Serum biomarkers REG1A and REG3A combined with the traditional CEA represent a novel nomogram for the screening and risk stratification of colorectal cancer.

BACKGROUND: To develop and validate a serum protein nomogram for colorectal cancer (CRC) screening. METHODS: The serum protein characteristics were extracted from an independent sample containing 30 colorectal cancer and 12 polyp tissues along with their paired samples, and different serum protein expression profiles were validated using RNA microarrays. The prediction model was developed in a training cohort that included 1345 patients clinicopathologically confirmed CRC and 518 normal participants, and data were gathered from November 2011 to January 2017. The lasso logistic regression model was employed for features selection and serum nomogram building. An internal validation cohort containing 576 CRC patients and 222 normal participants was assessed. RESULTS: Serum signatures containing 27 secreted proteins were significantly differentially expressed in polyps and CRC compared to paired normal tissue, and REG family proteins were selected as potential predictors. The C-index of the nomogram1 (based on Lasso logistic regression model) which contains REG1A, REG3A, CEA and age was 0.913 (95% CI, 0.899 to 0.928) and was well calibrated. Addition of CA199 to the nomogram failed to show incremental prognostic value, as shown in nomogram2 (based on logistic regression model). Application of the nomogram1 in the independent validation cohort had similar discrimination (C-index, 0.912 [95% CI, 0.890 to 0.934]) and good calibration. The decision curve (DCA) and clinical impact curve (ICI) analysis demonstrated that nomogram1 was clinically useful. CONCLUSIONS: This study presents a serum nomogram that included REG1A, REG3A, CEA and age, which can be convenient for screening of colorectal cancer.

Population-Specific gene expression profiles in prostate cancer: insights from Weighted Gene Co-expression Network Analysis (WGCNA).

This study investigates the genetic factors contributing to the disparity in prostate cancer incidence and progression among African American men (AAM) compared to European American men (EAM). The research focuses on employing Weighted Gene Co-expression Network Analysis (WGCNA) on public microarray data obtained from prostate cancer patients. The study employed WGCNA to identify clusters of genes with correlated expression patterns, which were then analyzed for their connection to population backgrounds. Additionally, pathway enrichment analysis was conducted to understand the significance of the identified gene modules in prostate cancer pathways. The Least Absolute Shrinkage and Selection Operator (LASSO) and Correlation-based Feature Selection (CFS) methods were utilized for selection of biomarker genes. The results revealed 353 differentially expressed genes (DEGs) between AAM and EAM. Six significant gene expression modules were identified through WGCNA, showing varying degrees of correlation with prostate cancer. LASSO and CFS methods pinpointed critical genes, as well as six common genes between both approaches, which are indicative of their vital role in the disease. The XGBoost classifier validated these findings, achieving satisfactory prediction accuracy. Genes such as APRT, CCL2, BEX2, MGC26963, and PLAU were identified as key genes significantly associated with cancer progression. In conclusion, the research underlines the importance of incorporating AAM and EAM population diversity in genomic studies, particularly in cancer research. In addition, the study highlights the effectiveness of integrating machine learning techniques with gene expression analysis as a robust methodology for identifying critical genes in cancer research.

Identification of a combined hypoxia and lactate metabolism prognostic signature in lung adenocarcinoma : Author.

BACKGROUND: In the tumor microenvironment (TME), a bidirectional relationship exists between hypoxia and lactate metabolism, with each component exerting a reciprocal influence on the other, forming an inextricable link. The aim of the present investigation was to develop a prognostic model by amalgamating genes associated with hypoxia and lactate metabolism. This model is intended to serve as a tool for predicting patient outcomes, including survival rates, the status of the immune microenvironment, and responsiveness to therapy in patients with lung adenocarcinoma (LUAD). METHODS: Transcriptomic sequencing data and patient clinical information specific to LUAD were obtained from comprehensive repositories of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). A compendium of genes implicated in hypoxia and lactate metabolism was assembled from an array of accessible datasets. Univariate and multivariate Cox regression analyses were employed. Additional investigative procedures, including tumor mutational load (TMB), microsatellite instability (MSI), functional enrichment assessments and the ESTIMATE, CIBERSORT, and TIDE algorithms, were used to evaluate drug sensitivity and predict the efficacy of immune-based therapies. RESULTS: A novel prognostic signature comprising five lactate and hypoxia-related genes (LHRGs), PKFP, SLC2A1, BCAN, CDKN3, and ANLN, was established. This model demonstrated that LUAD patients with elevated LHRG-related risk scores exhibited significantly reduced survival rates. Both univariate and multivariate Cox analyses confirmed that the risk score was a robust prognostic indicator of overall survival. Immunophenotyping revealed increased infiltration of memory CD4 + T cells, dendritic cells and NK cells in patients classified within the high-risk category compared to their low-risk counterparts. Higher probability of mutations in lung adenocarcinoma driver genes in high-risk groups, and the MSI was associated with the risk-score. Functional enrichment analyses indicated a predominance of cell cycle-related pathways in the high-risk group, whereas metabolic pathways were more prevalent in the low-risk group. Moreover, drug sensitivity analyses revealed increased sensitivity to a variety of drugs in the high-risk group, especially inhibitors of the PI3K-AKT, EGFR, and ELK pathways. CONCLUSIONS: This prognostic model integrates lactate metabolism and hypoxia parameters, offering predictive insights regarding survival, immune cell infiltration and functionality, as well as therapeutic responsiveness in LUAD patients. This model may facilitate personalized treatment strategies, tailoring interventions to the unique molecular profile of each patient's disease.

Establishing and Validating a novel Prognostic Model in the Initial Diagnosis of Non-small Cell Lung Cancer with Bone Metastases.

Background: The aim of this research is to establish and validate a prognostic model for predicting prognosis in non-small cell lung cancer (NSCLC) patients with bone metastases. Methods: Overall, 176 NSCLC patients with bone metastases were retrospectively evaluated in the research. We employed the LASSO-Cox regression method to select the candidate indicators for predicting the prognosis among NSCLC patients complicated with bone metastases. We employed the receiver operating characteristic curve (ROC) and the concordance index (C-index) to assess the discriminative ability. Results: Based on the LASSO-Cox regression analysis, 9 candidate indicators were screened to build the prognostic model. The prognostic model had a higher C-index in the training cohort (0.738, 95% CI: 0.680-0.796) and the validation cohort (0.660, 95% CI: 0.566-0.754) than the advanced lung cancer inflammation index (ALI). Furthermore, the AUCs of the 1-, 2-, and 3-year OS predictions for the prognostic model were higher than ALI in both cohorts. Kaplan-Meier curves and the estimated restricted mean survival time (RMST) values showed that the patients in the low-risk subgroup had the lower probabilities of cancer-specific mortality than high-risk subgroup. Conclusions: The prognostic model could provide clinicians with precise information and facilitate individualized treatment for patients with bone metastases.

Differential Expression of mRNA in Peripheral Blood Mononuclear Cells May Predict Postoperative Atrial Fibrillation in Coronary Artery Bypass Surgery Patients.

Postoperative Atrial Fibrillation (POAF) frequently follows Coronary Artery Bypass Grafting (CABG) surgery. This prospective study investigates genes linked to POAF in CABG patients, aiming to create a predictive model. Employing differential gene and methylation analyses, the study identified four genes (WARS2, CKAP2, CHI3L1, HSD17B6) associated with POAF. Preoperative plasma samples and clinical data were collected from 139 CABG patients, categorized into POAF (+) (43) and POAF (-) (96). Real-time quantitative PCR assessed gene expression, and a predictive model using the LASSO method demonstrated robust performance, with AUC values of 0.8895 in the training set and 0.7840 in the test set. This pioneering study integrates genomics and clinical data, suggesting WARS2, CKAP2, and CHI3L1 as potential indicators for POAF prediction.

Presenting a Multispectral Image Sensor for Quantification of Total Polyphenols in Low-Temperature Stressed Tomato Seedlings Using Hyperspectral Imaging.

Hyperspectral imaging was used to predict the total polyphenol content in low-temperature stressed tomato seedlings for the development of a multispectral image sensor. The spectral data with a full width at half maximum (FWHM) of 5 nm were merged to obtain FWHMs of 10 nm, 25 nm, and 50 nm using a commercialized bandpass filter. Using the permutation importance method and regression coefficients, we developed the least absolute shrinkage and selection operator (Lasso) regression models by setting the band number to ≥11, ≤10, and ≤5 for each FWHM. The regression model using 56 bands with an FWHM of 5 nm resulted in an R2 of 0.71, an RMSE of 3.99 mg/g, and an RE of 9.04%, whereas the model developed using the spectral data of only 5 bands with a FWHM of 25 nm (at 519.5 nm, 620.1 nm, 660.3 nm, 719.8 nm, and 980.3 nm) provided an R2 of 0.62, an RMSE of 4.54 mg/g, and an RE of 10.3%. These results show that a multispectral image sensor can be developed to predict the total polyphenol content of tomato seedlings subjected to low-temperature stress, paving the way for energy saving and low-temperature stress damage prevention in vegetable seedling production.

Identifying and evaluating a disulfidptosis-related gene signature to predict prognosis in colorectal adenocarcinoma patients.

Disulfidptosis, a regulated form of cell death, has been recently reported in cancers characterized by high SLC7A11 expression, including invasive breast carcinoma, lung adenocarcinoma, and hepatocellular carcinoma. However, its role in colon adenocarcinoma (COAD) has been infrequently discussed. In this study, we developed and validated a prognostic model based on 20 disulfidptosis-related genes (DRGs) using LASSO and Cox regression analyses. The robustness and practicality of this model were assessed via a nomogram. Subsequent correlation and enrichment analysis revealed a relationship between the risk score, several critical cancer-related biological processes, immune cell infiltration, and the expression of oncogenes and cell senescence-related genes. POU4F1, a significant component of our model, might function as an oncogene due to its upregulation in COAD tumors and its positive correlation with oncogene expression. In vitro assays demonstrated that POU4F1 knockdown noticeably decreased cell proliferation and migration but increased cell senescence in COAD cells. We further investigated the regulatory role of the DRG in disulfidptosis by culturing cells in a glucose-deprived medium. In summary, our research revealed and confirmed a DRG-based risk prediction model for COAD patients and verified the role of POU4F1 in promoting cell proliferation, migration, and disulfidptosis.

Identifying potential targets for preventing cancer progression through the PLA2G1B recombinant protein using bioinformatics and machine learning methods.

Lung cancer is the deadliest and most aggressive malignancy in the world. Preventing cancer is crucial. Therefore, the new molecular targets have laid the foundation for molecular diagnosis and targeted therapy of lung cancer. PLA2G1B plays a key role in lipid metabolism and inflammation. PLA2G1B has selective substrate specificity. In this paper, the recombinant protein molecular structure of PLA2G1B was studied and novel therapeutic interventions were designed to disrupt PLA2G1B activity and impede tumor growth by targeting specific regions or residues in its structure. Construct protein-protein interaction networks and core genes using R's "STRING" program. LASSO, SVM-RFE and RF algorithms identified important genes associated with lung cancer. 282 deg were identified. Enrichment analysis showed that these genes were mainly related to adhesion and neuroactive ligand-receptor interaction pathways. PLA2G1B was subsequently identified as developing a preventative feature. GSEA showed that PLA2G1B is closely related to α-linolenic acid metabolism. Through the analysis of LASSO, SVM-RFE and RF algorithms, we found that PLA2G1B gene may be a preventive gene for lung cancer.

Development and validation of a TAAbs and TAAs based non-invasive model for diagnosing lung cancer.

Background: Single Tumor-associated autoantibodies (TAAbs) and tumor-associated antigens (TAAs) have been found to have lower diagnostic efficacy in lung cancer. Our objective is to develop and validate a lung cancer prediction model that utilizes TAAbs and TAAs and to enhance the accuracy of lung cancer detection. Methods: 1830 subjects were randomly divided into training and validation sets at a 7:3 ratio for this study. Lasso regression analysis was used to remove collinear variables, whereas univariate logistic regression analysis was employed to identify potential independent risk factors for lung cancer. A diagnostic model was constructed using multivariate logistic analysis. The results were presented as a nomogram and assessed for various performance measures, including area under the curve, calibration curve, and decision curve analysis. Results: The diagnostic model was developed using gender, age, GAGE7, MAGE-A1, CA125, and CEA as variables. The training set had an AUC of 0.787, while the validation set had an AUC of 0.750. The calibration curves of the training and validation sets showed a strong agreement between anticipated and observed values. The nomogram performed better than any individual variable in both the training and validation sets in terms of net benefits for lung cancer detection, according to DCA analysis. Conclusions: This study proposes a diagnostic model for lung cancer that uses TAAbs and TAAs and incorporates individual characteristics. This model can be easily applied to personalized diagnosis.

Development of a Preoperative Prediction Model Based on Spectral CT to Evaluate Axillary Lymph Node With Macrometastases in Clinical T1/2N0 Invasive Breast Cancer.

OBJECTIVES: To develop a prediction model based on spectral computed tomography (CT) to evaluate axillary lymph node (ALN) with macrometastases in clinical T1/2N0 invasive breast cancer. METHODS: A total of 217 clinical T1/2N0 invasive breast cancer patients who underwent spectral CT scans were retrospectively enrolled and categorized into a training cohort (n = 151) and validation cohort (n = 66). These patients were classified into ALN nonmacrometastases (stage pN0 or pN0 [i+] or pN1mi) and ALN macrometastases (stage pN1-3) subgroups. The morphologic criteria and quantitative spectral CT parameters of the most suspicious ALN were measured and compared. Least absolute shrinkage and selection operator (Lasso) was used to screen predictive indicators to build a logistic model. The receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate the models. RESULTS: The combined arterial-venous phase spectral CT model yielded the best diagnostic performance in discrimination of ALN nonmacrometastases and ALN macrometastases with the highest AUC (0.963 in the training cohort and 0.945 in validation cohorts). Among single phase spectral CT models, the venous phase spectral CT model showed the best performance (AUC = 0.960 in the training cohort and 0.940 in validation cohorts). There was no significant difference in AUCs among the 3 models (DeLong test, P > .05 for each comparison). CONCLUSION: A Lasso-logistic model that combined morphologic features and quantitative spectral CT parameters based on contrast-enhanced spectral imaging potentially be used as a noninvasive tool for individual preoperative prediction of ALN status in clinical T1/2N0 invasive breast cancers.

Metaheuristic integrated machine learning classification of colon cancer using STFT LASSO and EHO feature extraction from microarray gene expressions.

The microarray gene expression data poses a tremendous challenge due to their curse of dimensionality problem. The sheer volume of features far surpasses available samples, leading to overfitting and reduced classification accuracy. Thus the dimensionality of microarray gene expression data must be reduced with efficient feature extraction methods to reduce the volume of data and extract meaningful information to enhance the classification accuracy and interpretability. In this research, we discover the uniqueness of applying STFT (Short Term Fourier Transform), LASSO (Least Absolute Shrinkage and Selection Operator), and EHO (Elephant Herding Optimisation) for extracting significant features from lung cancer and reducing the dimensionality of the microarray gene expression database. The classification of lung cancer is performed using the following classifiers: Gaussian Mixture Model (GMM), Particle Swarm Optimization (PSO) with GMM, Detrended Fluctuation Analysis (DFA), Naive Bayes classifier (NBC), Firefly with GMM, Support Vector Machine with Radial Basis Kernel (SVM-RBF) and Flower Pollination Optimization (FPO) with GMM. The EHO feature extraction with the FPO-GMM classifier attained the highest accuracy in the range of 96.77, with an F1 score of 97.5, MCC of 0.92 and Kappa of 0.92. The reported results underline the significance of utilizing STFT, LASSO, and EHO for feature extraction in reducing the dimensionality of microarray gene expression data. These methodologies also help in improved and early diagnosis of lung cancer with enhanced classification accuracy and interpretability.

Insights into inflammation and implications for the pathogenesis and long-term outcomes of endometrial cancer: genome-wide surveys and a clinical cohort study.

BACKGROUND: Despite evidence showing a connection between inflammation and endometrial cancer (EC) risk, the surveys on genetic correlation and cohort studies investigating the impact on long-term outcomes have yet to be refined. We aimed to address the impact of inflammation factors on the pathogenesis, progression and consequences of EC. METHODS: For the genetic correlation analyses, a two-sample of Mendelian randomization (MR) study was applied to investigate inflammation-related single-nucleotide polymorphisms involved with endometrial cancer from GWAS databases. The observational retrospective study included consecutive patients diagnosed with EC (stage I to IV) with surgeries between January 2010 and October 2020 at the Cancer Hospital of Shantou University Medical College. RESULTS: The 2-sample MR surveys indicated no causal relationship between inflammatory cytokines and endometrial cancer. 780 cases (median age, 55.0 years ) diagnosed with EC were included in the cohort and followed up for an average of 6.8 years. Increased inflammatory parameters at baseline were associated with a higher FIGO stage and invasive EC risk (odds ratios [OR] 1.01 to 4.20). Multivariate-cox regression suggested that multiple inflammatory indicators were significantly associated with overall survival (OS) and progression-free survival (PFS) (P < 0.05). Nomogram models based on inflammatory risk and clinical factors were developed for OS and PFS with C-index of 0.811 and 0.789, respectively. LASSO regression for the validation supported the predictive efficacy of inflammatory and clinical factors on the long-term outcomes of EC. CONCLUSIONS: Despite the fact that the genetic surveys did not show a detrimental impact of inflammatory cytokines on the endometrial cancer risk, our cohort study suggested that inflammatory level was associated with the progression and long-term outcomes of EC. This evidence may contribute to new strategies targeted at decreasing inflammation levels during EC therapy.

Integrative nomogram model based on anoikis-related genes enhances prognostic evaluation in colorectal cancer.

Background: Revealing the role of anoikis resistance plays in CRC is significant for CRC diagnosis and treatment. This study integrated the CRC anoikis-related key genes (CRC-AKGs) and established a novel model for improving the efficiency and accuracy of the prognostic evaluation of CRC. Methods: CRC-ARGs were screened out by performing differential expression and univariate Cox analysis. CRC-AKGs were obtained through the LASSO machine learning algorithm and the LASSO Risk-Score was constructed to build a nomogram clinical prediction model combined with the clinical predictors. In parallel, this work developed a web-based dynamic nomogram to facilitate the generalization and practical application of our model. Results: We identified 10 CRC-AKGs and a risk-related prognostic Risk-Score was calculated. Multivariate COX regression analysis indicated that the Risk-Score, TNM stage, and age were independent risk factors that significantly associated with the CRC prognosis(p < 0.05). A prognostic model was built to predict the outcome with satisfied accuracy (3-year AUC = 0.815) for CRC individuals. The web interactive nomogram (https://yuexiaozhang.shinyapps.io/anoikisCRC/) showed strong generalizability of our model. In parallel, a substantial correlation between tumor microenvironment and Risk-Score was discovered in the present work. Conclusion: This study reveals the potential role of anoikis in CRC and sets new insights into clinical decision-making in colorectal cancer based on both clinical and sequencing data. Also, the interactive tool provides researchers with a user-friendly interface to input relevant clinical variables and obtain personalized risk predictions or prognostic assessments based on our established model.

Development and validation of a LASSO prediction model for cisplatin induced nephrotoxicity: a case-control study in China.

BACKGROUND: Early identification of high-risk individuals with cisplatin-induced nephrotoxicity (CIN) is crucial for avoiding CIN and improving prognosis. In this study, we developed and validated a CIN prediction model based on general clinical data, laboratory indications, and genetic features of lung cancer patients before chemotherapy. METHODS: We retrospectively included 696 lung cancer patients using platinum chemotherapy regimens from June 2019 to June 2021 as the traing set to construct a predictive model using Absolute shrinkage and selection operator (LASSO) regression, cross validation, and Akaike's information criterion (AIC) to select important variables. We prospectively selected 283 independent lung cancer patients from July 2021 to December 2022 as the test set to evaluate the model's performance. RESULTS: The prediction model showed good discrimination and calibration, with AUCs of 0.9217 and 0.8288, sensitivity of 79.89% and 45.07%, specificity of 94.48% and 94.81%, in the training and test sets respectively. Clinical decision curve analysis suggested that the model has value for clinical use when the risk threshold ranges between 0.1 and 0.9. Precision-Recall (PR) curve shown in recall interval from 0.5 to 0.75: precision gradually declines with increasing Recall, up to 0.9. CONCLUSIONS: Predictive models based on laboratory and demographic variables can serve as a beneficial complementary tool for identifying high-risk populations with CIN.

A novel nomogram to predict the recurrence of hepatocellular carcinoma after liver transplantation using extended selection criteria.

BACKGROUND: Liver transplantations (LTs) with extended criteria have produced surgical results comparable to those obtained with traditional standards. However, it is not sufficient to predict hepatocellular carcinoma (HCC) recurrence after LT according to morphological criteria alone. The present study aimed to construct a nomogram for predicting HCC recurrence after LT using extended selection criteria. METHODS: Retrospective data on patients with HCC, including pathology, serological markers and follow-up data, were collected from January 2015 to April 2020 at Huashan Hospital, Fudan University, Shanghai, China. Logistic least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analyses were performed to identify and construct the prognostic nomogram. Receiver operating characteristic (ROC) curves, Kaplan-Meier curves, decision curve analyses (DCAs), calibration diagrams, net reclassification indices (NRIs) and integrated discrimination improvement (IDI) values were used to assess the prognostic capacity of the nomogram. RESULTS: A total of 301 patients with HCC who underwent LT were enrolled in the study. The nomogram was constructed, and the ROC curve showed good performance in predicting survival in both the development set (2/3) and the validation set (1/3) (the area under the curve reached 0.748 and 0.716, respectively). According to the median value of the risk score, the patients were categorized into the high- and low-risk groups, which had significantly different recurrence-free survival (RFS) rates (P < 0.01). Compared with the Milan criteria and University of California San Francisco (UCSF) criteria, DCA revealed that the new nomogram model had the best net benefit in predicting 1-, 3- and 5-year RFS. The nomogram performed well for calibration, NRI and IDI improvement. CONCLUSIONS: The nomogram, based on the Milan criteria and serological markers, showed good accuracy in predicting the recurrence of HCC after LT using extended selection criteria.

Intra-hospital microbiome variability is driven by accessibility and clinical activities.

The hospital environmental microbiome, which can affect patients' and healthcare workers' health, is highly variable and the drivers of this variability are not well understood. In this study, we collected 37 surface samples from the neonatal intensive care unit (NICU) in an inpatient hospital before and after the operation began. Additionally, healthcare workers collected 160 surface samples from five additional areas of the hospital. All samples were analyzed using 16S rRNA gene amplicon sequencing, and the samples collected by healthcare workers were cultured. The NICU samples exhibited similar alpha and beta diversities before and after opening, which indicated that the microbiome there was stable over time. Conversely, the diversities of samples taken after opening varied widely by area. Principal coordinate analysis (PCoA) showed the samples clustered into two distinct groups: high alpha diversity [the pediatric intensive care unit (PICU), pathology lab, and microbiology lab] and low alpha diversity [the NICU, pediatric surgery ward, and infection prevention and control (IPAC) office]. Least absolute shrinkage and selection operator (LASSO) classification models identified 156 informative amplicon sequence variants (ASVs) for predicting the sample's area of origin. The testing accuracy ranged from 86.37% to 100%, which outperformed linear and radial support vector machine (SVM) and random forest models. ASVs of genera that contain emerging pathogens were identified in these models. Culture experiments had identified viable species among the samples, including potential antibiotic-resistant bacteria. Though area type differences were not noted in the culture data, the prevalences and relative abundances of genera detected positively correlated with 16S sequencing data. This study brings to light the microbial community temporal and spatial variation within the hospital and the importance of pathogenic and commensal bacteria to understanding dispersal patterns for infection control. IMPORTANCE: We sampled surface samples from a newly built inpatient hospital in multiple areas, including areas accessed by only healthcare workers. Our analysis of the neonatal intensive care unit (NICU) showed that the microbiome was stable before and after the operation began, possibly due to access restrictions. Of the high-touch samples taken after opening, areas with high diversity had more potential external seeds (long-term patients and clinical samples), and areas with low diversity and had fewer (short-term or newborn patients). Classification models performed at high accuracy and identified biomarkers that could be used for more targeted surveillance and infection control. Though culturing data yielded viability and antibiotic-resistance information, it disproportionately detected the presence of genera relative to 16S data. This difference reinforces the utility of 16S sequencing in profiling hospital microbiomes. By examining the microbiome over time and in multiple areas, we identified potential drivers of the microbial variation within a hospital.

Intestinal mRNA expression profiles associated with mucosal healing in ustekinumab-treated Crohn's disease patients: bioinformatics analysis and prospective cohort validation.

BACKGROUND: Variations exist in the response of patients with Crohn's disease (CD) to ustekinumab (UST) treatment, but the underlying cause remains unknown. Our objective was to investigate the involvement of immune cells and identify potential biomarkers that could predict the response to interleukin (IL) 12/23 inhibitors in patients with CD. METHODS: The GSE207022 dataset, which consisted of 54 non-responders and 9 responders to UST in a CD cohort, was analyzed. Differentially expressed genes (DEGs) were identified and subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. Least absolute shrinkage and selection operator (LASSO) regression was used to screen the most powerful hub genes. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the predictive performances of these genes. Single-sample Gene Set Enrichment Analysis (ssGSEA) was used to estimate the proportions of immune cell types. These significantly altered genes were subjected to cluster analysis into immune cell-related infiltration. To validate the reliability of the candidates, patients prescribed UST as a first-line biologic in a prospective cohort were included as an independent validation dataset. RESULTS: A total of 99 DEGs were identified in the integrated dataset. GO and KEGG analyses revealed significant enrichment of immune response pathways in patients with CD. Thirteen genes (SOCS3, CD55, KDM5D, IGFBP5, LCN2, SLC15A1, XPNPEP2, HLA-DQA2, HMGCS2, DDX3Y, ITGB2, CDKN2B and HLA-DQA1), which were primarily associated with the response versus nonresponse patients, were identified and included in the LASSO analysis. These genes accurately predicted treatment response, with an area under the curve (AUC) of 0.938. T helper cell type 1 (Th1) cell polarization was comparatively strong in nonresponse individuals. Positive connections were observed between Th1 cells and the LCN2 and KDM5D genes. Furthermore, we employed an independent validation dataset and early experimental verification to validate the LCN2 and KDM5D genes as effective predictive markers. CONCLUSIONS: Th1 cell polarization is an important cause of nonresponse to UST therapy in patients with CD. LCN2 and KDM5D can be used as predictive markers to effectively identify nonresponse patients. TRIAL REGISTRATION: Trial registration number: NCT05542459; Date of registration: 2022-09-14; URL: https://www. CLINICALTRIALS: gov .

Identification of PANoptosis-related genes as prognostic indicators of thyroid cancer.

Background: Thyroid cancer (THCA) has become a common malignancy in recent years, with the mortality rate steadily increasing. PANoptosis is a unique kind of programmed cell death (PCD), including pyroptosis, necroptosis, and apoptosis, and is involved in the proliferation and prognosis of numerous cancers. This paper demonstrated the connection between PANoptosis-related genes and THCA based on the analyses of Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases, which have not been evaluated yet. Methods: We identified PANoptosis-related differentially expressed genes (PRDEGs) by multi-analyzing the TCGA-THCA and GEO datasets. To identify the significant PRDEGs, a prognostic model was constructed using least absolute shrinkage and selection operator regression (LASSO). The predictive values of the significant PRDEGs for THCA outcomes were determined using Cox regression analysis and nomograms. Gene enrichment analyses were performed. Finally, immunohistochemistry was carried out using the human protein atlas. Results: A LASSO regression model based on nine PRDEGs was constructed, and the prognostic value of key PRDEGs was explored via risk score. Univariate and multivariate Cox regression were implemented to identify further three significant PRDEGs closely related to distant metastasis, lymph node metastasis, and tumor stage. Then, a nomogram was constructed, which presented high predictive accuracy for 5 years survival of THCA patients. Gene enrichment analyses in THCA were strongly associated with PCD pathways. CASP6 presented significantly differential expression during clinical T stage, N stage, and PFI events (P < 0.05 for all) and demonstrated the highest degree of diagnostic efficacy in PRDEGs (HR: 2.060, 95 % CI: 1.170-3.628, P < 0.05). Immunohistochemistry showed CASP6 was more abundant in THCA tumor tissue. Conclusion: A potential prognostic role for PRDEGs in THCA was identified, providing a new direction for treatment. CASP6 may be a potential therapeutic target and a novel prognostic biomarker for THCA.

Identifying and assessing a prognostic model based on disulfidptosis-related genes: implications for immune microenvironment and tumor biology in lung adenocarcinoma.

Introduction: Lung cancer, with the highest global mortality rate among cancers, presents a grim prognosis, often diagnosed at an advanced stage in nearly 70% of cases. Recent research has unveiled a novel mechanism of cell death termed disulfidptosis, which is facilitated by glucose scarcity and the protein SLC7A11. Methods: Utilizing the least absolute shrinkage and selection operator (LASSO) regression analysis combined with Cox regression analysis, we constructed a prognostic model focusing on disulfidptosis-related genes. Nomograms, correlation analyses, and enrichment analyses were employed to assess the significance of this model. Among the genes incorporated into the model, CHRNA5 was selected for further investigation regarding its role in LUAD cells. Biological functions of CHRNA5 were assessed using EdU, transwell, and CCK-8 assays. Results: The efficacy of the model was validated through internal testing and an external validation set, with further evaluation of its robustness and clinical applicability using a nomogram. Subsequent correlation analyses revealed associations between the risk score and infiltration of various cancer types, as well as oncogene expression. Enrichment analysis also identified associations between the risk score and pivotal biological processes and KEGG pathways. Our findings underscore the significant impact of CHRNA5 on LUAD cell proliferation, migration, and disulfidptosis. Conclusion: This study successfully developed and validated a robust prognostic model centered on disulfidptosis-related genes, providing a foundation for predicting prognosis in LUAD patients.