Clinical Prognostic Models Research Articles

Development and validation of a clinical prediction model for glioma grade using machine learning.

Histopathological evaluation is currently the gold standard for grading gliomas; however, this technique is invasive. This study aimed to develop and validate a diagnostic prediction model for glioma by employing multiple machine learning algorithms to identify risk factors associated with high-grade glioma, facilitating the prediction of glioma grading. Data from 1114 eligible glioma patients were obtained from The Cancer Genome Atlas (TCGA) database, which was divided into a training set (n= 781) and a test set (n= 333). Fifty machine learning algorithms were employed, and the optimal algorithm was selected to construct a prediction model. The performance of the machine learning prediction model was compared to the clinical prediction model in terms of discrimination, calibration, and clinical validity to assess the performance of the prediction model. The area under the curve (AUC) values of the machine learning prediction models (training set: 0.870 vs. 0.740, test set: 0.863 vs. 0.718) were significantly improved from the clinical prediction models. Furthermore, significant improvement in discrimination was observed for the Integrated Discrimination Improvement (IDI) (training set: 0.230, test set: 0.270) and Net Reclassification Index (NRI) (training set: 0.170, test set: 0.170) from the clinical prognostic model. Both models showed a high goodness of fit and an increased net benefit. A strong prediction accuracy model can be developed using machine learning algorithms to screen for high-grade glioma risk predictors, which can serve as a non-invasive prediction tool for preoperative diagnostic grading of glioma.

DNMT3A promotes glioma growth and malignancy via TNF-α/NF-κB signaling pathway.

DNMT3A is the main molecule responsible for DNA methylation in cells. DNMT3A affects the progression of inflammation, degenerative diseases, and malignant tumors, and exhibits significant aberrantly expression in tumor tissues. Transcriptome data and relevant clinical information were downloaded from The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), and Gene Expression Omnibus (GEO) datasets. Differential expression analysis and prognostic analysis were conducted based on above statistics. We constructed a clinical prognostic model and identified DNMT3A as an independent prognostic factor to accurately predict patient prognosis. Differential gene enrichment analysis revealed that DNMT3A affects the progression of glioma through multiple pathways, among which the tumor necrosis factor-α (TNF-α)/nuclear factor-kappa B (NF-κB) pathway shows a strong correlation. Immunological analysis also revealed a certain correlation between DNMT3A and tumor immunity. We demonstrated through gene editing that DNMT3A can affect the release of TNF-α in cells, thereby affecting the progression of glioma. Functional experiments have also demonstrated that DNMT3A plays a crucial role in tumors. RNA-sequencing and survival analyses of lower-grade glioma (LGG) patients in TCGA, CGGA, and GEO cohorts showed that high DNMT3A expression correlated with poor prognosis of LGG patients. Univariate and multivariate Cox regression analyses showed that DNMT3A expression was an independent prognostic indicator in LGG. The prognosis prediction nomogram with age, World Health Organization (WHO) grading, and DNMT3A expression showed reliable performance in predicting the 1-, 3-, and 5-year overall survival (OS) of LGG patients. Functional enrichment analysis, gene set enrichment analysis (GSEA), and ESTIMATE algorithm analyses showed that DNMT3A expression was associated with the tumor infiltration of immune cells and predicted response to immunotherapy in two immunotherapy cohorts of pan-cancer patients. Furthermore, short hairpin RNA (shRNA)-mediated knockdown of DNMT3A in the LGG cell lines suppressed proliferation, migration, and invasion of LGG cells by downregulating the TNF-α/NF-κB signaling pathway. Our data showed that DNMT3A was a potential prognostic biomarker in glioma. DNMT3A promoted proliferation and malignancy of LGG cells through the TNF-α/NF-κB signaling pathway. DNMT3A is a promising therapeutic target for treating patients with LGG.

A clinical prognostic model of oxidative stress-related genes linked to tumor immune cell infiltration and the prognosis of ovarian cancer patients

BackgroundAccording to statistics, ovarian cancer (OV) is the most prevalent type of gynecologic malignancy and has the highest mortality rate of all gynecologic tumors. Although several studies have shown that oxidative stress (OS) contributes significantly to the onset and progression of cancer, the role of OS in OV needs to be investigated further. Thus, it is critical to comprehend the function of OS-related genes in OV. MethodsIn this study, all data related to the transcriptome and clinical status of the patients were retrieved from “The Cancer Genome Atlas” (TCGA) and “Gene Expression Omnibus” (GEO) databases. Using the unsupervised cluster analysis technique, all patients with OV were classified into two different subtypes (categories) based on the OS gene. All hub genes were screened using the weighted gene co-expression network analysis (WGCNA). Since the hub genes and the differentially expressed genes (DEGs) in both categories were found to intersect, the univariate Cox regression analysis was implemented. A multivariate Cox analysis was also performed to construct a novel clinical prognosis model, which was validated using data from the GEO cohort. In addition, the relationship between risk score and immune cell infiltration level was evaluated using CIBERSORT. Finally, qRT-PCR was used to confirm the expression of the genes used to construct the model. ResultsTwo subtypes of OS were obtained. The findings indicated that OS-C1 had a better survival outcome than OS-C2. The results of WGCNA yielded 112 hub genes. For univariate COX regression analyses, 49 OS-related trait genes were obtained. Finally, a clinical prognostic model containing two genes was constructed. This model could differentiate between patients with OV having varying years of survival in the TCGA and GEO cohorts. The model risk score was verified as an independent prognostic indicator. According to the results of CIBERSORT, many tumor-infiltrating immune cells were found to be significantly related to the risk score. Furthermore, the results revealed that patients with low-risk OV in the CTLA4 treatment group had a high likelihood of benefiting from immunotherapy. qRT-PCR results also showed that the expression of MARVELD1 and VSIG4 was high in the OV samples. ConclusionsAnalysis of the results suggested that the newly developed model, which contained two characteristic OS-related genes, could successfully predict the survival outcomes of all patients with OV. The findings of this study could offer valuable information and insights into the refinement of personalized therapy and immunotherapy for OV in the future.

Develop prediction model to help forecast advanced prostate cancer patients' prognosis after surgery using neural network.

The effect of surgery on advanced prostate cancer (PC) is unclear and predictive model for postoperative survival is lacking yet. We investigate the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) database, to collect clinical features of advanced PC patients. According to clinical experience, age, race, grade, pathology, T, N, M, stage, size, regional nodes positive, regional nodes examined, surgery, radiotherapy, chemotherapy, history of malignancy, clinical Gleason score (composed of needle core biopsy or transurethral resection of the prostate specimens), pathological Gleason score (composed of prostatectomy specimens) and prostate-specific antigen (PSA) are the potential predictive variables. All samples are divided into train cohort (70% of total, for model training) and test cohort (30% of total, for model validation) by random sampling. We then develop neural network to predict advanced PC patients' overall. Area under receiver operating characteristic curve (AUC) is used to evaluate model's performance. 6380 patients, diagnosed with advanced (stage III-IV) prostate cancer and receiving surgery, have been included. The model using all collected clinical features as predictors and based on neural network algorithm performs best, which scores 0.7058 AUC (95% CIs, 0.7021-0.7068) in train cohort and 0.6925 AUC (95% CIs, 0.6906-0.6956) in test cohort. We then package it into a Windows 64-bit software. Patients with advanced prostate cancer may benefit from surgery. In order to forecast their overall survival, we first build a clinical features-based prognostic model. This model is accuracy and may offer some reference on clinical decision making.

Steroid responsiveness in alcohol-associated hepatitis is linked to glucocorticoid metabolism, mitochondrial repair, and heat shock proteins.

Alcohol-associated hepatitis (AH) is one of the clinical presentations of alcohol-associated liver disease. AH has poor prognosis, and corticosteroids remain the mainstay of drug therapy. However, ~40% of patients do not respond to this treatment, and the mechanisms underlying the altered response to corticosteroids are not understood. The current study aimed to identify changes in hepatic protein expression associated with responsiveness to corticosteroids and prognosis in patients with AH. Patients with AH were enrolled based on the National Institute on Alcohol Abuse and Alcoholism inclusion criteria for acute AH and further confirmed by a diagnostic liver biopsy. Proteomic analysis was conducted on liver samples acquired from patients with AH grouped as nonresponders (AH-NR, n = 7) and responders (AH-R, n = 14) to corticosteroids, and nonalcohol-associated liver disease controls (n = 10). The definition of responders was based on the clinical prognostic model, the Lille Score, where a score < 0.45 classified patients as AH-R and a score > 0.45 as AH-NR. Primary outcomes used to assess steroid response were Lille Score (eg, improved liver function) and survival at 24 weeks. Reduced levels of the glucocorticoid receptor and its transcriptional co-activator, glucocorticoid modulatory element-binding protein 2, were observed in the hepatic proteome of AH-NR versus AH-R. The corticosteroid metabolizing enzyme, 11-beta-hydroxysteroid dehydrogenase 1, was increased in AH-NR versus AH-R along with elevated mitochondrial DNA repair enzymes, while several proteins of the heat shock pathway were reduced. Analysis of differentially expressed proteins in AH-NR who survived 24 weeks relative to AH-NR nonsurvivors revealed several protein expression changes, including increased levels of acute phase proteins, elevated coagulation factors, and reduced mast cell markers. This study identified hepatic proteomic changes that may predict responsiveness to corticosteroids and mortality in patients with AH.

Screening of diagnostic biomarkers for ferroptosis-related osteoarthritis and construction of a risk-prognosis model

Background: Osteoarthritis (OA) is the most prevalent and commonly chronic joint disease that frequently develops among the elderly population. It is not just a single tissue that is affected, but rather a pathology involving the entire joint. Among them, synovitis is a key pathological change in OA. Ferroptosis is a newly discovered form of cell death that results from the buildup of lipid peroxidation. However, the role and impact of it in OA are yet to be explored. Objective: The key to this work is to uncover the mechanisms of ferroptosis-related OA pathogenesis and develop more novel diagnostic biomarkers to facilitate the diagnostic and therapeutic of OA. Materials and methods: Download ferroptosis-related genes and OA synovial chip datasets separately from the FerrDB and Gene Expression Omnibus databases. Identify ferroptosis differentially expressed genes using R software, obtain the intersection genes through two machine learning algorithms, and obtain diagnostic biomarkers after logistic regression analysis. Verify the diagnostic and therapeutic efficacy of specific genes for OA through the construction of clinical risk prognostic models using ROC curves and nomogram. Simultaneously, correlations between specific genes and OA immune cell infiltration co-expression were constructed. Finally, verify the differential presentation of specific genes in OA and health control synovium. Results: Obtain 38 ferroptosis differentially expressed genes through screening. Based on machine learning algorithms and logistic regression analysis, select AGPS, BRD4, RBMS1, and EGR1 as diagnostic biomarker genes. The diagnostic and therapeutic efficacy of the four specific genes for OA has been validated by ROC curves and nomogram of clinical risk prognostic models. The analysis of immune cell infiltration and correlation suggests a close association between specific genes and OA immune cell infiltration. Further revealing the diagnostic value of specific genes for OA by the differential presentation analysis of their differential presentation in synovial tissue from OA and health control. Conclusion: This study identified four diagnostic biomarkers for OA that are associated with iron death. The establishment of a risk-prognostic model is conducive to the premature diagnosis of OA, evaluating functional recovery during rehabilitation, and guidance for subsequent treatment.

Immune-related lncRNAs signature and radiomics signature predict the prognosis and immune microenvironment of glioblastoma multiforme

BackgroundGlioblastoma multiforme (GBM) is the most common primary malignant brain tumor in adults. This study aimed to construct immune-related long non-coding RNAs (lncRNAs) signature and radiomics signature to probe the prognosis and immune infiltration of GBM patients.MethodsWe downloaded GBM RNA-seq data and clinical information from The Cancer Genome Atlas (TCGA) project database, and MRI data were obtained from The Cancer Imaging Archive (TCIA). Then, we conducted a cox regression analysis to establish the immune-related lncRNAs signature and radiomics signature. Afterward, we employed a gene set enrichment analysis (GSEA) to explore the biological processes and pathways. Besides, we used CIBERSORT to estimate the abundance of tumor-infiltrating immune cells (TIICs). Furthermore, we investigated the relationship between the immune-related lncRNAs signature, radiomics signature and immune checkpoint genes. Finally, we constructed a multifactors prognostic model and compared it with the clinical prognostic model.ResultsWe identified four immune-related lncRNAs and two radiomics features, which show the ability to stratify patients into high-risk and low-risk groups with significantly different survival rates. The risk score curves and Kaplan–Meier curves confirmed that the immune-related lncRNAs signature and radiomics signature were a novel independent prognostic factor in GBM patients. The GSEA suggested that the immune-related lncRNAs signature were involved in L1 cell adhesion molecular (L1CAM) interactions and the radiomics signature were involved signaling by Robo receptors. Besides, the two signatures was associated with the infiltration of immune cells. Furthermore, they were linked with the expression of critical immune genes and could predict immunotherapy’s clinical response. Finally, the area under the curve (AUC) (0.890,0.887) and C-index (0.737,0.817) of the multifactors prognostic model were greater than those of the clinical prognostic model in both the training and validation sets, indicated significantly improved discrimination.ConclusionsWe identified the immune-related lncRNAs signature and tradiomics signature that can predict the outcomes, immune cell infiltration, and immunotherapy response in patients with GBM.

Convined clinical prognostic model in colorectal cancer.

Current staging systems in patients with colorectal cancer (CRC) utilize relatively few patient characteristics in comparison to the breadth of information available. The objective of our study is to analyze the heterogeneous set of variables that may influence mortality and recurrence independently in patients with CCR, and prepare a predictive model of survival and recurrence. Data from 288 patients who had undergone scheduled surgery for stage I-III cancer of the colon and upper rectum were used to construct Cox models for DFS and overall CSS at five years. We have jointly examined clinical variables, serological markers and histological variables with the aim of identifying new prognostic factors. Internal and external validation was carried out on each of the nomograms obtained. Perineural invasion; high platelet-lymphocyte ratio (PLR) and the pN stage were the variables that emerged as an independent risk factor of recurrence. The variables related independently to overall CSS were the presence of blood in stools, high PLR and nodal involvement. We have created a predictive model of recurrence and mortality at 5years with data that is easily available (clinical, analytical and histological variables) which can help personalize the treatment and follow-up of patients with CRC. We also conducted an adequate internal and external validation.

A CIC-related-epigenetic factors-based model associated with prediction, the tumor microenvironment and drug sensitivity in osteosarcoma

Osteosarcoma is generally considered a cold tumor and is characterized by epigenetic alterations. Although tumor cells are surrounded by many immune cells such as macrophages, T cells may be suppressed, be inactivated, or not be presented due to various mechanisms, which usually results in poor prognosis and insensitivity to immunotherapy. Immunotherapy is considered a promising anti-cancer therapy in osteosarcoma but requires more research, but osteosarcoma does not currently respond well to this therapy. The cancer immunity cycle (CIC) is essential for anti-tumor immunity, and is epigenetically regulated. Therefore, it is possible to modulate the immune microenvironment of osteosarcoma by targeting epigenetic factors. In this study, we explored the correlation between epigenetic modulation and CIC in osteosarcoma through bioinformatic methods. Based on the RNA data from TARGET and GSE21257 cohorts, we identified epigenetic related subtypes by NMF clustering and constructed a clinical prognostic model by the LASSO algorithm. ESTIMATE, Cibersort, and xCell algorithms were applied to analyze the tumor microenvironment. Based on eight epigenetic biomarkers (SFMBT2, SP140, CBX5, HMGN2, SMARCA4, PSIP1, ACTR6, and CHD2), two subtypes were identified, and they are mainly distinguished by immune response and cell cycle regulation. After excluding ACTR6 by LASSO regression, the prognostic model was established and it exhibited good predictive efficacy. The risk score showed a strong correlation with the tumor microenvironment, drug sensitivity and many immune checkpoints. In summary, our study sheds a new light on the CIC-related epigenetic modulation mechanism of osteosarcoma and helps search for potential drugs for osteosarcoma treatment.

Identification and validation of a prognostic risk-scoring model for AML based on m7G-associated gene clustering.

Acute myeloid leukemia (AML) patients still suffer from poor 5-year survival and relapse after remission. A better prognostic assessment tool is urgently needed. New evidence demonstrates that 7-methylguanosine (m7G) methylation modifications play an important role in AML, however, the exact role of m7G-related genes in the prognosis of AML remains unclear. The study obtained AML expression profiles and clinical information from TCGA, GEO, and TARGET databases. Using the patient data from the TCGA cohort as the training set. Consensus clustering was performed based on 29 m7G-related genes. Survival analysis was performed by KM curves. The subgroup characteristic gene sets were screened using WGCNA. And tumor immune infiltration correlation analysis was performed by ssGSEA. The patients were classified into 3 groups based on m7G-related genebased cluster analysis, and the differential genes were screened by differential analysis and WGCNA. After LASSO regression analysis, 6 characteristic genes (including CBR1, CCDC102A, LGALS1, RD3L, SLC29A2, and TWIST1) were screened, and a prognostic risk-score model was constructed. The survival rate of low-risk patients was significantly higher than that of high-risk patients (p < 0.0001). The area under the curve values at 1, 3, and 5 years in the training set were 0.871, 0.874, and 0.951, respectively, indicating that this predictive model has an excellent predictive effect. In addition, after univariate and multivariate Cox regression screening, histograms were constructed with clinical characteristics and prognostic risk score models to better predict individual survival. Further analysis showed that the prognostic risk score model was associated with immune cell infiltration. These findings suggest that the scoring model and essential risk genes could provide potential prognostic biomarkers for patients with acute myeloid leukemia.

Retrospective cohort study for thrombocytopenia during concurrent chemoradiotherapy for rectal cancer.

The aim of this article was to establish the clinical prognostic models and identify the predictive radiation dosimetric parameters for thrombocytopenia during concurrent chemoradiotherapy for rectal cancer. In this retrospective cohort study, patients with rectal adenocarcinoma undergoing concurrent long-term chemoradiotherapy were included. The primary outcome of interest was grade 2 or higher (2+) thrombocytopenia (platelet(PLT) count <75,000/μL). Secondary outcomes included: grade 1 or higher thrombocytopenia (PLT count<100,000/μL) and the PLT count during chemoradiotherapy and its nadir. The risk prediction model was developed by logistic regression to identify clinical predictors of 2+ thrombocytopenia. Univariate linear regression models were used to test correlations between radiation dosimetric parameters and the absolute PLT count at nadirs. This retrospective cohort comprised 238 patients. Fifty-four (22.6%) patients developed thrombocytopenia during concurrent chemoradiotherapy, while 15 (6.3%) patients developed 2+ thrombocytopenia. Four independently associated risk factors, including age, Alb level, PLT count, and chemotherapy regimen, were included in the final model and used to form a 2+ thrombocytopenia probability estimation nomogram. The C-index was 0.87 (95% CI: 0.78-0.96). The calibration plot showed a moderate agreement, and the Brier score was 0.047 (95% CI: 0.025-0.070). The total absolute volume of bone marrow irradiated by 5 Gy, 10 Gy and 15 Gy of radiation (BM-V5ab, BM-V10ab, BM-V15ab), calculated by the volume of bone marrow multiplied by the corresponding Vx, were identified as new predictors. The nadir of PLT was found to be negatively correlated with BM-V5ab (β = -0.062, P =0.030), BM-V10ab (β = -0.065, P =0.030) and BM-V15ab (β = -0.064, P =0.042). The occurrence of 2+ thrombocytopenia during concurrent chemoradiotherapy for rectal cancer can be predicted by the patient's baseline status and chemoradiotherapy regimen, and low dose irradiation of bone marrow can affect the level of platelets during the treatment.

Clinical prognostic models in children with sepsis in low- and middle-income countries: a systematic review and meta-analysis.

Sepsis is the leading cause of child death worldwide, with the majority of these deaths occurring in low- and middle-income countries (LMICs). The aim of this systematic review and meta-analysis was to describe clinical prognostic scores and models for pediatric sepsis outcomes and assess the performance of these scores for predicting mortality in LMICs. Ovid Medline, CINAHL, Cochrane Library, EBSCO Global Health, and Web of Science, were searched through September 2022 for citations related to the development or validation of a clinical prognostic score or model among children with sepsis, conducted in LMIC. Titles, abstracts, and full texts were screened by two independent reviewers and data extracted included population characteristics, variables included, outcomes, and model performance. Risk of bias was assessed with the Prediction Model Risk of Bias Assessment Tool (PROBAST). 4,251 titles/abstracts and 315 full-text studies were screened, with 12 studies meeting inclusion criteria. Study countries included India, China, Egypt, Indonesia, Tanzania, and a multi-site study in Latin America. Prognostic scores/models included existing scores such as PELOD-2, pSOFA, PRISM, P-MODS, refractory shock criteria. There was high risk of bias in all studies. Meta-analysis was possible for pSOFA, PELOD-2, PRISM, and P-MODS, with pooled area under the receiver-operator characteristic curve of 0.86 (95%CI 0.78-0.94), 0.83 (95% CI 0.76-0.91), respectively. Relatively few clinical scores and models have been externally validated for prognostication and risk-stratification among children with sepsis in diverse LMIC settings. Notably there were no studies from low-income countries. Some potentially relevant studies were excluded due to lack of clarity regarding the presence of sepsis in the study populations. More widespread and standardized use of sepsis criteria may aid in better understanding the burden of sepsis and prognostic model performance at the bedside among children in LMICs. Further research to externally validate, implement and adapt these models is needed to account for challenges in use of these scores in resource-limited settings. https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022340126, PROSPERO [CRD42022340126].

Development and validation of a clinical prognostic model for BRAF V600E-mutated colorectal cancer patients based on pathological stage, microsatellite status, and primary tumor site.

To develop and validate a prognostic model for patients with BRAF V600E-mutated colorectal cancer. The clinical and pathological information of 206 patients with BRAF V600E-mutated colorectal cancer diagnosed in Cancer Hospital, Chinese Academy of Medical Sciences, and Peking Union Medical College from 2014 to 2021 was retrospectively collected. Least absolute shrinkage and selection operator (LASSO) regression, Cox regression, and nomograms were used to develop clinical prognostic models. The differentiation was measured using C-statistic, and the predicted variability was evaluated using the calibration curve. The prognostic model was externally validated using validation set data from 164 patients pooled from five studies. Our clinical prognostic model included three variables: pathological stage, microsatellite status, and primary tumor site. In internal validation, the model had a concordant index of 0.785 (95% CI [0.732-0.839]) and a concordant index of 0.754 (95% CI [0.698-0.810]) using pathological staging. External validation confirmed the robustness of the model with a consistency index of 0.670 (95% CI [0.617-0.724]) and a consistency index of 0.584 (95% CI [0.546-0.622]) using pathological staging. Likelihood ratio test results show that our model is better (internal validation, p = 5.141e-03; external validation, p = 2.728e-05). The calibration graph drawn based on the prediction and the actual situation is close to the 45° diagonal. By adding microsatellite status and primary tumor site on the basis of pathological stage, we improved the discriminability and prediction accuracy of the model and successfully established a prognosis model for patients with BRAF V600E mutation of colorectal cancer.

Correlation Between Portal Vein Diameter and Clinical Prognostic Scores in Patients with Liver Cirrhosis: A Cross-sectional Study

Introduction: Patients with liver cirrhosis develop portal hypertension, which leads to complications like splenomegaly, ascites, and oesophageal varices. Hepatic Venous Pressure Gradient (HVPG) measurement, the best available method to measure portal hypertension, is invasive and difficult to perform. Some studies suggest that an increased Portal Vein Diameter (PVD) on ultrasonography indicates portal hypertension and correlates with oesophageal varices. Studies correlating PVD with other complications of portal hypertension, like ascites and spleen size, are scarce. Aim: To correlate ultrasonographically measured PVD with clinical prognostic models: Child-Turcotte-Pugh (CTP) score and Model for End-stage Liver Disease (MELD), as well as with platelet count, in patients with liver cirrhosis. Materials and Methods: This was a cross-sectional study conducted in the Department of General Medicine in collaboration with the Department of Radiodiagnosis at Government Medical Collge, Chandigarh, India, from February 2021 to September 2021 over an eight-month period in a tertiary healthcare centre in North India. A total of 97 patients with cirrhosis, identified based on clinical features supported by laboratory tests and ultrasonography, were included in the study. A 6 mL of blood sample was collected from each patient for haemogram, biochemical tests (liver function tests and renal function tests), and coagulogram. Ultrasonography assessment of PVD, spleen size, and ascites was performed in a supine position using a right subcostal approach, after overnight fasting. The collected data were analysed using relevant statistical tests. Results: The mean age of the study population was 47.39 ± 12.64 years, with 73 (75.3%) males and 24 (24.7%) females. The most common aetiological factor for liver cirrhosis was alcohol, present in 52 (53.6%) patients. The mean PVD was found to be 12.31±2.71 mm. The correlation coefficient of PVD with spleen size was 0.3 with a p-value of 0.004, suggesting a positive correlation. The correlation coefficient of parameters like thrombocytopenia, CTP score, and MELD score was -0.2 (p-value=0.066), 0.1 (p-value=0.463), and 0.0 (p-value=0.725), respectively. Conclusion: Sonographically measured PVD cannot be used as a substitute for the clinical grading and staging of cirrhosis. Only a weak positive correlation was found between PVD and spleen size.

Precision prognosis of colorectal cancer: a multi-tiered model integrating microsatellite instability genes and clinical parameters.

Prognostic assessment for colorectal cancer (CRC) displays substantial heterogeneity, as reliance solely on traditional TNM staging falls short of achieving precise individualized predictions. The integration of diverse biological information sources holds the potential to enhance prognostic accuracy. To establish a comprehensive multi-tiered precision prognostic evaluation system for CRC by amalgamating gene expression profiles, clinical characteristics, and tumor microsatellite instability (MSI) status in CRC patients. We integrated genomic data, clinical information, and survival follow-up data from 483 CRC patients obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. MSI-related gene modules were identified using differential expression analysis and Weighted Gene Co-expression Network Analysis (WGCNA). Three prognostic models were constructed: MSI-Related Gene Prognostic Model (Model I), Clinical Prognostic Model (Model II), and Integrated Multi-Layered Prognostic Model (Model III) by combining clinical features. Model performance was assessed and compared using Receiver Operating Characteristic (ROC) curves, Kaplan-Meier analysis, and other methods. Six MSI-related genes were selected for constructing Model I (AUC = 0.724); Model II used two clinical features (AUC = 0.684). Compared to individual models, the integrated Model III exhibited superior performance (AUC = 0.825) and demonstrated good stability in an independent dataset (AUC = 0.767). This study successfully developed and validated a comprehensive multi-tiered precision prognostic assessment model for CRC, providing an effective tool for personalized medical management of CRC.

CRISPR-Cas9 screening develops an epigenetic and transcriptional gene signature for risk stratification and target prediction in neuroblastoma.

Objectives: Neuroblastoma (NB), a pediatric malignancy of the peripheral nervous system, is characterized by epigenetic and transcriptional (EP-TF) anomalies. This study aimed to develop an EP-TF clinical prognostic model for NB using CRISPR-Cas9 knockout screening. Results: An integrative analysis was conducted using CRISPR-Cas9 screening in vitro and in vivo with public NB datasets to identify 35EP-TF genes that exhibited the highest expression in NB and were highly dependent on cancer viability. After univariate analysis, 27 of these 35 genes were included in the least absolute shrinkage and selection operator screen. We established and biologically validated a prognostic EP-TF model encompassing RUVBL1, LARP7, GTF3C4, THAP10, SUPT16H, TIGD1, SUV39H2, TAF1A, SMAD9, and FEM1B across diverse NB cohorts. MYCN serves a potential upstream regulator of EP-TF genes. The high-risk subtype exhibited traits associated with the malignant cell cycle, MYCN-linked signaling and chromatin remodeling, all of which are correlated with poor prognosis and immunosuppression. MEK inhibitors have emerged as promising therapeutic agents for targeting most EP-TF risk genes in NB. Conclusion: Our novel prognostic model shows significant potential for predicting and evaluating the overall survival of NB patients, offering insights into therapeutic targets.

Single-cell RNA-Seq Elucidates the Crosstalk Between Cancer Stem Cells and the Tumor Microenvironment in Hepatocellular Carcinoma.

Background: The challenge of systemic treatment for hepatocellular carcinoma (HCC) stems from the development of drug resistance, primarily driven by the interplay between cancer stem cells (CSCs) and the tumor microenvironment (TME). However, there is a notable dearth of comprehensive research investigating the crosstalk between CSCs and stromal cells or immune cells within the TME of HCC. Methods: We procured single-cell RNA sequencing (scRNA-Seq) data from 16 patients diagnosed with HCC. Employing meticulous data quality control and cell annotation procedures, we delineated distinct CSCs subtypes and performed multi-omics analyses encompassing metabolic activity, cell communication, and cell trajectory. These analyses shed light on the potential molecular mechanisms governing the interaction between CSCs and the TME, while also identifying CSCs' developmental genes. By combining these developmental genes, we employed machine learning algorithms and RT-qPCR to construct and validate a prognostic risk model for HCC. Results: We successfully identified CSCs subtypes residing within malignant cells. Through meticulous enrichment analysis and assessment of metabolic activity, we discovered anomalous metabolic patterns within the CSCs microenvironment, including hypoxia and glucose deprivation. Moreover, CSCs exhibited aberrant activity in signaling pathways associated with lipid metabolism. Furthermore, our investigations into cell communication unveiled that CSCs possess the capacity to modulate stromal cells and immune cells through the secretion of MIF or MDK, consequently exerting regulatory control over the TME. Finally, through cell trajectory analysis, we found developmental genes of CSCs. Leveraging these genes, we successfully developed and validated a prognostic risk model (APCS, ADH4, FTH1, and HSPB1) with machine learning and RT-qPCR. Conclusions: By means of single-cell multi-omics analysis, this study offers valuable insights into the potential molecular mechanisms governing the interaction between CSCs and the TME, elucidating the pivotal role CSCs play within the TME. Additionally, we have successfully established a comprehensive clinical prognostic model through bulk RNA-Seq data.

Establishing a nomogram to predict refracture after percutaneous kyphoplasty by logistic regression.

Several studies have examined the risk factors for post-percutaneous kyphoplasty (PKP) refractures and developed many clinical prognostic models. However, no prior research exists using the Random Forest (RF) model, a favored tool for model development, to predict the occurrence of new vertebral compression fractures (NVCFs). Therefore, this study aimed to investigate the risk factors for the occurrence of post-PKP fractures, compare the predictive performance of logistic regression and RF models in forecasting post-PKP fractures, and visualize the logistic regression model. We collected clinical data from 349 patients who underwent PKP treatment at our institution from January 2018 to December 2021. Lasso regression was employed to select risk factors associated with the occurrence of NVCFs. Subsequently, logistic regression and RF models were established, and their predictive capabilities were compared. Finally, a nomogram was created. The variables selected using Lasso regression, including bone density, cement distribution, vertebral fracture location, preoperative vertebral height, and vertebral height restoration rate, were included in both the logistic regression and RF models. The area under the curves of the logistic regression and RF models were 0.868 and 0.786, respectively, in the training set and 0.786 and 0.599, respectively, in the validation set. Furthermore, the calibration curve of the logistic regression model also outperformed that of the RF model. The logistic regression model provided better predictive capabilities for identifying patients at risk for post-PKP vertebral fractures than the RF model.

Mortality risk factors in patients receiving ECPR after cardiac arrest: Development and validation of a clinical prognostic prediction model

BackgroundPrevious studies have shown an increasing trend of extracorporeal cardiopulmonary resuscitation (ECPR) use in patients with cardiac arrest (CA). Although ECPR have been found to reduce mortality in patients with CA compared with conventional cardiopulmonary resuscitation (CCPR), the mortality remains high. This study was designed to identify the potential mortality risk factors for ECPR patients for further optimization of patient management and treatment selection. MethodsWe conducted a prospective, multicentre study collecting 990 CA patients undergoing ECPR in 61 hospitals in China from January 2017 to May 2022 in CSECLS registry database. A clinical prediction model was developed using cox regression and validated with external data. ResultsThe data of 351 patients meeting the inclusion criteria before October 2021 was used to develop a prediction model and that of 68 patients after October 2021 for validation. Of the 351 patients with CA treated with ECPR, 227 (64.8%) patients died before hospital discharge. Multivariate analysis suggested that a medical history of cerebrovascular diseases, pulseless electrical activity (PEA)/asystole and higher Lactate (Lac) were risk factors for mortality while aged 45–60, higher pH and intra-aortic balloon pump (IABP) during ECPR have protective effects. Internal validation by bootstrap resampling was subsequently used to evaluate the stability of the model, showing moderate discrimination, especially in the early stage following ECPR, with a C statistic of 0.70 and adequate calibration with GOF chi-square = 10.4 (p = 0.50) for the entire cohort. Fair discrimination with c statistic of 0.65 and good calibration (GOF chi-square = 6.1, p = 0.809) in the external validation cohort demonstrating the model's ability to predict in-hospital death across a wide range of probabilities. ConclusionRisk factors have been identified among ECPR patients including a history of cerebrovascular diseases, higher Lac and presence of PEA or asystole. While factor such as age 45–60, higher pH and use of IABP have been found protective against in-hospital mortality. These factors can be used for risk prediction, thereby improving the management and treatment selection of patients for this resource-intensive therapy.

Construction and evaluation of a prognostic prediction model based on the mEGOS score for patients with Guillain-Barré syndrome

BackgroundGuillain-Barré syndrome (GBS) is an immune-mediated acute peripheral neuropathy in which up to 20% patients remain unable to walk independently after 6 months of onset. This study aimed to develop a clinical prognostic model based on the modified Erasmus GBS Outcome Score (mEGOS) for predicting the prognosis of GBS patients at 6 months of onset.MethodsThe clinical data of 201 GBS patients were retrospectively analyzed. According to the GBS disability score (GBS-DS) at 6 months of onset, patients were divided into a good prognosis group (GBS-DS &amp;lt;3 points) and a poor prognosis group (GBS-DS≥3 points). Univariate and multivariate analysis was used to screen out independent risk factors for poor prognosis, and a prediction model was accordingly constructed for GBS prognosis.ResultsThe mEGOS score, serum albumin (ALB) and fasting plasma glucose (FPG) were independent risk factors for poor prognosis in patients with GBS, and the above risk factors were used to construct a prognostic model of mEGOS-I and a nomogram. The receiver operating characteristic (ROC) curve showed that the area under curve (AUC) of mEGOS-I at admission and at 7 days of admission to predict poor prognosis at 6 months of GBS onset was 0.891 and 0.916, respectively, with sensitivities of 82.7% and 82.6% and specificities of 86.5% and 86.6%, respectively. Decision curve analysis showed that the nomogram had a very high clinical benefit.ConclusionTo our knowledge, this is the first report of the construction of a prognostic prediction model based on the mEGOS score, ALB, and FPG that can accurately and stably predict the prognosis of GBS patients at 6 months of onset.