Diagnostic Nomogram Research Articles

Integrative bioinformatics analysis reveals novel insights into osteoarthritis pathogenesis and diagnostic biomarkers

BackgroundOsteoarthritis (OA) is a prevalent joint disorder characterized by degeneration and inflammation. Understanding its molecular mechanisms is crucial for diagnosis and treatment.MethodsWe employed bioinformatics analyses to study OA using gene expression data. Differential expression analysis, weighted gene co-expression network analysis (WGCNA), and protein-protein interaction (PPI) network analysis were conducted. Enrichment analyses were performed to elucidate the biological significance of identified genes. Additionally, signature genes were identified using LASSO regression analysis, and a diagnostic nomogram was developed. qRT-PCR was conducted to confirm the expression levels of signature genes.ResultsWe identified 200 differentially expressed genes (DEGs) and a lightgreen module strongly correlated with OA. Within this module, 97 core genes were identified. Fifteen core lipopolysaccharide-related genes (LRGs) were found, enriched in immune and inflammatory pathways. Three hub genes (CCL3, ZFP36, and CCN1) emerged as potential biomarkers for OA diagnosis, with a nomogram showing high predictive accuracy, and validated by using clinical samples. Gene set enrichment analysis (GSEA) revealed distinct signaling pathways associated with the signature genes. Immunological analysis indicated altered immune profiles in OA, with the signature genes influencing immune cell infiltration and immune response pathways.ConclusionOur study provides insights into OA pathogenesis and identifies potential diagnostic biomarkers. The developed nomogram shows promise for accurate OA diagnosis. Furthermore, the signature genes play crucial roles in modulating the immune microenvironment in OA, suggesting their therapeutic potential.Clinical trial numberNot applicable.

Analysis and Validation of a Diagnostic Nomogram for Predicting the Risk of Acute Respiratory Failure for Non-HIV Related Pneumocystis Jirovecii Pneumonia Patients.

Pneumocystis Pneumonia (PCP), primarily affecting individuals with weakened immune systems, is a severe respiratory infection caused by pneumocystis jirovecii and can lead to acute respiratory failure (ARF). In this article, we explore the risk factors of ARF and propose a prognostic model of ARF for PCP patients. In this multi-center, retrospective study in 6secondary or tertiary academic hospitals in China, 120 PCP patients were screened from the Dryad database for the development of a predictive model. A total of 49 patients from Peking University People's Hospital were collected for external validation. Crucial clinical features of these patients are selected applying univariate and multivariate logistic regression analysis. We established an intuitive nomogram. Receiver operating characteristic (ROC) curve, calibration curve, decision curve analysis (DCA) and clinical impact curve (CIC) were plotted to evaluate the model's performance. A cohort of 120 patients formed the training cohort for the development of the model, with 49 patients constituting the test cohort. Univariate and multivariate logistic regression analysis identified five risk factors associated with ARF, which are age, fever, dyspnea, high neutrophil count and use of antibiotics. A nomogram was then proposed based on these factors. The area under the ROC curve (AUROC) in the development group has reached 0.8576, while the validation group has an AUROC of 0.7372, indicating commendable ability for predicting ARF. In addition, results for Hosmer-Lemeshow test indicate the effectiveness of our model. Furthermore, DCA and CIC curves demonstrate excellent clinical benefit. We present a nomogram for predicting ARF in non-HIV related PCP patients. The prognostic model may provide references in clinical medicine, promote timely treatment and improve therapeutic outcomes of PCP patients.

Establishment and Validation of Diagnostic and Prognostic Prediction Models for Liver Metastasis in Patients with Rectal Cancer: A SEER-based Study

Background: Rectal cancer is one of the most common gastrointestinal tumors, among which the liver is the most common site of distant metastasis and liver metastasis that leads to poor prognosis. This study aimed to develop and validate a diagnostic nomogram to predict the occurrence of rectal cancer with liver metastasis (RCLM) and a prognostic nomogram to predict cancer-specific survival (CSS) in RCML patients. Methods: Data on patients with rectal cancer diagnosed between 2010 and 2013 were collected from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate logistic regression and multivariate logistic regression were used to determine the independent risk factors of RCLM. Univariate Cox proportional hazards regression and multivariate Cox proportional hazards regression were used to identify independent prognostic factors for RCLM. This study then developed two novel nomograms, and the results were evaluated by receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Results: A total of 29367 patients with rectal cancer were included. Among them, 3403 patients (11.59%) had liver metastases at the time of diagnosis. The independent risk factors of RCLM included sex, race, tumor grade, AJCC N, CEA, marital status, tumor size, total number of primary tumors, and histological type. Age, chemotherapy, total number of primary tumors, surgery sites, and histological type were independent prognostic factors of patients with RCLM. The results of ROC curves, calibration curves, and DCA in the development, validation, and testing sets confirmed that the diagnostic nomogram can precisely predict the occurrence of RCLM. The results of ROC curves, calibration curves, DCA, C-indexes, and Kaplan–Meier (K-M) survival curves in the development, validation, and testing sets confirmed that the prognostic nomogram could precisely predict the prognosis of RCLM. Conclusion: The two nomograms are expected to be effective tools for predicting the risk of liver metastasis for patients with rectal cancer and personalized prognosis prediction for patients with RCLM, which may benefit clinical decision-making.

Risk factors of surgical compliance and impact on survival in patients with kidney cancer: a population-based, propensity score matching study.

To identify the impact of surgical compliance on survival in patients with kidney cancer and to explore the factors that influence surgical compliance. Clinical date of kidney cancer patients were identified from the SEER databases, and the patients were divided into surgical compliance group and surgical noncompliance group. Cox survival analysis and Kaplan-Meier curves were used to evaluate the effect of surgical compliance on overall survival (OS) and cancer-specific survival (CSS). A diagnostic nomogram was constructed to quantify individual differences in compliance, and receiver operating characteristics (ROC) curves and calibration curves were used to assess the accuracy of the nomogram. Propensity score matching (PSM) was performed to balance potential baseline confounding factors. Of the 133,950 patients eligible for surgical resection, 2,814 (2.1%) patients did not opt for surgery ultimately. Surgical noncompliance was associated with poor prognosis. In all patients, Cox regression analysis showed that surgical noncompliance was an independent predictor for OS [before: HR = 2.490, 95% CI 2.374-2.612, p < 0.001; after: HR = 2.380, 95% CI 2.202-2.573, p < 0.001] and CSS [before: HR = 2.490, 95% CI 2.318-2.675, p < 0.001; after: HR = 2.035, 95% CI 1.813-2.285, p < 0.001] of kidney cancer patients before and after PSM. Multivariable logistic regression revealed that older age, afro-american origin, lower household income, and advanced tumor grade were associated with surgical noncompliance. The ROC and calibration curves showed that the diagnostic nomogram had high predictive accuracy. Surgical compliance was an independent prognostic factor for OS and CSS in patients with kidney cancer, and surgical noncompliance was associated with poor survival.

Validation of a urine- based proteomics test to predict clinically significant prostate cancer: complementing mpMRI pathway.

INTRODUCTIONː Prostate cancer (PCa) is the most frequently diagnosed cancer among men. A major clinical need is to accurately predict clinically significant PCa (csPCa). A proteomics-based 19-biomarker model (19-BM) was previously developed using capillary electrophoresis-mass spectrometry (CE-MS) and validated in 1000 patients at risk for PCa. This study aimed to validate 19-BM in a multicenter prospective cohort of 101 biopsy-naive patients using current diagnostic pathways. METHODSː Urine samples from 101 patients with PCa were analyzed using CE-MS. All patients underwent MRI using a 3-T system. The 19-BM score was estimated using support vector machine-based software (MosaCluster v1.7.0), employing a previously established cut-off criterion of -0.07. Previously developed diagnostic nomograms were calculated along with MRI. RESULTSː Independent validation of 19-BM yielded a sensitivity of 77% and a specificity of 85% (AUC:0.81). This performance surpassed those of PSA (AUC:0.56) and PSA density (AUC:0.69). For PI-RADS≤ 3 patients, 19-BM showed a sensitivity of 86% and a specificity of 88%. Integrating 19-BM with MRI resulted in significantly better accuracy (AUC:0.90) compared to individual investigations alone (AUC19BM=0.81; p=0.004 and AUCMRI:0.79; p=0.001). Examining the decision curve analysis, 19-BM with MRI surpassed other approaches for the prevailing risk interval from a 30% cut-off. CONCLUSIONSː 19-BM exhibited favorable reproducibility for the prediction of csPCa. In patients with PI-RADS≤3, 19-BM correctly classified 88% of the patients with insignificant PCa at the cost of one missed csPCa patient. Utilizing the 19-BM test could prove valuable in complementing MRI and reducing the need for unnecessary biopsies.

Novel insights of disulfidptosis-mediated immune microenvironment regulation in atherosclerosis based on bioinformatics analyses

Atherosclerosis (AS) is the leading cause of coronary heart disease, which is the primary cause of death worldwide. Recent studies have identified disulfidptosis as a new type of cell death that may be involved in onset and development of many diseases. However, the role of disulfidptosis in AS is not clear. In this study, bioinformatics analysis and experiments in vivo and in vitro were performed to evaluate the potential relationship between disulfidptosis and AS. AS-related sequencing data were obtained from the Gene Expression Omnibus (GEO). Bioinformatics techniques were used to evaluate differentially expressed genes (DEGs) associated with disulfidptosis-related AS. Hub genes were screened using least absolute shrinkage and selection operator (LASSO) and random forests (RF) methods. In addition, we established a foam cell model in vitro and an AS mouse model in vivo to verify the expressions of hub genes. In addition, we constructed a diagnostic nomogram with hub genes to predict progression of AS. Finally, the consensus clustering method was used to establish two different subtypes, and associations between subtypes and immunity were explored. As the results, 9 disulfidptosis-related AS DEGs were identified from GSE28829 and GSE43292 datasets. Evaluation of DEGs using LASSO and RF methods resulted in identification of 4 hub genes (CAPZB, DSTN, MYL6, PDLIM1), which were analyzed for diagnostic value using ROC curve analysis and verified in vitro and in vivo. Furthermore, a nomogram including hub genes was established that accurately predicted the occurrence of AS. The consensus clustering algorithm was used to separate patients with early atherosclerotic plaques and patients with advanced atherosclerotic plaques into two disulfidptosis subtypes. Cluster B displayed higher levels of infiltrating immune cells, which indicated that patients in cluster B may have a positive immune response for progression of AS. In summary, disulfidptosis-related genes including CAPZB, DSTN, MYL6, and PDLIM1 may be diagnostic markers and therapeutic targets for AS. In addition, these genes are closely related to immune cells, which may inform immunotherapy for AS.

Identifying the Role of Oligodendrocyte Genes in the Diagnosis of Alzheimer's Disease through Machine Learning and Bioinformatics Analysis.

Due to the heterogeneity of Alzheimer's disease (AD), the underlying pathogenic mechanisms have not been fully elucidated. Oligodendrocyte (OL) damage and myelin degeneration are prevalent features of AD pathology. When oligodendrocytes are subjected to amyloid-beta (Aβ) toxicity, this damage compromises the structural integrity of myelin and results in a reduction of myelin-associated proteins. Consequently, the impairment of myelin integrity leads to a slowdown or cessation of nerve signal transmission, ultimately contributing to cognitive dysfunction and the progression of AD. Consequently, elucidating the relationship between oligodendrocytes and AD from the perspective of oligodendrocytes is instrumental in advancing our understanding of the pathogenesis of AD. Here, an attempt is made in this study to identify oligodendrocyte-related biomarkers of AD. AD datasets were obtained from the Gene Expression Omnibus database and used for consensus clustering to identify subclasses. Hub genes were identified through differentially expressed genes (DEGs) analysis and oligodendrocyte gene set enrichment. Immune infiltration analysis was conducted using the CIBERSORT method. Signature genes were identified using machine learning algorithms and logistic regression. A diagnostic nomogram for predicting AD was developed and validated using external datasets and an AD model. A small molecular compound was identified using the eXtreme Sum algorithm. 46 genes were found to be significantly correlated with AD progression by examining the overlap between DEGs and oligodendrocyte genes. Two subclasses of AD, Cluster A, and Cluster B, were identified, and 9 signature genes were identified using a machine learning algorithm to construct a nomogram. Enrichment analysis showed that 9 genes are involved in apoptosis and neuronal development. Immune infiltration analysis found differences in immune cell presence between AD patients and controls. External datasets and RT-qPCR verification showed variation in signature genes between AD patients and controls. Five small molecular compounds were predicted. It was found that 9 oligodendrocyte genes can be used to create a diagnostic tool for AD, which could help in developing new treatments.

External validation of a diagnostic nomogram based on clinical parameters and nutritional index for predicting vaginal invasion in stage IB–IIA cervical cancer

External validation of a diagnostic nomogram based on clinical parameters and nutritional index for predicting vaginal invasion in stage IB–IIA cervical cancer

Screening of pathologically significant diagnostic biomarkers in tears of thyroid eye disease based on bioinformatic analysis and machine learning.

Lacrimal gland enlargement is a common pathological change in patients with thyroid eye disease (TED). Tear fluid has emerged as a new source of diagnostic biomarkers, but tear-based diagnostic biomarkers for TED with high efficacy are still lacking. We aim to investigate genes associated with TED-associated lacrimal gland lesions. Additionally, we seek to identify potential biomarkers for diagnosing TED in tear fluid. We obtained two expression profiling datasets related to TED lacrimal gland samples from the Gene Expression Omnibus (GEO). Subsequently, we combined the two separate datasets and conducted differential gene expression analysis and weighted gene co-expression network analysis (WGCNA) on the obtained integrated dataset. The genes were employed for Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. The genes were intersected with the secretory proteins profile to get the potential proteins in the tear fluid. Machine learning techniques were then employed to identify optimal biomarkers and develop a diagnostic nomogram for predicting TED. Finally, gene set enrichment analysis (GSEA) and immune infiltration analysis were conducted on screened hub genes to further elucidate their potential mechanisms in TED. In our analysis of the integrated TED dataset, we identified 2,918 key module genes and 157 differentially expressed genes and finally obtained 84 lacrimal-associated key genes. Enrichment analysis disclosed that these 84 genes primarily pertain to endoplasmic reticulum organization. After intersecting with the secretory proteins, 13 lacrimal gland-associated secretory protein genes (LaSGs) were identified. The results from machine learning indicated the substantial diagnostic value of dyslexia associated gene (KIAA0319) and peroxiredoxin4 (PRDX4) in TED-associated lacrimal gland lesions. The two hub genes were chosen as candidate biomarkers in tear fluid and employed to establish a diagnostic nomogram. Furthermore, single-gene GSEA results and immune cell infiltration analysis unveiled immune dysregulation in the lacrimal gland of TED, with KIAA0319 and PRDX4 showing significant associations with infiltrating immune cells. We uncovered the distinct pathophysiology of TED-associated lacrimal gland enlargement compared to TED-associated orbital adipose tissue enlargement. We have demonstrated the endoplasmic reticulum-related pathways involved in TED-associated lacrimal gland lesions and established a diagnostic nomogram for TED utilizing KIAA0319 and PRDX4 through integrated bioinformatics analysis. This contribution offers novel insights for non-invasive, prospective diagnostic approaches in the context of TED.

Construction of a diagnostic nomogram model for lymph node metastasis in gastric cancer based on human cartilage glycoprotein-39, carbohydrate antigen 50, and serum ferritin

Construction of a diagnostic nomogram model for lymph node metastasis in gastric cancer based on human cartilage glycoprotein-39, carbohydrate antigen 50, and serum ferritin

Exploring diagnostic biomarkers of type 2 cardio-renal syndrome based on secreted proteins and bioinformatics analysis

Chronic heart failure (CHF) can induce chronic kidney disease (CKD), called type 2 cardio-renal syndrome (CRS2). The mechanism is not completely clear, and there is a lack of early warning biomarkers of CKD in the context of CHF. Two CKD, one CHF-PBMC and four CHF-cardiac tissue expression profile datasets were obtained from GEO database. Differential expression analysis and WGCNA were used to detect CKD key genes and CHF-related secreted proteins. Protein–protein interaction (PPI), functional enrichment, and cMAP analysis reveal potential mechanisms and drugs of CHF-related CKD. Five machine learning algorithms were used to screen candidate biomarkers, construct a diagnostic nomogram for CKD and validate it in two external cohorts. Clinical serum samples were collected in our hospital to evaluate the correlation and diagnostic value of biomarkers and CKD. 225 CKD key genes and 316 CHF-related secreted proteins were identified. Four key subgroups, including 204 genes, were identified as CRS2-related pathogenic genes by PPI analysis. Enrichment analysis revealed that the identified subgroups exhibited significant enrichment in cytokine action, immune responses, and inflammatory processes. The cMAP analysis highlighted metiradone as a drug with greater potential for therapeutic intervention for CRS2. Utilizing five machine learning algorithms, three hub genes (CD48, COL3A1, LOXL1) were pinpointed as potential biomarkers for CKD, and a nomogram model was constructed. Receiver operating characteristic analysis demonstrated that the nomogram’s area under the curve (AUC) exceeded 0.80 in both the CKD combined dataset and two external cohorts. In addition, the three biomarkers were significantly correlated with the glomerular filtration rate, and the AUC of the model predicting disease progression was 0.944. Furthermore, analysis of immune cell infiltration indicated a correlation between the three biomarkers and the infiltration fraction of macrophages, neutrophils, and other immune cells in CKD. Our clinical cohort validated the expression patterns of three biomarkers in serum, and the diagnostic model achieved an AUC of 0.876. CHF has the potential to facilitate the progression of CKD via the release of cardiac and PBMC secreted proteins. Furthermore, CD48, COL3A1, and LOXL1 have been identified as potential biomarkers for the detection of CKD.

Exploring shared biomarkers and shared pathways in insomnia and atherosclerosis using integrated bioinformatics analysis.

Insomnia (ISM) is one of the non-traditional drivers of atherosclerosis (AS) and an important risk factor for AS-related cardiovascular disease. Our study aimed to explore the shared pathways and diagnostic biomarkers of ISM-related AS using integrated bioinformatics analysis. We download the datasets from the Gene Expression Omnibus database and the GeneCards database. Weighted gene co-expression network analysis and gene differential expression analysis were applied to screen the AS-related gene set. The shared genes of ISM and AS were obtained by intersecting with ISM-related genes. Subsequently, candidate diagnostic biomarkers were identified by constructing protein-protein interaction networks and machine learning algorithms, and a nomogram was constructed. Moreover, to explore potential mechanisms, a comprehensive analysis of shared genes was carried out, including enrichment analysis, protein interactions, immune cell infiltration, and single-cell sequencing analysis. We successfully screened 61 genes shared by ISM and AS, of which 3 genes (IL10RA, CCR1, and SPI1) were identified as diagnostic biomarkers. A nomogram with excellent predictive value was constructed (the area under curve of the model constructed by the biomarkers was 0.931, and the validation set was 0.745). In addition, the shared genes were mainly enriched in immune and inflammatory response regulation pathways. The biomarkers were associated with a variety of immune cells, especially myeloid immune cells. We constructed a diagnostic nomogram based on IL10RA, CCR1, and SPI1 and explored the inflammatory-immune mechanisms, which indicated new insights for early diagnosis and treatment of ISM-related AS.

Obesity-related indices as predictors of lower extremity arterial disease in type 2 diabetes mellitus.

The aim of this study was to investigate the relationship between obesity and lower extremity arterial disease (LEAD) in patients with type 2 diabetes mellitus (T2DM). This retrospective study included 1821 patients with type 2 diabetes: 364 patients with LEAD and 1457 patients without LEAD. The patients were divided into training and internal test cohorts in a 7:3 ratio. LASSO regression analysis was used in the training cohort to filter relevant variables. Univariate and multivariate regression analyses were conducted to assess independent risk factors. A diagnostic nomogram was constructed and its discrimination was evaluated using the area under the ROC curve (AUC). The consistency was assessed using a calibration plot. The clinical application of the nomogram was evaluated by performing a decision curve analysis (DCA) and validated by an internal test cohort of the training cohorts. The LEAD group exhibited significantly higher values in obesity-related indices compared to the non-LEAD group, including waist circumference (WC), waist-to-hip ratio (WHR), waist-to-height ratio (WHtR), conicity index (CI), body adiposity index (BAI), and abdominal volume index (AVI). Multivariate analysis identified BMI, CI, BAI, and other parameters as independent risk factors for LEAD. A nomogram was constructed, and the AUC value of the nomogram was 0.746 in the training cohort and 0.663 in the internal test cohort. Obesity-related indices are associated with LEAD in patients with T2DM. Therefore, it is important to manage waist circumference and weight to reduce the risk of LEAD in patients with T2DM.

P37-1 A diagnostic nomogram for pathological invasiveness in early LUAD: integration of autoantibody and radiomics features

P37-1 A diagnostic nomogram for pathological invasiveness in early LUAD: integration of autoantibody and radiomics features

Integrative genomics unveils basement membrane-related diagnostic markers and therapeutic targets in esophageal squamous cell carcinoma

BackgroundEsophageal squamous cell carcinoma (ESCC) is often diagnosed at advanced stages due to the inherent limitations of current screening methodologies. Central to evaluating tumor invasion and prognostic assessment in ESCC is the integrity of the basement membrane (BM). However, current research on the implications of BM-related genes (BMRGs) in diagnosing ESCC remains sparse.MethodsWe performed a comprehensive analysis using single-cell RNA-sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) database, alongside gene expression profiles acquired from GEO and The Cancer Genome Atlas (TCGA) databases. This identified differentially expressed BMRGs in ESCC. Employing LASSO, RF, and SVM-RFE, we selected potential BM biomarkers and crafted a diagnostic nomogram for ESCC, validated by ROC curves and AUC values. We also explored immune infiltration and biological mechanisms through consensus clustering and GSVA, and utilized single cell trajectory analysis and GSCALite to study gene distributions and pathways. In vitro experiments further elucidated the role of these genes in ESCC carcinogenesis.ResultsHere, we discovered that ESCC cell types exhibited markedly elevated BM-related scores. Our analysis pinpointed seven BM genes upregulated and linked to immune infiltration, showcasing unique gene expression profiles and varying immune cell densities across the BM-related subtypes. Furthermore, a robust positive correlation was observed between these genes expression and EMT activity. The knockdown of BGN significantly suppressed cell proliferation, migration, invasion, while also augmenting cell viability following chemotherapy drug treatment.ConclusionOur study identified seven key BMRGs (BGN, LAMB3, SPARC, MMP1, LUM, COL4A1, and NELL2) and established a diagnostic nomogram for ESCC. Of noteworthy significance is the discovery of BGN as a promising drug target, indicating a novel strategy for future clinical combination therapies in ESCC.

HPV-16 E6 mutation and viral integration related host DNA methylation implicate the development and progression of cervical cancer

Background HPV-16 infection and viral-host integration are the most important risk factors for cervical cancer (CC). The aim of this study is to develop a new molecular strategy integrated both the viral and host genome variations identifying and monitoring CC. Method A total of 312 methylation and 538 RNA-seq datasets were collected from public databases to identify differentially methylated and expressed genes. HPV associated virus integration sites (VISs) were analysed using the ViMIC database. From September 2020 to August 2021, the 70 HPV-16 positive cases retrospectively collected from multi-centre cohorts were subjected to HPV-16 E6 deep sequencing and PCR-based host gene (ASTN1, DLX1, ITGA4, RXFP3, SOX17, ZNF671) methylation detection. RNAseq and expression validation (NNF671) were performed in C-33A cell line harbouring HPV D32E. Lasso and logistic regression algorithm were used to construct the CC diagnostic model. Results A positive correlation was observed between the average methylation level of CC patients and their pathological features including tumour stage (p = 0.0077) and HPV subtype (p < 0.001). ZNF671 was identified as a CC-specific methylation marker, with an impressive 93% sensitivity. Both HPV-16 D32E mutation and integration of HPV-16 down-regulated the ZNF671 expression. Finally, a CC diagnostic nomogram was developed by integrating ZNF671 methylation level and HPV E6 mutation feature, yielding an exceptional AUC of 0.997 (95% CI: 0.934–1.000). Conclusions Our study demonstrated HPV viral mutations are closely related to host gene epigenetic alterations in CC. Integration of the viral and host genetic information might be a new promising strategy for CC screening.

Potential biomarkers and immune characteristics for polycythemia vera-related atherosclerosis using bulk RNA and single-cell RNA datasets: a combined comprehensive bioinformatics and machine learning analysis.

Polycythemia vera (PV) is a myeloproliferative disease characterized by significantly higher hemoglobin levels and positivity for JAK2 mutation. Thrombosis is the main risk event of this disease. Atherosclerosis (AS) can markedly increase the risk of arterial thrombosis in patients with PV. The objectives of our study were to identify potential biomarkers for PV-related AS and to explore the molecular biological association between PV and AS. We extracted microarray datasets from the Gene Expression Omnibus (GEO) dataset for PV and AS. Common differentially expressed genes (CGs) were identified by differential expression analysis. Functional enrichment and protein-protein interaction (PPI) networks were constructed from the CG by random forest models using LASSO regression to identify pathogenic genes and their underlying processes in PV-related AS. The expression of potential biomarkers was validated using an external dataset. A diagnostic nomogram was constructed based on potential biomarkers to predict PV-related AS, and its diagnostic performance was assessed using ROC, calibration, and decision curve analyses. Subsequently, we used single-cell gene set enrichment analysis (GSEA) to analyze the immune signaling pathways associated with potential biomarkers. We also performed immune infiltration analysis of AS with "CIBERSORT" and calculated Pearson's correlation coefficients for potential biomarkers and infiltrating immune cells. Finally, we observed the expression of potential biomarkers in immune cells based on the single-cell RNA dataset. Fifty-two CGs were identified based on the intersection between up-regulated and down-regulated genes in PV and AS. Most biological processes associated with CGs were cytokines and factors associated with chemotaxis of immune cells. The PPI analysis identified ten hub genes, and of these, CCR1 and MMP9 were selected as potential biomarkers with which to construct a diagnostic model using machine learning methods and external dataset validation. These biomarkers could regulate Toll-like signaling, NOD-like signaling, and chemokine signaling pathways associated with AS. Finally, we determined that these potential biomarkers had a strong correlation with macrophage M0 infiltration. Further, the potential biomarkers were highly expressed in macrophages from patients with AS. We identified two CGs (CCR1 and MMP9) as potential biomarkers for PV-related AS and established a diagnostic model based on them. These results may provide insight for future experimental studies for the diagnosis and treatment of PV-related AS.

Establishment and Validation of a Nomogram for Identifying False Positives in Xpert MTB/RIF Rifampicin Resistance Test.

This study aimed to establish and validate a diagnostic nomogram for identifying false positives in the Xpert MTB/RIF (Xpert) for detection of rifampicin resistance (RIF-R). In this retrospective study, we collected basic patient characteristics and various clinical information from the electronic medical record database. Patients were randomly divided into training and validation groups in a 7:3 ratio. LASSO regression was used to screen variables and construct a diagnostic nomogram. The ROC curve, calibration curve, and decision curve analysis (DCA) were used to evaluate the performance of the nomogram. A total of 384 patients were included in the study, with 268 and 116 patients in the training and validation cohorts, respectively. Finally, probe mutations and probe delay were identified as the independent influencing factors. Using the mutation of probe E as a reference, probes A or C (OR = 51.07, P<0.001), probe D (OR = 7.48, P<0.001), and multiple probes (OR = 4.42, P=0.029) were identified as factors influencing false positives in Xpert for detection of RIF-R. Taking probe delay ΔCT <4 as a reference, ΔCT (4-5.9) (OR = 17.06, P=0.005) and ΔCT (6-7.9) (OR = 36.67, P<0.001) were noted to be the factors influencing false positives in Xpert for detection of RIF-R. Based on these two variables, we constructed a diagnostic nomogram. The area under the curve of the nomogram model was 0.847 and 0.850 for the training and validation groups, respectively. The calibration curves were consistent. The DCA revealed that the model achieved the greatest net benefit when the threshold probability was set between 6% and 71% in the training cohort and 6% and 70% in the validation cohort. The nomogram constructed can identify false positives in Xpert for detection of RIF-R and provides basis for clinicians to formulate diagnosis and treatment plans.

Development and validation of a diagnostic nomogram for frailty in cancer patients

BackgroundThe presence of frailty decreases the overall survival of cancer patients. An accurate and operational diagnostic method is needed to help clinicians choose the most appropriate treatment to improve patient outcomes. MethodsData were collected from 10 649 cancer patients who were prospectively enrolled in the Investigation on Nutritional Status and its Clinical Outcomes of Common Cancers (INSCOC) project in China from July 2013 to August 2022. The training cohort and validation cohort were randomly divided at a ratio of 7:3. The multivariable logistic regression analysis, multivariate Cox regression analyses, and the least absolute shrinkage and selection operator (LASSO) method were used to develop the nomogram. The concordance index and calibration curve were used to assess the diagnostic utility of the nomogram model. ResultsThe 10 risk factors associated with frailty in cancer patients were age, AJCC stage, liver cancer, hemoglobin, radiotherapy, surgery, hand grip strength (HGS), calf circumference (CC), PG-SGA score and QOL from the QLQ-C30. The diagnostic nomogram model achieved a good C index of 0.847 (95% CI, 0.832–0.862, P < 0.001) in the training cohort and 0.853 (95% CI, 0.83–0.876, P < 0.001) in the validation cohort. The prediction nomogram showed 1-, 3-, and 5-year mortality C indices in the training cohort of 0.708 (95% CI, 0.686–0.731), 0.655 (95% CI, 0.627–0.683), and 0.623 (95% CI, 0.568–0.678). The 1-, 3-, and 5-year C indices in the validation cohort were similarly 0.743 (95% CI, 0.711–0.777), 0.680 (95% CI, 0.639–0.722), and 0.629 (95% CI, 0.558–0.700). In addition, the calibration curves and decision curve analysis (DCA) were well-fitted for both the diagnostic model and prediction model. ConclusionsThe nomogram model provides an accurate method to diagnose frailty in cancer patients. Using this model could lead to the selection of more appropriate therapy and a better prognosis for cancer patients.

Relationships between systemic sclerosis and atherosclerosis: screening for mitochondria-related biomarkers.

Patients with systemic sclerosis (SSc) are known to have higher incidence of atherosclerosis (AS). Mitochondrial injuries in SSc can cause endothelial dysfunction, leading to AS; thus, mitochondria appear to be hubs linking SSc to AS. This study aimed to identify the mitochondria-related biomarkers of SSc and AS. We identified common differentially expressed genes (DEGs) in the SSc (GSE58095) and AS (GSE100927) datasets of the Gene Expression Omnibus (GEO) database. Considering the intersection between genes with identical expression trends and mitochondrial genes, we used the least absolute shrinkage and selection operator (LASSO) as well as random forest (RF) algorithms to identify four mitochondria-related hub genes. Diagnostic nomograms were then constructed to predict the likelihood of SSc and AS. Next, we used the CIBERSORT algorithm to evaluate immune infiltration in both disorders, predicted the transcription factors for the hub genes, and validated these genes for the two datasets. A total of 112 genes and 13 mitochondria-related genes were identified; these genes were then significantly enriched for macrophage differentiation, collagen-containing extracellular matrix, collagen binding, antigen processing and presentation, leukocyte transendothelial migration, and apoptosis. Four mitochondria-related hub DEGs (IFI6, FSCN1, GAL, and SGCA) were also identified. The nomograms showed good diagnostic values for GSE58095 (area under the curve (AUC) = 0.903) and GSE100927 (AUC = 0.904). Further, memory B cells, γδT cells, M0 macrophages, and activated mast cells were significantly higher in AS, while the resting memory CD4+ T cells were lower and M1 macrophages were higher in SSc; all of these were closely linked to multiple immune cells. Gene set enrichment analysis (GSEA) showed that IFI6 and FSCN1 were involved in immune-related pathways in both AS and SSc; GAL and SGCA are related to mitochondrial metabolism pathways in both SSc and AS. Twenty transcription factors (TFs) were predicted, where two TFs, namely BRCA1 and PPARγ, were highly expressed in both SSc and AS. Four mitochondria-related biomarkers were identified in both SSc and AS, which have high diagnostic value and are associated with immune cell infiltration in both disorders. Hence, this study provides new insights into the pathological mechanisms underlying SSc and AS. The specific roles and action mechanisms of these genes require further clinical validation in SSc patients with AS.