Comprehensive pan-cancer analysis of FUTs family as prognostic and immunity markers based on multi-omics data.

BACKGROUND: The dysregulation of fucosyltransferases (FUTs) contributes to alterations in fucosylated epitope expression, which serve as distinctive features of cancer cells. Nonetheless, a comprehensive elucidation of the prognostic biological marker and therapeutic target of the FUTs family in pan-cancer remains elusive. METHODS: Over 10,000 individuals' profiling information was examined, including information on 750 small molecule drugs, 33 types of cancer, and 24 types of immune cells. We focused on POFUT2's function and applied GSVA (Gene Set Variation Analysis) to calculate the FUT score. Survival and cancer pathways were found to be correlated with this score. After deriving a signature via univariate Cox and LASSO regression, we generated and analyzed the ROC curve and developed a nomogram. RESULTS: Our comprehensive analysis revealed epigenetic, genomic, and immunogenomic changes in FUTs, particularly POFUT2, resulting in aberrant expression. Elevated frequencies of CNV (Copy number variation), SNV (Single Nucleotide Variant), and hypermethylation were observed in FUTs. Additionally, the survival of patients with various types of cancers may be predicted by FUT expression. Immune response and prognosis in numerous types of cancer were found to be strongly linked to aberrant POFUT2 expression. Pathway analysis unveiled the role of FUTs in apoptosis, epithelial-to-mesenchymal transition (EMT), cell cycle, DNA damage response, RAS/MAPK, TSC/mTOR, PI3K/AKT, AR, ER, and RTK. A prognostic index for patients diagnosed with adrenocortical carcinoma (ACC) was established by applying a risk model incorporating nine FUTs and based on the findings of the GSVA. CONCLUSIONS: FUTs, particularly POFUT2, emerge as candidate targets for improving the outcomes of immune therapy. The significance of aberrant MUC12 expression, cancer immune therapy, and patient survival in the context of diverse malignancies is enhanced by the strong correlation observed among these factors. Our five-gene risk signature provides patients with ACC with an independent prognostic indicator, emphasizing the critical function of these genes in inhibiting the immune system's response in ACC.

Development of a prognostic model related to homologous recombination deficiency in glioma based on multiple machine learning.

Background: Despite advances in neuro-oncology, treatments of glioma and tools for predicting the outcome of patients remain limited. The objective of this research is to construct a prognostic model for glioma using the Homologous Recombination Deficiency (HRD) score and validate its predictive capability for glioma. Methods: We consolidated glioma datasets from TCGA, various cancer types for pan-cancer HRD analysis, and two additional glioma RNAseq datasets from GEO and CGGA databases. HRD scores, mutation data, and other genomic indices were calculated. Using machine learning algorithms, we identified signature genes and constructed an HRD-related prognostic risk model. The model's performance was validated across multiple cohorts. We also assessed immune infiltration and conducted molecular docking to identify potential therapeutic agents. Results: Our analysis established a correlation between higher HRD scores and genomic instability in gliomas. The model, based on machine learning algorithms, identified seven key genes, significantly predicting patient prognosis. Moreover, the HRD score prognostic model surpassed other models in terms of prediction efficacy across different cancers. Differential immune cell infiltration patterns were observed between HRD risk groups, with potential implications for immunotherapy. Molecular docking highlighted several compounds, notably Panobinostat, as promising for high-risk patients. Conclusions: The prognostic model based on the HRD score threshold and associated genes in glioma offers new insights into the genomic and immunological landscapes, potentially guiding therapeutic strategies. The differential immune profiles associated with HRD-risk groups could inform immunotherapeutic interventions, with our findings paving the way for personalized medicine in glioma treatment.

Identification of a novel prognostic model for gastric cancer utilizing glutamine-related genes.

Background: Glutamine metabolism presents a promising avenue for cancer prevention and treatment, but the underlying mechanisms in gastric cancer (GC) progression remain elusive. Methods: The TCGA-STAD and GEO GSE62254 datasets, containing gene expression, clinical information, and survival outcomes of GC, were meticulously examined. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were employed to excavate a key module (MEturquoise), which was used to intersect with glutamine metabolism-related genes (GMRGs) and differentially expressed genes (DEGs) to identify differentially expressed GMRGs (DE-GMRGs). LASSO and Cox Univariate analyses were implemented to determine risk model genes. Correlation of the risk model with clinical parameters, pathways, and tumor immune microenvironments, was analyzed, and its prognostic independence was validated by Cox analyses. Finally, reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was performed to validate the expression levels of MYB, LRFN4, LMNB2, and SLC1A5 in GC and para-carcinoma tissue. Results: The excavation of 4521 DEGs led to the discovery of the key MEturquoise module, which exhibited robust correlations with GC traits. The intersection analysis identified 42 DE-GMRGs, among which six genes showed consistency. Further LASSO analysis established MYB, LRFN4, LMNB2, and SLC1A5 as pivotal risk model genes. The risk model demonstrated associations with oncogenic and metabolism-related pathways, inversely correlating with responses to immune checkpoint blockade therapies. This risk model, together with "age", was validated to be an independent prognostic factor for GC. RT-qPCR result indicated that MYB, LRFN4, LMNB2, and SLC1A5 expressions were remarkably up-regulated in GC tissues comparison with para-carcinoma tissue. Conclusion: The present study has generated a novel risk module containing four DE-GMRGs for predicting the prognosis and the response to immune checkpoint blockade treatments for GC. This risk model provides new insights into the involvement of glutamine metabolism in GC, warranting further investigation.

Prediction of post-ESD esophageal stricture by a nomogram and risk factor analysis of ineffective oral steroids prophylaxis.

BACKGROUND AND AIMS: Several risk models for esophageal stricture after endoscopic submucosal dissection have been developed. However, some of them did not include the use of steroids in the risk analysis. Glucocorticoid sensitivity mediated by glucocorticoid receptor expression has not been discussed in this condition. METHODS: Clinical and endoscopic characteristics were included in the logistic regression model to establish a nomogram for stenosis prediction. The score for each risk factor was estimated. Risk factors of ineffective oral steroid prophylaxis were analyzed and glucocorticoid receptor expressions were detected by immunohistochemistry. RESULTS: Three hundred fourteen patients of endoscopic submucosal dissection for esophageal superficial neoplasms were included to develop the nomogram. The circumferential range(≤ 3/4, 3/4-1 or the whole circumference), longitudinal diameter reached 4 cm (yes or not) and lesion location (the cervical and upper thoracic part, the middle thoracic part or the lower thoracic part) consisted of the nomogram. Patients have a high risk of esophageal stricture if they have a total point greater than 36. In the simplified risk score model, the corresponding cutoff score was 1. 92 patients with oral steroid prophylaxis were separately analyzed and the circumferential mucosal defect involving 7/8 or more was an independent risk factor of ineffective prevention (OR 12.2, 95%CI 5.27-28.11). The expression of glucocorticoid receptor ß was higher in the stricture group (p = 0.042 for AOD; p = 0.016 for the scoring system). CONCLUSIONS: We established a nomogram for esophageal stricture prediction. Depending on the characteristics of lesions, it is possible to estimate the risk of stricture under routine post-ESD treatments (no steroids or oral steroids). Alternative treatments should be considered if the risk is extremely high, especially for patients with mucosal defects involving 7/8 or more of circumference in which oral steroid treatment tends to be ineffective. The higher glucocorticoid receptor ß may indicate potential glucocorticoid resistance.

The role of TMEM163 protein in thyroid microcarcinoma: expression pattern and clinical implications.

BACKGROUND: TMEM163 protein is a new zinc ion transporter whose regulatory role in tumors has yet to be discovered. This study aimed to analyze the expression pattern of TMEM163 in thyroid microcarcinoma and explore its potential molecular function and clinical value. METHODS: Differential analysis was performed to detect the expression pattern of TMEM163 in papillary thyroid carcinoma. Functional analysis was performed to explore the biological function of TMEM163. Logistic regression was performed to detect the relationship between TMEM163 expression and lymph node metastasis. A correlation analysis of the relationship between 163 and anoikis was performed. qRT-PCR and western blot were used to verify its expression in PTC tissues. The effect of TMEM163 on PTC cell function was studied by a series of in vitro cell experiments. The prediction model of lymph node metastasis was constructed based on the ultrasonic characteristics of PTMC and the expression of TMEM163. RESULTS: The expression of TMEM163 in PTC tissue was higher than in normal thyroid tissue. In vitro, silencing TMEM163 inhibited PTC cells' proliferation, migration, and invasion, while TMEM163 overexpression exhibited the opposite effect. In addition, down-regulating its expression can inhibit the cell cycle process and induce the apoptosis of tumor cells. In pathway analysis, we demonstrated that knockout of TMEM163 significantly increased p21 expression and inhibited BCL-2 expression. Logistic regression results suggested that the expression of TMEM163 combined with PTMC ultrasound characteristics helped predict lymph node metastasis. CONCLUSION: TMEM163 is highly expressed in PTC, which may be involved in the mechanism of anoikis, and can be used as a molecular marker to predict PTMC lymph node metastasis.

The Association between Leucocyte Telomere Length and Survival Outcomes in Patients with Cardiovascular Disease.

Background: We explore the association between leucocyte telomere length (LTL) and all-cause and cardiovascular disease (CVD)-specific death in CVD patients. Methods: We acquired 1599 CVD patients from a nationally representative US population survey for this study. We applied Kaplan-Meier curves, adjusted weighted Cox regression models, and restricted cubic spline to investigate the association between LTL and all-cause death. Additionally, we employed competing risk regression to assess the impact of LTL on cardiovascular-specific death, setting non-cardiovascular death as a competing event. Results: The overall mortality rate was 31.0% after a median follow-up of 13.9 years. Patients with shorter LTL exhibited a higher risk of all-cause death, with an adjusted hazard ratio (HR) of 1.25 (95% confidence interval (CI): 1.05-1.48). Restricted cubic spline illustrated a linear dose-response relationship. In gender-specific analyses, female patients with shorter LTL showed a higher risk of death (weighted HR, 1.79; 95% CI, 1.29-2.48), whereas this association was not observed in males (weighted HR, 0.90; 95% CI, 0.61-1.32). The Fine-Gray competing risk model revealed no significant relationship between LTL and cardiovascular-specific mortality but a significant association with non-cardiovascular death (adjusted HR, 1.24; 95% CI, 1.02-1.51). Conclusions: LTL is inversely associated with all-cause death in female CVD patients. The significant correlation between reduced LTL and increased all-cause mortality emphasizes LTL as a potential marker for tertiary prevention against cardiovascular disease.

Extrachromosomal circular DNAs in prostate adenocarcinoma: global characterizations and a novel prediction model.

Background: The role of focal amplifications and extrachromosomal circular DNA (eccDNA) is still uncertain in prostate adenocarcinoma (PRAD). Here, we first mapped the global characterizations of eccDNA and then investigate the characterization of eccDNA-amplified key differentially expressed encoded genes (eKDEGs) in the progression, immune response and immunotherapy of PRAD. Methods: Circular_seq was used in conjunction with the TCGA-PRAD transcriptome dataset to sequence, annotate, and filter for eccDNA-amplified differentially expressed coding genes (eDEGs) in PRAD and para-cancerous normal prostate tissues. Afterwards, risk models were created and eKDEGs linked to the PRAD prognosis were identified using Cox and Lasso regression analysis. The immune microenvironment of the risk model was quantified using a variety of immunological algorithms, which also identified its characteristics with regard to immunotherapy, immune response, and immune infiltration. Results: In this research, there was no significant difference in the size, type, and chromosomal distribution of eccDNA in PRAD and para-cancerous normal prostate tissues. However, 4,290 differentially expressed eccDNAs were identified and 1,981 coding genes were amplified. Following that, 499 eDEGs were tested in conjunction with the transcriptome dataset from TCGA-PRAD. By using Cox and Lasso regression techniques, ZNF330 and PITPNM3 were identified as eKDEGs of PRAD, and a new PRAD risk model was conducted based on this. Survival analysis showed that the high-risk group of this model was associated with poor prognosis and validated in external data. Immune infiltration analysis showed that the model risks affected immune cell infiltration in PRAD, not only mediating changes in immune cell function, but also correlating with immunophenotyping. Furthermore, the high-risk group was negatively associated with anti-CTLA-4/anti-PD-1 response and mutational burden. In addition, Tumor Immune Dysfunction and Exclusion analyses showed that high-risk group was more prone to immune escape. Drug sensitivity analyses identified 10 drugs, which were instructive for PRAD treatment. Conclusion: ZNF330 and PITPNM are the eKDEGs for PRAD, which can be used as potential new prognostic markers. The two-factor combined risk model can effectively assess the survival and prognosis of PRAD patients, but also can predict the different responses of immunotherapy to PRAD patients, which may provide new ideas for PRAD immunotherapy.

Microplastic transport and ecological risk in coastal intruded aquifers based on a coupled seawater intrusion and microplastic risk assessment model.

Seawater-groundwater interactions can enhance the migration process of microplastics to coastal aquifers, posing increased associated environmental risks. Here, we aim to analyze the relationship between seawater intrusion (SWI) and groundwater microplastic pollution in Laizhou Bay (LZB), which is a typical area of sea-land interactions. The results showed that modern seawater intrusion was the main process controlling the migration of microplastics. The detected microplastics in the study area showed a migration pattern from nearshore marine areas to groundwater aquifers along the SWI direction. In addition, the microplastics also reached the brine formed by palaeo-saltwater intrusion through hydraulic exchange between aquifers. By comparing the spatial distributions of different microplastic parameters, we found that nearshore fisheries, commercial, tourism, textile, and agricultural activities were the main sources of microplastics in groundwater in the study area. A risk assessment model of microplastics associated with SWI was further optimized in this study using a three-level classification system by assigning appropriate weights to different potential influencing factors. The results showed moderate comprehensive ecological risks associated with microplastics from seawater intrusion in the study area, with high microplastic enrichment risks. This study provides a scientific basis for future research on seawater-groundwater interactions and microplastic pollution in coastal regions.

Construction of a five-gene-based prognostic model for relapsed/refractory acute lymphoblastic leukemia.

BACKGROUND: Relapsed/refractory acute lymphoblastic leukemia (R/R ALL) continues to be a major cause of mortality in children worldwide, with around 15% of ALL patients experiencing relapse and approximately 10% eventually dying from the disease. Early identification of R/R ALL in children has posed a longstanding clinical challenge. METHOD: Genetic analysis of survival outcomes in pediatric patients with ALL from the TARGET-ALL dataset revealed five risk score factors identified through the intersection of differential genes (relapse/non-relapse) from the GSE17703 and GSE6092 databases. A risk score equation was formulated using these factors and validated against prognostic data from 46 ALL cases at our institution. Patients from multiple datasets were stratified into high and low-score groups based on this equation. Protein-protein interaction networks (PPI) were then constructed using the intersecting differential genes from all three datasets to identify hub nodes and predict interacting transcription factors. Additionally, genes related to cell pyroptosis with varying expression across these datasets were screened, and a multifactorial ROC curve (incorporating risk score and differential expression of pyroptosis-related genes) was generated. Furthermore, relationships among variables in the predictive model were depicted using a nomogram, and model efficacy was assessed through decision curve analysis (DCA). RESULTS: By analyzing the TARGET-ALL, GSE17703, and GSE6092 databases, we developed a prognostic risk assessment model for pediatric ALL incorporating BAG2, EPHA4, FBXO9, SNX10, and WNK1. Validation of this model was conducted using data from 46 pediatric ALL cases obtained from our institution. Following the identification of 27 differentially expressed genes, we constructed a PPI and identified the top 10 hub genes (PTPRC, BTK, LCK, PRKCQ, CD3D, CD27, CD3G, BLNK, RASGRP1, VPREB1). Using this network, we predicted the top 5 transcription factors (HOXB4, MYC, SOX2, E2F1, NANOG). ROC and DCA were conducted on pyroptosis-related genes exhibiting differential expression and risk scores. Subsequently, a nomogram was generated, demonstrating the effectiveness of the risk score in predicting prognosis for pediatric ALL patients. CONCLUSIONS: We have developed a risk prediction model for pediatric R/R ALL utilizing the genes BAG2, EPHA4, FBXO9, SNX10, and WNK1. This model provides a scientific foundation for early identification of R/R ALL in children.

A Predictive Model for Disseminated Intravascular Coagulopathy in Sepsis: An Observational Study.

Introduction: Sepsis remains a significant global health challenge due to its high morbidity and mortality rates. Disseminated Intravascular Coagulopathy (DIC) represents a critical complication of sepsis, contributing to increased mortality and economic burden. Despite various prognostic scoring systems, there is a lack of a specific model for DIC prediction in sepsis patients. Methods: This observational study included 336 sepsis patients. Clinical and laboratory data were collected, and prognoses were defined according to established criteria. Results: We enrolled 336 patients, with 304 in the non-DIC group and 32 in the DIC group. Patients with DIC had notably lower platelet (PLT) and higher levels of prothrombin time (PT), lactate (LAC), and procalcitonin (PCT) compared to those without DIC. Univariate and multivariate analyses identified risk factors associated with the DIC, showing that PLT (OR = 0.985, 95% CI 0.978-0.993, p < 0.001), PT level (OR = 1.140, 95% CI 1.004-1.295, p = 0.044), and LAC (OR = 1.101, 95% CI 0.989-1.226, p = 0.078) were related factors. A risk model was established, and its sensitivity and specificity in predicting DIC among sepsis patients were assessed by comparing it to the SOFA score. The area under the ROC curve for the model was 0.850, while the SOFA score was 0.813. With a model score >-2.12, the sensitivity for predicting DIC was 84.4%, and the specificity was 75.0%. Conclusion: Our study introduces a predictive model for DIC detection in sepsis patients, emphasizing the need for clinicians to focus on patients with high model scores for timely intervention.

Development and Validation of the Communities Geriatric Mild Cognitive Impairment Risk Calculator (CGMCI-Risk).

Objectives: The aim was to develop and validate the Communities Geriatric Mild Cognitive Impairment Risk Calculator (CGMCI-Risk), aiding community healthcare workers in the early identification of individuals at high risk of mild cognitive impairment (MCI). Methods: Based on nationally representative community survey data, backward stepwise regression was employed to screen the variables, and logistic regression was utilized to construct the CGMCI-Risk. Internal validation was conducted using bootstrap resampling, while external validation was performed using temporal validation. The area under the receiver operating characteristic curve (AUROC), calibration curve, and decision curve analysis (DCA) were employed to evaluate the CGMCI-Risk in terms of discrimination, calibration, and net benefit, respectively. Results: The CGMCI-Risk model included variables such as age, educational level, sex, exercise, garden work, TV watching or radio listening, Instrumental Activity of Daily Living (IADL), hearing, and masticatory function. The AUROC was 0.781 (95% CI = 0.766 to 0.796). The calibration curve showed strong agreement, and the DCA suggested substantial clinical utility. In external validation, the CGMCI-Risk model maintained a similar performance with an AUROC of 0.782 (95% CI = 0.763 to 0.801). Conclusions: CGMCI-Risk is an effective tool for assessing cognitive function risk within the community. It uses readily predictor variables, allowing community healthcare workers to identify the risk of MCI in older adults over a three-year span.

C6 and KLRG2 are pyroptosis subtype-related prognostic biomarkers and correlated with tumor-infiltrating lymphocytes in lung adenocarcinoma.

Pyroptosis plays an important role in lung adenocarcinoma (LUAD). In this study, we aimed to explore the pyroptosis-related gene (PRG) expression pattern and to identify promising pyroptosis-related biomarkers to improve the prognosis of LUAD. The gene expression profiles and clinical information of LUAD patients were downloaded from the Cancer Genome Atlas (TCGA), and validation cohort information was extracted from the Gene Expression Omnibus database. Gene expression data were analyzed using the limma package and visualized using the ggplot2 package as well as the pheatmap package in R software. Functional enrichment analysis was also performed for the 44 differentially expressed PRGs (DEPRGs). Then, consensus clustering revealed pyroptosis-related tumor subtypes, and differentially expressed genes (DEGs) were screened according to the subtypes. Next, univariate Cox and multivariate Cox regression analyses were used to identify independent prognostic PRGs. After overlapping DEGs and the Lasso regression analysis-based prognostic genes, the predictive risk model was established and validated. Correlation analysis between PRGs and clinicopathological variables was also explored. Finally, the TIMER and TISIDB databases were used to further explore the correlation analysis between immune cell infiltration levels, the risk score, and clinicopathological variables in the predictive risk model. A total of 52 genes from the PubMed were identified as PRGs, and 44 of the 52 genes were pooled as DEPRGs. The most significant GO term was "collagen trimer" (P = 2.46E-13), and KEGG analysis results indicated that 44 DEPRGs were significantly enriched in Salmonella infection (P < 0.001). Then, consensus clustering analysis divided LUAD patients into two clusters, and a total of 79 DEGs were identified according to these cluster subtypes. Subsequently, univariate and multivariate Cox regression analyses were used to identify 12 genes that could serve as independent prognostic indicators and we also performed Lasso regression analysis and screened 23 DEGs. After overlapping 23 DEGs and 12 genes, only 4 (KLRG2, MAPK4, C6 and SFRP5) of 12 genes were selected for the further exploration of the prognostic pattern. Survival analysis results indicated that this risk model effectively predicted the prognosis (P < 0.001). Combined with the correlation analysis results between the 4 genes and clinicopathological variables, C6 and KLRG2 were screened as prognostic genes. In this study, we constructed a predictive risk model and identified two pyroptosis subtype-related gene expression patterns to improve the prognosis of LUAD. Understanding the subtypes of LUAD is helpful for accurately characterizing the LUAD and developing personalized treatment.

Predictive nomogram of the clinical outcomes of colorectal cancer based on methylated SEPT9 and intratumoral IL-10+ Tregs infiltration.

BACKGROUND: Gene methylation and the immune-related tumor microenvironment (TME) are highly correlated in tumor progression and therapeutic efficacy. Although both of them can be used to predict the clinical outcomes of colorectal cancer (CRC) patients, their predictive value is still unsatisfactory. Whether a combination risk model comprising these two prediction parameters performs better predictive effectiveness than independent factor is still unclear. Methylated Septin9 (mSEPT9) is an early diagnosis biomarker of CRC, in this study, we aimed to investigate mSEPT9-related biomarkers of immunosuppressive TME and identify the value of the combination risk model in predicting the clinical outcomes of CRC. METHODS: Immunofluorescence staining was performed to clarify the correlation between intratumoral IL-10+ Treg infiltration and mSEPT9 in peripheral blood. Survival time, response to 5-fluorouracil (5-FU)-based chemotherapy and PD-1 blockade, and the probability of recurrence or metastasis were analyzed in study (197 CRC samples) and validation (195 CRC samples) sets to evaluate the efficacy of combination risk model. Potential mechanisms were explored by mRNA sequencing. RESULTS: Hypermethylated SEPT9 in the peripheral blood of patients with CRC (stage I-III, and stage IV with resectable M1) before radical resection was positively correlated with high intratumoral IL-10+ Treg infiltration. The high-risk model revealed poor overall survival and progression-free survival, inferior therapeutic response to 5-FU-based chemotherapy and PD-1 blockade, and high probability of recurrence or metastasis. The underlying mechanisms may be associated with the increase in mSEPT9 and mediation of IL-10 via methionine metabolic reprogramming-induced infiltration of IL-10+ Tregs in the TME, which promotes tumor progression and resistance to 5-FU-based chemotherapy and PD-1 blockade. CONCLUSIONS: The combination risk model of peripheral mSETP9 and intratumoral IL-10+ Treg infiltration in CRC can effectively predict prognosis, responsiveness to 5-FU-based chemotherapy and PD-1 blockade, and the probability of recurrence or metastasis. Therefore, this model can be used for precision treatment of CRC.

Integrated Identification and Immunotherapy Response Analysis of the Prognostic Signature Associated With m6A, Cuproptosis-Related, Ferroptosis-Related lncRNA in Endometrial Cancer.

BACKGROUND: Endometrial cancer (EC) stands as the predominant gynecological malignancy impacting the female reproductive system on a global scale. N6-methyladenosine, cuproptosis- and ferroptosis-related biomarker is beneficial to the prognostic of tumor patients. Nevertheless, the correlation between m6A-modified lncRNAs and ferroptosis, copper-induced apoptosis in the initiation and progression of EC remains unexplored in existing literature. AIMS: In this study, based on bioinformatics approach, we identified lncRNAs co-expressing with cuproptosis-, ferroptosis-, m6A- related lncRNAs from expression data of EC. By constructing the prognosis model in EC, we screened hub lncRNA signatures affecting prognosis of EC patients. Furthermore, the guiding value of m6A-modified ferroptosis-related lncRNA (mfrlncRNA) features was assessed in terms of prognosis, immune microenvironment, and drug sensitivity. METHOD: Our research harnessed gene expression data coupled with clinical insights derived from The Cancer Genome Atlas (TCGA) collection. To forge prognostic models, we adopted five machine learning approaches, assessing their efficacy through C-index and time-independent ROC analysis. We pinpointed prognostic indicators using the LASSO Cox regression approach. Moreover, we delved into the biological and immunological implications of the discovered lncRNA prognostic signatures. RESULTS: The survival rate for the low-risk group was markedly higher than that for the high-risk group, as evidenced by a significant log-rank test (p < 0.001). The LASSO Cox regression model yielded concordance indices of 0.76 for the training set and 0.77 for the validation set, indicating reliable prognostic accuracy. Enrichment analysis of gene functions linked the identified signature predominantly to endopeptidase inhibitor activity, highlighting the signature's potential implications. Additionally, immune function and drug density emphasized the importance of early diagnosis in EC. CONCLUSION: Five hub lncRNAs in EC were identified through constructing the prognosis model. Those genes might be potential biomarkers to provide valuable reference for targeted therapy and prognostic assessment of EC.

Exploring the impact of estrogenic endocrine disruptors on cervical cancer progression: A transcriptome analysis and prognostic model development.

Cervical cancer is the fourth most common cancer among women globally. The detrimental health effects of estrogenic endocrine disruptors (EED), such as bisphenol A (BPA) and phthalates, are recognized, but their role in cervical cancer progression remains unclear. To investigate this, a transcriptome analysis using bioinformatics was conducted. The Comparative Toxicogenomics Database (CTD) identified estrogen-responsive genes (ERGs) associated with EED. Cervical cancer expression and clinical data were sourced from The Cancer Genome Atlas (TCGA). The limma package identified differentially expressed ERGs (DERGs), which were further analyzed for molecular mechanisms through enrichment analysis. LASSO regression developed a prognostic risk score model, and COX analysis identified prognostic biomarkers. ssGSEA assessed immune tumor infiltration, and Autodock performed molecular docking. A total of 217 DERGs were linked to endocrine resistance, estrogen signaling, and the cell cycle. The prognostic risk score and nomogram based on DERGs were highly predictive of cervical cancer prognosis and could serve as independent risk factors. The risk score influenced the tumor immune microenvironment by affecting immune cell presence. SCARA3 and FASN emerged as independent prognostic factors, with molecular docking confirming strong binding between EED and FASN. DERGs can aid in creating a reliable prognostic model and predicting overall survival in cervical cancer patients, offering new insights into the impact of EED on cancer progression and highlighting environmental factors related to cancer risks and development.

Usefulness of hypochloremia at the time of discharge to predict prognosis in patients with chronic heart failure after hospitalization.

BACKGROUND: Hypochloremia has been suggested as a strong marker of mortality in hospitalized patients with heart failure (HF). This study aimed to clarify whether incorporating hypochloremia into pre-existing prognostic models improved the performance of the models. METHODS: We tested the prognostic value of hypochloremia (<97 mEq/L) measured at discharge in hospitalized patients with HF registered in the REALITY-AHF and NARA-HF studies. The primary outcome was 1-year mortality after discharge. RESULTS: Among 2496 patients with HF, 316 (12.6 %) had hypochloremia at the time of discharge, and 387 (15.5 %) deaths were observed within 1 year of discharge. The presence of hypochloremia was strongly associated with higher 1-year mortality compared to those without hypochloremia (log-rank: p < 0.001), and this association remained even after adjustment for the Get With the Guideline-HF risk model (GWTG-HF), anemia, New York Heart Association (NYHA) classification, and log-brain natriuretic peptide (BNP) [hazard ratio (HR) 1.64; p < 0.001]. Furthermore, adding hypochloremia to the prediction model composed of GWTG-HF + anemia + NYHA class + log-BNP yielded a numerically larger area under the curve (0.740 vs 0.749; p = 0.059) and significant improvement in net reclassification (0.159, p = 0.010). CONCLUSIONS: Incorporating the presence of hypochloremia at discharge into pre-existing risk prediction models provides incremental prognostic information for hospitalized patients with HF.

Development and validation of a nomogram for individualizing fall risk in patients with hematologic malignancies.

Inpatient falls are common adverse events especially for patients with hematologic malignancies. A fall-risk prediction model for patients with hematologic malignancies are still needed. Here we conducted a multicenter study that prospectively included 516 hospitalized patients with hematologic malignancies, and developed a nomogram for fall risk prediction. Patients were divided into the modeling group (n = 389) and the validation group (n = 127). A questionnaire containing sociodemographic factors, general health factors, disease-related factors, medication factors, and physical activity factors was administered to all patients. Logistic regression analysis revealed that peripheral neuropathy, pain intensity, Morse fall scale score, chemotherapy courses, and myelosuppression days were risk factors for falls in patients with hematologic malignancies. The nomogram model had a sensitivity of 0.790 and specificity of 0.800. The calibration curves demonstrated acceptable agreement between the predicted and observed outcomes. Therefore, the nomogram model has promising accuracy in predicting fall risk in patients with hematologic malignancies.

Predicting Long-Term Clinical Outcomes of Patients With Recurrent Pericarditis.

BACKGROUND: Recurrent pericarditis (RP) is a complex condition associated with significant morbidity. Prior studies have evaluated which variables are associated with clinical remission. However, there is currently no established risk-stratification model for predicting outcomes in these patients. OBJECTIVES: We developed a risk stratification model that can predict long-term outcomes in patients with RP and enable identification of patients with characteristics that portend poor outcomes. METHODS: We retrospectively studied a total of 365 consecutive patients with RP from 2012 to 2019. The primary outcome was clinical remission (CR), defined as cessation of all anti-inflammatory therapy with complete resolution of symptoms. Five machine learning survival models were used to calculate the likelihood of CR within 5 years and stratify patients into high-risk, intermediate-risk, and low-risk groups. RESULTS: Among the cohort, the mean age was 46 ± 15 years, and 205 (56%) were women. CR was achieved in 118 (32%) patients. The final model included steroid dependency, total number of recurrences, pericardial late gadolinium enhancement, age, etiology, sex, ejection fraction, and heart rate as the most important parameters. The model predicted the outcome with a C-index of 0.800 on the test set and exhibited a significant ability in stratification of patients into low-risk, intermediate-risk, and high-risk groups (log-rank test; P < 0.0001). CONCLUSIONS: We developed a novel risk-stratification model for predicting CR in RP. Our model can also aid in stratifying patients, with high discriminative ability. The use of an explainable machine learning model can aid physicians in making individualized treatment decision in RP patients.

A diagnostic signature developed based on the necroptosis-related genes and its association with immune infiltration in osteosarcoma.

Introduction: Osteosarcoma is a bone-derived malignancy that often leads to lung metastasis and death. Material and methods: The RNA-seq data of TARGET-osteosarcoma were collected from TARGET database. GSE16088 and GSE12865 datasets of osteosarcoma x from Gene Expression Database (GEO) were donwloaded. ConsensusClusterPlus was used for molecular subtype classification. Univariate Cox and Lasso regression was employed to develop a risk model. To analyze the regulatory effects of model feature genes on the malignant phenotype of osteosarcoma cell lines, qRT-PCR, Transwell and wound healing assays were performed. The abundance of immune cell infiltration was assessed using MCP-Counter, Gene Set Enrichment Analysis (GSEA), and ESTIMATE. The Tumor Immune Dysfunction and Exclusion (TIDE) software was employed to evaluate immunotherapy and response to conventional chemotherapy drugs. Results: Three clusters (C1, C2 and C3) were classified using 39 necroptosis score-associated genes. In general, C1 and C2 showed better prognosis outcome and lower death rate than C3. Specifically, C2 could benefit more from immunotherapy, while C3 was more sensitive to traditional medicines, and C1 had higher immune cell infiltration. Next, an 8-gene signature and a risk score model were developed, with a low risk score indicating better survival and immune cell infiltration. ROC analysis showed that 1-, 3-, and 5-year overall survival of osteosarcoma could be correctly predicted by the risk score model. Cellular experiments revealed that the model feature gene IFITM3 promoted the osteosarcoma cell migration and invasion. Furthermore, the overall survival of osteosarcoma patients from TARGET and validation datasets can be accurately evaluated using the nomogram model. Conclusions: Our prognostic model developed using necroptosis genes could facilitate the prognostic prediction for patients suffering from osteosarcoma, offering potential osteosarcoma targets.