and a Brier score of 0.12. The multimodal network outperformed the unimodal models on threshold-independent metrics and at MCC-optimised threshold, but it had similar classification performance as the image-only model at high-sensitivity thresholds. Analysis of permutation importance showed that key clinical features influential for the clinical data-based network included bleeding, lesion elevation, patient age and recent lesion growth. Grad-CAM visualisations showed that the image-based network focused on lesioned regions during classification rather than background artefacts.

Conclusions: A transfer learning-based, multimodal neural network can accurately identify malignant skin lesions from smartphone images and clinical data. External validation with larger, more diverse datasets is needed to assess the model's generalisability and support clinical adoption.

Objective: Hepatocellular carcinoma (HCC) is the most common primary liver cancer mainly caused by hepatitis viral infection. Early stage diagnosis is still challenging due to its asymptomatic behavior so there is an urgent need for effective biomarkers. This study aimed to identify effective diagnostic biomarker or therapeutic target for HCC.

Method: Label-free quantitative mass spectrometry was performed to analyze protein expression in HCC and control tissues. Protein-protein interaction (PPI) analysis was done using the STRING database and hub proteins were identified by Cytohubba. The survival analysis and expressions profiling of hub proteins were performed by using GEPIA. Functional and pathway enrichment analysis were carried out using Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG).

Results: A total of 1539 proteins were identified, of which 116 were differentially expressed proteins (DEPs). PPI network analysis revealed 10 hub proteins; EGFR, GAPDH, HSP90AA1, MMP9, PTPRC, CD44, ANXA5, PECAM1, MMP2, and CDK1. Among these, GAPDH, MMP9, ANXA5, HSP90AA1, and CDK1 were significantly associated with low survival rate (p ⩽ .05). Moreover, MMP9 and CDK1 were showed significantly increased expression in tumor tissues as compared to control (p ⩽ .05). The GO analysis based on biological process, cellular components and molecular function indicated that DEPs were enriched in stress response, vesicle and extracellular space, protein binding and enzyme activity. The KEGG pathway analysis showed that the thyroid hormone synthesis pathway is the most enriched.

Conclusion: The hub proteins GAPDH, HSP90AA1, MMP9, ANXA5, and CDK1 demonstrated significant prognostic potential, could be used as promising theragnostic biomarkers for HCC.

Hepatitis B virus (HBV) causes liver cancer, which is the third most common cause of cancer-related death worldwide. Chronic inflammation via HBV in the host hepatocytes causes hepatocyte remodeling (hepatocyte transformation and immortalization) and hepatocellular carcinoma (HCC). Recognizing cancer stages accurately to optimize early screening and diagnosis is a primary concern in the outlook of HBV-induced hepatocyte remodeling and liver cancer. Genomic signatures play important roles in addressing this issue. Recently, machine learning (ML) models and bioinformatics analysis have become very important in discovering novel genomic signatures for the early diagnosis, treatment, and prognosis of HBV-induced hepatic cell remodeling and HCC. We discuss the recent literature on the ML approach and bioinformatics analysis revealed novel genomic signatures for diagnosing and forecasting HBV-associated hepatocyte remodeling and HCC. Various genomic signatures, including various microRNAs and their associated genes, long noncoding RNAs (lncRNAs), and small nucleolar RNAs (snoRNAs), have been discovered to be involved in the upregulation and downregulation of HBV-HCC. Moreover, these genetic biomarkers also affect different biological processes, such as proliferation, migration, circulation, assault, dissemination, antiapoptosis, mitogenesis, transformation, and angiogenesis in HBV-infected hepatocytes.

Objective: This study aimed to identify biomarkers for colorectal cancer (CRC) with representative gene functions and high classification accuracy in tissue and blood samples.

Methods: We integrated CRC DNA methylation profiles from The Cancer Genome Atlas and comorbidity patterns of CRC to select biomarker candidates. We clustered these candidates near the promoter regions into multiple functional groups based on their functional annotations. To validate the selected biomarkers, we applied 3 machine learning techniques to construct models and compare their prediction performances.

Results: The 10 screened genes showed significant methylation differences in both tissue and blood samples. Our test results showed that 3-gene combinations achieved outstanding classification performance. Selecting 3 representative biomarkers from different genetic functional clusters, the combination of ADHFE1, ADAMTS5, and MIR129-2 exhibited the best performance across the 3 prediction models, achieving a Matthews correlation coefficient > .85 and an F1-score of .9.

Conclusions: Using integrated DNA methylation analysis, we identified 3 CRC-related biomarkers with remarkable classification performance. These biomarkers can be used to design a practical clinical toolkit for CRC diagnosis assistance and may also serve as candidate biomarkers for further clinical experiments through liquid biopsies.

Over a 20 year period, the journal Cancer Informatics has played an important role defining and forging a bridge between bioinformations and translational cancer research. The main focus of the journal has been to advance the prevention, diagnosis, and treatment of cancer. This involves the specialized intersection of genomics, molecular biology, data science, computer programing, statistics, communication theory, and the clinical sciences to answer important questions in the field of cancer research.

Backgrounds: Bladder cancer (BLCA) has a high degree of intratumor heterogeneity, which significantly affects patient prognosis. We performed single-cell analysis of BLCA tumors and organoids to elucidate the underlying mechanisms.

Methods: Single-cell RNA sequencing (scRNA-seq) data of BLCA samples were analyzed using Seurat, harmony, and infercnv for quality control, batch correction, and identification of malignant epithelial cells. Gene set enrichment analysis (GSEA), cell trajectory analysis, cell cycle analysis, and single-cell regulatory network inference and clustering (SCENIC) analysis explored the functional heterogeneity between malignant epithelial cell subpopulations. Cellchat was used to infer intercellular communication patterns. Co-expression analysis identified co-expression modules of the anti-apoptotic subpopulation. A prognostic model was constructed using hub genes and Cox regression, and nomogram analysis was performed. The tumor immune dysfunction and exclusion (TIDE) algorithm was applied to predict immunotherapy response.

Results: Organoids recapitulated the cellular and mutational landscape of the parent tumor. BLCA progression was characterized by mesenchymal features, epithelial-mesenchymal transition (EMT), immune microenvironment remodeling, and metabolic reprograming. An anti-apoptotic tumor subpopulation was identified, characterized by aberrant gene expression, transcriptional instability, and a high mutational burden. Key regulators of this subpopulation included CEBPB, EGR1, ELF3, and EZH2. This subpopulation interacted with immune and stromal cells through signaling pathways such as FGF, CXCL, and VEGF to promote tumor progression. Myofibroblast cancer-associated fibroblasts (mCAFs) and inflammatory cancer-associated fibroblasts (iCAFs) differentially contributed to metastasis. Protein-protein interaction (PPI) network analysis identified functional modules related to apoptosis, proliferation, and metabolism in the anti-apoptotic subpopulation. A 5-gene risk model was developed to predict patient prognosis, which was significantly associated with immune checkpoint gene expression, suggesting potential implications for immunotherapy.

Conclusions: We identified a distinct anti-apoptotic tumor subpopulation as a key driver of tumor progression with prognostic significance, laying the foundation for the development of new therapeutic strategies to improve patient outcomes.

Objectives: Immunogenic cell death (ICD) has been demonstrated to play a critical role in the development and progression of malignant tumors by modulating the anti-tumor immune response. However, its function in cervical cancer (CC) remains largely unexplored. In this study, we aimed to construct an ICD-related gene signature to predict patient prognosis and immune cell infiltration in CC.

Methods: The gene expression profiles and clinical data of CC were downloaded from The Cancer Genome Alas (TCGA) and Gene Expression Omnibus (GEO) datasets, serving as the training and testing groups, respectively. An ICD-related gene signature was developed using the LASSO-Cox model. The expression levels of the associated ICD-related genes were evaluated using single-cell data, CC cell lines, and clinical samples in vitro.

Results: Two ICD-associated subtypes (cluster 1 and cluster 2) were identified through consensus clustering. Patients classified into cluster 2 demonstrated higher levels of immune cell infiltration and exhibited a more favorable prognosis. Subsequently, an ICD-related gene signature comprising 3 genes (IL1B, IFNG, and FOXP3) was established for CC. Based on the median risk score, patients in both training and testing cohorts were segregated into high-risk and low-risk groups. Further analyses indicated that the estimated risk score functioned as an independent prognostic factor for CC and influenced immune cell abundance within the tumor microenvironment. The up-regulation of the identified ICD-related genes was further validated in CC cell lines and collected clinical samples.

Conclusion: In summary, the stratification based on ICD-related genes demonstrated strong efficacy in predicting patient prognosis and immune cell infiltration, which also provides valuable new perspectives for the diagnosis and prognosis of CC.

Objectives: Prostate cancer stem cells (CSCs) play an important role in cancer cell survival, proliferation, metastasis, and recurrence; thus, removing CSCs is important for complete cancer removal. However, the mechanisms underlying CSC functions remain largely unknown, making it difficult to develop new anticancer drugs targeting CSCs. Herein, we aimed to identify novel factors that regulate stemness and predict prognosis.

Methods: We reanalyzed 2 single-cell RNA sequencing data of prostate cancer (PCa) tissues using Seurat. We used gene set enrichment analysis (GSEA) to estimate CSCs and identified common upregulated genes in CSCs between these datasets. To investigate whether its expression levels change over CSC differentiation, we performed a trajectory analysis using monocle 3. In addition, GSEA helped us understand how the identified genes regulate stemness. Finally, to assess their clinical significance, we used the Cancer Genome Atlas database to evaluate their impact on prognosis.

Results: The expression of thioredoxin (TXN), a redox enzyme, was approximately 1.2 times higher in prostate CSCs than in PCa cells (P < 1 × 10^-10), and TXN expression decreased over CSC differentiation. In addition, GSEA suggested that intracellular signaling pathways, including MYC, may be involved in stemness regulation by TXN. Furthermore, TXN expression correlated with poor prognosis (P < .05) in PCa patients with high stemness.

Conclusions: Despite the limited sample size in our study and the need for further in vitro and in vivo experiments to demonstrate whether TXN functionally regulates prostate CSCs, our findings suggest that TXN may serve as a novel therapeutic target against CSCs. Moreover, TXN expression in CSCs could be a useful marker for predicting the prognosis of PCa patients.

Objectives: The TGF-β signaling pathway is widely acknowledged for its role in various aspects of cancer progression, including cellular invasion, epithelial-mesenchymal transition, and immunosuppression. Immune checkpoint inhibitors (ICIs) and pharmacological agents that target TGF-β offer significant potential as therapeutic options for cancer. However, the specific role of TGF-β in prognostic assessment and treatment strategies for breast cancer (BC) remains unclear.

Methods: The Cancer Genome Atlas (TCGA) database was utilized to develop a predictive model incorporating five TGF-β signaling-related genes (TSRGs). The GSE161529 dataset from the Gene Expression Omnibus was employed to conduct single-cell analyses aimed at further elucidating the characteristics of these TSRGs. Additionally, an unsupervised clustering algorithm was applied to categorize BC patients into two distinct groups based on the five TSRGs, with a focus on immune response and overall survival (OS). Further investigations were conducted to explore variations in pharmacotherapy and the tumor microenvironment across different patient cohorts and clusters.

Results: The predictive model for BC identified five TSRGs: FUT8, IFNG, ID3, KLF10, and PARD6A. Single-cell analysis revealed that IFNG is predominantly expressed in CD8+ T cells. Consensus clustering effectively categorized BC patients into two distinct clusters, with cluster B demonstrating a longer OS and a more favorable prognosis. Immunological assessments indicated a higher presence of immune checkpoints and immune cells in cluster B, suggesting a greater likelihood of responsiveness to ICIs.

Conclusion: The findings of this study highlight the potential of the TGF-β signaling pathway for prognostic classification and the development of personalized treatment strategies for BC patients, thereby enhancing our understanding of its significance in BC prognosis.