Least Absolute Shrinkage And Selection Operator Analysis Research Articles

Does FDG PET-Based Radiomics Have an Added Value for Prediction of Overall Survival in Non-Small Cell Lung Cancer?

Objectives: Radiomics and machine learning are innovative approaches to improve the clinical management of NSCLC. However, there is less information about the additive value of FDG PET-based radiomics compared with clinical and imaging variables. Methods: This retrospective study included 320 NSCLC patients who underwent PET/CT with FDG at initial staging. VOIs were placed on primary tumors only. We included a total of 94 variables, including 87 textural features extracted from PET studies, SUVmax, MTV, TLG, TNM stage, histology, age, and gender. We used the least absolute shrinkage and selection operator (LASSO) regression to select variables with the highest predictive value. Although several radiomics variables are available, the added value of these predictors compared with clinical and imaging variables is still under evaluation. Three hundred and twenty NSCLC patients were included in this retrospective study and underwent 18F-FDG PET/CT at initial staging. In this study, we evaluated 94 variables, including 87 textural features, SUVmax, MTV, TLG, TNM stage, histology, age, and gender. Image-based predictors were extracted from a volume of interest (VOI) positioned on the primary tumor. The least absolute shrinkage and selection operator (LASSO) Cox regression was used to reduce the number of variables and select only those with the highest predictive value. The predictive model implemented with the variables selected using the LASSO analysis was compared with a reference model using only a tumor stage and SUVmax. Results: NGTDM coarseness, SUVmax, and TNM stage survived the LASSO analysis and were used for the radiomic model. The AUCs obtained from the reference and radiomic models were 80.82 (95%CI, 69.01-92.63) and 81.02 (95%CI, 69.07-92.97), respectively (p = 0.98). The median OS in the reference model was 17.0 months in high-risk patients (95%CI, 11-21) and 113 months in low-risk patients (HR 7.47, p < 0.001). In the radiomic model, the median OS was 16.5 months (95%CI, 11-20) and 113 months in high- and low-risk groups, respectively (HR 9.64, p < 0.001). Conclusions: Our results indicate that a radiomic model composed using the tumor stage, SUVmax, and a selected radiomic feature (NGTDM_Coarseness) predicts survival in NSCLC patients similarly to a reference model composed only by the tumor stage and SUVmax. Replication of these preliminary results is necessary.

Predictive model for non-malignant portal vein thrombosis associated with cirrhosis based on inflammatory biomarkers.

Portal vein thrombosis (PVT), a complication of liver cirrhosis, is a major public health concern. PVT prediction is the most effective method for PVT diagnosis and treatment. To develop and validate a nomogram and network calculator based on clinical indicators to predict PVT in patients with cirrhosis. Patients with cirrhosis hospitalized between January 2016 and December 2021 at the First Hospital of Lanzhou University were screened and 643 patients with cirrhosis who met the eligibility criteria were retrieved. Following a 1:1 propensity score matching 572 patients with cirrhosis were screened, and relevant clinical data were collected. PVT risk factors were identified using the least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression analysis. Variance inflation factors and correlation matrix plots were used to analyze multicollinearity among the variables. A nomogram was constructed to predict the probability of PVT based on independent risk factors for PVT, and its predictive performance was verified using a receiver operating characteristic curve (ROC), calibration curves, and decision curve analysis (DCA). Finally, a network calculator was constructed based on the nomograms. This study enrolled 286 cirrhosis patients with PVT and 286 without PVT. LASSO analysis revealed 13 variables as strongly associated with PVT occurrence. Multivariate logistic regression analysis revealed nine indicators as independent PVT risk factors, including etiology, ascites, gastroesophageal varices, platelet count, D-dimer, portal vein diameter, portal vein velocity, aspartate transaminase to neutrophil ratio index, and platelet-to-lymphocyte ratio. LASSO and correlation matrix plot results revealed no significant multicollinearity or correlation among the variables. A nomogram was constructed based on the screened independent risk factors. The nomogram had excellent predictive performance, with an area under the ROC curve of 0.821 and 0.829 in the training and testing groups, respectively. Calibration curves and DCA revealed its good clinical performance. Finally, the optimal cutoff value for the total nomogram score was 0.513. The sensitivity and specificity of the optimal cutoff values were 0.822 and 0.706, respectively. A nomogram for predicting PVT occurrence was successfully developed and validated, and a network calculator was constructed. This can enable clinicians to rapidly and easily identify high PVT risk groups.

Further Clarification of Pain Management Complexity in Radiotherapy: Insights from Modern Statistical Approaches.

The primary objective of this study was to assess the adequacy of analgesic care in radiotherapy (RT) patients, with a secondary objective to identify predictive variables associated with pain management adequacy using a modern statistical approach, integrating the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and the Classification and Regression Tree (CART) analysis. This observational, multicenter cohort study involved 1387 patients reporting pain or taking analgesic drugs from 13 RT departments in Italy. The Pain Management Index (PMI) served as the measure for pain control adequacy, with a PMI score < 0 indicating suboptimal management. Patient demographics, clinical status, and treatment-related factors were examined to discern the predictors of pain management adequacy. Among the analyzed cohort, 46.1% reported inadequately managed pain. Non-cancer pain origin, breast cancer diagnosis, higher ECOG Performance Status scores, younger patient age, early assessment phase, and curative treatment intent emerged as significant determinants of negative PMI from the LASSO analysis. Notably, pain management was observed to improve as RT progressed, with a greater discrepancy between cancer (33.2% with PMI < 0) and non-cancer pain (73.1% with PMI < 0). Breast cancer patients under 70 years of age with non-cancer pain had the highest rate of negative PMI at 86.5%, highlighting a potential deficiency in managing benign pain in younger patients. The study underscores the dynamic nature of pain management during RT, suggesting improvements over the treatment course yet revealing specific challenges in non-cancer pain management, particularly among younger breast cancer patients. The use of advanced statistical techniques for analysis stresses the importance of a multifaceted approach to pain management, one that incorporates both cancer and non-cancer pain considerations to ensure a holistic and improved quality of oncological care.

Using a k-means clustering to identify novel phenotypes of acute ischemic stroke and development of its Clinlabomics models.

Acute ischemic stroke (AIS) is a heterogeneous condition. To stratify the heterogeneity, identify novel phenotypes, and develop Clinlabomics models of phenotypes that can conduct more personalized treatments for AIS. In a retrospective analysis, consecutive AIS and non-AIS inpatients were enrolled. An unsupervised k-means clustering algorithm was used to classify AIS patients into distinct novel phenotypes. Besides, the intergroup comparisons across the phenotypes were performed in clinical and laboratory data. Next, the least absolute shrinkage and selection operator (LASSO) algorithm was used to select essential variables. In addition, Clinlabomics predictive models of phenotypes were established by a support vector machines (SVM) classifier. We used the area under curve (AUC), accuracy, sensitivity, and specificity to evaluate the performance of the models. Of the three derived phenotypes in 909 AIS patients [median age 64 (IQR: 17) years, 69% male], in phenotype 1 (N = 401), patients were relatively young and obese and had significantly elevated levels of lipids. Phenotype 2 (N = 463) was associated with abnormal ion levels. Phenotype 3 (N = 45) was characterized by the highest level of inflammation, accompanied by mild multiple-organ dysfunction. The external validation cohort prospectively collected 507 AIS patients [median age 60 (IQR: 18) years, 70% male]. Phenotype characteristics were similar in the validation cohort. After LASSO analysis, Clinlabomics models of phenotype 1 and 2 were constructed by the SVM algorithm, yielding high AUC (0.977, 95% CI: 0.961-0.993 and 0.984, 95% CI: 0.971-0.997), accuracy (0.936, 95% CI: 0.922-0.956 and 0.952, 95% CI: 0.938-0.972), sensitivity (0.984, 95% CI: 0.968-0.998 and 0.958, 95% CI: 0.939-0.984), and specificity (0.892, 95% CI: 0.874-0.926 and 0.945, 95% CI: 0.923-0.969). In this study, three novel phenotypes that reflected the abnormal variables of AIS patients were identified, and the Clinlabomics models of phenotypes were established, which are conducive to individualized treatments.

Construction and validation of a prognostic model for stemness-related genes in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is the most common histological type of lung cancer with poor overall prognosis. Early identification of high-risk patients and individualized treatment can help extend the survival time of patients. This study aimed to construct and validate a prognostic prediction least absolute shrinkage and selection operator (LASSO) model for stemness-related genes in LUAD. Firstly, LUAD RNA-sequencing data and clinical data were downloaded from The Cancer Genome Atlas (TCGA) database. The tumor stemness index based on mRNA expression (mRNAsi) was calculated, and the relationship between mRNAsi and the survival prognosis as well as clinical features of LUAD patients was analyzed. Then, the weighted gene co-expression network analysis (WGCNA) method was used to screen for gene modules highly correlated with mRNAsi, and functional annotation [Gene Ontology (GO) analysis] and pathway enrichment analysis [Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis] were performed for the selected stemness-related gene module. Furthermore, prognosis-associated genes were determined from the stemness-related genes through univariate Cox analysis, and a prognostic model was constructed using LASSO analysis. Finally, a series of validations including survival curve analysis, receiver operating characteristic (ROC) curve analysis, and risk analysis were conducted for the prognostic model, and nomogram based on the risk model and various clinicopathological features were constructed. LUAD patients with high mRNAsi had a higher mortality rate than those with low mRNAsi. GO analysis showed that stemness-related genes were mainly involved in mRNA processing and extracellular matrix organization, while KEGG analysis revealed their involvement in cell cycle and PI3K-Akt signaling pathways. A prognostic model based on 12 stemness-related genes was constructed using LASSO regression. Validation of the prognostic model demonstrated its good accuracy in predicting the prognosis of LUAD patients. mRNAsi plays an important role in the occurrence and development of LUAD. This study successfully constructed a prognostic prediction LASSO model for stemness-related genes in LUAD, which can serve as a novel prognostic indicator for LUAD and may be an effective complement to the current Tumor Node Metastasis (TNM) clinical staging of LUAD.

Identification of important modules and biomarkers in diabetic cardiomyopathy based on WGCNA and LASSO analysis.

Diabetic cardiomyopathy (DCM) lacks specific and sensitive biomarkers, and its diagnosis remains a challenge. Therefore, there is an urgent need to develop useful biomarkers to help diagnose and evaluate the prognosis of DCM. This study aims to find specific diagnostic markers for diabetic cardiomyopathy. Two datasets (GSE106180 and GSE161827) from the GEO database were integrated to identify differentially expressed genes (DEGs) between control and type 2 diabetic cardiomyopathy. We assessed the infiltration of immune cells and used weighted coexpression network analysis (WGCNA) to construct the gene coexpression network. Then we performed a clustering analysis. Finally, a diagnostic model was built by the least absolute shrinkage and selection operator (LASSO). A total of 3066 DEGs in the GSE106180 and GSE161827 datasets. There were differences in immune cell infiltration. According to gene significance (GS) > 0.2 and module membership (MM) > 0.8, 41 yellow Module genes and 1474 turquoise Module genes were selected. Hub genes were mainly related to the "proteasomal protein catabolic process", "mitochondrial matrix" and "protein processing in endoplasmic reticulum" pathways. LASSO was used to construct a diagnostic model composed of OXCT1, CACNA2D2, BCL7B, EGLN3, GABARAP, and ACADSB and verified it in the GSE163060 and GSE175988 datasets with AUCs of 0.9333 (95% CI: 0.7801-1) and 0.96 (95% CI: 0.8861-1), respectively. H9C2 cells were verified, and the results were similar to the bioinformatics analysis. We constructed a diagnostic model of DCM, and OXCT1, CACNA2D2, BCL7B, EGLN3, GABARAP, and ACADSB were potential biomarkers, which may provide new insights for improving the ability of early diagnosis and treatment of diabetic cardiomyopathy.

NCAPH drives breast cancer progression and identifies a gene signature that predicts luminal a tumour recurrence.

Luminal A tumours generally have a favourable prognosis but possess the highest 10-year recurrence risk among breast cancers. Additionally, a quarter of the recurrence cases occur within 5 years post-diagnosis. Identifying such patients is crucial as long-term relapsers could benefit from extended hormone therapy, while early relapsers might require more aggressive treatment. We conducted a study to explore non-structural chromosome maintenance condensin I complex subunit H's (NCAPH) role in luminal A breast cancer pathogenesis, both in vitro and in vivo, aiming to identify an intratumoural gene expression signature, with a focus on elevated NCAPH levels, as a potential marker for unfavourable progression. Our analysis included transgenic mouse models overexpressing NCAPH and a genetically diverse mouse cohort generated by backcrossing. A least absolute shrinkage and selection operator (LASSO) multivariate regression analysis was performed on transcripts associated with elevated intratumoural NCAPH levels. We found that NCAPH contributes to adverse luminal A breast cancer progression. The intratumoural gene expression signature associated with elevated NCAPH levels emerged as a potential risk identifier. Transgenic mice overexpressing NCAPH developed breast tumours with extended latency, and in Mouse Mammary Tumor Virus (MMTV)-NCAPHErbB2 double-transgenic mice, luminal tumours showed increased aggressiveness. High intratumoural Ncaph levels correlated with worse breast cancer outcome and subpar chemotherapy response. A 10-gene risk score, termed Gene Signature for Luminal A 10 (GSLA10), was derived from the LASSO analysis, correlating with adverse luminal A breast cancer progression. The GSLA10 signature outperformed the Oncotype DX signature in discerning tumours with unfavourable outcomes, previously categorised as luminal A by Prediction Analysis of Microarray 50 (PAM50) across three independent human cohorts. This new signature holds promise for identifying luminal A tumour patients with adverse prognosis, aiding in the development of personalised treatment strategies to significantly improve patient outcomes.

Host transcriptomics and machine learning for secondary bacterial infections in patients with COVID-19: a prospective, observational cohort study

Viral respiratory tract infections are frequently complicated by secondary bacterial infections. This studyaimed to use machine learning to predict the risk of bacterial superinfection in SARS-CoV-2-positive individuals. In this prospective, multicentre, observational cohort study done in nine centres in six countries (Australia, Indonesia, Singapore, Italy, Czechia, and France) blood samples and RNA sequencing were used to develop a robust model of predicting secondary bacterial infections in the respiratory tract of patients with COVID-19. Eligible participants were older than 18 years, had known or suspected COVID-19, and symptoms of a recent respiratory infection. A control cohort of participants without COVID-19 who were older than 18 years and with no infection symptoms was also recruited from one Australian centre. In the pre-analysis phase, data were filtered to include only individuals with complete blood transcriptomics and patient data (ie, age, sex, location, and WHO severity score at the time of sample collection). The dataset was then divided randomly (4:1) into a training set (80%) and a test set (20%). Gene expression data in the training set and control cohort were used for differential expression analysis. Differentially expressed genes, along with WHO severity score, location, age, and sex, were used for feature selection with least absolute shrinkage and selection operator (LASSO) in the training set. For LASSO analysis, samples were excluded if gene expression data were not obtained at study admission, no longitudinal clinical information was available, a bacterial infection at the time of study admission was present, or a fungal infection in the absence of a bacterial infection was detected. LASSO regression was performed using three subsets of predictor variables: patient data alone, gene expression data alone, or a combination of patient data and gene expression data. The accuracy of the resultant models was tested on data from the test set. Between March, 2020, and October, 2021, we recruited 536 SARS-CoV-2-positive individuals and between June, 2013, and January, 2020, we recruited 74 participants into the control cohort. After prefiltering analysis and other exclusions, samples from 158 individuals were analysed in the training set and 47 in the test set. The expression of seven host genes (DAPP1, CST3, FGL2, GCH1, CIITA, UPP1, and RN7SL1) in the blood at the time of study admission was identified by LASSO as predictive of the risk of developing a secondary bacterial infection of the respiratory tract more than 24 h after study admission. Specifically, the expression of these genes in combination with a patient's WHO severity score at the time of study enrolment resulted in an area under the curve of 0·98 (95% CI 0·89-1·00), a true positive rate (sensitivity) of 1·00 (95% CI 1·00-1·00), and a true negative rate (specificity) of 0·94 (95% CI 0·89-1·00) in the test cohort. The combination of patient data and host transcriptomics at hospital admission identified all seven individuals in the training and test sets who developed a bacterial infection of the respiratory tract 5-9 days after hospital admission. These data raise the possibility that host transcriptomics at the time of clinical presentation, together with machine learning, can forward predict the risk of secondary bacterial infections and allow for the more targeted use of antibiotics in viral infection. Snow Medical Research Foundation, the National Health and Medical Research Council, the Jack Ma Foundation, the Helmholtz-Association, the A2 Milk Company, National Institute of Allergy and Infectious Disease, and the Fondazione AIRC Associazione Italiana per la Ricerca contro il Cancro.

Exploration of potential shared gene signatures between periodontitis and multiple sclerosis

BackgroundAlthough periodontitis has previously been reported to be linked with multiple sclerosis (MS), but the molecular mechanisms and pathological interactions between the two remain unclear. This study aims to explore potential crosstalk genes and pathways between periodontitis and MS.MethodsPeriodontitis and MS data were obtained from the Gene Expression Omnibus (GEO) database. Shared genes were identified by differential expression analysis and weighted gene co-expression network analysis (WGCNA). Then, enrichment analysis for the shared genes was carried out by multiple methods. The least absolute shrinkage and selection operator (LASSO) regression was used to obtain potential shared diagnostic genes. Furthermore, the expression profile of 28 immune cells in periodontitis and MS was examined using single-sample GSEA (ssGSEA). Finally, real-time quantitative fluorescent PCR (qRT-PCR) and immune histochemical staining were employed to validate Hub gene expressions in periodontitis and MS samples.ResultsFAM46C, SLC7A7, LY96, CFI, DDIT4L, CD14, C5AR1, and IGJ genes were the shared genes between periodontitis, and MS. GO analysis revealed that the shared genes exhibited the greatest enrichment in response to molecules of bacterial origin. LASSO analysis indicated that CFI, DDIT4L, and FAM46C were the most effective shared diagnostic biomarkers for periodontitis and MS, which were further validated by qPCR and immunohistochemical staining. ssGSEA analysis revealed that T and B cells significantly influence the development of MS and periodontitis.ConclusionsFAM46C, SLC7A7, LY96, CFI, DDIT4L, CD14, C5AR1, and IGJ were the most important crosstalk genes between periodontitis, and MS. Further studies found that CFI, DDIT4L, and FAM46C were potential biomarkers in periodontitis and MS.

An interpretable machine learning model for stroke recurrence in patients with symptomatic intracranial atherosclerotic arterial stenosis.

Symptomatic intracranial atherosclerotic stenosis (SICAS) is the most common etiology of ischemic stroke and one of the main causes of high stroke recurrence. The recurrence of stroke is closely related to the prognosis of ischemic stroke. This study aims to develop a machine learning model based on high-resolution vessel wall imaging (HR-VWI) to predict the risk of stroke recurrence in SICAS. This study retrospectively collected data from 180 SICAS stroke patients treated at the hospital between 2020.01 and 2022.01. Relevant imaging and clinical data were collected, and follow-up was conducted. The dataset was divided into a training set and a validation set in a ratio of 7:3. We employed the least absolute shrinkage and selection operator (LASSO) regression to perform a selection on the baseline data, laboratory tests, and neuroimaging data generated by HR-VWI scans collected from the training set. Finally, five machine learning techniques, including logistic regression model (LR), support vector machine (SVM), Gaussian naive Bayes (GNB), Complement naive Bayes (CNB), and k-nearest neighbors algorithm (kNN), were employed to develop a predictive model for stroke recurrence. Shapley Additive Explanation (SHAP) was used to provide visualization and interpretation for each patient. The model's effectiveness was evaluated using average accuracy, sensitivity, specificity, precision, f1 score, PR curve, calibration curve, and decision curve analysis. LASSO analysis revealed that "history of hypertension," "homocysteine level," "NWI value," "stenosis rate," "intracranial hemorrhage," "positive remodeling," and "enhancement grade" were independent risk factors for stroke recurrence in SICAS patients. In 10-fold cross-validation, the area under the curve (AUC) ranged from 0.813 to 0.912 in ROC curve analysis. The area under the precision-recall curve (AUPRC) ranged from 0.655 to 0.833, with the Gaussian Naive Bayes (GNB) model exhibiting the best ability to predict stroke recurrence in SICAS. SHAP analysis provided interpretability for the machine learning model and revealed essential factors related to the risk of stroke recurrence in SICAS. A precise machine learning-based prediction model for stroke recurrence in SICAS has been established to assist clinical practitioners in making clinical decisions and implementing personalized treatment measures.

Network pharmacology combines machine learning, molecular simulation dynamics and experimental validation to explore the mechanism of acetylbinankadsurin A in the treatment of liver fibrosis

Ethnopharmacological relevanceThe Kadsura coccinea (Lem.) A. C. Smith is known as “Heilaohu” of the Tujia ethnomedicine in China. It has anti-tumor, anti-oxidation, anti-HIV, anti-inflammatory and liver protective effects, used to treat diseases such as rheumatoid arthritis, cancer, gastritis and hepatitis. In this research, we investigated the anti-fibrotic effect and possible mechanisms of acetylbinankadsurin A (ACBA) in vitro and in vivo, which is a natural compound derived from roots of K. coccinea. Aim of the studyWe try to evaluate the efficacy of ACBA in the treatment of liver fibrosis and to explore the underlying mechanisms of ACBA by network pharmacology, machine learning, molecular docking, molecular dynamics simulations, and experimental assessment. Materials and methodsACBA was isolated from the CH2Cl2 layer of the roots of K. coccinea through column chromatographic techniques. The structure of ACBA was determined by using 1D and 2D NMR. CCl4-induced C57BL/6 mouse liver fibrosis models were established to evaluate the anti-fibrosis effects of ACBA in vivo. The molecular targets of ACBA and liver fibrosis were obtained from various databases, then constructed a protein-protein interaction (PPI) networks through the STRING database. Gene ontology (GO) enrichment and kyoto encyclopedia of genes and genomes (KEGG) analysis were applied using the “clusterProfiler” R package. Furthermore, the key genes for ACBA treatment of liver fibrosis were identified by the least absolute shrinkage and selection operator (LASSO). Molecular docking and molecular dynamics simulations were also carried out. Finally, the target and pathway of ACBA were verified by immunofluorescence staining, RT-PCR and Western blot. ResultFirst, ACBA attenuated CCl4-induced liver injury and fibrosis in vivo. These findings were accompanied by decreased expression of α-SMA and collagen I. Second, ACBA significantly decreased serum levels of ALT, AST, TNF-α and IL-6. Then, we identified 133 potential targets of ACBA and 7987 targets of liver fibrosis. KEGG analysis showed that ACBA could regulate the drug metabolism - cytochrome P450, fructose and mannose metabolism, IL-17 and NF-κB signaling pathways. Next, six core targets was screened by LASSO analysis including AKR1B1, PFKFB3, EPHA3, CDK1, CCR1 and CYP3A4. Molecular docking showed that ACBA has a good binding affinity for CCR1. Furthermore, compared with CCR1 inhibitor BX-471, The results of molecular simulation dynamics showed that ACBA was stable in binding with CCR1. Consistently, ACBA remarkably downregulated the expression of CCR1, p-NF-κBp65, p-IκBα, p-STAT1 and TNF-α proteins, which were upregulated in CCl4-induced hepatic fibrosis and LPS-THP-1 cells. ConclusionOur results suggest that ACBA significantly attenuated CCl4-induced liver fibrosis in histopathological and in serum level. This effect may be mediated by CCR1, NF-κB and STAT1 signalling.

Transcriptomic correlates of cell cycle checkpoints with distinct prognosis, molecular characteristics, immunological regulation, and therapeutic response in colorectal adenocarcinoma.

Colorectal adenocarcinoma (COAD), accounting for the most common subtype of colorectal cancer (CRC), is a kind of malignant digestive tumor. Some cell cycle checkpoints (CCCs) have been found to contribute to CRC progression, whereas the functional roles of a lot of CCCs, especially the integrated role of checkpoint mechanism in the cell cycle, remain unclear. The Genomic Data Commons (GDC) The Cancer Genome Atlas (TCGA) COAD cohort was retrieved as the training dataset, and GSE24551 and GSE29623 were downloaded from Gene Expression Omnibus (GEO) as the validation datasets. A total of 209 CCC-related genes were derived from the Gene Ontology Consortium and were subsequently enrolled in the univariate, multivariate, and least absolute shrinkage and selection operator (LASSO) Cox regression analyses, finally defining a CCC signature. Cell proliferation and Transwell assay analyses were utilized to evaluate the functional roles of signature-related CCCs. The underlying CCC signature, molecular characteristics, immune-related features, and therapeutic response were finally estimated. The Genomics of Drug Sensitivity in Cancer (GDSC) database was employed for the evaluation of chemotherapeutic responses. The aberrant gene expression of CCCs greatly contributed to COAD development and progression. Univariate Cox regression analysis identified 27 CCC-related genes significantly affecting the overall survival (OS) of COAD patients; subsequently, LASSO analysis determined a novel CCC signature. Noticeably, CDK5RAP2, MAD1L1, NBN, RGCC, and ZNF207 were first identified to be correlated with the prognosis of COAD, and it was proven that all of them were significantly correlated with the proliferation and invasion of HCT116 and SW480 cells. In TCGA COAD cohort, CCC signature robustly stratified COAD patients into high and low CCC score groups (median OS: 57.24 months vs. unreached, p< 0.0001), simultaneously, with the good AUC values for OS prediction at 1, 2, and 3 years were 0.74, 0.78, and 0.77. Furthermore, the prognostic capacity of the CCC signature was verified in the GSE24551 and GSE29623 datasets, and the CCC signature was independent of clinical features. Moreover, a higher CCC score always indicated worse OS, regardless of clinical features, histological subtypes, or molecular subgroups. Intriguingly, functional enrichment analysis confirmed the CCC score was markedly associated withextracellular, matrix and immune (chemokine)-related signaling, cell cycle-related signaling, and metabolisms. Impressively, a higher CCC score was positively correlated with a majority of chemokines, receptors, immunostimulators, and anticancer immunity, indicating a relatively immune-promoting microenvironment. In addition, GSE173839, GSE25066, GSE41998, and GSE194040 dataset analyses of the underlying CCC signature suggested that durvalumab with olaparib and paclitaxel, taxane-anthracycline chemotherapy, neoadjuvant cyclophosphamide/doxorubicin with ixabepilone or paclitaxel, and immunotherapeutic strategies might be suitable for COAD patients with higher CCC score. Eventually, the GDSC database analysis showed that lower CCC scores were likely to be more sensitive to 5-fluorouracil, bosutinib, gemcitabine, gefitinib, methotrexate, mitomycin C, and temozolomide, while patients with higher CCC score seemed to have a higher level of sensitivity to bortezomib and elesclomol. The novel CCC signature exhibited a good ability for prognosis prediction for COAD patients, and the CCC score was found to be highly correlated with molecular features, immune-related characteristics, and therapeutic responses, which would greatly promote clinical management and precision medicine for COAD.

Imaging-Derived Biomarkers Integrated with Clinical and Laboratory Values Predict Recurrence of Hepatocellular Carcinoma After Liver Transplantation.

To investigate the prognostic value of computed tomography (CT) derived imaging biomarkers in hepatocellular carcinoma (HCC) recurrence after liver transplantation (LT) and develop a predictive nomogram model. This retrospective study included 178 patients with histopathologically confirmed HCC who underwent liver transplantation between 2007 and 2021 at the two academic liver centers. We evaluated dedicated imaging features from baseline multiphase contrast-enhanced CT supplemented by several clinical findings and laboratory parameters. Time-to-recurrence was estimated by Kaplan-Meier analysis. Univariable Cox proportional hazard regression and multivariable Least Absolute Shrinkage and Selection Operator (LASSO) regression were used to assess independent prognostic factors for recurrence. A nomogram model was then built based on the independent factors selected through LASSO regression, to predict the probabilities of HCC recurrence at one, three, and five years. The rate of HCC recurrence after LT was 17.4% (31 of 178). The LASSO analysis revealed six independent predictors associated with an elevated risk of tumor recurrence. These predictors included the presence of peritumoral enhancement, the presence of over three tumor lesions, the largest tumor diameter greater than 3 cm, serum alpha-fetoprotein (AFP) levels exceeding 400 ng/mL, and the presence of a tumor capsule. Conversely, a history of bridging therapies was found to be correlated with a reduced risk of HCC recurrence. In addition, Kaplan-Meier curves showed patients with irregular margin, satellite nodules, or small lesions displayed shorter time-to-recurrence. Our nomogram demonstrated good performance, yielding a C-index of 0.835 and AUC values of 0.86, 0.88, and 0.85 for the predictions of 1-year, 3-year, and 5-year TTR, respectively. Imaging parameters derived from baseline contrast-enhanced CT showing malignant behavior and aggressive growth patterns, along with serum AFP and history of bridging therapies, show potential as biomarkers for predicting HCC recurrence after transplantation.

Establishment and Validation of the Novel Necroptosis-related Genes for Predicting Stemness and Immunity of Hepatocellular Carcinoma via Machine- learning Algorithm

Background:: Necroptosis, a recently identified mechanism of programmed cell death, exerts significant influence on various aspects of cancer biology, including tumor cell proliferation, stemness, metastasis, and immunosuppression. However, the role of necroptosis-related genes (NRGs) in Hepatocellular Carcinoma (HCC) remains elusive. Methods:: In this study, we assessed the mutation signature, copy number variation, and expression of 37 NRGs in HCC using the TCGA-LIHC dataset. We further validated our results using the ICGC-LIRI-JP dataset. To construct our prognostic model, we utilized the least absolute shrinkage and selection operator (LASSO), and evaluated the predictive efficacy of the NRGs-score using various machine learning algorithms, including K-M curves, time-ROC curves, univariate and multivariate Cox regression, and nomogram. In addition, we analyzed immune infiltration using the CIBERSOFT and ssGSEA algorithms, calculated the stemness index through the one-class logistic regression (OCLR) algorithm, and performed anti-cancer stem cells (CSCs) drug sensitivity analysis using oncoPredict. Finally, we validated the expression of the prognostic NRGs through qPCR both in vitro and in vivo. Results:: About 18 out of 37 NRGs were found to be differentially expressed in HCC and correlated with clinical outcomes. To construct a prognostic model, six signature genes (ALDH2, EZH2, PGAM5, PLK1, SQSTM1, and TARDBP) were selected using LASSO analysis. These genes were then employed to categorize HCC patients into two subgroups based on NRGs-score (low vs. high). A high NRGs score was associated with a worse prognosis. Furthermore, univariate and multivariate Cox regression analyses were performed to confirm the NRGs-score as an independent risk factor. These analyses revealed strong associations between NRGs-score and critical factors, such as AFP, disease stage, and tumor grade in the HCC cohort. NRGs-score effectively predicted the 1-, 3-, and 5-year survival of HCC patients. Immune infiltration analysis further revealed that the expression of immune checkpoint molecules was significantly enhanced in the high NRGs-score group. Stemness analysis in the HCC cohort showed that NRGs-score was positively correlated with mRNA stemness index, and patients with high NRGs-score were sensitive to CSCs inhibitors. The findings from the external validation cohort provided confirmation that the NRGs-score presented a trait with universal applicability in accurately predicting the survival of HCC. Additionally, the six prognostic genes were consistently differentially expressed in both the HCC cell line and the mouse HCC model. Conclusions:: Our study demonstrated the pivotal role of NRGs in promoting stemness and immune suppression in HCC and established a robust model which could successfully predict HCC prognosis.

Head and neck squamous cell carcinoma-specific prognostic signature and drug sensitive subtypes based on programmed cell death-related genes.

As a complex group of malignancies, head and neck squamous cell carcinoma (HNSC) is one of the leading causes of cancer mortality. This study aims to establish a reliable clinical classification and gene signature for HNSC prognostic prediction and precision treatments. A consensus clustering analysis was performed to group HNSC patients in The Cancer Genome Atlas (TCGA) database based on genes linked to programmed cell death (PCD). Differentially expressed genes (DEGs) between subtypes were identified using the "limma" R package. The TCGA prognostic signature and PCD-related prognostic genes were found using a least absolute shrinkage and selection operator (LASSO) regression analysis and univariate Cox regression analysis. The robustness of the LASSO analysis was validated using datasets GSE65858 and GSE41613. A cell counting kit-8 (CCK-8) test, Western blot, and real-time reverse transcriptase-polymerase chain reaction (RT-qPCR) were used to evaluate the expression and viability of prognostic genes. Four molecular subtypes were identified in PCD-related genes. Subtype C4 had the best prognosis and the highest immune score, while subtype C1 exhibited the most unfavorable outcomes. Three hundred shared DEGs were identified among the four subtypes, and four prognostic genes (CTLA4, CAMK2N1, PLAU and CALML5) were used to construct a TCGA-HNSC prognostic model. High-risk patients manifested poorer prognosis, more inflammatory pathway enrichment, and lower immune cell infiltration. High-risk patients were more prone to immune escape and were more likely to be resistant to Cisplatin and 5-Fluorouracil. Prognosis prediction was validated in external datasets. The expression of CTLA4, CAMK2N1, PLAU and CALML5 was enhanced in CAL-27 and SCC-25 cell lines, and CALML5 inhibited CAL-27 and SCC-25 cell viability. This study shares novel insights into HNSC classification and provides a reliable PCD-related prognostic signature for prognosis prediction and treatment for patients with HNSC.

Bioinformatics analysis reveals the potential common genes and immune characteristics between atrial fibrillation and periodontitis.

Atrial fibrillation (AF) and periodontitis, both classified under chronic inflammatory diseases, share common etiologies, including genetic factors and immune pathways. However, the exact mechanisms are still poorly understood. This study aimed to explore the potential common genes and immune characteristics between AF and periodontitis. Gene expression datasets for AF and periodontitis were downloaded from the Gene Expression Omnibus (GEO) database. Differential expression analysis was used to identify common genes in the training set. Functional analyses, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, were conducted to elucidate the underlying mechanisms. Hub genes were further screened based on expression levels, receiver operating characteristic (ROC) curves, and least absolute shrinkage and selection operator (LASSO) regression. Then, based on the expression levels and ROC values of the hub genes in the validation set, the target genes were identified. Finally, immune cell infiltration analysis was performed on the AF and periodontitis datasets in the training set using the "CIBERSORT" R package. The relationships between target genes, infiltrating immune cells, and inflammatory factors were also investigated. In addition, AF susceptibility, atrial fibrosis, inflammatory infiltration, and RGS1 protein expression in rat models of periodontitis were assessed through invivo electrophysiology experiments, Masson's trichrome staining, hematoxylin-eosin staining, immunohistochemistry, and western blotting, respectively. A total of 21 common genes were identified between AF and periodontitis among the differentially expressed genes. After evaluating gene expression levels, ROC curves, and LASSO analysis, four significant genes between AF and periodontitis were identified, namely regulator of G-protein signaling 1 (RGS1), annexin A6 (ANXA6), solute carrier family 27 member 6 (SLC27A6), and ficolin 1 (FCN1). Further validation confirmed that RGS1 was the optimal shared target gene for AF and periodontitis. Immune cell infiltration analysis revealed that neutrophils and T cells play an important role in the pathogenesis of both diseases. RGS1 showed a significant positive correlation with activated memory CD4 T cells and gamma-delta T cells and a negative correlation with CD8 T cells and regulatory T cells in both training sets. Moreover, RGS1 was positively correlated with classical pro-inflammatory cytokines IL1β and IL6. In periodontitis rat models, AF susceptibility, atrial fibrosis, and inflammatory infiltration were significantly increased, and RGS1 expression in the atrial tissue was upregulated. A common gene between AF and periodontitis, RGS1 appears central in linking the two conditions. Immune and inflammatory responses may underlie the interaction between AF and periodontitis.

Identification of hub genes and their correlation with immune infiltrating cells in membranous nephropathy: an integrated bioinformatics analysis

BackgroundMembranous nephropathy (MN) is a chronic glomerular disease that leads to nephrotic syndrome in adults. The aim of this study was to identify novel biomarkers and immune-related mechanisms in the progression of MN through an integrated bioinformatics approach.MethodsThe microarray data were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between MN and normal samples were identified and analyzed by the Gene Ontology analysis, the Kyoto Encyclopedia of Genes and Genomes analysis and the Gene Set Enrichment Analysis (GSEA) enrichment. Hub The hub genes were screened and identified by the weighted gene co-expression network analysis (WGCNA) and the least absolute shrinkage and selection operator (LASSO) algorithm. The receiver operating characteristic (ROC) curves evaluated the diagnostic value of hub genes. The single-sample GSEA analyzed the infiltration degree of several immune cells and their correlation with the hub genes.ResultsWe identified a total of 574 DEGs. The enrichment analysis showed that metabolic and immune-related functions and pathways were significantly enriched. Four co-expression modules were obtained using WGCNA. The candidate signature genes were intersected with DEGs and then subjected to the LASSO analysis, obtaining a total of 6 hub genes. The ROC curves indicated that the hub genes were associated with a high diagnostic value. The CD4+ T cells, CD8+ T cells and B cells significantly infiltrated in MN samples and correlated with the hub genes.ConclusionsWe identified six hub genes (ZYX, CD151, N4BP2L2-IT2, TAPBP, FRAS1 and SCARNA9) as novel biomarkers for MN, providing potential targets for the diagnosis and treatment.

Investigating the prognostic role of lncRNAs associated with disulfidptosis-related genes in clear cell renal cell carcinoma.

Renal cell carcinoma (RCC) is a grave malignancy that poses a significant global health burden with over 400,000 new cases annually. Disulfidptosis, a newly discovered programmed cell death process, is linked to the actin cytoskeleton, which plays a vital role in maintaining cell shape and survival. The role of disulfidptosis is poorly depicted in the clear cell histologic variant of RCC (ccRCC). Three sets of ccRCC cohorts, ICGC_RECA-EU (n =91), GSE76207 (n =32) and TCGA-KIRC (n =607), were included in our study, the batch effect of which was removed using the "combat" function. Correlation was calculated using the "rcorr" function of the "Hmisc" package for Pearson analysis, which was visualized using the "pheatmap" package. Principal component analysis was performed by the "vegan" package, visualized using the "scatterplot3d" package. Long non-coding RNAs (lncRNAs) associated with disulfidptosis were screened out using least absolute shrinkage and selection operator (LASSO) and COX analysis. Tumor mutation, immune landscaping and immunotherapy prediction were performed for further characterization of two risk groups. A total of 1822 disulfidptosis-related lncRNAs was selected, among which 308 lncRNAs were found to be significantly associated with the clinical outcome of ccRCC patients. We retained 11 disulfidptosis-related lncRNAs, namely, AP000439.3, RP11-417E7.1, RP11-119D9.1, LINC01510, SNHG3, AC156455.1, RP11-291B21.2, EMX2OS, AC093850.2, HAGLR and RP11-389C8.2, through LASSO and COX analysis for prognosis model construction, which displayed satisfactory accuracy (area under the curve, AUC, values all above 0.6 in multiple cohorts) in stratification of ccRCC prognosis. A nomogram model was constructed by integrating clinical factors with risk score, which further enhanced the prediction efficacy (AUC values all above 0.7 in multiple cohorts). We found that patients of male gender, higher clinical stages and advanced pathological T stage were inclined to have higher risk score values. Dactinomycin_1911, Vinblastine_1004, Daporinad_1248 and Vinorelbine_2048 were identified as promising candidate drugs for treating ccRCC patients of higher risk score value. Moreover, patients of higher risk value were prone to be resistant to immunotherapy. We developed a prognosis predicting model based on 11 selected disulfidptosis-related lncRNAs, the efficacy of which was verified in different cohorts. Furthermore, we delineated an intricate portrait of tumor mutation, immune topography and pharmacosensitivity evaluations within disparate risk stratifications.

Computed tomography texture-based models for predicting KIT exon 11 mutation of gastrointestinal stromal tumors

BackgroundKIT exon 11 mutation in gastrointestinal stromal tumors (GISTs) is associated with treatment strategies. However, few studies have shown the role of imaging-based texture analysis in KIT exon 11 mutation in GISTs. In this study, we aimed to show the association between computed tomography (CT)-based texture features and KIT exon 11 mutation. MethodsNinety-five GISTs confirmed by surgery and identified with mutational genotype of KIT were included in this study. By amplifying the samples using over-sampling technique, a total of 183 region of interest (ROI) segments were extracted from 63 patients as training cohort. The 63 new ROI segments were extracted from the 63 patients as internal validation cohort. Thirty-two patients who underwent KIT exon 11 mutation test during 2021–2023 was selected as external validation cohort. The textural parameters were evaluated both in training cohort and validation cohort. Least absolute shrinkage and selection operator (LASSO) algorithms and logistic regression analysis were used to select the discriminant features. ResultsThree of textural features were obtained using LASSO analysis. Logistic regression analysis showed that patients’ age, tumor location and radiomics features were significantly associated with KIT exon 11 mutation (p < 0.05). A nomogram was developed based on the associated factors. The area under the curve (AUC) of clinical features, radiomics features and their combination in training cohort was 0.687 (95 % CI: 0.604–0.771), 0.829 (95 % CI: 0.768–0.890) and 0.874 (95 % CI: 0.822–0.926), respectively. The AUC of radiomics features in internal validation cohort and external cohort was 0.880 (95 % CI: 0.796–0.964) and 0.827 (95%CI: 0.667–0.987), respectively. ConclusionThe CT texture-based model can be used to predict KIT exon 11 mutation in GISTs.

Identification of hub biomarkers and immune-related pathways participating in the progression of Kawasaki disease by integrated bioinformatics analysis

BackgroundKawasaki disease (KD) is a systemic vasculitis that commonly affects children and its etiology remains unknown. Growing evidence suggests that immune-mediated inflammation and immune cells in the peripheral blood play crucial roles in the pathophysiology of KD. The objective of this research was to find important biomarkers and immune-related mechanisms implicated in KD, along with their correlation with immune cells in the peripheral blood. Material/MethodsGene microarray data from the Gene Expression Omnibus (GEO) was utilized in this study. Three datasets, namely GSE63881 (341 samples), GSE73463 (233 samples), and GSE73461 (279 samples), were obtained. To find intersecting genes, we employed differentially expressed genes (DEGs) analysis and weighted gene co-expression network analysis (WGCNA). Subsequently, functional annotation, construction of protein–protein interaction (PPI) networks, and Least Absolute Shrinkage and Selection Operator (LASSO) regression were performed to identify hub genes. The accuracy of these hub genes in identifying KD was evaluated using the receiver operating characteristic curve (ROC). Furthermore, Gene Set Variation Analysis (GSVA) was employed to explore the composition of circulating immune cells within the assessed datasets and their relationship with the hub gene markers. ResultsWGCNA yielded eight co-expression modules, with one hub module (MEblue module) exhibiting the strongest association with acute KD. 425 distinct genes were identified. Integrating WGCNA and DEGs yielded a total of 277 intersecting genes. By conducting LASSO analysis, five hub genes (S100A12, MMP9, TLR2, NLRC4 and ARG1) were identified as potential biomarkers for KD. The diagnostic value of these five hub genes was demonstrated through ROC curve analysis, indicating their high accuracy in diagnosing KD. Analysis of the circulating immune cell composition within the assessed datasets revealed a significant association between KD and various immune cell types, including activated dendritic cells, neutrophils, immature dendritic cells, macrophages, and activated CD8 T cells. Importantly, all five hub genes exhibited strong correlations with immune cells. ConclusionActivated dendritic cells, neutrophils, and macrophages were closely associated with the pathogenesis of KD. Furthermore, the hub genes (S100A12, MMP9, TLR2, NLRC4, and ARG1) are likely to participate in the pathogenic mechanisms of KD through immune-related signaling pathways.