Predictive model using four ferroptosis-related genes accurately predicts gastric cancer prognosis

BACKGROUND Gastric cancer(GC)is a common malignancy of the digestive system.According to global 2018 cancer data,GC has the fifth-highest incidence and the thirdhighest fatality rate among malignant tumors.More than 60%of GC are linked to infection with Helicobacter pylori(H.pylori),a gram-negative,active,microaerophilic,and helical bacterium.This parasite induces GC by producing toxic factors,such as cytotoxin-related gene A,vacuolar cytotoxin A,and outer membrane proteins.Ferroptosis,or iron-dependent programmed cell death,has been linked to GC,although there has been little research on the link between H.pylori infection-related GC and ferroptosis.AIM To identify coregulated differentially expressed genes among ferroptosis-related genes(FRGs)in GC patients and develop a ferroptosis-related prognostic model with discrimination ability.METHODS Gene expression profiles of GC patients and those with H.pylori-associated GC were obtained from The Cancer Genome Atlas and Gene Expression Omnibus(GEO)databases.The FRGs were acquired from the FerrDb database.A ferroptosis-related gene prognostic index(FRGPI)was created using least absolute shrinkage and selection operator–Cox regression.The predictive ability of the FRGPI was validated in the GEO cohort.Finally,we verified the expression of the hub genes and the activity of the ferroptosis inducer FIN56 in GC cell lines and tissues.RESULTS Four hub genes were identified(NOX4,MTCH1,GABARAPL2,and SLC2A3)and shown to accurately predict GC and H.pylori-associated GC.The FRGPI based on the hub genes could independently predict GC patient survival;GC patients in the high-risk group had considerably worse overall survival than did those in the low-risk group.The FRGPI was a significant predictor of GC prognosis and was strongly correlated with disease progression.Moreover,the gene expression levels of common immune checkpoint proteins dramatically increased in the highrisk subgroup of the FRGPI cohort.The hub genes were also confirmed to be highly overexpressed in GC cell lines and tissues and were found to be primarily localized at the cell membrane.The ferroptosis inducer FIN56 inhibited GC cell proliferation in a dose-dependent manner.CONCLUSION In this study,we developed a predictive model based on four FRGs that can accurately predict the prognosis of GC patients and the efficacy of immunotherapy in this population.

Construction and validation of a pancreatic cancer prognostic model based on genes related to the hypoxic tumor microenvironment

BACKGROUND Pancreatic cancer is one of the most lethal malignancies,characterized by poor prognosis and low survival rates.Traditional prognostic factors for pancreatic cancer offer inadequate predictive accuracy,often failing to capture the complexity of the disease.The hypoxic tumor microenvironment has been recognized as a significant factor influencing cancer progression and resistance to treatment.This study aims to develop a prognostic model based on key hypoxia-related molecules to enhance prediction accuracy for patient outcomes and to guide more effective treatment strategies in pancreatic cancer.AIM To develop and validate a prognostic model for predicting outcomes in patients with pancreatic cancer using key hypoxia-related molecules.METHODS This pancreatic cancer prognostic model was developed based on the expression levels of the hypoxia-associated genes CAPN2,PLAU,and CCNA2.The results were validated in an independent dataset.This study also examined the correlations between the model risk score and various clinical features,components of the immune microenvironment,chemotherapeutic drug sensitivity,and metabolism-related pathways.Real-time quantitative PCR verification was conducted to confirm the differential expression of the target genes in hypoxic and normal pancreatic cancer cell lines.RESULTS The prognostic model demonstrated significant predictive value,with the risk score showing a strong correlation with clinical features:It was significantly associated with tumor grade(G)(bP<0.01),moderately associated with tumor stage(T)(aP<0.05),and significantly correlated with residual tumor(R)status(bP<0.01).There was also a significant negative correlation between the risk score and the half-maximal inhibitory concentration of some chemotherapeutic drugs.Furthermore,the risk score was linked to the enrichment of metabolism-related pathways in pancreatic cancer.CONCLUSION The prognostic model based on hypoxia-related genes effectively predicts pancreatic cancer outcomes with improved accuracy over traditional factors and can guide treatment selection based on risk assessment.

Interaction between inflammatory bowel disease,physical activity,and myokines:Assessment of serum irisin levels

Inflammatory bowel disease(IBD),including Crohn’s disease and ulcerative colitis,showed a wide spectrum of intestinal and extra-intestinal manifestations,which rendered the patients physically inactive and impaired their quality of life.It has been found that physical activity is a non-pharmacological intervention that improves the quality of life for those patients.Irisin is one member of the myokines secreted by muscle contraction during exercise and could be used as an antiinflammatory biomarker in assessing the physical activity of IBD patients.In addition,experimental studies showed that exogenous irisin significantly decreased the inflammatory markers and the histological changes of the intestinal mucosa observed in experimental colitis.Furthermore,irisin produces changes in the diversity of the microbiota.Therefore,endogenous or exogenous irisin,via its anti-inflammatory effects,will improve the health of IBD patients and will limit the barriers to physical activity in patients with IBD.

Lipid metabolism-related long noncoding RNA RP11-817I4.1 promotes fatty acid synthesis and tumor progression in hepatocellular carcinoma

BACKGROUND Hepatocellular carcinoma(HCC)is one of the most common types of tumors.The influence of lipid metabolism disruption on the development of HCC has been demonstrated in published studies.AIM To establish an HCC prognostic model for lipid metabolism-related long non-coding RNAs(LMR-lncRNAs)and conduct in-depth research on the specific role of novel LMR-lncRNAs in HCC.METHODS Correlation and differential expression analyses of The Cancer Genome Atlas data were used to identify differentially expressed LMR-lncRNAs.Quantitative real-time polymerase chain reaction analysis was used to evaluate the expression of LMR-lncRNAs.Nile red staining was employed to observe intracellular lipid levels.The interaction between RP11-817I4.1,miR-3120-3p,and ATP citrate lyase(ACLY)was validated through the performance of dual-luciferase reporter gene and RIP assays.RESULTS Three LMR-lncRNAs(negative regulator of antiviral response,RNA transmembrane and coiled-coil domain family 1 antisense RNA 1,and RP11-817I4.1)were identified as predictive markers for HCC patients and were utilized in the construction of risk models.Additionally,proliferation,migration,and invasion were reduced by RP11-817I4.1 knockdown.An increase in lipid levels in HCC cells was significantly induced by RP11-817I4.1 through the miR-3120-3p/ACLY axis.CONCLUSION LMR-lncRNAs have the capacity to predict the clinical characteristics and prognoses of HCC patients,and the discovery of a novel LMR-lncRNAs,RP11-817I4.1,revealed its role in promoting lipid accumulation,thereby accelerating the onset and progression of HCC.

Evaluating the influence of sarcopenia and myosteatosis on clinical outcomes in gastric cancer patients undergoing immune checkpoint inhibitor

BACKGROUND The development and progression of gastric cancer(GC)are closely linked to the nutritional status of patients.Although immunotherapy has been demonstrated to be clinically effective,the relationships of sarcopenia and myosteatosis with the use of immune checkpoint inhibitors(ICIs)in patients with gastric cancer remain to be characterized.METHODS We performed a retrospective study of patients who were undergoing immuno-therapy for GC.For the evaluation of sarcopenia,the optimal cut-off value for the skeletal muscle index was established using receiver operating characteristic analysis of data obtained from pre-treatment computed tomography images at the L3 vertebral level.Myosteatosis was defined using the mean skeletal muscle density(SMD),with a threshold value of<41 Hounsfield units(HU)for patients with a body mass index(BMI)<25 kg/m^(2)and<33 HU for those with a BMI≥25 kg/m^(2).The log-rank test was used to compare progression-free survival(PFS)and overall survival(OS),and a Cox proportional hazard model was used to identify prognostic factors.Nomograms were developed to predict the PFS and OS of patients on the basis of the results of multivariate analyses.RESULTS We studied 115 patients who were undergoing ICI therapy for GC,of whom 27.4%had sarcopenia and 29.8%had myosteatosis.Patients with sarcopenia or myosteatosis had significantly shorter PFS and OS than those without these conditions.Furthermore,both sarcopenia and myosteatosis were found to be independent predictors of PFS and OS in patients with GC administering an ICI.The prediction models created for PFS and OS were associated with C-indexes of 0.758 and 0.781,respectively.CONCLUSION The presence of sarcopenia or myosteatosis is a reliable predictor of the clinical outcomes of patients with GC who are undergoing treatment with an ICI.

Identification and validation of a pyroptosis-related prognostic model for colorectal cancer based on bulk and single-cell RNA sequencing data

BACKGROUND Pyroptosis impacts the development of malignant tumors,yet its role in colorectal cancer(CRC)prognosis remains uncertain.AIM To assess the prognostic significance of pyroptosis-related genes and their association with CRC immune infiltration.METHODS Gene expression data were obtained from The Cancer Genome Atlas(TCGA)and single-cell RNA sequencing dataset GSE178341 from the Gene Expression Omnibus(GEO).Pyroptosis-related gene expression in cell clusters was analyzed,and enrichment analysis was conducted.A pyroptosis-related risk model was developed using the LASSO regression algorithm,with prediction accuracy assessed through K-M and receiver operating characteristic analyses.A nomo-gram predicting survival was created,and the correlation between the risk model and immune infiltration was analyzed using CIBERSORTx calculations.Finally,the differential expression of the 8 prognostic genes between CRC and normal samples was verified by analyzing TCGA-COADREAD data from the UCSC database.RESULTS An effective pyroptosis-related risk model was constructed using 8 genes-CHMP2B,SDHB,BST2,UBE2D2,GJA1,AIM2,PDCD6IP,and SEZ6L2(P<0.05).Seven of these genes exhibited differential expression between CRC and normal samples based on TCGA database analysis(P<0.05).Patients with higher risk scores demonstrated increased death risk and reduced overall survival(P<0.05).Significant differences in immune infiltration were observed between low-and high-risk groups,correlating with pyroptosis-related gene expression.CONCLUSION We developed a pyroptosis-related prognostic model for CRC,affirming its correlation with immune infiltration.This model may prove useful for CRC prognostic evaluation.

Transient receptor potential channels as predictive marker and potential indicator of chemoresistance in colon cancer

Transient receptor potential(TRP)channels are strongly associated with colon cancer development and progression.This study leveraged a multivariate Cox regression model on publicly available datasets to construct a TRP channels-associated gene signature,with further validation of signature in real world samples from our hospital treated patient samples.Kaplan-Meier(K-M)survival analysis and receiver operating characteristic(ROC)curves were employed to evaluate this gene signature’s predictive accuracy and robustness in both training and testing cohorts,respectively.Additionally,the study utilized the CIBERSORT algorithm and single-sample gene set enrichment analysis to explore the signature’s immune infiltration landscape and underlying functional implications.The support vector machine algorithm was applied to evaluate the signature’s potential in predicting chemotherapy outcomes.The findings unveiled a novel three TRP channels-related gene signature(MCOLN1,TRPM5,and TRPV4)in colon adenocarcinoma(COAD).The ROC and K-M survival curves in the training dataset(AUC=0.761;p=1.58e-05)and testing dataset(AUC=0.699;p=0.004)showed the signature’s robust predictive capability for the overall survival of COAD patients.Analysis of the immune infiltration landscape associated with the signature revealed higher immune infiltration,especially an increased presence of M2 macrophages,in high-risk group patients compared to their low-risk counterparts.High-risk score patients also exhibited potential responsiveness to immune checkpoint inhibitor therapy,evident through increased CD86 and PD-1 expression profiles.Moreover,the TRPM5 gene within the signature was highly expressed in the chemoresistance group(p=0.00095)and associated with poor prognosis(p=0.036)in COAD patients,highlighting its role as a hub gene of chemoresistance.Ultimately,this signature emerged as an independent prognosis factor for COAD patients(p=6.48e-06)and expression of model gene are validated by public data and real-world patients.Overall,this bioinformatics study provides valuable insights into the prognostic implications and potential chemotherapy resistance mechanisms associated with TRPs-related genes in colon cancer.

Use of machine learning models for the prognostication of liver transplantation: A systematic review

BACKGROUND Liver transplantation(LT)is a life-saving intervention for patients with end-stage liver disease.However,the equitable allocation of scarce donor organs remains a formidable challenge.Prognostic tools are pivotal in identifying the most suitable transplant candidates.Traditionally,scoring systems like the model for end-stage liver disease have been instrumental in this process.Nevertheless,the landscape of prognostication is undergoing a transformation with the integration of machine learning(ML)and artificial intelligence models.AIM To assess the utility of ML models in prognostication for LT,comparing their performance and reliability to established traditional scoring systems.METHODS Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines,we conducted a thorough and standardized literature search using the PubMed/MEDLINE database.Our search imposed no restrictions on publication year,age,or gender.Exclusion criteria encompassed non-English studies,review articles,case reports,conference papers,studies with missing data,or those exhibiting evident methodological flaws.RESULTS Our search yielded a total of 64 articles,with 23 meeting the inclusion criteria.Among the selected studies,60.8%originated from the United States and China combined.Only one pediatric study met the criteria.Notably,91%of the studies were published within the past five years.ML models consistently demonstrated satisfactory to excellent area under the receiver operating characteristic curve values(ranging from 0.6 to 1)across all studies,surpassing the performance of traditional scoring systems.Random forest exhibited superior predictive capabilities for 90-d mortality following LT,sepsis,and acute kidney injury(AKI).In contrast,gradient boosting excelled in predicting the risk of graft-versus-host disease,pneumonia,and AKI.CONCLUSION This study underscores the potential of ML models in guiding decisions related to allograft allocation and LT,marking a significant evolution in the field of prognostication.

Preoperative controlling nutritional status as an optimal prognostic nutritional index to predict the outcome for colorectal cancer

BACKGROUND The controlling nutritional status(CONUT)score effectively reflects a patient’s nutritional status,which is closely related to cancer prognosis.This study invest-igated the relationship between the CONUT score and prognosis after radical surgery for colorectal cancer,and compared the predictive ability of the CONUT score with other indexes.AIM To analyze the predictive performance of the CONUT score for the survival rate of colorectal cancer patients who underwent potentially curative resection.METHODS This retrospective analysis included 217 patients with newly diagnosed colorectal.The CONUT score was calculated based on the serum albumin level,total lymphocyte count,and total cholesterol level.The cutoff value of the CONUT score for predicting prognosis was 4 according to the Youden Index by the receiver operating characteristic curve.The associations between the CONUT score and the prognosis were performed using Kaplan-Meier curves and Cox regression analysis.RESULTS Using the cutoff value of the CONUT score,patients were stratified into CONUT low(n=189)and CONUT high groups(n=28).The CONUT high group had worse overall survival(OS)(P=0.013)and relapse-free survival(RFS)(P=0.015).The predictive performance of CONUT was superior to the modified Glasgow prognostic score,the prognostic nutritional index,and the neutrophil-to-lymphocyte ratio.Meanwhile,the predictive performances of CONUT+tumor node metastasis(TNM)stage for 3-year OS[area under the receiver operating characteristics curve(AUC)=0.803]and 3-year RFS(AUC=0.752)were no less than skeletal muscle mass index(SMI)+TNM stage.The CONUT score was negatively correlated with SMI(P<0.01).CONCLUSION As a nutritional indicator,the CONUT score could predict long-term outcomes after radical surgery for colorectal cancer,and its predictive ability was superior to other indexes.The correlation between the CONUT score and skeletal muscle may be one of the factors that play a predictive role.

Unraveling the enigma:A comprehensive review of solid pseudopapillary tumor of the pancreas

Solid pseudopapillary tumor of the pancreas(SPTP)is a rare neoplasm predom-inantly observed in young females.Pathologically,CTNNB1 mutations,β-catenin nuclear accumulation,and subsequent Wnt-signaling pathway activation are the leading molecular features.Accurate preoperative diagnosis often relies on imaging techniques and endoscopic biopsies.Surgical resection remains the mainstay treatment.Risk models,such as the Fudan Prognostic Index,show promise as predictive tools for assessing the prognosis of SPTP.Establishing three types of metachronous liver metastasis can be beneficial in tailoring individu-alized treatment and follow-up strategies.Despite advancements,challenges persist in understanding its etiology,establishing standardized treatments for unresectable or metastatic diseases,and developing a widely recognized grading system.This comprehensive review aims to elucidate the enigma by consolidating current knowledge on the epidemiology,clinical presentation,pathology,molecular characteristics,diagnostic methods,treatment options,and prognostic factors.

Establishment and evaluation of a prognostic model for patients with unresectable gastric cancer liver metastases

BACKGROUND Liver metastases(LM)is the primary factor contributing to unfavorable outcomes in patients diagnosed with gastric cancer(GC).The objective of this study is to analyze significant prognostic risk factors for patients with GCLM and develop a reliable nomogram model that can accurately predict individualized prognosis,thereby enhancing the ability to evaluate patient outcomes.AIM To analyze prognostic risk factors for GCLM and develop a reliable nomogram model to accurately predict individualized prognosis,thereby enhancing patient outcome assessment.METHODS Retrospective analysis was conducted on clinical data pertaining to GCLM(type III),admitted to the Department of General Surgery across multiple centers of the Chinese PLA General Hospital from January 2010 to January 2018.The dataset was divided into a development cohort and validation cohort in a ratio of 2:1.In the development cohort,we utilized univariate and multivariate Cox regression analyses to identify independent risk factors associated with overall survival in GCLM patients.Subsequently,we established a prediction model based on these findings and evaluated its performance using receiver operator characteristic curve analysis,calibration curves,and clinical decision curves.A nomogram was created to visually represent the prediction model,which was then externally validated using the validation cohort.RESULTS A total of 372 patients were included in this study,comprising 248 individuals in the development cohort and 124 individuals in the validation cohort.Based on Cox analysis results,our final prediction model incorporated five independent risk factors including albumin levels,primary tumor size,presence of extrahepatic metastases,surgical treatment status,and chemotherapy administration.The 1-,3-,and 5-years Area Under the Curve values in the development cohort are 0.753,0.859,and 0.909,respectively;whereas in the validation cohort,they are observed to be 0.772,0.848,and 0.923.Furthermore,the calibration curves demonstrated excellent consistency between observed values and actual values.Finally,the decision curve analysis curve indicated substantial net clinical benefit.CONCLUSION Our study identified significant prognostic risk factors for GCLM and developed a reliable nomogram model,demonstrating promising predictive accuracy and potential clinical benefit in evaluating patient outcomes.

Prognostic value of PEA3 subfamily gene expression in cholangiocarcinoma

BACKGROUND Cholangiocarcinoma(CCA)is a lethal malignancy with limited treatment options and poor prognosis.The PEA3 subfamily of E26 transformation specific genes:ETV1,ETV4,and ETV5 are known to play significant roles in various cancers by influencing cell proliferation,invasion,and metastasis.AIM To analyze PEA3 subfamily gene expression levels in CCA and their correlation with clinical parameters to determine their prognostic value for CCA.METHODS The expression levels of PEA3 subfamily genes in pan-cancer and CCA data in the cancer genome atlas and genotype-tissue expression project databases were analyzed with R language software.Survival curve and receiver operating characteristic analyses were performed using the SurvMiner,Survival,and Procr language packages.The gene expression profiling interactive analysis 2.0 database was used to analyze the expression levels of PEA3 subfamily genes in different subtypes and stages of CCA.Web Gestalt was used to perform the gene ontology/Kyoto encyclopedia of genes and genomes(GO/KEGG)analysis,and STRING database analysis was used to determine the genes and proteins related to PEA3 subfamily genes.RESULTS ETV1,ETV4,and ETV5 expression levels were significantly increased in CCA.There were significant differences in ETV1,ETV4,and ETV5 expression levels among the different subtypes of CCA,and predictive analysis revealed that only high ETV1 and ETV4 expression levels were significantly associated with shorter overall survival in patients with CCA.GO/KEGG analysis revealed that PEA3 subfamily genes were closely related to transcriptional misregulation in cancer.In vitro and in vivo experiments revealed that PEA3 silencing inhibited the invasion and metastasis of CCA cells.CONCLUSION The expression level of ETV4 may be a predictive biomarker of survival in patients with CCA.

A Meta-Analysis of the Prognostic and Clinicopathological Significance of circZFR in Human Gastrointestinal Cancers

Background: Studies of gastrointestinal (GIT) cancers have shown that circZFR could be involved in the development and progression of various GIT cancers. However, small sample sizes limit the clinical significance of these studies. Here, a meta-analysis was conducted to ascertain the actual involvement of circZFR in the development and prognosis of GIT cancers. Methods: PubMed, Embase, Web of Science, and the Cochrane Library were searched up to December 31, 2023. Hazard ratios (HRs) or odds ratios (ORs) with 95% confidence intervals (CIs) were pooled to evaluate the association between circZFR expression and overall survival (OS). Publication bias was measured using the funnel plot and Egger’s test. Results: 10 studies having 659 participants were enrolled for meta-analysis. High circZFR expression was associated with poor OS (HR = 1.4, 95% CI: 1.20, 1.70). High circZFR expression also predicted larger tumor size (OR = 4.38, 95% CI 2.65, 7.25), advanced clinical stage (OR = 5.33, 95% CI 3.10, 9.16), and tendency for distant metastasis (OR = 2.89, 95% CI: 1.62, 5.11), but was not related to age, gender, and histological grade. Conclusions: In summary, high circZFR expression was associated with poor OS, larger tumor size, advanced stage cancer and tendency for distant metastasis. These findings suggested that circZFR could be a prognostic marker for GIT cancers.

Development of a prognostic scoring system for hepatic vena cava Budd-Chiari syndrome with hepatocellular carcinoma

Background:Hepatocellular carcinoma(HCC)is a serious complication of hepatic vena cava Budd-Chiari syndrome(HVC-BCS)that significantly reduces the survival time of patients.Our study aimed to analyze the prognostic factors influencing the survival of HVC-BCS patients with HCC and to develop a prognostic scoring system.Methods:The clinical and follow-up data of 64 HVC-BCS patients with HCC who received invasive treatment at the First Affiliated Hospital of Zhengzhou University between January 2015 and December 2019 were retrospectively analyzed.Kaplan-Meier curves and log-rank tests were used to analyze the survival curve of patients and the difference in prognoses between the groups.Univariate and multivariate Cox regression analyses were performed to analyze the influence of biochemical,tumor,and etiological characteristics on the total survival time of patients,and a new prognostic scoring system was developed according to the regression coefficients of the independent predictors in the statistical model.The prediction efficiency was evaluated using the time-dependent receiver operating characteristics curve and concordance index.Results:Multivariate analysis showed that serum albumin level<34 g/L[hazard ratio(HR)=4.207,95%confidence interval(CI):1.816-8.932,P=0.001],maximum tumor diameter>7 cm(HR=8.623,95%CI:3.771-19.715,P<0.001),and inferior vena cava stenosis(HR=3.612,95%CI:1.646-7.928,P=0.001)were independent predictors of survival.A prognostic scoring system was developed according to the above-mentioned independent predictors,and patients were classified into grades A,B,C and D.Significant differences in survival were found among the four groups.Conclusions:This study successfully developed a prognostic scoring system for HVC-BCS patients with HCC,which is helpful for clinical evaluation of patient prognosis.

A pilot clinical study to evaluate feasibility of using single patient classifier as a prognostic test in stage Ⅱ-Ⅲ gastric cancer patients

Objective:Precision medicine approaches emphasize the importance of reliable prognostic tools for guiding individualized therapy decisions.In this study,we evaluated the clinical feasibility of the single patient classifier(SPC)test,a new clinical-grade prognostic assay,in stageⅡ-Ⅲgastric cancer patients.Methods:A prospective multicenter study was conducted,involving 237 patients who underwent gastrectomy between September 2019 and August 2020 across nine hospitals.The SPC test was employed to stratify patients into risk groups,and its feasibility and performance were evaluated.The primary endpoint was the proportion of the cases in which the test results were timely delivered before selecting postoperative treatment.Furthermore,3-year disease-free survivals of risk groups were analyzed.Results:The SPC test met the primary endpoint criteria.The 99.5%of SPC tests were timely delivered to hospitals before the postoperative treatment started.In a clinical setting,the median time from the specimen transfer to laboratory to the result delivery to hospital was 4 d.Furthermore,3-year disease-free survivals were significantly different between risk groups classified with SPC tests.Conclusions:This study highlights the SPC test's feasibility in offering crucial information timely delivered for making informed decisions regarding postoperative treatment strategies.It also provides evidence to support the implementation of a future prospective clinical trial aimed at evaluating the clinical utility of the SPC test in guiding personalized treatment decisions for gastric cancer patients.

Advancing prognostic precision in gastric cancer with an immunoinflammatory index

Gastric cancer remains a major global health challenge with high morbidity and mortality rates.Recent advancements in immunology and inflammation research have highlighted the crucial roles that these biological processes play in tumor progression and patient outcomes.This has sparked new interest in developing prognostic biomarkers that integrate these two key biological processes.In this letter,we discuss the recent study by Ba et al,which proposed a novel prognostic immunoinflammatory index for patients with gastric cancer.We underscore the importance of this research,its potential impact on medical practice,and the prospective avenues for further investigation in this rapidly emerging area of study.

A Prognostic Model Based on Colony Stimulating Factors-related Genes in Triple-negative Breast Cancer

Objective Triple-negative breast cancer(TNBC)is the breast cancer subtype with the worst prognosis,and lacks effective therapeutic targets.Colony stimulating factors(CSFs)are cytokines that can regulate the production of blood cells and stimulate the growth and development of immune cells,playing an important role in the malignant progression of TNBC.This article aims to construct a novel prognostic model based on the expression of colony stimulating factors-related genes(CRGs),and analyze the sensitivity of TNBC patients to immunotherapy and drug therapy.Methods We downloaded CRGs from public databases and screened for differentially expressed CRGs between normal and TNBC tissues in the TCGA-BRCA database.Through LASSO Cox regression analysis,we constructed a prognostic model and stratified TNBC patients into high-risk and low-risk groups based on the colony stimulating factors-related genes risk score(CRRS).We further analyzed the correlation between CRRS and patient prognosis,clinical features,tumor microenvironment(TME)in both high-risk and low-risk groups,and evaluated the relationship between CRRS and sensitivity to immunotherapy and drug therapy.Results We identified 842 differentially expressed CRGs in breast cancer tissues of TNBC patients and selected 13 CRGs for constructing the prognostic model.Kaplan-Meier survival curves,time-dependent receiver operating characteristic curves,and other analyses confirmed that TNBC patients with high CRRS had shorter overall survival,and the predictive ability of CRRS prognostic model was further validated using the GEO dataset.Nomogram combining clinical features confirmed that CRRS was an independent factor for the prognosis of TNBC patients.Moreover,patients in the high-risk group had lower levels of immune infiltration in the TME and were sensitive to chemotherapeutic drugs such as 5-fluorouracil,ipatasertib,and paclitaxel.Conclusion We have developed a CRRS-based prognostic model composed of 13 differentially expressed CRGs,which may serve as a useful tool for predicting the prognosis of TNBC patients and guiding clinical treatment.Moreover,the key genes within this model may represent potential molecular targets for future therapies of TNBC.

UBE2C as an Immune-Related Biomarker for Breast Cancer:A Study Based on Multiple Databases

Objective To screen the target genes that are associated with survival of breast cancer(BRCA) and explore their prognostic values and immune correlations with BRCA using multiple databases..Methods The microarray expression datasets of BRCA were downloaded from the Gene Expresssion Omnibus database(GEO) and analyzed to obtain differentially expressed genes(DEGs). Hub genes were obtained by constructing and visualizing the protein-protein interaction network of DEGs. The key gene was determined using R language, STRING, and Cytoscape, and the differential expression of the key gene was verified using external datasets The Cancer Genome Atlas(TCGA) and quantitative real-time PCR(q RT-PCR) for BRCA tissues of 37 patients. The prognostic value and immunological correlation of UBE2C in BRCA were explored using R language, TIMER, and Gene Set Enrichment Analysis(GSEA).Results Of 10 hub genes seleceed from 302 DEGS, UBE2C was identified as the gene associated with BRCA survival. The expression of UBE2C was differentially upregulated in BRCA, as verified by TCGA and q RT-PCR. Prognostic analysis revealed that UBE2C served as an independent prognostic factor. High expression of UBE2C was associated with decreased immune infiltration levels of B cells, CD4+ T cells, CD8+ T cells, macrophages, and myeloid dendritic cells in BRCA tissue. The expression of UBE2C in BRCA showed a significant correlation with immune checkpoints genes PDCD1, CD274, and CTLA4 expressions. There was a positive correlation between the expression of UBE2C and the tumor mutational burden and microsatellite instability. GSEA demonstrated that UBE2C expression significantly enriched 786 immune-related gene sets.Conclusions UBE2C expression in BRCA tissues is closely related to the BRCA immune microenvironment and showes predictive values on the survivals and prognosis of BRCA patients and the effecacy of immunotherapy. UBE2C may be an potential immune-related prognostic biomarker for BRCA.

Validation of prognostic scores for predicting acute liver failure and in-hospital death in patients with dengue-induced severe hepatitis

BACKGROUND Acute liver failure(ALF)in dengue is rare but fatal.Early identification of patients who are at risk of ALF is the key strategy to improve survival.AIM To validate prognostic scores for predicting ALF and in-hospital mortality in dengue-induced severe hepatitis(DISH).METHODS We retrospectively reviewed 2532 dengue patients over a period of 16 years(2007-2022).Patients with DISH,defined as transaminases>10 times the normal reference level and DISH with subsequent ALF,were included.Univariate regre-ssion analysis was used to identify factors associated with outcomes.Youden’s index in conjunction with receiver operating characteristic(ROC)analysis was used to determine optimal cut-off values for prognostic scores in predicting ALF and in-hospital death.Area under the ROC(AUROC)curve values were compared using paired data nonparametric ROC curve estimation.RESULTS Of 193 DISH patients,20 developed ALF(0.79%),with a mortality rate of 60.0%.International normalized ratio,bilirubin,albumin,and creatinine were indepen-dent predictors associated with ALF and death.Prognostic scores showed excel-lent performance:Model for end-stage liver disease(MELD)score≥15 predicted ALF(AUROC 0.917,sensitivity 90.0%,specificity 88.4%)and≥18 predicted death(AUROC 0.823,sensitivity 86.9%,specificity 89.1%);easy albumin-bilirubin(ALBI)score≥-30 predicted ALF and death(ALF:AUROC 0.835,sensitivity80.0%,specificity 72.2%;death:AUROC 0.808,sensitivity 76.9%,specificity 69.3%);ALBI score≥-2 predicted ALF and death(ALF:AUROC 0.806,sensitivity 80.0%,specificity 77.4%;death:AUROC 0.799,sensitivity 76.9%,specificity 74.3%).Platelet-ALBI score also showed good performance in predicting ALF and death(AUROC=0.786 and 0.699,respectively).MELD and EZ-ALBI scores had similar performance in predicting ALF(Z=1.688,P=0.091)and death(Z=0.322,P=0.747).CONCLUSION MELD score is the best predictor of ALF and death in DISH patients.EZ-ALBI score,a simpler yet effective score,shows promise as an alternative prognostic tool in dengue patients.

Review on Lithium-ion Battery PHM from the Perspective of Key PHM Steps

Prognostics and health management(PHM)has gotten considerable attention in the background of Industry 4.0.Battery PHM contributes to the reliable and safe operation of electric devices.Nevertheless,relevant reviews are still continuously updated over time.In this paper,we browsed extensive literature related to battery PHM from 2018to 2023 and summarized advances in battery PHM field,including battery testing and public datasets,fault diagnosis and prediction methods,health status estimation and health management methods.The last topic includes state of health estimation methods,remaining useful life prediction methods and predictive maintenance methods.Each of these categories is introduced and discussed in details.Based on this survey,we accordingly discuss challenges left to battery PHM,and provide future research opportunities.This research systematically reviews recent research about battery PHM from the perspective of key PHM steps and provide some valuable prospects for researchers and practitioners.