A machine learning framework develops a DNA replication stress model for predicting clinical outcomes and therapeutic vulnerability in primary prostate cancer

Histology-resolved proteomics reveals distinct tumor and stromal profiles in low- and high-grade prostate cancer

BACKGROUND

Prostate cancer is one of the most frequently diagnosed cancers in men. Prostate tumor staging and disease aggressiveness are evaluated based on the Gleason scoring system, which is further used to direct clinical intervention. The Gleason scoring system provides an estimate of tumor aggressiveness through quantitation of the serum level of prostate specific antigen (PSA) and histologic assessment of Grade Group, determined by the Gleason Grade of the tumor specimen.

METHODS

To improve our understanding of the proteomic characteristics differentiating low- versus high-grade prostate cancer tumors, we performed a deep proteomic characterization of laser microdissected epithelial and stromal subpopulations from surgically resected tissue specimens from patients with Gleason 6 (n = 23 specimens from n = 15 patients) and Gleason 9 (n = 15 specimens from n = 15 patients) prostate cancer via quantitative high-resolution liquid chromatography-tandem mass spectrometry analysis.

RESULTS

In total, 789 and 295 grade-specific significantly altered proteins were quantified in the tumor epithelium and tumor-involved stroma, respectively. Benign epithelial and stromal populations were not inherently different between Gleason 6 versus Gleason 9 specimens. Notably, 598 proteins were exclusively significantly altered between Gleason 9 (but not Gleason 6) tumor-involved stroma and benign stroma, including several proteins involved in cholesterol biosynthesis and nucleotide metabolism.

CONCLUSIONS

Proteomic alterations between Gleason 6 versus Gleason 9 were exclusive to the disease microenvironment, observed in both the tumor epithelium and tumor-involved stroma. Further, the molecular alterations measured in the tumor-involved stroma from Gleason 9 cases relative to the benign stroma have unique significance in disease aggressiveness, development, and/or progression. Our data provide supportive evidence of a need for further investigations into targeting stromal reservoirs of cholesterol and/or deoxynucleoside triphosphates in PCa tumors and further highlight the necessity for independent examination of the TME epithelial and stromal compartments.

Investigating the Prognostic Role of Telomerase-Related Cellular Senescence Gene Signatures in Breast Cancer Using Machine Learning

Background: Telomeres and cellular senescence are critical biological processes implicated in cancer development and progression, including breast cancer, through their influence on genomic stability and modulation of the tumor microenvironment. Methods: This study integrated bulk RNA sequencing and single-cell RNA sequencing (scRNA-seq) data to establish a gene signature associated with telomere maintenance and cellular senescence for prognostic prediction in breast cancer. Telomere-related genes (TEGs) and senescence-associated genes were curated from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A comprehensive machine learning framework incorporating 101 algorithmic combinations across 10 survival modeling approaches, including random survival forests and ridge regression, was employed to develop a robust prognostic model. Results: A set of 19 key telomere- and senescence-related genes was identified as the optimal prognostic signature. The model demonstrated strong predictive accuracy and was successfully validated in multiple independent cohorts. Functional enrichment analyses indicated significant associations with immune responses and aging-related pathways. Single-cell transcriptomic analysis revealed marked cellular heterogeneity, identifying distinct subpopulations (fibroblasts and immune cells) with divergent risk scores and biological pathway activity. Additionally, pseudo-time trajectory analysis and intercellular communication mapping provided insights into the dynamic evolution of the tumor microenvironment. Immunohistochemical (IHC) validation using data from the Human Protein Atlas confirmed differential protein expression between normal and tumor tissues for several of the selected genes, reinforcing their biological relevance and clinical utility. Conclusions: This study presents a novel 19-gene telomere- and senescence-associated signature with strong prognostic value in breast cancer. These findings enhance our understanding of tumor heterogeneity and may inform precision oncology approaches and future therapeutic strategies.

Radiomics based on multiple machine learning methods for diagnosing early bone metastases not visible on CT images

OBJECTIVES

This study utilizes [99mTc]-methylene diphosphate (MDP) single photon emission computed tomography (SPECT) images as a reference standard to evaluate whether the integration of radiomics features from computed tomography (CT) and machine learning algorithms can identify microscopic early bone metastases. Additionally, we also determine the optimal machine learning approach.

MATERIALS AND METHODS

We retrospectively studied 63 patients with early bone metastasis from July 2020 to March 2023. The ITK-SNAP software was used to delineate early bone metastases and normal bone tissue in SPECT images of each patient, which were then registered onto CT images to outline the volume of interest (VOI). The VOI includes 63 early bone metastasis volumes and 63 normal bone tissue volumes. 126 VOIs were randomly distributed in a 7:3 ratio between the training and testing groups, and 944 radiomics features were extracted from every VOI. We established 20 machine learning models using 5 feature selection algorithms and 4 classification methods. Evaluate the performance of the model using the area under the receiver operating characteristic curve (AUC).

RESULTS

Most machine learning models demonstrated outstanding discriminative capacity, with AUCs higher than 0.70. Notably, the K-Nearest Neighbors (KNN) classifier exhibited significant performance improvement compared to the other four classifiers. Specifically, the model constructed utilizing eXtreme Gradient Boosting (XGBoost) feature selection method integrated with KNN classifier achieved the maximum AUC, which is 0.989 in the training set and 0.975 in the testing set.

CONCLUSIONS

Radiomics features integrated with machine learning methods can identify early bone metastases that are not visible on CT images. In our analysis, KNN is considered the optimal classification method.

Development and validation of the PHM-CPA model to predict in-hospital mortality for cirrhotic patients with acute kidney injury

BACKGROUND

The presence of acute kidney injury (AKI) significantly increases in-hospital mortality risk for cirrhotic patients. Early prognosis prediction for these patients is crucial. We aimed to develop and validate a machine learning model for in-hospital mortality prediction for cirrhotic patients with AKI.

METHODS

Data from cirrhotic patients with AKI hospitalized at the First Affiliated Hospital of Zhejiang University between January 1, 2013, and December 31, 2020 were used to train and validate an extreme Gradient Boosting model to predict in-hospital mortality risk. The Boruta algorithm was used for variable selection. The optimal model was selected and named as PHM-CPA (Prediction of in-Hospital Mortality for Cirrhotic Patients with AKI). The PHM-CPA model was then externally validated in patients from eICU Collaborative Research Database (eICU-CRD) and Medical Information Mart for Intensive Care III dataset (MIMIC). The predictive performance of PHM-CPA model was compared with that of logistic regression (LR) model and 25 previously reported models.

RESULTS

A total of 519 cirrhotic patients with AKI were enrolled in model training cohort, of whom 118 (23%) died during hospitalization. Fifteen variables from common laboratory tests were selected to develop the PHM-CPA model. The PHM-CPA model achieved an AUROC of 0.816 (95% CI, 0.763-0.861) in the internal validation cohort and 0.787 (95% CI, 0.745-0.830) in the external validation cohort. The PHM-CPA model consistently outperformed the LR model and 25 previously reported models.

CONCLUSION

We developed and validated the PHM-CPA model, comprising readily available clinical variables, which demonstrated superior performance and calibration in predicting in-hospital mortality for cirrhotic patients with AKI.

Exploring the molecular interface of gene expression dynamics and prostate cancer susceptibility in response to HBCD exposure

Hexabromocyclododecane (HBCD), a brominated flame retardant, is linked to various health implications, including prostate cancer. This study explored the molecular mechanisms and potential biomarkers associated with HBCD exposure using data from the Comparative Toxicogenomics Database (CTD) and The Cancer Genome Atlas (TCGA). A total of 7,147 differentially expressed genes (DEGs) and 46 differentially expressed miRNAs were identified, with significant enrichment in cancer-related pathways and xenobiotic metabolism. Protein-protein interaction (PPI) network construction and enrichment analyses revealed four hub genes: DNAJC12, PKMYT1, RRM2, and SLC12A5. These genes displayed notable expression changes in response to HBCD exposure and were strongly correlated with survival outcomes in prostate cancer patients, as demonstrated by Cox regression and ROC curve analyses. Additionally, miRNA correlation analyses indicated robust positive associations, highlighting a coordinated regulatory network. Experimental expression analyses on HBCD-treated cell lines further validated these findings. This study sheds light on the significant impact of HBCD on gene and miRNA expression in prostate cancer, emphasizing the potential of the identified hub genes and miRNAs as prognostic biomarkers and therapeutic targets. By elucidating the pathways and regulatory networks influenced by HBCD, the findings provide a foundation for developing strategies to mitigate its carcinogenic effects and improve outcomes for prostate cancer patients.

Dynamics of spindle assembly and position checkpoints: Integrating molecular mechanisms with computational models

Mitotic checkpoints orchestrate cell division through intricate molecular networks that ensure genomic stability. While experimental research has uncovered key aspects of checkpoint function, the complexity of protein interactions and spatial dynamics necessitates computational modeling for a deeper, system-level understanding. This review explores mathematical frameworks-from ordinary differential equations to stochastic simulations, which reveal checkpoint dynamics across multiple scales, encompassing models ranging from simple protein interactions to whole-system simulations with thousands of parameters. These approaches have elucidated fundamental properties, including bistable switches driving spindle assembly checkpoint (SAC) activation, spatial organization principles underlying spindle position checkpoint (SPOC) signaling, and critical system-level features ensuring checkpoint robustness. This study evaluates diverse modeling approaches, from rule-based models to chemical organization theory, highlighting their successful application in predicting protein localization patterns and checkpoint response dynamics validated through live-cell imaging. Contemporary challenges persist in integrating spatial and temporal scales, refining parameter estimation, and enhancing spatial modeling fidelity. However, recent advances in single-molecule imaging, data-driven algorithms, and machine learning techniques, particularly deep learning for parameter optimization, present transformative opportunities for improving model accuracy and predictive power. By bridging molecular mechanisms with system-level behaviors through validated computational frameworks, this review offers a comprehensive perspective on the mathematical modeling of cell cycle control, with practical implications for cancer research and therapeutic development.

The Potential Biological Roles and Clinical Significance of Anaphase-Promoting Complex Subunit 1 in Colorectal Cancer

BackgroundAnaphase-promoting complex subunit 1 (ANAPC1) is a regulator of cellular mitosis and an important factor in tumorigenesis. To date, a comprehensive assessment of the potential role, biological behaviours, and clinical significance of ANAPC1 in colorectal cancer (CRC) is still lacking.Materials and methodsThis study integrated 2329 mRNA expression data, single-cell RNA sequencing (scRNA-seq), and internal immunohistochemistry of 416 tissue samples to comprehensively evaluate the abnormal expression pattern of ANAPC1 in CRC. It also incorporated evidence from immune infiltration analysis, functional enrichment analysis, and weighted gene co-expression network analysis to explore the biological behaviour of ANAPC1 in CRC. In addition, in vitro cell biology experiments such as real-time polymerase chain reaction (RT-PCR), western blot (WB), cholecystokinin 8 (CCK-8), wound healing, cell cycle, and apoptosis assays were conducted to verify the potential effect of ANAPC1 on CRC cells.ResultsANAPC1 mRNA was significantly overexpressed in CRC tissue (SMD = 2.07, 95% CI 1.59-2.55, P < .05) and malignant epithelial cells (P < .05). Validation at the protein level similarly confirmed the overexpression of ANAPC1 in CRC tissue (P < .05). ANAPC1 in CRC may play a role in abnormal ribosome biogenesis, DNA replication, ATP-dependent activity acting on DNA, nuclear division, chromosome segregation, and other pathways. In vitro experiments demonstrated that HCT-116 cells with ANAPC1 knockdown had reduced proliferation and migration abilities, increased cell apoptosis rate, and altered cell cycle distribution. In addition, CRC patients with low ANAPC1 expression were more likely to benefit from treatment with immune checkpoint inhibitors. ANAPC1 was significantly downregulated in malignant epithelial cells of CRC treated with PD-1 inhibitors (P < .05).ConclusionANAPC1 may have a positive impact on the development of CRC by being involved in pathways related to DNA replication, chromosome segregation, and ribosomes.

Comprehensive analysis and validation of TP73 as a biomarker for calcium oxalate nephrolithiasis using machine learning and in vivo and in vitro experiments

Calcium oxalate (CaOx) nephrolithiasis constitutes approximately 75% of nephrolithiasis cases, resulting from the supersaturation and deposition of CaOx crystals in renal tissues. Despite their prevalence, precise biomarkers for CaOx nephrolithiasis are lacking. With advances in high-throughput sequencing, we aimed to identify biomarkers of CaOx nephrolithiasis by combining two CaOx nephrolithiasis datasets (GSE73680 and GSE117518). Utilizing weighted gene co-expression network analysis (WGCNA) and four machine learning, we identified six hub genes (DLK2, BHLHA15, C12orf5, ICMT, LOXHD1, and TP73) as potential biomarkers. Additionally, CIBERSORT immune infiltration analysis suggested that these core genes may influence immune cell recruitment and infiltration in CaOx nephrolithiasis. Then, TP73 emerged as a significant hub gene in CaOx nephrolithiasis via receiver operating characteristic (ROC) analysis (AUC = 0.885). Furthermore, the role of TP73 was validated in CaOx nephrolithiasis rat models induced by 1% ethylene glycol, as well as clinical samples and renal tubular epithelial cell models treated with 1 mM oxalate. Immunohistochemistry, RNA-Sequencing, and RT-qPCR experiments demonstrated an increased expression of TP73 in CaOx nephrolithiasis rat models and clinical samples. After transfection with TP73 lentivirus, CCK-8 assays suggested that TP73 could inhibit the proliferation of HK-2 and NRK-52E cells. In oxalate-induced cell models, dihydroethidium staining and flow cytometry apoptosis assays indicated that TP73 could enhance ROS levels and cell apoptosis. In summary, our study preliminarily identified TP73 as a diagnostic biomarker and elucidated the promoting role of TP73 in CaOx nephrolithiasis, providing a deeper understanding of the clinical diagnosis and pathogenesis.

Bulk and single-cell RNA sequencing analyses coupled with multiple machine learning to develop a glycosyltransferase associated signature in colorectal cancer

BACKGROUND

This study aims to identify key glycosyltransferases (GTs) in colorectal cancer (CRC) and establish a robust prognostic signature derived from GTs.

METHODS

Utilizing the AUCell, UCell, singscore, ssgsea, and AddModuleScore algorithms, along with correlation analysis, we redefined genes related to GTs in CRC at the single-cell RNA level. To improve risk model accuracy, univariate Cox and lasso regression were employed to discover a more clinically subset of GTs in CRC. Subsequently, the efficacy of seven machine learning algorithms for CRC prognosis was assessed, focusing on survival outcomes through nested cross-validation. The model was then validated across four independent external cohorts, exploring variations in the tumor microenvironment (TME), response to immunotherapy, mutational profiles, and pathways of each risk group. Importantly, we identified potential therapeutic agents targeting patients categorized into the high-GARS group.

RESULTS

In our research, we classified CRC patients into distinct subgroups, each exhibiting variations in prognosis, clinical characteristics, pathway enrichments, immune infiltration, and immune checkpoint genes expression. Additionally, we established a Glycosyltransferase-Associated Risk Signature (GARS) based on machine learning. GARS surpasses traditional clinicopathological features in both prognostic power and survival prediction accuracy, and it correlates with higher malignancy levels, providing valuable insights into CRC patients. Furthermore, we explored the association between the risk score and the efficacy of immunotherapy.

CONCLUSION

A prognostic model based on GTs was developed to forecast the response to immunotherapy, offering a novel approach to CRC management.

Research and application of omics and artificial intelligence in cancer

Cancer has a high incidence and lethality rate, which is a significant threat to human health. With the development of high-throughput technologies, different types of cancer genomics data have been accumulated, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics. A comprehensive analysis of various omics data is needed to understand the underlying mechanisms of tumor development. However, integrating such a massive amount of data is one of the main challenges today. Artificial intelligence (AI) techniques such as machine learning are now becoming practical tools for analyzing and understanding multi-omics data on diseases. Enabling great optimization of existing research paradigms for cancer screening, diagnosis, and treatment. In addition, intelligent healthcare has received widespread attention with the development of healthcare informatization. As an essential part of innovative healthcare, practical, intelligent prognosis analysis and personalized treatment for cancer patients are also necessary. This paper introduces the advanced multi-omics data analysis technology in recent years, presents the cases and advantages of the combination of both omics data and AI applied to cancer diseases, and finally briefly describes the challenges faced by multi-omics analysis and AI at the current stage, aiming to provide new perspectives for oncology research and the possibility of personalized cancer treatment.

Machine learning-based integration develops a mitophagy-related lncRNA signature for predicting the progression of prostate cancer: a bioinformatic analysis

Prostate cancer remains a complex and challenging disease, necessitating innovative approaches for prognosis and therapeutic guidance. This study integrates machine learning techniques to develop a novel mitophagy-related long non-coding RNA (lncRNA) signature for predicting the progression of prostate cancer. Leveraging the TCGA-PRAD dataset, we identify a set of four key lncRNAs and formulate a riskscore, revealing its potential as a prognostic indicator. Subsequent analyses unravel the intricate connections between riskscore, immune cell infiltration, mutational landscapes, and treatment outcomes. Notably, the pan-cancer exploration of YEATS2-AS1 highlights its pervasive impact, demonstrating elevated expression across various malignancies. Furthermore, drug sensitivity predictions based on riskscore guide personalized chemotherapy strategies, with drugs like Carmustine and Entinostat showing distinct suitability for high and low-risk group patients. Regression analysis exposes significant correlations between the mitophagy-related lncRNAs, riskscore, and key mitophagy-related genes. Molecular docking analyses reveal promising interactions between Cyclophosphamide and proteins encoded by these genes, suggesting potential therapeutic avenues. This comprehensive study not only introduces a robust prognostic tool but also provides valuable insights into the molecular intricacies and potential therapeutic interventions in prostate cancer, paving the way for more personalized and effective clinical approaches.

Computer-aided drug discovery strategies for novel therapeutics for prostate cancer leveraging next-generating sequencing data

INTRODUCTION

Prostate cancer (PC) is the most common malignancy and accounts for a significant proportion of cancer deaths among men. Although initial therapy success can often be observed in patients diagnosed with localized PC, many patients eventually develop disease recurrence and metastasis. Without effective treatments, patients with aggressive PC display very poor survival. To curb the current high mortality rate, many investigations have been carried out to identify efficacious therapeutics. Compared to de novo drug designs, computational methods have been widely employed to offer actionable drug predictions in a fast and cost-efficient way. Particularly, powered by an increasing availability of next-generation sequencing molecular profiles from PC patients, computer-aided approaches can be tailored to screen for candidate drugs.

AREAS COVERED

Herein, the authors review the recent advances in computational methods for drug discovery utilizing molecular profiles from PC patients. Given the uniqueness in PC therapeutic needs, they discuss in detail the drug discovery goals of these studies, highlighting their translational values for clinically impactful drug nomination.

EXPERT OPINION

Evolving molecular profiling techniques may enable new perspectives for computer-aided approaches to offer drug candidates for different tumor microenvironments. With ongoing efforts to incorporate new compounds into large-scale high-throughput screens, the authors envision continued expansion of drug candidate pools.

Architecting the metabolic reprogramming survival risk framework in LUAD through single-cell landscape analysis: three-stage ensemble learning with genetic algorithm optimization

Recent studies have increasingly revealed the connection between metabolic reprogramming and tumor progression. However, the specific impact of metabolic reprogramming on inter-patient heterogeneity and prognosis in lung adenocarcinoma (LUAD) still requires further exploration. Here, we introduced a cellular hierarchy framework according to a malignant and metabolic gene set, named malignant & metabolism reprogramming (MMR), to reanalyze 178,739 single-cell reference profiles. Furthermore, we proposed a three-stage ensemble learning pipeline, aided by genetic algorithm (GA), for survival prediction across 9 LUAD cohorts (n = 2066). Throughout the pipeline of developing the three stage-MMR (3 S-MMR) score, double training sets were implemented to avoid over-fitting; the gene-pairing method was utilized to remove batch effect; GA was harnessed to pinpoint the optimal basic learner combination. The novel 3 S-MMR score reflects various aspects of LUAD biology, provides new insights into precision medicine for patients, and may serve as a generalizable predictor of prognosis and immunotherapy response. To facilitate the clinical adoption of the 3 S-MMR score, we developed an easy-to-use web tool for risk scoring as well as therapy stratification in LUAD patients. In summary, we have proposed and validated an ensemble learning model pipeline within the framework of metabolic reprogramming, offering potential insights for LUAD treatment and an effective approach for developing prognostic models for other diseases.

Machine learning-based investigation of regulated cell death for predicting prognosis and immunotherapy response in glioma patients

Glioblastoma is a highly aggressive and malignant type of brain cancer that originates from glial cells in the brain, with a median survival time of 15 months and a 5-year survival rate of less than 5%. Regulated cell death (RCD) is the autonomous and orderly cell death under genetic control, controlled by precise signaling pathways and molecularly defined effector mechanisms, modulated by pharmacological or genetic interventions, and plays a key role in maintaining homeostasis of the internal environment. The comprehensive and systemic landscape of the RCD in glioma is not fully investigated and explored. After collecting 18 RCD-related signatures from the opening literature, we comprehensively explored the RCD landscape, integrating the multi-omics data, including large-scale bulk data, single-cell level data, glioma cell lines, and proteome level data. We also provided a machine learning framework for screening the potentially therapeutic candidates. Here, based on bulk and single-cell sequencing samples, we explored RCD-related phenotypes, investigated the profile of the RCD, and developed an RCD gene pair scoring system, named RCD.GP signature, showing a reliable and robust performance in predicting the prognosis of glioblastoma. Using the machine learning framework consisting of Lasso, RSF, XgBoost, Enet, CoxBoost and Boruta, we identified seven RCD genes as potential therapeutic targets in glioma and verified that the SLC43A3 highly expressed in glioma grades and glioma cell lines through qRT-PCR. Our study provided comprehensive insights into the RCD roles in glioma, developed a robust RCD gene pair signature for predicting the prognosis of glioma patients, constructed a machine learning framework for screening the core candidates and identified the SLC43A3 as an oncogenic role and a prediction biomarker in glioblastoma.

Genomics and Artificial Intelligence: Prostate Cancer

Artificial intelligence (AI) is revolutionizing prostate cancer genomics research. By leveraging machine learning and deep learning algorithms, researchers can rapidly analyze vast genomic datasets to identify patterns and correlations that may be missed by traditional methods. These AI-driven insights can lead to the discovery of novel biomarkers, enhance the accuracy of diagnosis, and predict disease progression and treatment response. As such, AI is becoming an indispensable tool in the pursuit of personalized medicine for prostate cancer.

Key Proteins of Replication Stress Response and Cell Cycle Control as Cancer Therapy Targets

Replication stress (RS) is a characteristic state of cancer cells as they tend to exchange precision of replication for fast proliferation and increased genomic instability. To overcome the consequences of improper replication control, malignant cells frequently inactivate parts of their DNA damage response (DDR) pathways (the ATM-CHK2-p53 pathway), while relying on other pathways which help to maintain replication fork stability (ATR-CHK1). This creates a dependency on the remaining DDR pathways, vulnerability to further destabilization of replication and synthetic lethality of DDR inhibitors with common oncogenic alterations such as mutations of TP53, RB1, ATM, amplifications of MYC, CCNE1 and others. The response to RS is normally limited by coordination of cell cycle, transcription and replication. Inhibition of WEE1 and PKMYT1 kinases, which prevent unscheduled mitosis entry, leads to fragility of under-replicated sites. Recent evidence also shows that inhibition of Cyclin-dependent kinases (CDKs), such as CDK4/6, CDK2, CDK8/19 and CDK12/13 can contribute to RS through disruption of DNA repair and replication control. Here, we review the main causes of RS in cancers as well as main therapeutic targets-ATR, CHK1, PARP and their inhibitors.

Sustained pigmentation causes DNA damage and invokes translesion polymerase Polκ for repair in melanocytes

Melanin protects skin cells from ultraviolet radiation-induced DNA damage. However, intermediates of eumelanin are highly reactive quinones that are potentially genotoxic. In this study, we systematically investigate the effect of sustained elevation of melanogenesis and map the consequent cellular repair response of melanocytes. Pigmentation increases γH2AX foci, DNA abasic sites, causes replication stress and invokes translesion polymerase Polκ in primary human melanocytes, as well as mouse melanoma cells. Confirming the causal link, CRISPR-based genetic ablation of tyrosinase results in depigmented cells with low Polκ levels. During pigmentation, Polκ activates replication stress response and keeps a check on uncontrolled proliferation of cells harboring melanin-damaged DNA. The mutational landscape observed in human melanoma could in part explain the error-prone bypass of DNA lesions by Polκ, whose absence would lead to genome instability. Thereby, translesion polymerase Polκ is a critical response of pigmenting melanocytes to combat melanin-induced DNA alterations. Our study illuminates the dark side of melanin and identifies (eu)melanogenesis as a key missing link between tanning response and mutagenesis, mediated via the necessary evil translesion polymerase, Polκ.

A DNA Replication Stress-Based Prognostic Model for Lung Adenocarcinoma

Tumor cells endure continuous DNA replication stress, which opens the way to cancer development. Despite previous research, the prognostic implications of DNA replication stress on lung adenocarcinoma (LUAD) have yet to be investigated. Here, we aimed to investigate the potential of DNA replication stress-related genes (DNARSs) in predicting the prognosis of individuals with LUAD. Differentially expressed genes (DEGs) originated from the TCGA-LUAD dataset, and we constructed a 10-gene LUAD prognostic model based on DNARSs-related DEGs (DRSDs) using Cox regression analysis. The receiver operating characteristic (ROC) curve demonstrated excellent predictive capability for the LUAD prognostic model, while the Kaplan-Meier survival curve indicated a poorer prognosis in a high-risk (HR) group. Combined with clinical data, the Riskscore was found to be an independent predictor of LUAD prognosis. By incorporating Riskscore and clinical data, we developed a nomogram that demonstrated a capacity to predict overall survival and exhibited clinical utility, which was validated through the calibration curve, ROC curve, and decision curve analysis curve tests, confirming its effectiveness in prognostic evaluation. Immune analysis revealed that individuals belonging to the low-risk (LR) group exhibited a greater abundance of immune cell infiltration and higher levels of immune function. We calculated the immunopheno score and TIDE scores and tested them on the IMvigor210 and GSE78220 cohorts and found that individuals categorized in the LR group exhibited a higher likelihood of deriving therapeutic benefits from immunotherapy intervention. Additionally, we predicted that patients classified in the HR group would demonstrate enhanced sensitivity to Docetaxel using anti-tumor drugs. To summarize, we successfully developed and validated a prognostic model for LUAD by incorporating DNA replication stress as a key factor.