Correlation Between Basal Metabolic Rate and Clinical Outcomes in Gastric Cancer Patients: A Retrospective Study.

OBJECTIVES: Hypermetabolism is associated with clinical prognosis of cancer patients. The aim of this study was to explore the association between basal metabolic rate (BMR) and postoperative clinical outcomes in gastric cancer patients. METHODS: We collected data of 958 gastric cancer patients admitted at our center from June 2014 to December 2018. The optimal cutoff value of BMR (BMR ≤1149 kcal/day) was obtained using the X-tile plot. Logistic and Cox regression analyses were then performed to evaluate the relevant influencing factors of clinical outcomes. Finally, R software was utilized to construct the nomogram. RESULTS: A total of 213 patients were defined as having a lower basal metabolic rate (LBMR). Univariate and multivariate analyses showed that gastric cancer patients with LBMR were more prone to postoperative complications and had poor long-term overall survival (OS). The established nomogram had good predictive power to assess the risk of OS in gastric cancer patients after radical gastrectomy (c-index was 0.764). CONCLUSIONS: Overall, LBMR on admission is associated with the occurrence of postoperative complications in gastric cancer patients, and this population has a poorer long-term survival. Therefore, there should be more focus on the perioperative management of patients with this risk factor before surgery.

A nomogram based on nutritional and inflammatory parameters to predict DMFS and identify beneficiaries of adjuvant chemotherapy in IVA-stage nasopharyngeal carcinoma.

OBJECTIVE: This study aims to develop a nomogram integrating inflammation (NLR), Prognostic Nutritional Index (PNI), and EBV DNA (tumor burden) to achieve personalized treatment and prediction for stage IVA NPC. Furthermore, it endeavors to pinpoint specific subgroups that may derive significant benefits from S-1 adjuvant chemotherapy. METHODS: A total of 834 patients diagnosed with stage IVA NPC were enrolled in this study and randomly allocated into training and validation cohorts. Multivariate Cox analyses were conducted to identify independent prognostic factors for constructing the nomogram. The predictive and clinical utility of the nomogram was assessed through measures including the AUC, calibration curve, DCA, and C-indexes. IPTW was employed to balance baseline characteristics across the population. Kaplan-Meier analysis and log-rank tests were utilized to evaluate the prognostic value. RESULTS: In our study, we examined the clinical features of 557 individuals from the training cohort and 277 from the validation cohort. The median follow-up period was 50.1 and 49.7 months, respectively. For the overall cohort, the median follow-up duration was 53.8 months. The training and validation sets showed 3-year OS rates of 87.7% and 82.5%, respectively. Meanwhile, the 3-year DMFS rates were 95.9% and 84.3%, respectively. We created a nomogram that combined PNI, NRI, and EBV DNA, resulting in high prediction accuracy. Risk stratification demonstrated substantial variations in DMFS and OS between the high and low risk groups. Patients in the high-risk group benefited significantly from the IC + CCRT + S-1 treatment. In contrast, IC + CCRT demonstrated non-inferior 3-year DMFS and OS compared to IC + CCRT + S-1 in the low-risk population, indicating the possibility of reducing treatment intensity. CONCLUSIONS: In conclusion, our nomogram integrating NLR, PNI, and EBV DNA offers precise prognostication for stage IVA NPC. S-1 adjuvant chemotherapy provides notable benefits for high-risk patients, while treatment intensity reduction may be feasible for low-risk individuals.

Triglyceride-inflammation score established on account of random survival forest for predicting survival in patients with nasopharyngeal carcinoma: a retrospective study.

Objective: This study aimed to establish an effective prognostic model based on triglyceride and inflammatory markers, including neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), and platelet-to-lymphocyte ratio (PLR), to predict overall survival (OS) in patients with nasopharyngeal carcinoma (NPC). Additionally, we aimed to explore the interaction and mediation between these biomarkers in their association with OS. Methods: A retrospective review was conducted on 259 NPC patients who had blood lipid markers, including triglyceride and total cholesterol, as well as parameters of peripheral blood cells measured before treatment. These patients were followed up for over 5 years, and randomly divided into a training set (n=155) and a validation set (n=104). The triglyceride-inflammation (TI) score was developed using the random survival forest (RSF) algorithm. Subsequently, a nomogram was created. The performance of the prognostic model was measured by the concordance index (C-index), time-dependent receiver operating characteristic (ROC) curve, and decision curve analysis (DCA). The interaction and mediation between the biomarkers were further analyzed. Bioinformatics analysis based on the GEO dataset was used to investigate the association between triglyceride metabolism and immune cell infiltration. Results: The C-index of the TI score was 0.806 in the training set, 0.759 in the validation set, and 0.808 in the entire set. The area under the curve of time-dependent ROC of TI score in predicting survival at 1, 3, and 5 years were 0.741, 0.847, and 0.871 respectively in the training set, and 0.811, 0.837, and 0.758 in the validation set, then 0.771, 0.848, and 0.862 in the entire set, suggesting that TI score had excellent performance in predicting OS in NPC patients. Patients with stage T1-T2 or M0 had significantly lower TI scores, NLR, and PLR, and higher LMR compared to those with stage T3-T3 or M1, respectively. The nomogram, which integrated age, sex, clinical stage, and TI score, demonstrated good clinical usefulness and predictive ability, as evaluated by the DCA. Significant interactions were found between triglyceride and NLR and platelet, but triglyceride did not exhibit any medicating effects in the inflammatory markers. Additionally, NPC tissues with active triglyceride synthesis exhibited high immune cell infiltration. Conclusion: The TI score based on RSF represents a potential prognostic factor for NPC patients, offering convenience and economic advantages. The interaction between triglyceride and NLR may be attributed to the effect of triglyceride metabolism on immune response.

Significance of NKX2-1 as a biomarker for clinical prognosis, immune infiltration, and drug therapy in lung squamous cell carcinoma.

Background: This study was performed to determine the biological processes in which NKX2-1 is involved and thus its role in the development of lung squamous cell carcinoma (LUSC) toward improving the prognosis and treatment of LUSC. Methods: Raw RNA sequencing (RNA-seq) data of LUSC from The Cancer Genome Atlas (TCGA) were used in bioinformatics analysis to characterize NKX2-1 expression levels in tumor and normal tissues. Survival analysis of Kaplan-Meier curve, the time-dependent receiver operating characteristic (ROC) curve, and a nomogram were used to analyze the prognosis value of NKX2-1 for LUSC in terms of overall survival (OS) and progression-free survival (PFS). Then, differentially expressed genes (DEGs) were identified, and Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), and Gene Set Enrichment Analysis (GSEA) were used to clarify the biological mechanisms potentially involved in the development of LUSC. Moreover, the correlation between the NKX2-1 expression level and tumor mutation burden (TMB), tumor microenvironment (TME), and immune cell infiltration revealed that NKX2-1 participates in the development of LUSC. Finally, we studied the effects of NKX2-1 on drug therapy. To validate the protein and gene expression levels of NKX2-1 in LUSC, we employed immunohistochemistry(IHC) datasets, The Gene Expression Omnibus (GEO) database, and qRT-PCR analysis. Results: NKX2-1 expression levels were significantly lower in LUSC than in normal lung tissue. It significantly differed in gender, stage and N classification. The survival analysis revealed that high expression of NKX2-1 had shorter OS and PFS in LUSC. The multivariate Cox regression hazard model showed the NKX2-1 expression as an independent prognostic factor. Then, the nomogram predicted LUSC prognosis. There are 51 upregulated DEGs and 49 downregulated DEGs in the NKX2-1 high-level groups. GO, KEGG and GSEA analysis revealed that DEGs were enriched in cell cycle and DNA replication.The TME results show that NKX2-1 expression was positively associated with mast cells resting, neutrophils, monocytes, T cells CD4 memory resting, and M2 macrophages but negatively associated with M1 macrophages. The TMB correlated negatively with NKX2-1 expression. The pharmacotherapy had great sensitivity in the NKX2-1 low-level group, the immunotherapy is no significant difference in the NKX2-1 low-level and high-level groups. The analysis of GEO data demonstrated concurrence with TCGA results. IHC revealed NKX2-1 protein expression in tumor tissues of both LUAD and LUSC. Meanwhile qRT-PCR analysis indicated a significantly lower NKX2-1 expression level in LUSC compared to LUAD. These qRT-PCR findings were consistent with co-expression analysis of NKX2-1. Conclusion: We conclude that NKX2-1 is a potential biomarker for prognosis and treatment LUSC. A new insights of NKX2-1 in LUSC is still needed further research.

A predictive nomogram for short-term outcomes of myasthenia gravis patients treated with low-dose rituximab.

BACKGROUND: This study aims to establish and validate a predictive nomogram for the short-term clinical outcomes of myasthenia gravis (MG) patients treated with low-dose rituximab. METHODS: We retrospectively reviewed 108 patients who received rituximab of 600 mg every 6 months in Huashan Hospital and Tangdu Hospital. Of them, 76 patients from Huashan Hospital were included in the derivation cohort to develop the predictive nomogram, which was externally validated using 32 patients from Tangdu Hospital. The clinical response is defined as a ≥ 3 points decrease in QMG score within 6 months. Both clinical and genetic characteristics were included to screen predictors via multivariate logistic regression. Discrimination and calibration were measured by the area under the receiver operating characteristic curve (AUC-ROC) and Hosmer-Lemeshow test, respectively. RESULTS: Disease duration (OR = 0.987, p = 0.032), positive anti-muscle-specific tyrosine kinase antibodies (OR = 19.8, p = 0.007), and genotypes in FCGR2A rs1801274 (AG: OR = 0.131, p = 0.024;GG:OR = 0.037, p = 0.010) were independently associated with clinical response of post-rituximab patients. The nomogram identified MG patients with clinical response with an AUC-ROC (95% CI) of 0.875 (0.798-0.952) in the derivation cohort and 0.741(0.501-0.982) in the validation cohort. Hosmer-Lemeshow test showed a good calibration (derivation: Chi-square = 3.181, p = 0.923; validation: Chi-square = 8.098, p = 0.424). CONCLUSIONS: The nomogram achieved an optimal prediction of short-term outcomes in patients treated with low-dose rituximab.

Development and validation of a risk nomogram model for predicting peripheral neuropathy in patients with type 2 diabetes mellitus.

Objective: Diabetic peripheral neuropathy frequently occurs and presents severely in individuals suffering from type 2 diabetes mellitus, representing a significant complication. The objective of this research was to develop a risk nomogram for DPN, ensuring its internal validity and evaluating its capacity to predict the condition. Methods: In this retrospective analysis, Suqian First Hospital's cohort from January 2021 to June 2022 encompassed 397 individuals diagnosed with T2DM. A random number table method was utilized to allocate these patients into two groups for training and validation, following a 7:3 ratio. By applying univariate and multivariable logistic regression, predictive factors were refined to construct the nomogram. The model's prediction accuracy was assessed through metrics like the ROC area, HL test, and an analysis of the calibration curve. DCA further appraised the clinical applicability of the model. Emphasis was also placed on internal validation to confirm the model's dependability and consistency. Results: Out of 36 evaluated clinicopathological characteristics, a set of four, duration, TBIL, TG, and DPVD, were identified as key variables for constructing the predictive nomogram. The model exhibited robust discriminatory power, evidenced by an AUC of 0.771 (95% CI: 0.714-0.828) in the training cohort and an AUC of 0.754 (95% CI: 0.663-0.845) in the validation group. The congruence of the model's predictions with actual findings was corroborated by the calibration curve. Furthermore, DCA affirmed the clinical value of the model in predicting DPN. Conclusion: This research introduces an innovative risk nomogram designed for the prediction of diabetic peripheral neuropathy in individuals suffering from type 2 diabetes mellitus. It offers a valuable resource for healthcare professionals to pinpoint those at elevated risk of developing this complication. As a functional instrument, it stands as a viable option for the prognostication of DPN in clinical settings.

A Prediction Model for Prolonged Hospital Length of Stay (ProLOS) in Coronavirus Disease 2019 (COVID-19) Patients.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is an acute respiratory infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). With the normalization of COVID-19 globally, it is crucial to construct a prediction model that enables clinicians to identify patients at risk for ProLOS based on demographics and serum inflammatory biomarkers. METHODS: The study included hospitalized patients with a confirmed diagnosis of COVID-19. These patients were randomly grouped into a training (80%) and a test (20%) cohort. The LASSO regression and ten-fold cross-validation method were applied to filter variables. The training cohort utilized multifactorial logistic regression analyses to identify the independent factors of ProLOS in COVID-19 patients. A 4-variable nomogram was created for clinical use. ROC curves were plotted, and the area under the curve (AUC) was calculated to evaluate the model's discrimination; calibration analysis was planned to assess the validity of the nomogram, and decision curve analysis (DCA) was used to evaluate the clinical usefulness of the model. RESULTS: The results showed that among 310 patients with COVID-19, 80 had extended hospitalization (80/310). Four independent risk factors for COVID-19 patients were identified: age, coexisting chronic respiratory diseases, white blood cell count (WBC), and serum albumin (ALB). A nomogram based on these variables was created. The AUC in the training cohort was 0.808 (95% CI: 0.75 - 0.8671), and the AUC in the test cohort was 0.815 (95% CI: 0.7031 - 0.9282). The model demonstrates good calibration and can be used with threshold probabilities ranging from 0% to 100% to obtain clinical net benefits. CONCLUSIONS: A predictive model has been created to accurately predict whether the hospitalization duration of COVID-19 patients will be prolonged. This model incorporates serum WBC, ALB levels, age, and the presence of chronic respiratory system diseases.

Prediction of Subsequent Vertebral Fracture After Acute Osteoporotic Fractures from Clinical and Paraspinal Muscle Features.

To construct a nomogram based on clinical factors and paraspinal muscle features to predict vertebral fractures occurring after acute osteoporotic vertebral compression fracture (OVCF). We retrospectively enrolled 307 patients with acute OVCF between January 2013 and August 2022, and performed magnetic resonance imaging of the L3/4 and L4/5 intervertebral discs (IVDs) to estimate the cross-sectional area (CSA) and degree of fatty infiltration (FI) of the paraspinal muscles. We also collected clinical and radiographic data. We used univariable and multivariable Cox proportional hazards models to identify factors that should be included in the predictive nomogram. Post-OVCF vertebral fracture occurred within 3, 12, and 24 months in 33, 69, and 98 out of the 307 patients (10.8%, 22.5%, and 31.9%, respectively). Multivariate analysis revealed that this event was associated with percutaneous vertebroplasty treatment, higher FI at the L3/4 IVD levels of the psoas muscle, and lower relative CSA of functional muscle at the L4/5 IVD levels of the multifidus muscle. Area under the curve values for subsequent vertebral fracture at 3, 12, and 24 months were 0.711, 0.724, and 0.737, respectively, indicating remarkable accuracy of the nomogram. We developed a model for predicting post-OVCF vertebral fracture from diagnostic information about prescribed treatment, FI at the L3/4 IVD levels of the psoas muscle, and relative CSA of functional muscle at the L4/5 IVD levels of the multifidus muscle. This model could facilitate personalized predictions and preventive strategies.

Gefitinib metabolism-related lncRNAs for the prediction of prognosis, tumor microenvironment and drug sensitivity in lung adenocarcinoma.

The complete compound of gefitinib is effective in the treatment of lung adenocarcinoma. However, the effect on lung adenocarcinoma (LUAD) during its catabolism has not yet been elucidated. We carried out this study to examine the predictive value of gefitinib metabolism-related long noncoding RNAs (GMLncs) in LUAD patients. To filter GMLncs and create a prognostic model, we employed Pearson correlation, Lasso, univariate Cox, and multivariate Cox analysis. We combined risk scores and clinical features to create nomograms for better application in clinical settings. According to the constructed prognostic model, we performed GO/KEGG and GSEA enrichment analysis, tumor immune microenvironment analysis, immune evasion and immunotherapy analysis, somatic cell mutation analysis, drug sensitivity analysis, IMvigor210 immunotherapy validation, stem cell index analysis and real-time quantitative PCR (RT-qPCR) analysis. We built a predictive model with 9 GMLncs, which showed good predictive performance in validation and training sets. The calibration curve demonstrated excellent agreement between the expected and observed survival rates, for which the predictive performance was better than that of the nomogram without a risk score. The metabolism of gefitinib is related to the cytochrome P450 pathway and lipid metabolism pathway, and may be one of the causes of gefitinib resistance, according to analyses from the Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Immunological evasion and immunotherapy analysis revealed that the likelihood of immune evasion increased with risk score. Tumor microenvironment analysis found most immune cells at higher concentrations in the low-risk group. Drug sensitivity analysis found 23 sensitive drugs. Twenty-one of these drugs exhibited heightened sensitivity in the high-risk group. RT-qPCR analysis validated the characteristics of 9 GMlncs. The predictive model and nomogram that we constructed have good application value in evaluating the prognosis of patients and guiding clinical treatment.

A first trimester prediction model and nomogram for gestational diabetes mellitus based on maternal clinical risk factors in a resource-poor setting.

BACKGROUND: The implementation of universal screening for Gestational Diabetes Mellitus (GDM) is challenged by several factors key amongst which is limited resources, hence the continued reliance on risk factor-based screening. Effective identification of high-risk women early in pregnancy may enable preventive intervention. This study aimed at developing a GDM prediction model based on maternal clinical risk factors that are easily assessable in the first trimester of pregnancy in a population of Nigerian women. METHODS: This was a multi-hospital prospective observational cohort study of 253 consecutively selected pregnant women from which maternal clinical data was collected at 8-12 weeks gestational age. Diagnosis of GDM was made via a one-step 75-gram Oral Glucose Tolerance Test (OGTT) at 24-28 weeks of gestation. A GDM prediction model and nomogram based on selected maternal clinical risk factors was developed using multiple logistic regression analysis, and its performance was assessed by Receiver Operator Curve (ROC) analysis. Data analysis was carried out using Statistical Package for Social Sciences (SPSS) version 25 and Python programming language (version 3.0). RESULTS: Increasing maternal age, higher body mass index (BMI), a family history of diabetes mellitus in first-degree relative and previous history of foetal macrosomia were the major predictors of GDM. The model equation was: LogitP = 6.358 - 0.066 × Age - 0.075 × First trimester BMI - 1.879 × First-degree relative with diabetes mellitus - 0.522 × History of foetal macrosomia. It had an area under the receiver operator characteristic (ROC) curve (AUC) of 0.814 (95% CI: 0.751-0.877; p-value < 0.001), and at a predicted probability threshold of 0.745, it had a sensitivity of 79.2% and specificity of 74.5%. CONCLUSION: This first trimester prediction model reliably identifies women at high risk for GDM development in the first trimester, and the nomogram enhances its practical applicability, contributing to improved clinical outcomes in the study population.

Abnormal coagulation after hepatectomy in patients with normal preoperative coagulation function.

BACKGROUND: To explore the risk factors for postoperative abnormal coagulation (PAC) and establish a predictive model for patients with normal preoperative coagulation function who underwent hepatectomy. MATERIALS AND METHODS: A total of 661 patients with normal preoperative coagulation function who underwent hepatectomy between January 2015 and December 2021 at the First Affiliated Hospital of Sun Yat-sen University were divided into two groups: the postoperative abnormal coagulation group (PAC group, n = 362) and the normal coagulation group (non-PAC group, n = 299). Univariate and multivariate logistic analyses were used to identify the risk factors for PAC. RESULTS: The incidence of PAC in 661 patients who underwent hepatectomy was 54.8% (362/661). The least absolute shrinkage and selection operator (LASSO) method was used for multivariate logistic regression analysis. The preoperative international normalized ratio (INR), intraoperative succinyl gelatin infusion and major hepatectomy were found to be independent risk factors for PAC. A nomogram for predicting the PAC after hepatectomy was constructed. The model presented a receiver operating characteristic (ROC) curve of 0.742 (95% confidence interval (CI): 0.697-0.786) in the training cohort. The validation set demonstrated a promising ROC of 0.711 (95% CI: 0.639-0.783), and the calibration curve closely approximated the true incidence. Decision curve analysis (DCA) was performed to assess the clinical usefulness of the predictive model. The risk of PAC increased when the preoperative international normalized ratio (INR) was greater than 1.025 and the volume of intraoperative succinyl gelatin infusion was greater than 1500 ml. CONCLUSION: The PAC is closely related to the preoperative INR, intraoperative succinyl gelatin infusion and major hepatectomy. A three-factor prediction model was successfully established for predicting the PAC after hepatectomy.

Analysis of Bladder Cancer Staging Prediction Using Deep Residual Neural Network, Radiomics, and RNA-Seq from High-Definition CT Images.

Bladder cancer has recently seen an alarming increase in global diagnoses, ascending as a predominant cause of cancer-related mortalities. Given this pressing scenario, there is a burgeoning need to identify effective biomarkers for both the diagnosis and therapeutic guidance of bladder cancer. This study focuses on evaluating the potential of high-definition computed tomography (CT) imagery coupled with RNA-sequencing analysis to accurately predict bladder tumor stages, utilizing deep residual networks. Data for this study, including CT images and RNA-Seq datasets for 82 high-grade bladder cancer patients, were sourced from the TCIA and TCGA databases. We employed Cox and lasso regression analyses to determine radiomics and gene signatures, leading to the identification of a three-factor radiomics signature and a four-gene signature in our bladder cancer cohort. ROC curve analyses underscored the strong predictive capacities of both these signatures. Furthermore, we formulated a nomogram integrating clinical features, radiomics, and gene signatures. This nomogram's AUC scores stood at 0.870, 0.873, and 0.971 for 1-year, 3-year, and 5-year predictions, respectively. Our model, leveraging radiomics and gene signatures, presents significant promise for enhancing diagnostic precision in bladder cancer prognosis, advocating for its clinical adoption.

A nomogram to predict mortality in patients with severe fever with thrombocytopenia syndrome.

Severe fever with thrombocytopenia syndrome (SFTS) is an acute infectious disease caused by a novel Bunyavirus infection with low population immunity and high mortality rate. Lacking specific therapies, the treatment measures vary with the severity of the disease, therefore, a case control study involved 394 SFTS patients was taken to determine risk factors for mortality. Comparative clinical data from the first 24 h after admission was collected through the electronic medical record system. Independent risk factors for death of SFTS were identified through univariate and multivariate binary logistic regression analyses. The results of the logistic regression were visualized using a nomogram which was created by downloading RMS package in the R program. In our study, four independent mortality risk factors were identified: advanced age(mean 70.45 ± 7.76 years), MODS, elevated APTT, and D-dimer. The AUC of the nomogram was 0.873 (0.832, 0.915), and the model passes the calibration test namely Unreliability test with P = 0.958, showing that the model's predictive ability is excellent. The nomogram to determine the risk of death in SFTS efficiently provide a basis for clinical decision-making for treatment.

Investigation of ENO2 as a promising novel marker for the progression of colorectal cancer with microsatellite instability-high.

BACKGROUND: Microsatellite instability-high (MSI-H) has emerged as a significant biological characteristic of colorectal cancer (CRC). Studies reported that MSI-H CRC generally had a better prognosis than microsatellite stable (MSS)/microsatellite instability-low (MSI-L) CRC, but some MSI-H CRC patients exhibited distinctive molecular characteristics and experienced a less favorable prognosis. In this study, our objective was to explore the metabolic transcript-related subtypes of MSI-H CRC and identify a biomarker for predicting survival outcomes. METHODS: Single-cell RNA sequencing (scRNA-seq) data of MSI-H CRC patients were obtained from the Gene Expression Omnibus (GEO) database. By utilizing the copy number variation (CNV) score, a malignant cell subpopulation was identified at the single-cell level. The metabolic landscape of various cell types was examined using metabolic pathway gene sets. Subsequently, functional experiments were conducted to investigate the biological significance of the hub gene in MSI-H CRC. Finally, the predictive potential of the hub gene was assessed using a nomogram. RESULTS: This study revealed a malignant tumor cell subpopulation from the single-cell RNA sequencing (scRNA-seq) data. MSI-H CRC was clustered into two subtypes based on the expression profiles of metabolism-related genes, and ENO2 was identified as a hub gene. Functional experiments with ENO2 knockdown and overexpression demonstrated its role in promoting CRC cell migration, invasion, glycolysis, and epithelial-mesenchymal transition (EMT) in vitro. High expression of ENO2 in MSI-H CRC patients was associated with worse clinical outcomes, including increased tumor invasion depth (p = 0.007) and greater likelihood of perineural invasion (p = 0.015). Furthermore, the nomogram and calibration curves based on ENO2 showed potential prognosis predictive performance. CONCLUSION: Our findings suggest that ENO2 serves as a novel prognostic biomarker and is associated with the progression of MSI-H CRC.

Development and validation of a predictive model for the risk of sarcopenia in the older adults in China.

BACKGROUND: Sarcopenia is a progressive age-related disease that can cause a range of adverse health outcomes in older adults, and older adults with severe sarcopenia are also at increased short-term mortality risk. The aim of this study was to construct and validate a risk prediction model for sarcopenia in Chinese older adults. METHODS: This study used data from the 2015 China Health and Retirement Longitudinal Study (CHARLS), a high-quality micro-level data representative of households and individuals aged 45 years and older adults in China. The study analyzed 65 indicators, including sociodemographic indicators, health-related indicators, and biochemical indicators. RESULTS: 3454 older adults enrolled in the CHARLS database in 2015 were included in the final analysis. A total of 997 (28.8%) had phenotypes of sarcopenia. Multivariate logistic regression analysis showed that sex, Body Mass Index (BMI), Mean Systolic Blood Pressure (MSBP), Mean Diastolic Blood Pressure (MDBP) and pain were predictive factors for sarcopenia in older adults. These factors were used to construct a nomogram model, which showed good consistency and accuracy. The AUC value of the prediction model in the training set was 0.77 (95% CI = 0.75-0.79); the AUC value in the validation set was 0.76 (95% CI = 0.73-0.79). Hosmer-Lemeshow test values were P = 0.5041 and P = 0.2668 (both P > 0.05). Calibration curves showed significant agreement between the nomogram model and actual observations. ROC and DCA showed that the nomograms had good predictive properties. CONCLUSIONS: The constructed sarcopenia risk prediction model, incorporating factors such as sex, BMI, MSBP, MDBP, and pain, demonstrates promising predictive capabilities. This model offers valuable insights for clinical practitioners, aiding in early screening and targeted interventions for sarcopenia in Chinese older adults.

A clinical-radiomics nomogram based on dual-layer spectral detector CT to predict cancer stage in pancreatic ductal adenocarcinoma.

BACKGROUND: This study aimed to evaluate the efficacy of radiomics signatures derived from polyenergetic images (PEIs) and virtual monoenergetic images (VMIs) obtained through dual-layer spectral detector CT (DLCT). Moreover, it sought to develop a clinical-radiomics nomogram based on DLCT for predicting cancer stage (early stage: stage I-II, advanced stage: stage III-IV) in pancreatic ductal adenocarcinoma (PDAC). METHODS: A total of 173 patients histopathologically diagnosed with PDAC and who underwent contrast-enhanced DLCT were enrolled in this study. Among them, 49 were in the early stage, and 124 were in the advanced stage. Patients were randomly categorized into training (n = 122) and test (n = 51) cohorts at a 7:3 ratio. Radiomics features were extracted from PEIs and 40-keV VMIs were reconstructed at both arterial and portal venous phases. Radiomics signatures were constructed based on both PEIs and 40-keV VMIs. A radiomics nomogram was developed by integrating the 40-keV VMI-based radiomics signature with selected clinical predictors. The performance of the nomogram was assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curves analysis (DCA). RESULTS: The PEI-based radiomics signature demonstrated satisfactory diagnostic efficacy, with the areas under the ROC curves (AUCs) of 0.92 in both the training and test cohorts. The optimal radiomics signature was based on 40-keV VMIs, with AUCs of 0.96 and 0.94 in the training and test cohorts. The nomogram, which integrated a 40-keV VMI-based radiomics signature with two clinical parameters (tumour diameter and normalized iodine density at the portal venous phase), demonstrated promising calibration and discrimination in both the training and test cohorts (0.97 and 0.91, respectively). DCA indicated that the clinical-radiomics nomogram provided the most significant clinical benefit. CONCLUSIONS: The radiomics signature derived from 40-keV VMI and the clinical-radiomics nomogram based on DLCT both exhibited exceptional performance in distinguishing early from advanced stages in PDAC, aiding clinical decision-making for patients with this condition.

A Competing Risk Nomogram for Prediction of Prognosis in Patients With Primary Squamous Cell Thyroid Carcinoma.

Objective: Primary squamous cell thyroid carcinoma (PSCTC) is an extremely rare carcinoma, accounting for less than 1% of all thyroid carcinomas. However, the factors contributing to PSCTC outcomes remain unclear. This study aimed to identify the prognostic factors and develop a prognostic predictive model for patients with PSCTC. Methods: The analysis included patients diagnosed with thyroid carcinoma between 1975 and 2016 from the Surveillance, Epidemiology, and End Results database. Prognostic differences among the 5 pathological types of thyroid carcinomas were analyzed. To determine prognostic factors in PSCTC patients, the Cox regression model and Fine-Gray competing risk model were utilized. Based on the Fine-Gray competing risk model, a nomogram was established for predicting the prognosis of patients with PSCTC. Results: A total of 198,757 thyroid carcinoma patients, including 218 PSCTC patients, were identified. We found that PSCTC and anaplastic thyroid cancer had the worst prognosis among the 5 pathological types of thyroid carcinoma (P < .001). According to univariate and multivariate Cox regression analyses, age (71-95 years) was an independent risk factor for poorer overall survival and disease-specific survival in PSCTC patients. Using Fine-Gray regression analysis, the total number of in situ/malignant tumors for patient (Number 1) (≥2) was identified as an independent protective factor for prognosis of PSCTC. The area under the curve, the concordance index (C-index), calibration curves and decision curve analysis revealed that the nomogram was capable of predicting the prognosis of PSCTC patients accurately. Conclusion: The competing risk nomogram is highly accurate in predicting prognosis for patients with PSCTC, which may help clinicians to optimize individualized treatment decisions.

Development and assessment of an RNA editing-based risk model for the prognosis of cervical cancer patients.

RNA editing, as an epigenetic mechanism, exhibits a strong correlation with the occurrence and development of cancers. Nevertheless, few studies have been conducted to investigate the impact of RNA editing on cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC). In order to study the connection between RNA editing and CESC patients' prognoses, we obtained CESC-related information from The Cancer Genome Atlas (TCGA) database and randomly allocated the patients into the training group or testing group. An RNA editing-based risk model for CESC patients was established by Cox regression analysis and least absolute shrinkage and selection operator (LASSO). According to the median score generated by this RNA editing-based risk model, patients were categorized into subgroups with high and low risks. We further constructed the nomogram by risk scores and clinical characteristics and analyzed the impact of RNA editing levels on host gene expression levels and adenosine deaminase acting on RNA. Finally, we also compared the biological functions and pathways of differentially expressed genes (DEGs) between different subgroups by enrichment analysis. In this risk model, we screened out 6 RNA editing sites with significant prognostic value. The constructed nomogram performed well in forecasting patients' prognoses. Furthermore, the level of RNA editing at the prognostic site exhibited a strong correlation with host gene expression. In the high-risk subgroup, we observed multiple biological functions and pathways associated with immune response, cell proliferation, and tumor progression. This study establishes an RNA editing-based risk model that helps forecast patients' prognoses and offers a new understanding of the underlying mechanism of RNA editing in CESC.

Six-gene prognostic signature for non-alcoholic fatty liver disease susceptibility using machine learning.

BACKGROUND: nonalcoholic fatty liver disease (NAFLD) is a common liver disease affecting the global population and its impact on human health will continue to increase. Genetic susceptibility is an important factor influencing its onset and progression, and there is a lack of reliable methods to predict the susceptibility of normal populations to NAFLD using appropriate genes. METHODS: RNA sequencing data relating to nonalcoholic fatty liver disease was analyzed using the "limma" package within the R software. Differentially expressed genes were obtained through preliminary intersection screening. Core genes were analyzed and obtained by establishing and comparing 4 machine learning models, then a prediction model for NAFLD was constructed. The effectiveness of the model was then evaluated, and its applicability and reliability verified. Finally, we conducted further gene correlation analysis, analysis of biological function and analysis of immune infiltration. RESULTS: By comparing 4 machine learning algorithms, we identified SVM as the optimal model, with the first 6 genes (CD247, S100A9, CSF3R, DIP2C, OXCT 2 and PRAMEF16) as predictive genes. The nomogram was found to have good reliability and effectiveness. Six genes' receiver operating characteristic curves (ROC) suggest an essential role in NAFLD pathogenesis, and they exhibit a high predictive value. Further analysis of immunology demonstrated that these 6 genes were closely connected to various immune cells and pathways. CONCLUSION: This study has successfully constructed an advanced and reliable prediction model based on 6 diagnostic gene markers to predict the susceptibility of normal populations to NAFLD, while also providing insights for potential targeted therapies.

A nomogram based on the SII3 and clinical indicators predicts survival in patients with nasopharyngeal carcinoma treated with PD-1 inhibitors.

Numerous inflammatory indicators have been demonstrated to be strongly correlated with tumor prognosis. However, the association between inflammatory indicators and the prognosis of patients with nasopharyngeal carcinoma (NPC) receiving treatment with programmed death receptor-1 (PD-1) immunosuppressant monoclonal antibodies remains uncertain. Inflammatory indicators in peripheral blood were collected from 161 NPC patients at 3 weeks after initial PD-1 treatment. Through univariate and multivariate analyses, as well as nomogram and survival analyses, we aimed to identify independent prognostic factors related to 1-year progression-free survival (PFS). Subsequently, a prognostic nomogram was devised, and its predictive and discriminating abilities were assessed utilizing calibration curves and the concordance index. Our univariate and multivariate analyses indicated that age (P = .012), M stage (P < .001), and systemic immune-inflammation index (SII) during the third week following initial PD-1 treatment (SII3, P = .005) were independently correlated with the 1-year PFS of NPC patients after PD-1 treatment. Notably, we constructed a novel nomogram based on the SII3, age, and M stage. Importantly, utilizing the derived cutoff point from the nomogram, the high-risk group exhibited significantly shorter PFS than did the low-risk group (P < .001). Furthermore, the nomogram demonstrated a greater concordance index for PFS than did the tumor node metastasis stage within the entire cohort. We successfully developed a nomogram that integrates the SII3 and clinical markers to accurately predict the 1-year PFS of NPC patients receiving PD-1 inhibitor treatment.