ReProMSig: an integrative platform for development and application of reproducible multivariable models for cancer prognosis supporting guideline-based transparent reporting.

Adequate reporting is essential for evaluating the performance and clinical utility of a prognostic prediction model. Previous studies indicated a prevalence of incomplete or suboptimal reporting in translational and clinical studies involving development of multivariable prediction models for prognosis, which limited the potential applications of these models. While reporting templates introduced by the established guidelines provide an invaluable framework for reporting prognostic studies uniformly, there is a widespread lack of qualified adherence, which may be due to miscellaneous challenges in manual reporting of extensive model details, especially in the era of precision medicine. Here, we present ReProMSig (Reproducible Prognosis Molecular Signature), a web-based integrative platform providing the analysis framework for development, validation and application of a multivariable prediction model for cancer prognosis, using clinicopathological features and/or molecular profiles. ReProMSig platform supports transparent reporting by presenting both methodology details and analysis results in a strictly structured reporting file, following the guideline checklist with minimal manual input needed. The generated reporting file can be published together with a developed prediction model, to allow thorough interrogation and external validation, as well as online application for prospective cases. We demonstrated the utilities of ReProMSig by development of prognostic molecular signatures for stage II and III colorectal cancer respectively, in comparison with a published signature reproduced by ReProMSig. Together, ReProMSig provides an integrated framework for development, evaluation and application of prognostic/predictive biomarkers for cancer in a more transparent and reproducible way, which would be a useful resource for health care professionals and biomedical researchers.

An immunogenic cell death-related gene expression signature in predicting prognosis of pancreatic ductal adenocarcinoma.

BACKGROUND: Immunogenic cell death (ICD) has been identified as regulated cell death, which is sufficient to activate the adaptive immune response. This study aimed to research ICD-related genes and create a gene model to predict pancreatic ductal adenocarcinoma (PAAD) patients' prognosis. METHODS: The RNA sequencing and clinical data were downloaded from the TGCA and GEO databases. The PAAD samples were classified into two subtypes based on the expression levels of ICD-related genes using consensus clustering. Based on the differentially expressed genes (DEGs), a prognostic scoring model was constructed using LASSO regression and Cox regression, and the scoring model was used to predict the prognosis of PAAD patients. Moreover, colony formation assay was performed to confirm the prognostic value of those genes. RESULTS: We identified two ICD cluster by consensus clustering, and found that the the ICD-high group was closely associated with immune-hot phenotype, favorable clinical outcomes. We established an ICD-related prognostic model which can predict the prognosis of pancreatic ductal adenocarcinoma. Moreover, depletion of NT5E, ATG5, FOXP3, and IFNG inhibited the colony formation ability of pancreatic cancer cell. CONCLUSION: We identified a novel classification for PAAD based on the expression of ICD-related genes, which may provide a potential strategy for therapeutics against PAAD.

Novel molecular insights into pyroptosis in triple-negative breast cancer prognosis and immunotherapy.

BACKGROUND: Patients with triple-negative breast cancer (TNBC) often have a poor prognostic outcome. Current treatment strategies cannot benefit all TNBC patients. Previous findings suggested pyroptosis as a novel target for suppressing cancer development, although the relationship between TNBC and pyroptosis-related genes (PRGs) was still unclear. METHODS: Gene expression data and clinical follow-up of TNBC patients were collected from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) and Gene Expression Omnibus (GEO). PRGs were screened using weighted gene co-expression network analysis. Cox regression analysis and the least absolute shrinkage and selection operator (i.e. LASSO) technique were applied to construct a pyroptosis-related prognostic risk score (PPRS) model, which was further combined with the clinicopathological characteristics of TNBC patients to develop a survival decision tree and a nomogram. The model was used to calculate the PPRS, and then the overall survival, immune infiltration, immunotherapy response and drug sensitivity of TNBC patients were analyzed based on the PPRS. RESULTS: The PPRS model was closely related to clinicopathological features and can independently and accurately predict the prognosis of TNBC. According to normalized PPRS, patients in different cohorts were divided into two groups. Compared with the high-PPRS group, the low-PPRS group had significantly higher ESTIMATE (i.e. Estimation of STromal and Immune cells in MAlignantTumours using Expression data) score, immune score and stromal score, and it also had overexpressed immune checkpoints and significantly reduced Tumor Immune Dysfunction and Exclusion (TIDE) score, as well as higher sensitivity to paclitaxel, veliparib, olaparib and talazoparib. A decision tree and nomogram based on PPRS and clinical characteristics can improve the prognosis stratification and survival prediction for TNBC patients. CONCLUSIONS: A PPRS model was developed to predict TNBC patients' immune characteristics and response to immunotherapy, chemotherapy and targeted therapy, as well as their survival outcomes.

Integrative multi-omics analysis unravels the host response landscape and reveals a serum protein panel for early prognosis prediction for ARDS.

BACKGROUND: The multidimensional biological mechanisms underpinning acute respiratory distress syndrome (ARDS) continue to be elucidated, and early biomarkers for predicting ARDS prognosis are yet to be identified. METHODS: We conducted a multicenter observational study, profiling the 4D-DIA proteomics and global metabolomics of serum samples collected from patients at the initial stage of ARDS, alongside samples from both disease control and healthy control groups. We identified 28-day prognosis biomarkers of ARDS in the discovery cohort using the LASSO method, fold change analysis, and the Boruta algorithm. The candidate biomarkers were validated through parallel reaction monitoring (PRM) targeted mass spectrometry in an external validation cohort. Machine learning models were applied to explore the biomarkers of ARDS prognosis. RESULTS: In the discovery cohort, comprising 130 adult ARDS patients (mean age 72.5, 74.6% male), 33 disease controls, and 33 healthy controls, distinct proteomic and metabolic signatures were identified to differentiate ARDS from both control groups. Pathway analysis highlighted the upregulated sphingolipid signaling pathway as a key contributor to the pathological mechanisms underlying ARDS. MAP2K1 emerged as the hub protein, facilitating interactions with various biological functions within this pathway. Additionally, the metabolite sphingosine 1-phosphate (S1P) was closely associated with ARDS and its prognosis. Our research further highlights essential pathways contributing to the deceased ARDS, such as the downregulation of hematopoietic cell lineage and calcium signaling pathways, contrasted with the upregulation of the unfolded protein response and glycolysis. In particular, GAPDH and ENO1, critical enzymes in glycolysis, showed the highest interaction degree in the protein-protein interaction network of ARDS. In the discovery cohort, a panel of 36 proteins was identified as candidate biomarkers, with 8 proteins (VCAM1, LDHB, MSN, FLG2, TAGLN2, LMNA, MBL2, and LBP) demonstrating significant consistency in an independent validation cohort of 183 patients (mean age 72.6 years, 73.2% male), confirmed by PRM assay. The protein-based model exhibited superior predictive accuracy compared to the clinical model in both the discovery cohort (AUC: 0.893 vs. 0.784; Delong test, P < 0.001) and the validation cohort (AUC: 0.802 vs. 0.738; Delong test, P = 0.008). INTERPRETATION: Our multi-omics study demonstrated the potential biological mechanism and therapy targets in ARDS. This study unveiled several novel predictive biomarkers and established a validated prediction model for the poor prognosis of ARDS, offering valuable insights into the prognosis of individuals with ARDS.

A practical online prediction platform to predict the survival status of laryngeal squamous cell carcinoma after 5 years.

OBJECTIVE: Currently, there are few practical tools for predicting the prognosis of laryngeal squamous cell carcinoma (LSCC). This study aims to establish a model and a convenient online prediction platform to predict whether LSCC patients will survive 5 years after diagnosis, providing a reference for further evaluation of patient prognosis. METHODS: This is a retrospective study based on data collected from two centers. Center 1 included 117 LSCC patients with survival prognosis data, and center 2 included 33 patients, totaling 150 patients. All data were divided into independent training sets (60 %) and testing sets (40 %). Eight machine learning (ML) algorithms were used to establish models with 11 clinical parameters as input features. The accuracy, sensitivity, specificity, and receiver operating characteristic curve (ROC) of the testing set were used to evaluate the models, and the best model was selected. The model was then developed into a website-based 5-year survival status prediction platform for LSCC. In addition, we also used the SHapley Additive exPlanations (SHAP) tool to conduct interpretability analysis on the parameters of the model. RESULTS: The LSCC 5-year survival status prediction model using the support vector machine (SVM) algorithm achieved the best results, with accuracy, sensitivity, specificity, and area under the ROC curve (AUC) of 85.0 %, 87.5 %, 75.0 %, and 81.2 % respectively. The online platform for predicting the 5-year survival status of LSCC based on this model was successfully established. The SHAP analysis shows that the clinical stage is the most important feature of the model. CONCLUSION: This study successfully established a ML model and a practical online prediction platform to predict the survival status of laryngeal cancer patients after 5 years, which may help clinicians to better evaluate the prognosis of LSCC.

Genes associated with N6-methyladenosine regulators provide insight into the prognosis and immune response to renal clear cell carcinoma.

As one of the most common messenger ribonucleic acid modifications in eukaryotic organisms, N6-methyladenosine (m6A) is involved in a wide variety of biological functions. The imbalance of m6A RNA modification may be linked to cancer and other disorders, according to a growing body of studies. Its effects on clear cell renal cell carcinoma (KIRC) have not been well discussed, though. Here, we acquired the expression patterns of 23 important regulators of m6A RNA modification and assess how they might fare in KIRC. We observed that 17 major m6A RNA modification regulatory factors had a substantial predictive influence on KIRC. Using the "ConsensusCluster" program, we defined two groupings (Cluster 1 and Cluster 2) depending on the expression of the aforementioned 17 key m6A RNA methylation regulators. The Cluster 2 has a less favorable outcome and is strongly related with a lesser immune microenvironment, according to the findings. We also developed a strong risk profile for three m6A RNA modifiers (METTL14, YTHDF1, and LRPPRC) using multivariate Cox regression analysis. According to further research, the aforementioned risk profile could serve as an independent predicting factor for KIRC, and the chemotherapy response sensitivity was analyzed between two risk groups. Moreover, to effectively forecast the future outlook of KIRC clients, we established a novel prognostic approach according to gender, age, histopathological level, clinical stage, and risk score. Finally, the function of hub gene METTL14 was validated by cell proliferation and subcutaneous graft tumor in mice. In conclusion, we discovered that m6A RNA modifiers play an important role in controlling KIRC and created a viable risk profile as a marker of prediction for KIRC clients.

Establishing a prognostic model of chromatin modulators and identifying potential drug candidates in renal clear cell patients.

BACKGROUND: Renal carcinoma is a common malignant tumor of the urinary system. Advanced renal carcinoma has a low 5-year survival rate and a poor prognosis. More and more studies have confirmed that chromatin regulators (CRs) can regulate the occurrence and development of cancer. This article investigates the functional and prognostic value of CRs in renal carcinoma patients. METHODS: mRNA expression and clinical information were obtained from The Cancer Genome Atlas database. Univariate Cox regression analysis and LASSO regression analysis were used to select prognostic chromatin-regulated genes and use them to construct a risk model for predicting the prognosis of renal cancer. Differences in prognosis between high-risk and low-risk groups were compared using Kaplan-Meier analysis. In addition, we analyzed the relationship between chromatin regulators and tumor immune infiltration, and explored differences in drug sensitivity between risk groups. RESULTS: We constructed a model consisting of 11 CRs to predict the prognosis of renal cancer patients. We not only successfully validated its feasibility, but also found that the 11 CR-based model was an independent prognostic factor. Functional analysis showed that CRs were mainly enriched in cancer development-related signalling pathways. We also found through the TIMER database that CR-based models were also associated with immune cell infiltration and immune checkpoints. At the same time, the genomics of drug sensitivity in cancer database was used to analyze the commonly used drugs of renal clear cell carcinoma patients. It was found that patients in the low-risk group were sensitive to medicines such as axitinib, pazopanib, sorafenib, and gemcitabine. In contrast, those in the high-risk group may be sensitive to sunitinib. CONCLUSION: The chromatin regulator-related prognostic model we constructed can be used to assess the prognostic risk of patients with clear cell renal cell carcinoma. The results of this study can bring new ideas for targeted therapy of clear cell renal carcinoma, helping doctors to take corresponding measures in advance for patients with different risks.

Suppression of innate and acquired immunity in severe hand foot and mouth disease caused by EV71 infections in children.

Hand, foot, and mouth disease (HFMD) is a common children infectious disease caused by human enteroviruses. Most of the cases have minimal symptoms, however, some patients may develop serious neurological, cardiac complications, or even death. The pathological mechanism leading to severe HFMD is not clearly understood, and the immunological status of the individual patient may play an important role. Transcriptomes of peripheral blood mononuclear cells from EV71-infected patients (n = 45) and healthy controls (n = 36) were examined. Immune pathways were up-regulated in patients with mild disease symptoms (n = 11, M) compared to the healthy controls (n = 36, H), demonstrating an effective anti-viral response upon EV71 infection. However, in patients with severe symptoms (n = 23, S) as well as severe patients following treatment (n = 11, A), their innate and acquired immune pathways were down-regulated, indicating a global immunity suppression. Such immune suppression characteristics could thus provide an opportunity for early EV-71 infection prognosis prediction. Based on our cohort, an SVM model using RNA-seq expression levels of five genes (MCL1, ZBTB37, PLEKHM1P, IFNAR2 and YEATS2) was developed and achieved a high ROC-AUC (91·3%) in predicting severe HFMD. Meanwhile, qPCR fold-changes method was performed based three genes (MCL1, IFNAR2 and YEATS2) on additional cohort. This qPCR method achieved a ROC-AUC of 78.6% in predicting severe HFMD, which the patients could be distinguished in 2-3 h. Therefore, our models demonstrate the possibility of HFMD severity prediction based on the selected biomarkers that predict severe HFMD effectively.

Mitochondria-related lncRNAs: predicting prognosis, tumor microenvironment and treatment response in lung adenocarcinoma.

Lung cancer is the most common type of malignant tumor that affects people in China and even across the globe, as it exhibits the highest rates of morbidity and mortality. Lung adenocarcinoma (LUAD) is a type of lung cancer with a very high incidence. The purpose of this study was to identify potential biomarkers that could be used to forecast the prognosis and improve the existing therapy options for treating LUAD. Clinical and RNA sequencing data of LUAD patients were retrieved from the TCGA database, while the mitochondria-associated gene sets were acquired from the MITOMAP database. Thereafter, Pearson correlation analysis was carried out to screen mitochondria-associated lncRNAs. Furthermore, univariate Cox and Lasso regression analyses were used for the initial screening of the target lncRNAs for prognostic lncRNAs before they could be incorporated into a multivariate Cox Hazard ratio model. Then, the clinical data, concordance index, Kaplan-Meier (K-M) curves, and the clinically-relevant subjects that were approved by the Characteristic Curves (ROC) were employed for assessing the model's predictive value. Additionally, the differences in immune-related functions and biological pathway enrichment between high- and low-risk LUAD groups were examined. Nomograms were developed to anticipate the OS rates of the patients within 1-, 3-, and 5 years, and the differences in drug sensitivity and immunological checkpoints were compared. In this study, 2175 mitochondria-associated lncRNAs were screened. Univariate, multivariate, and Lasso Cox regression analyses were carried out to select 13 lncRNAs with an independent prognostic significance, and a prognostic model was developed. The OS analysis of the established prognostic prediction model revealed significant variations between the high- and low-risk patients. The AUC-ROC values after 1, 3, and 5 years were seen to be 0.746, 0.692, and 0.726, respectively. The results suggested that the prognostic model riskscore could be used as an independent prognostic factor that differed from the other clinical characteristics. After analyzing the findings of the study, it was noted that both the risk groups showed significant differences in their immune functioning, immunological checkpoint genes, and drug sensitivity. The prognosis of patients with LUAD could be accurately and independently predicted using a risk prediction model that included 13 mitochondria-associated lncRNAs.

Prognostic risk factors of serous ovarian carcinoma based on mesenchymal stem cell phenotype and guidance for therapeutic efficacy.

BACKGROUND: Epithelial ovarian cancer is the leading cause of death from gynecologic cancer, in which serous ovarian carcinoma (SOC) is the most common histological subtype. Although PARP inhibitors (PARPi) and antiangiogenics have been accepted as maintenance treatment in SOC, response to immunotherapy of SOC patients is limited. METHODS: The source of transcriptomic data of SOC was from the Cancer Genome Atlas database and Gene Expression Omnibus. The abundance scores of mesenchymal stem cells (MSC scores) were estimated for each sample by xCell. Weighted correlation network analysis is correlated the significant genes with MSC scores. Based on prognostic risk model construction with Cox regression analysis, patients with SOC were divided into low- and high-risk groups. And distribution of immune cells, immunosuppressors and pro-angiogenic factors in different risk groups was achieved by single-sample gene set enrichment analysis. The risk model of MSC scores was further validated in datasets of immune checkpoint blockade and antiangiogenic therapy. In the experiment, the mRNA expression of prognostic genes related to MSC scores was detected by real-time polymerase chain reaction, while the protein level was evaluated by immunohistochemistry. RESULTS: Three prognostic genes (PER1, AKAP12 and MMP17) were the constituents of risk model. Patients classified as high-risk exhibited worse prognosis, presented with an immunosuppressive phenotype, and demonstrated high micro-vessel density. Additionally, these patients were insensitive to immunotherapy and would achieve a longer overall survival with antiangiogenesis treatment. The validation experiments showed that the mRNA of PER1, AKAP12, and MMP17 was highly expressed in normal ovarian epithelial cells compared to SOC cell lines and there was a positive correlation between protein levels of PER1, AKAP12 and MMP17 and metastasis in human ovarian serous tumors. CONCLUSION: This prognostic model established on MSC scores can predict prognosis of patients and provide the guidance for patients receiving immunotherapy and molecular targeted therapy. Because the number of prognostic genes was fewer than other signatures of SOC, it will be easily accessible on clinic.

Metabolic classifications of renal cell carcinoma reveal intrinsic connections with clinical and immune characteristics.

BACKGROUND: Kidney cancer undergoes a dramatic metabolic shift and has demonstrated responsiveness to immunotherapeutic intervention. However, metabolic classification and the associations between metabolic alterations and immune infiltration in Renal cell carcinoma still remain elucidative. METHODS: Unsupervised consensus clustering was conducted on the TCGA cohorts for metabolic classification. GESA, mRNAsi, prognosis, clinical features, mutation load, immune infiltration and differentially expressed gene differences among different clusters were compared. The prognosis model and nomograms were constructed based on metabolic gene signatures and verified using external ICGC datasets. Immunohistochemical results from Human Protein Atlas database and Tongji hospital were used to validate gene expression levels in normal tissues and tumor samples. CCK8, apoptosis analysis, qPCR, subcutaneously implanted murine models and flowcytometry analysis were applied to investigate the roles of ACAA2 in tumor progression and anti-tumor immunity. RESULTS: Renal cell carcinoma was classified into 3 metabolic subclusters and the subcluster with low metabolic profiles displayed the poorest prognosis, highest invasiveness and AJCC grade, enhanced immune infiltration but suppressive immunophenotypes. ACAA2, ACAT1, ASRGL1, AKR1B10, ABCC2, ANGPTL4 were identified to construct the 6 gene-signature prognosis model and verified both internally and externally with ICGC cohorts. ACAA2 was demonstrated as a tumor suppressor and was associated with higher immune infiltration and elevated PD-1 expression of CD8+ T cells. CONCLUSIONS: Our research proposed a new metabolic classification method for RCC and revealed intrinsic associations between metabolic phenotypes and immune profiles. The identified gene signatures might serve as key factors bridging tumor metabolism and tumor immunity and warrant further in-depth investigations.

Construction of a prognostic risk score model based on the ARHGAP family to predict the survival of osteosarcoma.

BACKGROUND: Osteosarcoma (OS) is the most common primary malignancy of bone tumors. More and more ARHGAP family genes have been confirmed are to the occurrence, development, and invasion of tumors. However, its significance in osteosarcoma remains unclear. In this study, we aimed to identify the relationship between ARHGAP family genes and prognosis in patients with OS. METHODS: OS samples were retrieved from the TCGA and GEO databases. We then performed LASSO regression analysis and multivariate COX regression analysis to select ARHGAP family genes to construct a risk prognosis model. We then validated this prognostic model. We utilized ESTIMATE and CIBERSORT algorithms to calculate the stroma and immune scores of samples, as well as the proportions of tumor infiltrating immune cells (TICs). Finally, we conducted in vivo and in vitro experiments to investigate the effect of ARHGAP28 on osteosarcoma. RESULTS: We selected five genes to construct a risk prognosis model. Patients were divided into high- and low-risk groups and the survival time of the high-risk group was lower than that of the low-risk group. The high-risk group in the prognosis model constructed had relatively poor immune function. GSEA and ssGSEA showed that the low-risk group had abundant immune pathway infiltration. The overexpression of ARHGAP28 can inhibit the proliferation, migration, and invasion of osteosarcoma cells and tumor growth in mice, and IHC showed that overexpression of ARHGAP28 could inhibit the proliferation of tumor cells. CONCLUSION: We constructed a risk prognostic model based on five ARHGAP family genes, which can predict the overall survival of patients with osteosarcoma, to better assist us in clinical decision-making and individualized treatment.

Machine learning prognosis model based on patient-reported outcomes for chronic heart failure patients after discharge.

BACKGROUND: Patient-reported outcomes (PROs) can be obtained outside hospitals and are of great significance for evaluation of patients with chronic heart failure (CHF). The aim of this study was to establish a prediction model using PROs for out-of-hospital patients. METHODS: CHF-PRO were collected in 941 patients with CHF from a prospective cohort. Primary endpoints were all-cause mortality, HF hospitalization, and major adverse cardiovascular events (MACEs). To establish prognosis models during the two years follow-up, six machine learning methods were used, including logistic regression, random forest classifier, extreme gradient boosting (XGBoost), light gradient boosting machine, naive bayes, and multilayer perceptron. Models were established in four steps, namely, using general information as predictors, using four domains of CHF-PRO, using both of them and adjusting the parameters. The discrimination and calibration were then estimated. Further analyze were performed for the best model. The top prediction variables were further assessed. The Shapley additive explanations (SHAP) method was used to explain black boxes of the models. Moreover, a self-made web-based risk calculator was established to facilitate the clinical application. RESULTS: CHF-PRO showed strong prediction value and improved the performance of the models. Among the approaches, XGBoost of the parameter adjustment model had the highest prediction performance with an area under the curve of 0.754 (95% CI: 0.737 to 0.761) for death, 0.718 (95% CI: 0.717 to 0.721) for HF rehospitalization and 0.670 (95% CI: 0.595 to 0.710) for MACEs. The four domains of CHF-PRO, especially the physical domain, showed the most significant impact on the prediction of outcomes. CONCLUSION: CHF-PRO showed strong prediction value in the models. The XGBoost models using variables based on CHF-PRO and the patient's general information provide prognostic assessment for patients with CHF. The self-made web-based risk calculator can be conveniently used to predict the prognosis for patients after discharge. CLINICAL TRIAL REGISTRATION: URL: http://www.chictr.org.cn/index.aspx ; Unique identifier: ChiCTR2100043337.

Development and validation of a prognosis risk score model for preterm birth among pregnant women who had antenatal care visit, Northwest, Ethiopia, retrospective follow-up study.

BACKGROUND: Prematurity is the leading cause of neonatal morbidity and mortality, specifically in low-resource settings. The majority of prematurity can be prevented if early interventions are implemented for high-risk pregnancies. Developing a prognosis risk score for preterm birth based on easily available predictors could support health professionals as a simple clinical tool in their decision-making. Therefore, the study aims to develop and validate a prognosis risk score model for preterm birth among pregnant women who had antenatal care visit at Debre Markos Comprehensive and Specialized Hospital, Ethiopia. METHODS: A retrospective follow-up study was conducted among a total of 1,132 pregnant women. Client charts were selected using a simple random sampling technique. Data were extracted using structured checklist prepared in the Kobo Toolbox application and exported to STATA version 14 and R version 4.2.2 for data management and analysis. Stepwise backward multivariable analysis was done. A simplified risk prediction model was developed based on a binary logistic model, and the model's performance was assessed by discrimination power and calibration. The internal validity of the model was evaluated by bootstrapping. Decision Curve Analysis was used to determine the clinical impact of the model. RESULT: The incidence of preterm birth was 10.9%. The developed risk score model comprised of six predictors that remained in the reduced multivariable logistic regression, including age < 20, late initiation of antenatal care, unplanned pregnancy, recent pregnancy complications, hemoglobin < 11 mg/dl, and multiparty, for a total score of 17. The discriminatory power of the model was 0.931, and the calibration test was p > 0.05. The optimal cut-off for classifying risks as low or high was 4. At this cut point, the sensitivity, specificity and accuracy is 91.0%, 82.1%, and 83.1%, respectively. It was internally validated and has an optimism of 0.003. The model was found to have clinical benefit. CONCLUSION: The developed risk-score has excellent discrimination performance and clinical benefit. It can be used in the clinical settings by healthcare providers for early detection, timely decision making, and improving care quality.

Sarcopenia and a 5-mRNA risk module as a combined factor to predict prognosis for patients with stomach adenocarcinoma.

BACKGROUND: Sarcopenia is an important factor affecting the prognostic outcomes in adult cancer patients. Gastric cancer is considered an age-related disease and is one of the leading causes of global cancer mortality. We aimed to establish an effective age-related model at a molecular level to predict the prognosis of patients with gastric cancer. METHODS: TCGA STAD (stomach adenocarcinoma) and NCBI GEO database were utilized in this study to explore the expression, clinical relevance and prognostic value of age-related mRNAs in stomach adenocarcinoma through an integrated bioinformatics analysis. WGCNA co-expression network, Univariate Cox regression analysis, LASSO regression and Multivariate Cox regression analysis were implemented to construct an age-related prognostic signature. RESULTS: As a result, sarcopenia is not only an unfavorable factor for OS (overall survival) in patients with tumor of gastric (HR: 1.707, 95%CI: 1.437-2.026), but also increases the risk of postoperative complications in patients with gastric cancer (OR: 2.904, 95%CI: 2.150-3.922). A panel of 5 mRNAs (DCBLD1, DLC1, IGFBP1, RNASE1 and SPC24) were identified to dichotomize patients with significantly different OS and independently predicted the OS in TCGA STAD (HR = 3.044, 95%CI = 2.078-4.460, P < 0.001). CONCLUSION: The study provided novel insights to understand STAD at a molecular level and indicated that the 5 mRNAs might act as independent promising prognosis biomarkers for STAD. Sarcopenia and the 5-mRNA risk module as a combined factor to predict prognosis may play an important role in clinical diagnosis.

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OBJECTIVES: To construct a prognosis risk model based on long noncoding RNAs (lncRNAs) related to cuproptosis and to evaluate its application in assessing prognosis risk of bladder cancer patients. METHODS: RNA sequence data and clinical data of bladder cancer patients were downloaded from the Cancer Genome Atlas database. The correlation between lncRNAs related to cuproptosis and bladder cancer prognosis was analyzed with Pearson correlation analysis, univariate Cox regression, Lasso regression, and multivariate Cox regression. Then a cuproptosis-related lncRNA prognostic risk scoring equation was constructed. Patients were divided into high-risk and low-risk groups based on the median risk score, and the immune cell abundance between the two groups were compared. The accuracy of the risk scoring equation was evaluated using Kaplan-Meier survival curves, and the application of the risk scoring equation in predicting 1, 3 and 5-year survival rates was evaluated using receiver operating characteristic (ROC) curves. Univariate and multivariate Cox regression were used to screen for prognostic factors related to bladder cancer patients, and a prognostic risk assessment nomogram was constructed, the accuracy of which was evaluated with calibration curves. RESULTS: A prognostic risk scoring equation for bladder cancer patients was constructed based on nine cuproptosis-related lncRNAs. Immune infiltration analysis showed that the abundances of M0 macrophages, M1 macrophages, M2 macrophages, resting mast cells and neutrophils in the high-risk group were significantly higher than those in the low-risk group, while the abundances of CD8+ T cells, helper T cells, regulatory T cells and plasma cells in the low-risk group were significantly higher than those in the high-risk group (all P<0.05). Kaplan-Meier survival curve analysis showed that the total survival and progression-free survival of the low-risk group were longer than those of the high-risk group (both P<0.01). Univariate and multivariate Cox analysis showed that the risk score, age and tumor stage were independent factors for patient prognosis. The ROC curve analysis showed that the area under the curve (AUC) of the risk score in predicting 1, 3 and 5-year survival was 0.716, 0.697 and 0.717, respectively. When combined with age and tumor stage, the AUC for predicting 1-year prognosis increased to 0.725. The prognostic risk assessment nomogram for bladder cancer patients constructed based on patient age, tumor stage, and risk score had a prediction value that was consistent with the actual value. CONCLUSIONS: A bladder cancer patient prognosis risk assessment model based on cuproptosis-related lncRNA has been successfully constructed in this study. The model can predict the prognosis of bladder cancer patients and their immune infiltration status, which may also provide a reference for tumor immunotherapy.

Establishment and validation of a novel prognostic model for non-virus-related hepatocellular carcinoma.

OBJECTIVE: The incidence of non-virus-related hepatocellular carcinoma (NV-HCC) in hepatocellular carcinoma (HCC) is steadily increasing. The aim of this study was to establish a prognostic model to evaluate the overall survival (OS) of NV-HCC patients. METHODS: Overall, 261 patients with NV-HCC were enrolled in this study. A prognostic model was developed by using LASSO-Cox regression analysis. The prognostic power was appraised by the concordance index (C-index), and the time-dependent receiver operating characteristic curve (TD-ROC). Kaplan-Meier (K-M) survival analysis was used to evaluate the predictive ability in the respective subgroups stratified by the prognostic model risk score. A nomogram for survival prediction was established by integrating the prognostic model, TNM stage, and treatment. RESULTS: According to the LASSO-Cox regression results, the number of nodules, lymphocyte-to-monocyte ratio (LMR), prognostic nutritional index (PNI), alkaline phosphatase (ALP), aspartate aminotransferase (AST)/alanine aminotransferase (ALT) ratio (SLR) and C-reactive protein (CRP) were included for prognostic model construction. The C-index of the prognostic model was 0.759 (95% CI 0.723-0.797) in the development cohort and 0.796 (95% CI 0.737-0.855) in the validation cohort, and its predictive ability was better than TNM stage and treatment. The TD-ROC showed similar results. K-M survival analysis showed that NV-HCC patients with low risk scores had a better prognosis (P < 0.05). A nomogram based on the prognostic model, TNM stage, and treatment was constructed with sufficient discriminatory power with C-indexes of 0.78 and 0.85 in the development and validation cohort, respectively. CONCLUSION: For NV-HCC, this prognostic model could predict an OS benefit for patients, which may assist clinicians in designing individualized therapeutic strategies.

Synthesizing time-series wound prognosis factors from electronic medical records using generative adversarial networks.

Wound prognostic models not only provide an estimate of wound healing time to motivate patients to follow up their treatments but also can help clinicians to decide whether to use a standard care or adjuvant therapies and to assist them with designing clinical trials. However, collecting prognosis factors from Electronic Medical Records (EMR) of patients is challenging due to privacy, sensitivity, and confidentiality. In this study, we developed time series medical generative adversarial networks (GANs) to generate synthetic wound prognosis factors using very limited information collected during routine care in a specialized wound care facility. The generated prognosis variables are used in developing a predictive model for chronic wound healing trajectory. Our novel medical GAN can produce both continuous and categorical features from EMR. Moreover, we applied temporal information to our model by considering data collected from the weekly follow-ups of patients. Conditional training strategies were utilized to enhance training and generate classified data in terms of healing or non-healing. The ability of the proposed model to generate realistic EMR data was evaluated by TSTR (test on the synthetic, train on the real), discriminative accuracy, and visualization. We utilized samples generated by our proposed GAN in training a prognosis model to demonstrate its real-life application. Using the generated samples in training predictive models improved the classification accuracy by 6.66-10.01% compared to the previous EMR-GAN. Additionally, the suggested prognosis classifier has achieved the area under the curve (AUC) of 0.875, 0.810, and 0.647 when training the network using data from the first three visits, first two visits, and first visit, respectively. These results indicate a significant improvement in wound healing prediction compared to the previous prognosis models.

Ferroptosis-based molecular prognostic model for adrenocortical carcinoma based on least absolute shrinkage and selection operator regression.

BACKGROUND: This study aimed to find ferroptosis-related genes linked to clinical outcomes of adrenocortical carcinoma (ACC) and assess the prognostic value of the model. METHODS: We downloaded the mRNA sequencing data and patient clinical data of 78 ACC patients from the TCGA data portal. Candidate ferroptosis-related genes were screened by univariate regression analysis, machine-learning least absolute shrinkage, and selection operator (LASSO). A ferroptosis-related gene-based prognostic model was constructed. The effectiveness of the prediction model was accessed by KM and ROC analysis. External validation was done using the GSE19750 cohort. A nomogram was generated. The prognostic accuracy was measured and compared with conventional staging systems (TNM stage). Functional analysis was conducted to identify biological characterization of survival-associated ferroptosis-related genes. RESULTS: Seventy genes were identified as survival-associated ferroptosis-related genes. The prognostic model was constructed with 17 ferroptosis-related genes including STMN1, RRM2, HELLS, FANCD2, AURKA, GABARAPL2, SLC7A11, KRAS, ACSL4, MAPK3, HMGB1, CXCL2, ATG7, DDIT4, NOX1, PLIN4, and STEAP3. A RiskScore was calculated for each patient. KM curve indicated good prognostic performance. The AUC of the ROC curve for predicting 1-, 3-, and 5- year(s) survival time was 0.975, 0.913, and 0.915 respectively. The nomogram prognostic evaluation model showed better predictive ability than conventional staging systems. CONCLUSION: We constructed a prognosis model of ACC based on ferroptosis-related genes with better predictive value than the conventional staging system. These efforts provided candidate targets for revealing the molecular basis of ACC, as well as novel targets for drug development.

Global analysis of DNA methylation in hepatocellular carcinoma via a whole-genome bisulfite sequencing approach.

Alterations in DNA methylation patterns are considered early events in hepatocellular carcinoma (HCC). However, their mechanism and significance remain to be elucidated. We studied the genome-wide DNA methylation landscape of HCC by applying whole-genome bisulfite sequencing (WGBS) techonlogy. Overall, HCC exhibits a genome-wide hypomethylation pattern. After further annotation, we obtained 590 differentially hypermethylated genes (hyper-DMGs) and 977 differentially hypomethylated genes (hypo-DMGs) from three groups. Hyper-DMGs were mainly involved in ascorbate and alternate metabolism pathways, while hypo-DMGs were mainly involved in focal adhesion. By integrating the DMGs with HCC-related differentially expressed genes (DEGs) and DMGs from the TCGA database, we constructed prognostic model based on thirteen aberrantly methylated DEGs, and verified our prognostic model in GSE14520 dataset. This study compares the patterns of global epigenomic DNA methylation during the development of HCC, focusing on the role of DNA methylation in the early occurrence and development of HCC, providing a direction for future research on its epigenetic mechanism.