Risk Stratification Model Research Articles

The integration of multidisciplinary approaches revealed PTGES3 as a novel drug target for breast cancer treatment

BackgroundThe main challenge in personalized treatment of breast cancer (BC) is how to integrate massive amounts of computing resources and data. This study aimed to identify a novel molecular target that might be effective for BC prognosis and for targeted therapy by using network-based multidisciplinary approaches.MethodsDifferentially expressed genes (DEGs) were first identified based on ESTIMATE analysis. A risk model in the TCGA-BRCA cohort was constructed using the risk score of six DEGs and validated in external and clinical in-house cohorts. Subsequently, independent prognostic factors in the internal and external cohorts were evaluated. Cell viability CCK-8 and wound healing assays were performed after PTGES3 siRNA was transiently transfected into the BC cell lines. Drug prediction and molecular docking between PTGES3 and drugs were further analyzed. Cell viability and PTGES3 expression in two BC cell lines after drug treatment were also investigated.ResultsA novel six-gene signature (including APOOL, BNIP3, F2RL2, HINT3, PTGES3 and RTN3) was used to establish a prognostic risk stratification model. The risk score was an independent prognostic factor that was more accurate than clinicopathological risk factors alone in predicting overall survival (OS) in BC patients. A high risk score favored tumor stage/grade but not OS. PTGES3 had the highest hazard ratio among the six genes in the signature, and its mRNA and protein levels significantly increased in BC cell lines. PTGES3 knockdown significantly inhibited BC cell proliferation and migration. Three drugs (gedunin, genistein and diethylstilbestrol) were confirmed to target PTGES3, and genistein and diethylstilbestrol demonstrated stronger binding affinities than did gedunin. Genistein and diethylstilbestrol significantly inhibited BC cell proliferation and reduced the protein and mRNA levels of PTGES3.ConclusionsPTGES3 was found to be a novel drug target in a robust six-gene prognostic signature that may serve as a potential therapeutic strategy for BC.

Prediction of lymph node status in stage T1 esophageal squamous cell carcinoma.

263 Background: With the development of endoscopic therapy and the introduction of immunotherapies, it is more important for preserving the esophagus and uninvaded lymph nodes to avoid the damages involving the long-term quality of life and normal immune function caused by esophagectomy with radical lymphadenectomy. Methods: We collected patients with T1 ESCC who underwent endoscopic resection or esophagectomy from 2014 to 2021 in three cancer centers. The primary end point was presence of lymph node metastasis (LNM) in surgical resection specimens or detection of metastases during follow-up. Univariate and multivariate logistic regression were used to screen the clinicopathological features related to LNM. Using the function 'lrm' constructed the prediction model. The area under the curve (AUC) of receiver operating characteristic and calibration graphs were used to assess the performance of the model. An external validation was conducted to evaluate the generalization of the model. We then prospectively collected 28 surgically resected tissue samples of T1 ESCC to further supplement the model from the cellular and molecular level, and used 6 markers (CK, CD34, D2-40, CD4, CD8, FOXP3) and DAPI dye to process multiple immunofluorescence (mIF) staining. Afterwards, we analyzed the density, spatial distribution of microvessels, lymphatic capillaries and tumor-associated immune cells and the interaction between them and malignant cells in the tumor microenvironment of T1 ESCC to explore the relationship between tumor microenvironment and risk of LNM. Results: 1109 patients with T1 ESCC were included. 653 patients from two centers constituted the training cohort, and 456 patients from the third center served as the validation cohort. Multivariate logistic regression based on the training cohort showed that tumor invasion into the submucosa, poor histological grade, and lymphovascular invasion were risk factors for LNM in T1 ESCC. Subsequently, we constructed a model based on three variables to predict the risk of LNM in T1 ESCC. The model demonstrated good discriminative ability (AUC 0.858, 95%CI 0.826-0.890). After mIF staining of 28 prospectively collected T1 ESCC tissue samples, we observed that microvessels and lymphatic capillaries were abundant in the mucosa in lymph node-positive samples but not in lymph node-negative samples. In addition, the average density of FOXP3+ T cells in samples with and without metastasis was 253/mm2 and 26/mm2 (P < 0.001), and the average density of CD8+ T cells was 112/mm2 and 432/mm2 (P < 0.001) in quantitative analysis, respectively. Conclusions: This study (RENMIN-237) developed a well-performing LNM risk stratification model of T1 ESCC to reduce both overtreatment and undertreatment, and we found that FOXP3+ T cells in the tumor microenvironment are a risk factor for LNM, while CD8+ T cells can serve as a protective factor against LNM.

Postoperative troponin surveillance to detect myocardial infarction: an observational cohort modelling study

BackgroundClinical presentation of postoperative myocardial infarction (POMI) is often silent. Several international guidelines recommend routine troponin surveillance in patients at risk. We compared how these different guidelines select patients for surveillance after noncardiac surgery with our established risk stratification model. MethodsWe used outcome data from two prospective studies: Measurement of Exercise Tolerance before Surgery (METS) and Troponin Elevation After Major non-cardiac Surgery (TEAMS). We compared the major American, Canadian, and European guideline recommendations for troponin surveillance with our established risk stratification model. For each guideline and model, we quantified the number of patients requiring monitoring, % POMI detected, sensitivity, specificity, diagnostic odds ratio, and number needed to screen (NNS). ResultsMETS and TEAMS contributed 2350 patients, of whom 319 (14%) had myocardial injury, 61 (2.5%) developed POMI, and 14 (0.6%) died. Our risk stratification model selected fewer patients for troponin monitoring (20%), compared with the Canadian (78%) and European (79%) guidelines. The sensitivity to detect POMI was highest with the Canadian and European guidelines (0.85; 95% confidence interval [CI] 0.74–0.92). Specificity was highest using the American guidelines (0.91; 95% CI 0.90–0.92). Our risk stratification model had the best diagnostic odds ratio (2.5; 95% CI 1.4–4.2) and a lower NNS (21 vs 35) compared with the guidelines. ConclusionsMost postoperative myocardial infarctions were detected by the Canadian and European guidelines but at the cost of low specificity and a higher number of patients undergoing screening. Patient selection based on our risk stratification model was optimal.

Clinical predictors of left ventricular thrombus after myocardial infarction as detected by magnetic resonance imaging.

The diagnosis of a left ventricular (LV) thrombus in patients with ST-segment elevation myocardial infarction (STEMI) remains challenging. The aim of the current study is to characterize clinical predictors for LV thrombus formation, as detected by cardiac magnetic resonance imaging (CMRI). We retrospectively evaluated 337 consecutive STEMI patients. All patients underwent transthoracic echocardiography (TTE) and CMRI during their index hospitalization. We developed a novel risk stratification model (ThrombScore) to identify patients at risk of developing an LV thrombus. CMRI revealed the presence of LV thrombus in 34 patients (10%), of whom 33 (97%) had experienced an anterior wall myocardial infarction (MI), and the majority (77%) had at least mildly reduced left ventricular ejection fraction (LVEF < 45%). The sensitivity for thrombus formation of the first and second TTE was 5.9% and 59%, respectively. Multivariate logistic regression model revealed that elevated C-reactive protein levels, lack of ST-segment elevation (STe) resolution, elevated creatine phosphokinase levels, and STe in anterior ECG leads are robust independent predictors for developing an LV thrombus. These variables were incorporated to construct the ThrombScore: a simple six-point risk model. The odds ratio for developing thrombus per one-point increase in the score was 3.2 (95% CI 2.1-5.01; p < 0.001). The discrimination analysis of the model revealed a c-statistic of 0.86 for thrombus development. The model identified three distinct categories (I, II, and III) with corresponding thrombus incidences of 0%, 1.6%, and 27.6%, respectively. ThrombScore is a simple and practical clinical model for risk stratification of thrombus formation in patients with STEMI.

Arrhythmogenic Right Ventricular Cardiomyopathy in Children: A Systematic Review.

Arrhythmogenic right ventricular cardiomyopathy (ARVC) is an inherited disease characterized by the progressive replacement of the normal myocardium by fibroadipocytic tissue. The importance of an early diagnosis is supported by a higher risk of sudden cardiac death in the pediatric population. We reviewed the literature on diagnosis, risk stratification, and prognosis in the pediatric population with ARVC. In case reports which analyzed children with ARVC, the most common sign was ventricular tachycardia, frequently presenting as dizziness, syncope, or even cardiac arrest. Currently, there is no gold standard for diagnosing ARVC in children. Nevertheless, genetic analysis may provide a proper diagnosis tool for asymptomatic cases. Although risk stratification is recommended in patients with ARVC, a validated prediction model for risk stratification in children is still lacking; thus, it is a matter of further research. In consequence, even though ARVC is a relatively rare condition in children, it negatively impacts the survival and clinical outcomes of the patients. Therefore, appropriate and validated diagnostic and risk stratification tools are crucial for the early detection of children with ARVC, ensuring a prompt therapeutic intervention.

Evaluation of Relationship of Existence of Cardiovascular Disease and Incidence of Mortality and Renal Replacement Therapy in Corona-Positive Patients Admitted in ICU

Background: The coronavirus pandemic in 2020 was one of the biggest issues in the world, causing catastrophic respiratory, multi-organ problems and deaths. These effects are caused by both the virus itself and the drugs used to treat patients. Mortality rates were higher among those with underlying diseases. Patients with a history of cardiovascular disease (CVD) and those with a high risk of myocardial involvement by COVID-19 are particularly vulnerable to mortality. Clinicians and policymakers should consider these findings when developing risk stratification models. According to these studies, coronary artery disease (CAD) may increase mortality and the need for renal replacement therapy, primarily because of comorbidities rather than a direct effect of the disease itself. Objectives: To determine if all risk factors affected mortality, this study examined all risk factors. Methods: In the present study, patients with a positive history of CVD, hypertension, diabetes mellitus, dyslipidemia, hypothyroidism, chronic kidney disease and chest computed tomography (CT) scan compatible with coronavirus, who were admitted to intensive care unit (ICU) and underwent mechanical support, were included. Results: The study included 150 patients who were randomly selected. A total of 43% females and 57% males made up the study population, with a mean age of 65.3 ± 13.5. Creatinine levels in the mortality group were 1.60 mg/dL, while in the non-mortality group, they were 1.19 mg/dL. Hypertension was the most common risk factor among patients (60.9%). The mortality rate was 10.6% (16 out of 150 patients). There was a significant association between acute kidney injury during admission (P value = 0.005) and past use of corticosteroids (P value = 0.016), while the need for dialysis (P value = 0.052) was not significant. Conclusions: There was a significant difference in creatinine mean between the groups with and without mortality (P = 0.044) between the two groups. Mortality was not significantly affected by other factors. In addition, our study indicated that CAD, as well as other cardiac diseases and risk factors, can lead to higher mortality rates and the need for renal replacement therapy, which is largely due to the burden of comorbidities, rather than their direct effect.

Radiomic biomarkers for platinum-refractory head and neck cancer in the era of immunotherapy.

Immune checkpoint inhibitors (ICI) are recommended as the first-line therapy for platinum-refractory head and neck squamous cell carcinoma (HNSCC), a disease with a poor prognosis. However, biomarkers in this situation are rare. The objective was to identify radiomic features-associated biomarkers to guide the prognosis and treatment opinions in the era of ICI. A total of 31 platinum-refractory HNSCC patients were retrospectively enrolled. Of these, 65.5% (20/31) received ICI-based therapy and 35.5% (11/31) did not. Radiomic features of the primary site at the onset of recurrent metastatic (R/M) status were extracted. Prognostic and predictive radiomic biomarkers were analysed. The median overall survival from R/M status (R/M OS) was 9.6 months. Grey-level co-occurrence matrix-associated texture features were the most important in identifying the patients with or without 9-month R/M death. A radiomic risk-stratification model was established and equally separated the patients into high-, intermittent- and lower-risk groups (1-year R/M death rate, 100.0% vs. 70.8% vs. 27.1%, p = 0.001). Short-run high grey-level emphasis (SRHGE) was more suitable than programmed death ligand 1 (PD-L1) expression in selecting whether patients received ICI-based therapy. Radiomic features were effective prognostic and predictive biomarkers. Future studies are warranted.

Risk Stratification and Management of Intermediate-Risk Acute Pulmonary Embolism.

Pulmonary embolism (PE) is the third most common cause of cardiovascular death and necessitates prompt, accurate risk assessment at initial diagnosis to guide treatment and reduce associated mortality. Intermediate-risk PE, defined as the presence of right ventricular (RV) dysfunction in the absence of hemodynamic compromise, carries a significant risk for adverse clinical outcomes and represents a unique diagnostic challenge. While small clinical trials have evaluated advanced treatment strategies beyond standard anticoagulation, such as thrombolytic or endovascular therapy, there remains continued debate on the optimal care for this patient population. Here, we review the most recent risk stratification models, highlighting differences between prediction scores and their limitations, and discuss the utility of serologic biomarkers and imaging modalities to detect right ventricular dysfunction. Additionally, we examine current treatment recommendations including anticoagulation strategies, use of thrombolytics at full and reduced doses, and utilization of invasive treatment options. Current knowledge gaps and ongoing studies are highlighted.

Differentiation and risk stratification of basal cell carcinoma with deep learning on histopathologic images and measuring nuclei and tumor microenvironment features.

Nuclear pleomorphism and tumor microenvironment (TME) play a critical role in cancer development and progression. Identifying most predictive nuclei and TME features of basal cell carcinoma (BCC) may provide insights into which characteristics pathologists can use to distinguish and stratify this entity. To develop an automated workflow based on nuclei and TME features from basaloid cell tumor regions to differentiate BCC from trichoepithelioma (TE) and stratify BCC into high-risk (HR) and low-risk (LR) subtypes, and to identify the nuclear and TME characteristics profile of different basaloid cell tumors. The deep learning systems were trained on 161 H&E -stained sections which contained 51 sections of HR-BCC, 50 sections of LR-BCC and 60 sections of TE from one institution (D1), and externally and independently validated on D2 (46 sections) and D3 (76 sections), from 2015 to 2022. 60%, 20% and 20% of D1 data were randomly splitted for training, validation and testing, respectively. The framework comprised four stages: tumor regions identification by multi-head self-attention (MSA) U-Net, nuclei segmentation by HoVer-Net, quantitative feature by handcrafted extraction, and differentiation and risk stratification classifier construction. Pixel accuracy, precision, recall, dice score, intersection over union (IoU) and area under the curve (AUC) were used to evaluate the performance of tumor segmentation model and classifiers. MSA-U-Net model detected tumor regions with 0.910 precision, 0.869 recall, 0.889 dice score and 0.800 IoU. The differentiation classifier achieved 0.977±0.0159, 0.955±0.0181, 0.885±0.0237 AUC in D1, D2 and D3, respectively. The most discriminative features between BCC and TE contained Homogeneity, Elongation, T-T_meanEdgeLength, T-T_Nsubgraph, S-T_HarmonicCentrality, S-S_Degrees. The risk stratification model can well predict HR-BCC and LR-BCC with 0.920±0.0579, 0.839±0.0176, 0.825±0.0153 AUC in D1, D2 and D3, respectively. The most discriminative features between HR-BCC and LR-BCC comprised IntensityMin, Solidity, T-T_minEdgeLength, T-T_Coreness, T-T_Degrees, T-T_Betweenness, S-T_Degrees. This framework hold potential for future use as a second opinion helping inform diagnosis of BCC, and identify nuclei and TME features related with malignancy and tumor risk stratification.

A Model Based on Automated Urinalysis Parameters for Urothelial Carcinoma Risk Stratification in Suspected Patients.

The aim of this study was to develop and validate a risk stratification model for the screening of patients with suspected urothelial carcinoma (UC). We enrolled 671 consecutive patients with suspected UC and generated a risk stratification model based on urinary parameters by using an automated urinalysis analyzer (Sysmex UN-9000). All patients received urine cytology examination from January 1, 2019, to October 31, 2022. Out of the 671 patients, 191 (28.5%) were ultimately diagnosed with UC. The four features associated with the presence of malignancy on multivariable analysis can be summarized by using the mnemonic UC-PAAS: UC, protein vs. creatinine ratio (P/C), age, atypical cells (Atyp.C), and small round epithelial cell (SRC). Major criteria include Atyp.C ≥ 0.1/μL (2 points) and age ≥ 65 years (2 points); minor criteria include SRC ≥ 2.7/μL (1 point) and abnormal P/C results (1 point). The model evidenced good discrimination (area under the curve = 0.802, 95% confidence interval [0.756, 0.848]) in the training group. A UC-PAAS cutoff of more than 4 points identified a high-risk population, of whom 37 of 59 (62.7%) had UC; the negative predictive value was 0.867. The validation group yielded similar findings. We present a urinalysis-based screening model, the UC-PAAS, that may serve as an accessory clinical tool for the evaluation of patients with suspected UC, because the model identifies patients at higher risk who require closer follow-up than others or additional examinations.

Prognostic value of lymph node-to-primary tumor ratio of PET standardized uptake value for nasopharyngeal carcinoma: a recursive partitioning risk stratification analysis.

Induction chemotherapy (IC) combined with concurrent chemoradiotherapy has become the standard treatment for locoregionally advanced nasopharyngeal carcinoma (LA-NPC). Data on the prognostic value of the lymph node-to-primary tumor ratio (NTR) of positron emission tomography (PET) standardized uptake value (SUV) for patients treated with IC were limited. To evaluate the prognostic value of the SUV NTR for patients with LA-NPC treated with IC. In all, 467 patients with pretreatment 18F-fluorodeoxyglucose PET/computed tomography (CT) scans between September 2017 and November 2020 were retrospectively reviewed. The receiver operating characteristic (ROC) analysis was used to determine the optimal cut-off value of SUV NTR. Kaplan-Meier method was used to evaluate survival rates. The recursive partitioning analysis (RPA) was performed to construct a risk stratification model. The optimal cutoff value of SUV NTR was 0.74. Multivariate analyses showed that SUV NTR and overall stage were independent predictors for distant metastasis-free survival (DMFS) and regional recurrent-free survival (RRFS). Therefore, an RPA model based on the endpoint of DMFS was generated and categorized the patients into three distinct risk groups: RPA I (low risk: SUV NTR < 0.74 and stage III), RPA II (medium risk: SUV NTR < 0.74 and stage IVa, or SUV NTR ⩾ 0.74 and stage III), and RPA III (high risk: SUV NTR ⩾ 0.74 and stage IVa), with a 3-year DMFS of 98.9%, 93.4%, and 84.2%, respectively. ROC analysis showed that the RPA model had superior predictive efficacy than the SUV NTR or overall stage alone. SUV NTR was an independent prognosticator for distant metastasis and regional recurrence in locoregionally advanced NPC. The RPA risk stratification model based on SUV NTR provides improved DMFS and RRFS prediction over the eighth edition of the TNM (Tumor Node Metastasis) staging system.

Prognostic model for unresectable hepatocellular carcinoma treated with dual PD-1 and angiogenesis blockade therapy

Background and aimsDual programmed death 1 (PD-1) and angiogenesis blockade therapy is a frontline treatment for hepatocellular carcinoma (HCC). An accepted model for survival prediction and risk stratification in individual...

Clinical parameter-based prediction model for neurosyphilis risk stratification.

Accurately predicting neurosyphilis prior to a lumbar puncture (LP) is critical for the prompt management of neurosyphilis. However, a valid and reliable model for this purpose is still lacking. This study aimed to develop a nomogram for the accurate identification of neurosyphilis in patients with syphilis. The training cohort included 9,504 syphilis patients who underwent initial neurosyphilis evaluation between 2009 and 2020, while the validation cohort comprised 526 patients whose data were prospectively collected from January 2021 to September 2021. Neurosyphilis was observed in 35.8% (3,400/9,504) of the training cohort and 37.6% (198/526) of the validation cohort. The nomogram incorporated factors such as age, male gender, neurological and psychiatric symptoms, serum RPR, a mucous plaque of the larynx and nose, a history of other STD infections, and co-diabetes. The model exhibited good performance with concordance indexes of 0.84 (95% CI, 0.83-0.85) and 0.82 (95% CI, 0.78-0.86) in the training and validation cohorts, respectively, along with well-fitted calibration curves. This study developed a precise nomogram to predict neurosyphilis risk in syphilis patients, with potential implications for early detection prior to an LP.

Predictive models for lymph node metastasis in endometrial cancer: A systematic review and bibliometric analysis.

Lymph node metastasis is associated with a poorer prognosis in endometrial cancer. The objective was to synthesize and critically appraise existing predictive models for lymph node metastasis risk stratification in endometrial cancer. This study is a systematic review. We searched the Web of Science for articles reporting models predicting lymph node metastasis in endometrial cancer, with a systematic review and bibliometric analysis conducted based upon which. Risk of bias was assessed by the Prediction model Risk Of BiAS assessment Tool (PROBAST). A total of 64 articles were included in the systematic review, published between 2010 and 2023. The most common articles were "development only." Traditional clinicopathological parameters remained the mainstream in models, for example, serum tumor marker, myometrial invasion and tumor grade. Also, models based upon gene-signatures, radiomics and digital histopathological images exhibited an acceptable self-reported performance. The most frequently validated models were the Mayo criteria, which reached a negative predictive value of 97.1%-98.2%. Substantial variability and inconsistency were observed through PROBAST, indicating significant between-study heterogeneity. A further bibliometric analysis revealed a relatively weak link between authors and organizations on models predicting lymph node metastasis in endometrial cancer. A number of predictive models for lymph node metastasis in endometrial cancer have been developed. Although some exhibited promising performance as they demonstrated adequate to good discrimination, few models can currently be recommended for clinical practice due to lack of independent validation, high risk of bias and low consistency in measured predictors. Collaborations between authors, organizations and countries were weak. Model updating, external validation and collaborative research are urgently needed. None.

Identification of cell surface markers for acute myeloid leukemia prognosis based on multi-model analysis.

Given the extremely high inter-patient heterogeneity of acute myeloid leukemia (AML), the identification of biomarkers for prognostic assessment and therapeutic guidance is critical. Cell surface markers (CSMs) have been shown to play an important role in AML leukemogenesis and progression. In the current study, we evaluated the prognostic potential of all human CSMs in 130 AML patients from The Cancer Genome Atlas (TCGA) based on differential gene expression analysis and univariable Cox proportional hazards regression analysis. By using multi-model analysis, including Adaptive LASSO regression, LASSO regression, and Elastic Net, we constructed a 9-CSMs prognostic model for risk stratification of the AML patients. The predictive value of the 9-CSMs risk score was further validated at the transcriptome and proteome levels. Multivariable Cox regression analysis showed that the risk score was an independent prognostic factor for the AML patients. The AML patients with high 9-CSMs risk scores had a shorter overall and event-free survival time than those with low scores. Notably, single-cell RNA-sequencing analysis indicated that patients with high 9-CSMs risk scores exhibited chemotherapy resistance. Furthermore, PI3K inhibitors were identified as potential treatments for these high-risk patients. In conclusion, we constructed a 9-CSMs prognostic model that served as an independent prognostic factor for the survival of AML patients and held the potential for guiding drug therapy.

Development of a prognostic model based on different disulfidptosis related genes typing for kidney renal clear cell carcinoma.

Background: Kidney renal clear cell carcinoma (KIRC) is a common and clinically significant subtype of kidney cancer. A potential therapeutic target in KIRC is disulfidptosis, a novel mode of cell death induced by disulfide stress. The aim of this study was to develop a prognostic model to explore the clinical significance of different disulfidptosis gene typings from KIRC. Methods: A comprehensive analysis of the chromosomal localization, expression patterns, mutational landscape, copy number variations, and prognostic significance of 10 disulfide death genes was conducted. Patients were categorized into distinct subtypes using the Non-negative Matrix Factorization (NMF) typing method based on disulfidptosis gene expression patterns. Weighted Gene Co-expression Network Analysis (WGCNA) was used on the KIRC dataset to identify differentially expressed genes between subtype clusters. A risk signature was created using LASSO-Cox regression and validated by survival analysis. An interaction between risk score and immune cell infiltration, tumor microenvironment characteristics and pathway enrichment analysis were investigated. Results: Initial findings highlight the differential expression of specific DRGs in KIRC, with genomic instability and somatic mutation analysis revealing key insights into their role in cancer progression. NMF clustering differentiates KIRC patients into subgroups with distinct survival outcomes and immune profiles, and hierarchical clustering identifies gene modules associated with key biological and clinical parameters, leading to the development of a risk stratification model (LRP8, RNASE2, CLIP4, HAS2, SLC22A11, and KCTD12) validated by survival analysis and predictive of immune infiltration and drug sensitivity. Pathway enrichment analysis further delineates the differential molecular pathways between high-risk and low-risk patients, offering potential targets for personalized treatment. Lastly, differential expression analysis of model genes between normal and KIRC cells provides insights into the molecular mechanisms underlying KIRC, highlighting potential biomarkers and therapeutic targets. Conclusion: This study contributes to the understanding of KIRC and provides a potential prognostic model using disulfidptosis gene for personalized management in KIRC patients. The risk signature shows clinical applicability and sheds light on the biological mechanisms associated with disulfide-induced cell death.

Clinical Characteristics and Outcomes of AIDS-Related Burkitt Lymphoma in China: A Retrospective Single-Center Study.

Objectives: Studies on the prognosis and risk stratification of patients with acquired immune deficiency syndrome (AIDS) - related Burkitt lymphoma (AR-BL) are rare. We aim to construct a novel model to improve the risk assessment of these patients. Methods: We retrospectively analyzed the clinical data of 34 patients over the past 10 years and the factors associated with progression-free survival (PFS) and overall survival (OS) were evaluated in univariate and multivariate Cox models. Then, the novel model consisting of screened factors was compared with the existing models. Results: With a 37-month median follow-up, the overall 2-year PFS and OS rates were 40.50% and 36.18%, respectively. The OS of patients who received chemotherapy was better compared with those without chemotherapy (P = .0012). Treatment with an etoposide, prednisone, oncovin, cyclophosphamide, and hydroxydaunorubicin-based regimen was associated with longer OS and PFS compared with a cyclophosphamide, doxorubicin, vincristine, and prednisone-based regimen (OS, P = .0002; PFS, P = .0158). Chemotherapy (hazard ratio [HR] = 0.075; 95% confidence interval [CI], 0.009-0.614) and Eastern Cooperative Oncology Group Performance Status (ECOG PS) 2 to 4 (HR = 4.738; 95% CI, 1.178-19.061) were independent prognostic factors of OS in multivariate analysis and we established a novel prognostic risk stratification model named GZ8H model with chemotherapy and ECOG PS. Conclusion: GZ8H showed better stratification ability than the international prognostic index (IPI) or Burkitt lymphoma IPI (BL-IPI). Furthermore, the C-index of the nomogram used to predict OS was 0.884 in the entire cohort and the calibration curve showed excellent agreement between the predicted and actual results of OS. No human immunodeficiency virus-related factors were found to be associated with OS and PFS of AR-BL patients in our study. Overall, the clinical characteristics and outcomes in AR-BL were shown and prognostic factors for OS and PFS were identified in this study.

Development of Non-Genetic Risk Stratification Model of Severe Allopurinol Hypersensitivity (NoG-ALLOH Score): A Multicenter Study in Thailand

Background The side effect of allopurinol was severe allopurinol hypersensitivity (SAH). There are two major causes including genetic risk factor, HLA-B*58:01 and non-genetic risk factors including female, old age, renal impairment, inadequate starting dosage of allopurinol and received diuretic. Although HLA-B*58:01 testing is recommended in Thai, the several problems including cost, limitation of the testing and the delay of the result. Therefore, we aimed to use these non-genetic risk factors for developing the model to predict SAH. Method The study was a retrospective observational incidence density sampling. SAH cases were collected from tertiary care medical center which some cases were referred from primary care medical center; where most non-SAH cases were collected from primary care hospitals and tertiary care medical centers. The data of non-genetic factors particularly sex, age, renal function, co-diuresis, starting dosage of allopurinol and serum uric acid (SUA) of non-SAH cases selected the first description of allopurinol. The binary prevalence-weighted logistic regression statistical method was used to develop the prediction models. Three models were developed following general practice. Result Totally, there were 209 cases of SAH and 23,068 cases of non-SAH. Factors that were associated with the development of SAH within 90 days were female, old age (¿ 65 years old), renal impairment, inadequate starting dosage of allopurinol, co-medication(s) with diuretic and high SUA before prescription of allopurinol. Model 1a and model 1b were applied for patients who did not have and have SUA when starting allopurinol, respectively. Model 2 was applied for patients who had all non-genetic risk factors and started allopurinol within 60 days but have not SAH. The area under the receiver operating characteristic curve for model 1a, model 1b and model 2 were 0.72, 0.81 and 0.82, respectively (Figure 1). The performance for each predictions SAH were good. Conclusion Model 1a and model 1b predict SAH for the patients who had their first prescribed allopurinol, model 2 predicts SAH for the patients who had been taken allopurinol within 60 days but no SAH. The scoring system of each model helps clinician to prescribe allopurinol in real clinical practice before the patients develop SAH. The score of 0-1%, 1-2% and 2-100% indicates the low, moderate and high risk, respectively. The low-risk group can start allopurinol. The moderate-risk group considers to start allopurinol with closed monitoring of SAH. The high-risk group suggests to change to other urate lowering agents for preventing SAH (Table 1).

Establishing a risk stratification model to identify clinically high-risk N0 breast cancer who could benefit from regional nodal irradiation: a single institute analysis.

The purpose of this real-world study was to investigate the risk factors for developing recurrence among patients with pathological T1-3N0 breast cancer (BC) treated with breast-conserving surgery (BCS) followed by whole breast irradiation alone (WBI) and identify those clinically high-risk BCs who could benefit from regional nodal irradiation (RNI). Female BC patients treated at Shanghai Ruijin hospital from 2009 to 2016 were retrospectively reviewed. The disease-free survival (DFS), breast cancer specific survival (BCSS) and overall survival (OS) were estimated by the Kaplan-Meier method, and survival differences were compared with the log-rank test. Univariate and multivariate analysis was performed using Cox proportional hazards regression analysis. An external validation was conducted by using SEER database. A total of 622 BC patients treated with BCS+WBI alone were included. With a median follow-up of 82 months, the 7-year OS, BCSS and DFS for the entire cohort was 97%, 99% and 91%, respectively. Multivariable Cox analysis indicated that tumor size (p=0.006), tumor location (p=0.033), lymphovascular invasion (LVI) status (p=0.0028) and Ki-67 index (p=0.051) were independent risk factors for DFS. A scoring system was developed using these four factors and the 7-year DFS and OS were 97% and 96% for patients with 0-1 risk factors, 95% and 82% for patients with ≥2 risk factors (p<0.0001 for DFS, and p=0.0063 for OS). Based on tumor size and tumor location, an external validation by demonstrated that the 7-year OS was 90% and 88% for patients with 0-1 risk factor, which was significantly better than those defined as high-risk BC patients (82%, p<0.0001). By using our institute database, we establish a risk stratification system for identifying sub-group of pN0 BC patients, who are at high risk for developing recurrence. The results of our study support tailored RT decision-making according to individual risks, which needed to be confirmed in further studies.

Constructing a personalized prognostic risk model for colorectal cancer using machine learning and multi-omics approach based on epithelial-mesenchymal transition-related genes.

The progression and the metastatic potential of colorectal cancer (CRC) are intricately linked to the epithelial-mesenchymal transition (EMT) process. The present study harnesses the power of machine learning combined with multi-omics data to develop a risk stratification model anchored on EMT-associated genes. The aim is to facilitate personalized prognostic assessments in CRC. We utilized publicly accessible gene expression datasets to pinpoint EMT-associated genes, employing a CoxBoost algorithm to sift through these genes for prognostic significance. The resultant model, predicated on gene expression levels, underwent rigorous independent validation across various datasets. Our model demonstrated a robust capacity to segregate CRC patients into distinct high- and low-risk categories, each correlating with markedly different survival probabilities. Notably, the risk score emerged as an independent prognostic indicator for CRC. High-risk patients were characterized by an immunosuppressive tumor milieu and a heightened responsiveness to certain chemotherapeutic agents, underlining the model's potential in steering tailored oncological therapies. Moreover, our research unearthed a putative repressive interaction between the long non-coding RNA PVT1 and the EMT-associated genes TIMP1 and MMP1, offering new insights into the molecular intricacies of CRC. In essence, our research introduces a sophisticated risk model, leveraging machine learning and multi-omics insights, which accurately prognosticates outcomes for CRC patients, paving the way for more individualized and effective oncological treatment paradigms.