Cancer Informatics最新文献

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.

Objectives: The objective of the Prediction Augmented Screening Initiative (PASI) pilot application was to design and implement a clinical tool to optimize the lung cancer screening (LCS) workflow for providers. The Boston Informatics Group (BIG) at the Department of Veterans Affairs (VA) developed the Enabling Technologies for Rapid Learning Health Systems Platform (ENTHRALL) to support delivery of knowledge in a Learning Health System (LHS) framework. The BIG leveraged ENTHRALL to implement the PASI pilot application on a very short timeline. The application uses VA data to estimate patients' benefit from LCS based on National Cancer Institute (NCI) models, allowing proactive outreach to patients with high predicted benefit from LCS.

Methods: The application was designed utilizing ENTHRALL infrastructure, including optimized nightly data pulls to gather patient information, Natural Language Processing to extract smoking history, and a user interface (UI). Cross-functional collaboration allowed the use of the NCI's peer-reviewed prediction algorithm to provide daily patient benefit scores.

Results: The UI displays patients in descending order of benefit, delivering a prioritized list to providers. Clinicians can fill in information and track patient status to assist with their outreach activities. For the pilot, only patients meeting USPSTF LCS criteria (the current field standard) were displayed. Five VA stations were included.

Conclusions: Utilizing the VA BIG's ENTHRALL framework for an LHS, the group demonstrated their ability to design and deliver a new application within 3 months of inception, which was successfully utilized at 5 VA hospitals. The VA's capability to rapidly build clinically relevant applications will help it become an LHS tailored to current problems impacting the Veteran. Due to the success of the pilot, the clinical research team got approval to expand their study. The BIG is working on a non-pilot build.

Background: Carbonic anhydrase 9 (CA9) plays a crucial role in pH regulation and adaptation under hypoxic conditions in the tumor microenvironment. Despite its known involvement in the progression of specific cancers, a comprehensive pan-cancer examination of the prognostic value and biological implications of CA9 has not been performed. This study systematically explored the diverse roles of CA9 across multiple cancer types.

Methods: Bioinformatics methods were applied via extensive datasets from TCGA, GTEx, CPTAC, CancerSEA, and the public literature. We systematically analyzed the associations between CA9 expression profiles and various clinical parameters, prognosis, immune infiltration, immune-related genes, TMB, MSI, and tumor stemness scores. Additionally, a single-cell functional analysis was conducted.

Results: CA9 was significantly upregulated in 29 out of 33 cancer types, indicating high discriminatory ability between tumor and normal tissues. Elevated CA9 expression correlated with poor OS and PFIs in multiple cancers, such as GBMLGG, CESC, LUAD, KIPAN, GBM, THYM, LIHC, THCA, PAAD, and KICH. In 39 cancers, CA9 expression was predominantly negatively correlated with the infiltration of 22 immune cell infiltrations. It was also associated with TMB in 12 tumors and with MSI in 9. Single-cell analysis revealed positive links between CA9 and essential processes such as hypoxia, metastasis, angiogenesis, and stemness.

Conclusion: This study provides compelling evidence that CA9 is a potential pan-cancer prognostic marker and diagnostic tool. The associations of CA9 with immune components and determinants of immunotherapy response indicate the importance of CA9 in advancing cancer research and personalized treatment strategies.

Objectives: Multimodal deep learning models have the potential to significantly improve survival predictions and treatment planning for cancer patients. These models integrate diverse data modalities using early, intermediate, or late fusion techniques. However, many existing multimodal models either underperform or show only marginal improvements over unimodal models. To establish the true efficacy of multimodal survival prediction models, it is critical to demonstrate consistent and substantial advantages over unimodal counterparts.

Methods: In this paper, we introduce the Robust Multimodal Survival Model (RMSurv), a novel discrete late fusion model that leverages synthetic data generation to compute time-dependent weights for various modalities. RMSurv utilizes up to 6 distinct data modalities from The Cancer Genome Atlas Program (TCGA) non-small cell lung cancer and the TCGA pan-cancer datasets to predict overall survival over a period of 10 years. The key innovations of RMSurv are the calculation of time-dependent late fusion weights using a synthetically generated dataset and a new statistical feature normalization technique to enhance the interpretability and accuracy of discrete survival predictions. We evaluate the performance of the proposed method and several alternatives with cross validation using the concordance index, and vary the number of modalities included. We also create a late fusion simulation to highlight the complex relationships of multimodal fusion.

Results: In our experiments, RMSurv outperforms the best unimodal model's Concordance index (C-Index) by 0.0273 on the 6-modal TCGA Lung Adenocarcinoma (LUAD) dataset. Existing late and early fusion methods improved the C-index by only 0.0143 and 0.0072, respectively. RMSurv also performs best on the combined TCGA non-small-cell lung cancer dataset and the TCGA pan-cancer dataset.

Conclusions: These advancements underscore RMSurv's potential as a powerful approach for survival prediction, establishing robust multimodal benefits, and setting a new benchmark for survival prediction models in pan-cancer settings.

The complexity of cancer has long challenged the medical community, driving the need for improved early detection and treatment. Artificial intelligence (AI) has profoundly impacted oncology research in recent decades, resulting in innovative diagnostic and therapeutic approaches. This review synthesizes the critical applications of AI in oncology, focusing on 4 key areas: medical imaging, digital pathology, robotic surgery, and drug discovery. We highlight the role of AI in cancer diagnosis and treatment by reviewing key studies and machine learning methods, and we address the field's current technical and ethical challenges. AI models have significantly enhanced the accuracy of medical imaging by efficiently detecting lesions and disease sites, leading to earlier and more precise diagnoses. In digital pathology, AI tools aid in risk prediction and facilitate the examination of extensive tissue sample sets for patterns and markers, simplifying the pathologists' tasks. AI-powered robotic surgery provides different levels of automation, leading to precise and minimally invasive procedures that not only improve surgical outcomes but also lower readmission rates, hospital stays, and infection risks. Moreover, AI expedites the process of discovering cancer therapies by identifying potential lead compounds, predicting drug reactions, and repurposing current medications. In the past decade, several AI-developed drugs have successfully entered clinical trials. These significant advancements underscore the expanding role of AI in shaping the future of cancer diagnosis and treatment. Although standardization, transparency, and equitable implementation must be addressed, AI brings hope for more personalized and effective therapies.

Objective: The aim of this study was to investigate the role and mechanism of CD79A⁺ B cells in mediating the microenvironment of breast cancer and the relationship with the prognosis of breast cancer.

Methods: Single-cell RNA sequencing and bulk RNA sequencing analysis were combined to annotate breast cancer cell subtypes, perform cell communication and trajectory analysis. CD79A-related signature was constructed by LASSO and multivariate Cox analysis. CD79A⁺ B cell subsets in the tumor microenvironment were explored by immunoanalysis and multiple immunofluorescence analysis.

Results: There were communication relationships between CD79A⁺ B cells and multiple cell types. A prognostic risk signature containing 6 genes was constructed by combining the TCGA dataset. The immune profile analysis showed that the low-risk group showed a higher immune response. In addition, multiple immunofluorescence analysis showed an attraction between CD79A⁺ B cells and tumor cells, and patients with high CD79A⁺ B cells expression had significantly higher survival rates.

Conclusion: This study comprehensively explored the heterogeneity of CD79A⁺ B cells through transcriptome analysis and chromatin analysis, which contributes to an in-depth understanding of the function of CD79A⁺ B cells in biological processes as well as the molecular mechanism of breast carcinogenesis, providing a theoretical basis for treatment and prevention.

Objective: Osteosarcoma (OS) is a rare and complex form of cancer that mostly affects children and adolescents. Pain is a common symptom for patients in OS which causes significant unhappiness and persistent aches. To date, there is minimal knowledge on the mechanisms underlying OS induced pain and few treatment options for patients. Previous genetic studies have demonstrated that the panel of four genes, artemin (ARTN), persephin (PSPN), glial cell line-derived neurotropic factor (GDNF), and neurturin (NRTN) are associated with the regulation of pain processing in OS and analgesic responses.

Methods: In the present study, by utilising a scRNA-seq OS dataset, we aimed to measure the gene expression levels of four pain related genes, and compare them between the different cell types in human OS tissues and cell lines.

Results: Within a complex and diverse range of cell types in OS tissues, including osteoblastic OS cells, carcinoma associated fibroblasts (CAFs), B cells, myeloid cells 1, myeloid cells 2, NK/T cells, plasmocytes, ARTN and NRTN genes had the highest expression in Osteoblastic OS cells, GDNF gene had a peak expression in carcinoma associated fibroblasts, and PSPN gene in endothelial cells. In addition, all four genes showed deferential pattern of expression in 16 OS cell lines.

Conclusion: Future studies should investigate the potential to target deferentially expressed pain-related genes in specific cell types of OS for therapeutic benefit to improve the quality of life for patients living with pain caused by OS.