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Cancer transcriptomic data are used extensively to interrogate the prognostic value of targeted genes, yet basic scientists and clinicians have predominantly relied on univariable survival analysis for this purpose. This method often fails to capture the full prognostic potential and contextual relevance of the genes under study, inadvertently omitting a group of genes we term univariable missed-opportunity prognostic (UMOP) genes. Recognizing the complexity of revealing multifaceted prognostic implications, especially when extending the analysis to include various covariates and thresholds, we present the Cancer Gene Prognosis Atlas (CGPA). This platform greatly enhances gene-centric biomarker research across cancer types by offering an interactive and user-friendly interface for highly customized, in-depth prognostic analysis. CGPA notably supports data-driven exploration of gene pairs and gene-hallmark relationships, elucidating key composite biological mechanisms like synthetic lethality and immunosuppression. It further expands its capabilities to assess multi-gene panels using both public and user-provided data, facilitating a seamless mechanism-to-machine analysis. Additionally, CGPA features a designated portal for discovering prognostic gene modules using curated cancer immunotherapy data. Ultimately, CGPA's comprehensive, accessible tools allow cancer researchers, including those without statistical expertise, to precisely investigate the prognostic landscape of genes, customizing the model to fit specific research hypotheses and enhancing biomarker discovery and validation through a synergy of mechanistic and data-driven strategies., Competing Interests: Conflict of interest: The authors declare no potential conflicts of interest.

This report presents the largest collection of gamma-delta T cell receptor (γδ TCR) reads in human cancer to date, analyzing about 11,000 patient tumor samples across 33 cancer types using the TRUST4 algorithm. Despite γδ T cells being a small fraction of the T cell population, they play a key role in both innate and adaptive immunity. Our comprehensive analysis reveals their significant presence across all cancer types, specifically highlighting the diverse spectrum and clonality patterns of their γδ receptors. This research highlights the complex roles of γδ T cells in tumor tissues and their potential as prognostic biomarkers. We also demonstrate the utility of T cell receptor gamma (TRG) and delta (TRD) gene expression values from standard RNA-seq data. Ultimately, our work establishes a fundamental resource for future tumor-infiltrating γδ T cell research and may facilitate the development of novel γδ-T-cell-based therapeutic strategies. Together, we demonstrate the strong diversity and prognostic potential of γδ T cells in multiple cancer types.

Background: Parkinson's disease is a neurological disorder that can cause gait disturbance, leading to mobility issues and falls. Early diagnosis and prediction of freeze episodes are essential for mitigating symptoms and monitoring the disease., Objective: This review aims to evaluate the use of artificial intelligence (AI)-based gait evaluation in diagnosing and managing Parkinson's disease, and to explore the potential benefits of this technology for clinical decision-making and treatment support., Methods: A thorough review of published literature was conducted to identify studies, articles, and research related to AI-based gait evaluation in Parkinson's disease., Results: AI-based gait evaluation has shown promise in preventing freeze episodes, improving diagnosis, and increasing motor independence in patients with Parkinson's disease. Its advantages include higher diagnostic accuracy, continuous monitoring, and personalized therapeutic interventions., Conclusion: AI-based gait evaluation systems hold great promise for managing Parkinson's disease and improving patient outcomes. They offer the potential to transform clinical decision-making and inform personalized therapies, but further research is needed to determine their effectiveness and refine their use., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Wu, Cao, Liang and Wu.)

Identifying novel and reliable prognostic biomarkers for predicting patient survival outcomes is essential for deciding personalized treatment strategies for diseases such as cancer. Numerous feature selection techniques have been proposed to address the high-dimensional problem in constructing prediction models. Not only does feature selection lower the data dimension, but it also improves the prediction accuracy of the resulted models by mitigating overfitting. The performances of these feature selection methods when applied to survival models, on the other hand, deserve further investigation. In this paper, we construct and compare a series of prediction-oriented biomarker selection frameworks by leveraging recent machine learning algorithms, including random survival forests, extreme gradient boosting, light gradient boosting and deep learning-based survival models. Additionally, we adapt the recently proposed prediction-oriented marker selection (PROMISE) to a survival model (PROMISE-Cox) as a benchmark approach. Our simulation studies indicate that boosting-based approaches tend to provide superior accuracy with better true positive rate and false positive rate in more complicated scenarios. For demonstration purpose, we applied the proposed biomarker selection strategies to identify prognostic biomarkers in different modalities of head and neck cancer data., (© The Author(s) 2023. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics.)

Cancer prognosis prediction is critical to the clinical decision-making process. Currently, the high availability of transcriptome datasets allows us to extract the gene modules with promising prognostic values. However, the biomarker identification is greatly challenged by tumor and patient heterogeneity. In this study, a framework of three subnetwork-based strategies is presented, incorporating hypothesis-driven, data-driven, and literature-based methods with informative visualization to prioritize candidate genes. By applying the proposed approaches to a head and neck squamous cell cancer (HNSCC) transcriptome dataset, we successfully identified multiple HNSCC-specific gene modules with improved prognostic values and mechanism information compared with the standard gene panel selection methods. The proposed framework is general and can be applied to any type of omics data. Overall, the study demonstrates and supports the use of the subnetwork-based approach for distilling reliable and biologically meaningful prognostic factors., Competing Interests: C.H.C. has received honoraria from Sanofi, Merck, and Brooklyn ImmunoTherapeutics, and Exelixis for serving in ad hoc scientific advisory boards. All other authors declare no conflict interest., (© 2022 The Authors.)

CD19 CAR T-cell therapy with axicabtagene ciloleucel (axi-cel) for relapsed or refractory (R/R) large B cell lymphoma (LBCL) may lead to durable remissions, however, prolonged cytopenias and infections may occur. In this single center retrospective study of 85 patients, we characterized immune reconstitution and infections for patients remaining in remission after axi-cel for LBCL. Prolonged cytopenias (those occurring at or after day 30 following infusion) were common with >= grade 3 neutropenia seen in 21/70 (30-0%) patients at day 30 and persisting in 3/31 (9-7%) patients at 1 year. B cells were undetectable in 30/34 (88-2%) patients at day 30, but were detected in 11/19 (57-9%) at 1 year. Median IgG levels reached a nadir at day 180. By contrast, CD4 T cells decreased from baseline and were persistently low with a median CD4 count of 155 cells/μl at 1 year after axi-cel (n=19, range 33 - 269). In total, 23/85 (27-1%) patients received IVIG after axi-cel, and 34/85 (40-0%) received G-CSF. Infections in the first 30 days occurred in 31/85 (36-5%) patients, of which 11/85 (12-9%) required intravenous antibiotics or hospitalization ("severe") and were associated with cytokine release syndrome (CRS), neurotoxicity, tocilizumab use, corticosteroid use, and bridging therapy on univariate analyses. After day 30, 7 severe infections occurred, with no late deaths due to infection. Prolonged cytopenias are common following axi-cel therapy for LBCL and typically recover with time. Most patients experience profound and prolonged CD4 T cell immunosuppression without severe infection.

Head and neck squamous cell carcinoma (HNSCC) is an aggressive epithelial malignancy characterized by frequent mutations and metastasis. Long noncoding RNAs (lncRNAs) have been implicated in tumorigenesis and serve as novel prognostic biomarkers in different cancers. To enhance our understanding of lncRNAs that may have biological significance in HNSCC and may serve as prognostic biomarkers, we globally profiled lncRNAs in HNSCC by analyzing the RNA-seq data from The Atlas of Noncoding RNAs in Cancer (TANRIC) database. Of 3576 lncRNAs, we identified 926 (higher-688, lower-238) lncRNAs with a 2-fold abundance difference among the forty HNSCC and paired adjacent normal tissue. We investigated differential abundance of lncRNAs based on TP53 mutation and p16 status. We found 133 lncRNAs to have differential abundance by 2-fold among the mutant vs wild-type TP53 samples, whereas among p16-negative vs positive samples, we identified 710 lncRNAs with the same criteria. Meanwhile, analysis of the 15 most abundant lncRNAs in the tumor samples identified five lncRNAs whose higher abundance was associated with poor overall patient survival. Among these five, higher abundance of LINC00460 associated with poor patient survival in an independent cohort of 82 HNSCC patients. To further evaluate the potential function of LINC00460 , we performed lncRNA-mRNAs co-expression analysis and found that higher abundance of LINC00460 associated with cancer-related biological pathways including EMT and other inflammatory response pathways. In summary, we report LINC00460 is more abundant in tumors compared to adjacent normal tissue and that it may serve as a potential prognostic biomarker in HNSCC., Competing Interests: None., (AJTR Copyright © 2020.)