Development Of Risk Model Research Articles

Development of a Macrophage-Related Risk Model for Metastatic Melanoma.

As a metastasis-prone malignancy, the metastatic form and location of melanoma seriously affect its prognosis. Although effective surgical methods and targeted drugs are available to enable the treatment of carcinoma in situ, for metastatic tumors, the diagnosis, prognosis assessment and development of immunotherapy are still pending. This study aims to integrate multiple bioinformatics approaches to identify immune-related molecular targets viable for the treatment and prognostic assessment of metastatic melanoma, thus providing new strategies for its use as an immunotherapy. Immunoinfiltration analysis revealed that M1-type macrophages have significant infiltration differences in melanoma development and metastasis. In total, 349 genes differentially expressed in M1-type macrophages and M2-type macrophages were extracted from the MSigDB database. Then we derived an intersection of these genes and 1111 melanoma metastasis-related genes from the GEO database, and 31 intersected genes identified as melanoma macrophage immunomarkers (MMIMs) were obtained. Based on MMIMs, a risk model was constructed using the Lasso algorithm and regression analysis, which contained 10 genes (NMI, SNTB2, SLC1A4, PDE4B, CLEC2B, IFI27, COL1A2, MAF, LAMP3 and CCDC69). Patients with high+ risk scores calculated via the model have low levels of infiltration by CD8+ T cells and macrophages, which implies a poor prognosis for patients with metastatic cancer. DCA decision and nomogram curves verify the high sensitivity and specificity of this model for metastatic cancer patients. In addition, 28 miRNAs, 90 transcription factors and 29 potential drugs were predicted by targeting the 10 MMIMs derived from this model. Overall, we developed and validated immune-related prognostic models, which accurately reflected the prognostic and immune infiltration characteristics of patients with melanoma metastasis. The 10 MMIMs may also be prospective targets for immunotherapy.

Treating Noise and Anomalies in Vehicle Trajectories From an Experiment With a Swarm of Drones

Unmanned aerial systems, known as “drones”, are relatively new in collecting traffic data. Data from drone videography can have potential applications for traffic research. Drones can record the vehicles from their aerial point-of-view and provide their naturalistic driving behavior. Processing raw data from drones to remove noise and anomalies is crucial to ensure that the data are fit for subsequent applications, e.g., the development of traffic flow or crash risk models. This study uses a part of the pNEUMA dataset, a large dataset with almost half a million trajectories captured by a swarm of drones over Athens, Greece. This novel dataset offers an opportunity to analyze the data attributes and treat the noise and outliers in the data. We use a combination of smoothing filters and Extreme Gradient Boosting with adaptive regularization to process the speed and acceleration profiles of the vehicle trajectories in the dataset. Our approach can help prospective data users treat this or similar trajectory datasets alternatively to applying manual thresholds and assist in accelerating research in microscopic traffic analysis.

Mining Prognostic Biomarkers of Thyroid Cancer Patients Based on the Immune-Related Genes and Development of a Reliable Prognostic Risk Model.

Tumor immunity serves an essential role in the occurrence and development of thyroid cancer (THCA). The aim of this study is to establish an immune-related prognostic model for THCA patients by using immune-related genes (IRGs). Wilcox test was used to screen the differentially expressed immune-related genes (DEIRGs) in THCA and normal tissues, then the DEIRGs related to prognosis were identified using univariate Cox regression analysis. According to The Cancer Genome Atlas (TCGA) cohort, we developed a least absolute shrinkage and selection operator (LASSO) regression prognostic model and performed validation analyses regard to the predictive value of the model in internal (TCGA) and external (International Cancer Genome Consortium) cohorts respectively. Finally, we analyzed the correlation among the prognostic model, clinical variables, and immune cell infiltration. Eighty-two of 2,498 IRGs were differentially expressed between THCA and normal tissues, and 18 of them were related to prognosis. LASSO Cox regression analysis identified seven DEIRGs with the greatest prognostic value to construct the prognostic model. The risk model showed high predictive value for the survival of THCA in two independent cohorts. The risk score according to the risk model was positively associated with poor survival and the infiltration levels of immune cells, it can evaluate the prognosis of THCA patients independent of any other clinicopathologic feature. The prognostic value and genetic alternations of seven risk genes were evaluated separately. Our study established and verified a dependable prognostic model associated with immune for THCA, both the identified IRGs and immune-related risk model were clinically significant, which is conducive to promoting individualized immunotherapy against THCA.

Development and validation of competitive risk model for older women with metaplastic breast cancer

BackgroundMetaplastic breast cancer (MpBC) is a rare histological subtype of breast cancer. This study aims to establish a competitive risk model for older women with MpBC to predict patients’ survival accurately.MethodsData on patients diagnosed with MpBC from 2010 to 2019 are from the Surveillance, Epidemiology and End Results (SEER) program in the United States. All patients were randomly assigned to the training set and validation set. The proportional sub-distribution risk model was used in the training set to analyze the risk factors affecting patient death. Based on the risk factors for cancer-specific mortality (CSM) in patients, we constructed a competitive risk model to predict patients’ 1-, 3-, and 5-year cancer-specific survival. Then we used the concordance index (C-index), the calibration curve and the area under the receiver operating characteristic curve (AUC) to validate the discrimination and accuracy of the model.ResultsOne thousand, four hundred twelve older women with MpBC were included in this study. Age, T stage, N stage, M stage, tumor size, surgery and radiotherapy were risk factors for CSM. We established a competitive risk model to predict 1-, 3-, and 5-year cancer-specific survival in older women with MpBC. The C-index of the model was 0.792 in the training set and 0.744 in the validation set. The calibration curves in the training and validation sets showed that the model’s predicted values were almost consistent with the actual observed values. The AUC results show that the prediction model has good accuracy.ConclusionWe developed a competitive risk model based on these risk factors to predict cancer-specific survival in older women with MpBC. The validation results of the model show that it is a very effective and reliable prediction tool. This predictive tool allows doctors and patients to make individualized clinical decisions.

Development of a novel copper metabolism-related risk model to predict prognosis and tumor microenvironment of patients with stomach adenocarcinoma.

Background: Stomach adenocarcinoma (STAD) is the fourth highest cause of cancer mortality worldwide. Alterations in copper metabolism are closely linked to cancer genesis and progression. We aim to identify the prognostic value of copper metabolism-related genes (CMRGs) in STAD and the characteristic of the tumor immune microenvironment (TIME) of the CMRG risk model. Methods: CMRGs were investigated in the STAD cohort from The Cancer Genome Atlas (TCGA) database. Then, the hub CMRGs were screened out with LASSO Cox regression, followed by the establishment of a risk model and validated by GSE84437 from the Expression Omnibus (GEO) database. The hub CMRGs were then utilized to create a nomogram. TMB (tumor mutation burden) and immune cell infiltration were investigated. To validate CMRGs in immunotherapy response prediction, immunophenoscore (IPS) and IMvigor210 cohort were employed. Finally, data from single-cell RNA sequencing (scRNA-seq) was utilized to depict the properties of the hub CMRGs. Results: There were 75 differentially expressed CMRGs identified, 6 of which were linked with OS. 5 hub CMRGs were selected by LASSO regression, followed by construction of the CMRG risk model. High-risk patients had a shorter life expectancy than those low-risk. The risk score independently predicted STAD survival through univariate and multivariate Cox regression analyses, with ROC calculation generating the highest results. This risk model was linked to immunocyte infiltration and showed a good prediction performance for STAD patients' survival. Furthermore, the high-risk group had lower TMB and somatic mutation counters and higher TIDE scores, but the low-risk group had greater IPS-PD-1 and IPS-CTLA4 immunotherapy prediction, indicating a higher immune checkpoint inhibitors (ICIs) response, which was corroborated by the IMvigor210 cohort. Furthermore, those with low and high risk showed differential susceptibility to anticancer drugs. Based on CMRGs, two subclusters were identified. Cluster 2 patients had superior clinical results. Finally, the copper metabolism-related TIME of STAD was concentrated in endothelium, fibroblasts, and macrophages. Conclusion: CMRG is a promising biomarker of prognosis for patients with STAD and can be used as a guide for immunotherapy.

How to Gain Confidence in the Results of Internal Risk Models? Approaches and Techniques for Validation

The development of risk models for managing portfolios of financial institutions and insurance companies requires, both from the regulatory and management points of view, a strong validation of the quality of the results provided by internal risk models. In Solvency II, for instance, regulators ask for independent validation reports from companies who apply for the approval of their internal models. We analyze here various ways to enable management and regulators to gain confidence in the quality of models. It all starts by ensuring a good calibration of the risk models and the dependencies between the various risk drivers. Then, by applying stress tests to the model and various empirical analyses, in particular the probability integral transform, we can build a full and credible framework to validate risk models.

Development and validation of a simple general population lung cancer risk model including AHRR-methylation

IntroductionScreening reduces lung cancer mortality of high-risk populations. Currently proposed screening eligibility criteria only identify half of those individuals, who later develop lung cancer. This study aimed to develop and validate a sensitive and simple model for predicting 10-year lung cancer risk. MethodsUsing the 1991–94 examination of The Copenhagen City Heart Study in Denmark, 6,820 former or current smokers from the general population were followed for lung cancer within 10 years after examination. Logistic regression of baseline variables (age, sex, education, chronic obstructive pulmonary disease, family history of lung cancer, smoking status and cumulative smoking, secondhand smoking, occupational exposures to dust and fume, body mass index, lung function, plasma C-reactive protein, and AHRR(cg05575921) methylation) identified the best predictive model. The model was validated among 3,740 former or current smokers from the 2001-03 examination, also followed for 10 years. A simple risk chart was developed with Poisson regression. ResultsAge, sex, education, smoking status, cumulative smoking, and AHRR(cg05575921) methylation identified 65 of 88 individuals who developed lung cancer in the validation cohort. The highest risk group, consisting of less educated men aged >65 with current smoking status and cumulative smoking >20 pack-years, had absolute 10-year risks varying from 4% to 16% by AHRR(cg05575921) methylation. ConclusionA simple risk chart including age, sex, education, smoking status, cumulative smoking, and AHRR(cg05575921) methylation, identifies individuals with 10-year lung cancer risk from below 1% to 16%. Including AHRR(cg05575921) methylation in the eligibility criteria for screening identifies smokers who would benefit the most from screening.

Metabolic profiling of COVID-19 severity and mortality: A targeted metabolomics approach

Abstract The analysis of metabolites in response to SARS-CoV-2 infection provides a snapshot of the endogenous host metabolism and its role in shaping the interaction with SARS-CoV-2. In this study, using a targeted metabolomics approach, the metabolic signatures of severity and mortality were studied in COVID-19 patients. Our analysis revealed survival and severity-associated shifts in the metabolome of COVID-19 patients, where amino acids, particularly tryptophan and arginine, and fatty acid metabolism were altered in severe patients as compared to mild and asymptomatic cases. Herein, we also report alterations in the sums and ratios of several essential amino acids as well as kynurenine to tryptophan ratio, which is an indicator of indoleamine-2,3-dioxygenase (IDO) activity. Our findings suggest that metabolites of creatine including symmetric dimethylarginine, asymmetric dimethylarginine, and 1-methylhistidine can be used as biomarkers to identify patients at risk of poor outcomes in COVID-19, and that lower levels of these metabolites are associated with higher survival probabilities. Additionally, elevated levels of short chain acylcarnitines and carnitine palmitoyltransferase 1 and 2 enzymes indicators have been observed in critically ill COVID-19 patients, indicating their potential usefulness as predictors of hospital mortality and poor survival outcome, likely due to increased demand for energy, inflammation, and mitochondrial dysfunction. The metabolome analysis of COVID-19 patients can improve our understanding of disease severity by providing insight into the relationship between metabolites and disease progression, which can lead to the development of potential therapeutics and clinical risk models. This work was supported by Emergency Response Grant ID: 252

Abstract 5411: Efficient development of prognostic tests for detecting cancer risk using proteomic technology

Abstract Background: Prognostic models for assessing future health outcomes can be developed using time-to-event (also known as “survival”) data. This methodology is ubiquitous in statistical literature and in the analysis of cancer outcomes, but its use in high-dimensional analyses tends to be limited as the methods are difficult to implement in a machine learning environment. Additionally, development of certified prognostic clinical tests using proteomic biomarkers for detecting future cancer risk can be time-consuming, prone to overfitting issues, and difficult to navigate. We demonstrate the utility of combining SomaScan® proteomic data with pipeline machine learning tools and survival analysis methodology to identify powerful and robust LDT-certifiable prognostic tests for assessing future risk of cancer. Methods: Data pipeline and analysis tools were developed using R. In addition to standard machine learning techniques, statistical methods include elastic net AFT models, subsampling survival techniques, and metrics for assessing predictive survival models. The pipeline takes the analyst from data processing and QC through identification of optimal models for prediction of clinical endpoints, and then through validation on a hold-out test set. The tools include an assessment of model robustness against sample handling issues, longitudinal stability, the impacts of assay noise on model performance, effects of putative interferents, and risk of failure during CLIA validation in the lab. We demonstrate the utility of the tools and methods for development of a lung cancer risk model. Results: Analysis time for validation of an optimal clinical model was reduced by at least 80%, resulting in the development of 7 LDT-certified tests within 3 years, including a test for lung cancer risk. Inclusion of methods that allow for subsampling and penalized regression using AFT models show improved predictive performance and identification of top features related to clinical endpoints. Conclusion: Not only are powerful, prognostic tests do-able, but they can be LDT certified in an efficient manner and made to be robust to real-life lab settings. Survival analysis in a machine-learning setting allow us to leverage proteomic technology in new ways, leading to tests that assess future cancer risk, which can be used for precision medicine applications. Citation Format: Yolanda Hagar, Leigh Alexander, Jessica Chadwick, Gargi Datta, Joe Gogain, Rachel Ostroff, Clare Paterson, Laura Sampson, Caleb Scheidel, Sama Shrestha, Amy Zhang, Michael Hinterberg. Efficient development of prognostic tests for detecting cancer risk using proteomic technology. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5411.

Development and Validation of a Prognostic Risk Model for Patients with Advanced Melanoma Treated with Immune Checkpoint Inhibitors.

Risk stratification tools for patients with advanced melanoma (AM) treated with immune checkpoint inhibitors (ICI) are lacking. We identified a new prognostic model associated with overall survival (OS). A total of 318 treatment naïve patients with AM receiving ICI were collected from a multi-centre retrospective cohort study. LASSO Cox regression identified independent prognostic factors associated with OS. Model validation was carried out on 500 iterations of bootstrapped samples. Harrel's C-index was calculated and internally validated to outline the model's discriminatory performance. External validation was carried out in 142 advanced melanoma patients receiving ICI in later lines. High white blood cell count (WBC), high lactate dehydrogenase (LDH), low albumin, Eastern Cooperative Oncology Group (ECOG) performance status ≥1, and the presence of liver metastases were included in the model. Patients were parsed into 3 risk groups: favorable (0-1 factors) OS of 52.9 months, intermediate (2-3 factors) OS 13.0 months, and poor (≥4 factors) OS 2.7 months. The C-index of the model from the discovery cohort was 0.69. External validation in later-lines (N = 142) of therapy demonstrated a c-index of 0.65. Liver metastases, low albumin, high LDH, high WBC, and ECOG≥1 can be combined into a prognostic model for AM patients treated with ICI.

External validation and updating of prediction models of bleeding risk in patients with cancer receiving anticoagulants

ObjectivePatients with cancer are at increased bleeding risk, and anticoagulants increase this risk even more. Yet, validated bleeding risk models for prediction of bleeding risk in patients with cancer are...

Biology of Pre-cancers and Opportunities for Cancer Interception - lesson from colorectal cancer susceptibility syndromes.

Hereditary gastrointestinal cancer is associated with molecular and neoplastic precursors which have revealed much about sporadic carcinogenesis. Therefore, an appreciation of constitutional and somatic events linked to these syndromes have provided a useful model for the development of risk models and preventative strategies. In this review we focus of two of the best characterised syndromes, Lynch syndrome (LS) and familial adenomatous polyposis (FAP). Our understanding of the neoplasia-immune interaction in LS has contributed to the development of immune mediated therapies including cancer preventing vaccines and immunotherapy for cancer precursors. Chemoprevention in LS with aspirin and non-steroidal anti-inflammatory drugs has also translated into clinical cancer, however the efficacy of such agents in FAP remains elusive when cancer is applied as an endpoint in trials rather than the use of 'indirect' endpoints such as polyp burden, and requires further elucidation of biological mechanisms in FAP. Finally, we review controversies in gastrointestinal surveillance for LS and FAP, including limitations and opportunities of upper and lower GI endoscopy in the prevention and early detection of cancer.

The role of lactate metabolism-related LncRNAs in the prognosis, mutation, and tumor microenvironment of papillary thyroid cancer.

Lactate, a byproduct of glucose metabolism, is primarily utilized for gluconeogenesis and numerous cellular and organismal life processes. Interestingly, many studies have demonstrated a correlation between lactate metabolism and tumor development. However, the relationship between long non-coding RNAs (lncRNAs) and lactate metabolism in papillary thyroid cancer (PTC) remains to be explored. Lactate metabolism-related lncRNAs (LRLs) were obtained by differential expression and correlation analyses, and the risk model was further constructed by least absolute shrinkage and selection operator analysis (Lasso) and Cox analysis. Clinical, immune, tumor mutation, and enrichment analyses were performed based on the risk model. The expression level of six LRLs was tested using RT-PCR. This study found several lncRNAs linked to lactate metabolism in both The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) datasets. Using Cox regression analysis, 303 lactate LRLs were found to be substantially associated with prognosis. Lasso was done on the TCGA cohort. Six LRLs were identified as independent predictive indicators for the development of a PTC prognostic risk model. The cohort was separated into two groups based on the median risk score (0.39717 -0.39771). Subsequently, Kaplan-Meier survival analysis and multivariate Cox regression analysis revealed that the high-risk group had a lower survival probability and that the risk score was an independent predictive factor of prognosis. In addition, a nomogram that can easily predict the 1-, 3-, and 5-year survival rates of PTC patients was established. Furthermore, the association between PTC prognostic factors and tumor microenvironment (TME), immune escape, as well as tumor somatic mutation status was investigated in high- and low-risk groups. Lastly, gene expression analysis was used to confirm the differential expression levels of the six LRLs. In conclusion, we have constructed a prognostic model that can predict the prognosis, mutation status, and TME of PTC patients. The model may have great clinical significance in the comprehensive evaluation of PTC patients.

Telomere-related genes as potential biomarkers to predict endometriosis and immune response: Development of a machine learning-based risk model.

Endometriosis (EM) is an aggressive, pleomorphic, and common gynecological disease. Its clinical presentation includes abnormal menstruation, dysmenorrhea, and infertility, which seriously affect the patient's quality of life. However, the pathogenesis underlying EM and associated regulatory genes are unknown. Telomere-related genes (TRGs) were uploaded from TelNet. RNA-sequencing (RNA-seq) data of EM patients were obtained from three datasets (GSE5108, GSE23339, and GSE25628) in the GEO database, and a random forest approach was used to identify telomere signature genes and build nomogram prediction models. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analysis were used to identify the pathways involved in the action of the signature genes. Finally, the CAMP database was used to screen drugs for potential use in EM treatment. Fifteen total genes were screened as EM-telomere differentially expressed genes. Further screening by machine learning obtained six genes as characteristic predictive of EM. Immuno-infiltration analysis of the telomeric genes showed that expressions including macrophages and natural killer cells were significantly higher in cluster A. Further enrichment analysis showed that the differential genes were mainly enriched in biological pathways like cell cycle and extracellular matrix. Finally, the Connective Map database was used to screen 11 potential drugs for EM treatment. TRGs play a crucial role in EM development, and are associated with immune infiltration and act on multiple pathways, including the cell cycle. Telomere signature genes can be valuable predictive markers for EM.

Development of an immune-related gene prognostic risk model and identification of an immune infiltration signature in the tumor microenvironment of colon cancer

BackgroundColon cancer is a common and highly malignant tumor. Its incidence is increasing rapidly with poor prognosis. At present, immunotherapy is a rapidly developing treatment for colon cancer. The aim of this study was to construct a prognostic risk model based on immune genes for early diagnosis and accurate prognostic prediction of colon cancer.MethodsTranscriptome data and clinical data were downloaded from the cancer Genome Atlas database. Immunity genes were obtained from ImmPort database. The differentially expressed transcription factors (TFs) were obtained from Cistrome database. Differentially expressed (DE) immune genes were identified in 473 cases of colon cancer and 41 cases of normal adjacent tissues. An immune-related prognostic model of colon cancer was established and its clinical applicability was verified. Among 318 tumor-related transcription factors, differentially expressed transcription factors were finally obtained, and a regulatory network was constructed according to the up-down regulatory relationship.ResultsA total of 477 DE immune genes (180 up-regulated and 297 down-regulated) were detected. We developed and validated twelve immune gene models for colon cancer, including SLC10A2, FABP4, FGF2, CCL28, IGKV1-6, IGLV6-57, ESM1, UCN, UTS2, VIP, IL1RL2, NGFR. The model was proved to be an independent prognostic variable with good prognostic ability. A total of 68 DE TFs (40 up-regulated and 23 down-regulated) were obtained. The regulation network between TF and immune genes was plotted by using TF as source node and immune genes as target node. In addition, Macrophage, Myeloid Dendritic cell and CD4+ T cell increased with the increase of risk score.ConclusionWe developed and validated twelve immune gene models for colon cancer, including SLC10A2, FABP4, FGF2, CCL28, IGKV1-6, IGLV6-57, ESM1, UCN, UTS2, VIP, IL1RL2, NGFR. This model can be used as a tool variable to predict the prognosis of colon cancer.

IDF2022-0987 Development of a Clinical Risk Model for Targeted Screening of Undiagnosed Diabetes in Asian Dental Settings

IDF2022-0987 Development of a Clinical Risk Model for Targeted Screening of Undiagnosed Diabetes in Asian Dental Settings

Prediction of thrombosis in polycythemia vera: Development and validation of a multiple factor-based prognostic score system

BackgroundThrombosis is an important cause of death in patients with polycythemia vera (PV). The conventional stratification of thrombosis may ignore some potential risk factors. ObjectivesThis study aimed to develop and validate a multiple factor-based prediction model of thrombosis for the 2016 World Health Organization-dened PV. MethodsClinical and next-generation sequencing data from 2 cohorts of patients with PV were analyzed. Multivariable Cox regression analyses were conducted for the identification of thrombotic risk factors and model development. ResultsThe study involved 372 patients in the training cohort and another 195 patients in the external validation cohort. Multivariable analyses indicated that age ≥60 years (hazard ratio [HR] 2.56, 95% CI 1.51-4.35, P < .001), cardiovascular risk factors (HR 4.22, 95% CI 2.00-8.92, P < .001), at least 1 high-risk mutation for thrombosis (mutations in DNMT3A, ASXL1, or BCOR/BCORL1) (HR 4.35, 95% CI 2.62-7.21, P < .001), and previous thrombosis (HR 5.93, 95% CI 3.29-10.68, P < .001) were independent risk factors of thrombosis. After assigning coefficient-weighted scores to each risk factor mentioned above, a multiple factor-based prognostic score system of thrombosis (MFPS-PV) was developed, classifying patients into low-risk, intermediate-risk, and high-risk groups. Patients in the 3 groups had notably different thrombosis-free survival rates (P < .001). The MFPS-PV outperformed the conventional model in discrimination power (C-statistic: 0.87 [95% CI 0.83-0.91] vs 0.80 [95% CI 0.74-0.86]). The MFPS-PV was well calibrated and remained consistent during external validation. ConclusionThe MFPS-PV, integrating genetic and clinical characteristics for the first time, shows excellent accuracy and utility for thrombosis prediction in WHO-defined PV.

Development and Validation of a Prognostic Risk Model Based on Nature Killer Cells for Serous Ovarian Cancer.

Nature killer (NK) cells are increasingly considered important in tumor microenvironment, but their role in predicting the prognosis of ovarian cancer has not been revealed. This study aimed to develop a prognostic risk model for ovarian cancer based on NK cells. Firstly, differentially expressed genes (DEGs) of NK cells were found by single-cell RNA-sequencing dataset analysis. Based on six NK-cell DEGs identified by univariable, Lasso and multivariable Cox regression analyses, a prognostic risk model for serous ovarian cancer was developed in the TCGA cohort. This model was then validated in three external cohorts, and evaluated as an independent prognostic factor by multivariable Cox regression analysis together with clinical characteristics. With the investigation of the underlying mechanism, a relation between a higher risk score of this model and more immune activities in tumor microenvironment was revealed. Furthermore, a detailed inspection of infiltrated immunocytes indicated that not only quantity, but also the functional state of these immunocytes might affect prognostic risk. Additionally, the potential of this model to predict immunotherapeutic response was exhibited by evaluating the functional state of cytotoxic T lymphocytes. To conclude, this study introduced a novel prognostic risk model based on NK-cell DEGs, which might provide assistance for the personalized management of serous ovarian cancer patients.

Development of a Dose-Adjusted Polygenic Risk Model for Anthracycline-Induced Cardiotoxicity.

Anthracyclines, which are effective chemotherapeutic agents, cause cardiac dysfunction in up to 57% of patients. The cumulative anthracycline dose is a crucial predictor of cardiotoxicity; however, the cumulative dose alone cannot explain all cardiotoxic events. Strongly associated genetic variants in SLC28A3 , UGT1A6 , and RARG contribute to anthracycline-induced cardiotoxicity in pediatric patients and may help identify those most susceptible. This study aimed to examine how these pharmacogenetic effects are modulated by cumulative anthracycline doses in the development of cardiotoxicity. A total of 595 anthracycline-treated children were genotyped and cardiotoxicity cases were identified. A dose-stratified analysis was performed to compare the contributions of SLC28A3 rs7853758, UGT1A6 rs17863783, and RARG rs2229774 variants to the development of cardiotoxicity in low-dose (<150 mg/m 2 cumulative dose) and high-dose (>250 mg/m 2 cumulative dose) patient groups. Logistic regression was used to model the relationships between the cumulative anthracycline dose, genetic variants, and cardiotoxicity in the full cohort. At < 150 mg/m 2 cumulative anthracycline dose, the SLC28A3 protective variant did not reach statistical significance [odds ratio (OR) 0.46 (95% confidence interval (CI) 0.10-1.45), P = 0.23], but it was statistically significant at doses >250 mg/m 2 [OR 0.43 (95% CI 0.22-0.78), P = 0.0093]. Conversely, the UGT1A6 and RARG risk variants were either statistically significant or approaching significance at doses <150 mg/m 2 [OR 7.18 (95% CI 1.78-28.4), P = 0.0045 for UGT1A6 and OR 2.76 (95% CI 0.89-7.63), P = 0.057 for RARG ], but not at doses >250 mg/m 2 [OR 2.91 (95% CI 0.80-11.0), P = 0.10; OR 1.56 (95% CI 0.89-2.75), P = 0.12]. These findings suggest that the SLC28A3 variant imparts more significant protection for patients receiving higher anthracycline doses, whereas the UGT1A6 and RARG risk variants significantly increased the risk of cardiotoxicity at low anthracycline doses.

Development and validation of a novel risk model in newly diagnosed de novo bone metastatic prostate cancer (M1b): a retrospective study.

Previous studies suggested that bone metastasis has a significant effect on the time of progression to metastatic castration-resistant prostate cancer (CRPC) for newly diagnosed de novo bone metastatic hormone-sensitive prostate cancer (mHSPC). Nevertheless, the effect of different bone metastasis sites was not fully evaluated. This study aimed to develop and validate a novel bone metastatic risk model. We enrolled 122 patients who were newly diagnosed with de novo bone metastatic prostate cancer following primary androgen deprivation based therapy at our institution from January 2008 to June 2021. The metastatic bone sites were classified into six sites: skull; cervical, thoracic, and lumbar vertebrae; chest (ribs and sternum); pelvis; upper limbs; and lower limbs. We calculated the bone metastatic score (BMS) for each site: 0 points were assigned for non-metastasis and 1 point was assigned for metastasis. The X-tile was adopted to acquire optimal cutoff points of BMS. We defined high-risk group (HRG) as BMS ≥ 3 and low-risk group (LRG) as BMS < 3. The new bone risk stratification was validated by calculating the area under the receiver operating characteristic curve (AUC). Subsequently, the relevant clinical prognostic variables were added to construct a predictive nomogram for predicting CRPC. The median patient age was 73 years. Most patients had Gleason score ≤8 (93 cases, 76.2%). The median follow-up duration was 11.5 months (range: 2-92 months). Eighty-six patients progressed to CRPC during the follow-up. The most common bone metastatic site was the pelvis (90.2%). The median BMS was 4. Seventy-six patients had HRG, while forty-six had LRG. The 1-, 2-, and 3-year AUCs for H/LRG were 0.620, 0.754, and 0.793, respectively. The HRG was associated with earlier time to CRPC. A nomogram based on four parameters (Gleason score, H/LRG, prostate-specific antigen [PSA] nadir, and time to PSA nadir) was developed to predict CRPC. Internal validation using bootstrapping demonstrated good accuracy for predicting the CRPC (C-index: 0.727). The calibration analysis demonstrated that the model performed well. We established a novel H/LRG risk model for newly diagnosed de novo bone metastatic prostate cancer, which provided evidence to support clinical decision-making.