Predictor Of Prognosis In Patients Research Articles

Development and implementation of a prognostic model for clear cell renal cell carcinoma based on heterogeneous TLR4 expression

ObjectiveClear cell renal cell carcinoma (ccRCC) is the most common subtype among renal cell carcinomas and has the worst prognosis, originating from renal tubular epithelial cells. Toll-like receptor 4 (TLR4) plays a crucial role in ccRCC proliferation, infiltration, and metastasis. The aim of this study was to construct a prognostic scoring model for ccRCC based on TLR4 expression heterogeneity and to explore its association with immune infiltration, thereby providing insights for the treatment and prognostic evaluation of ccRCC. MethodsUsing R software, a differential analysis was conducted on normal samples and ccRCC samples, and in conjunction with the KEGG database, a correlation analysis for the clear cell renal cell carcinoma pathway (hsa05211) was carried out. We observed the expression heterogeneity of TLR4 in the TCGA-KIRC cohort and identified its related differential genes (TRGs). Based on the expression levels of TRGs, consensus clustering was employed to identify TLR4-related subtypes, and further clustering heatmaps, principal component, and single-sample gene set enrichment analyses were conducted. Overlapping differential genes (ODEGs) between subtypes were analysed, and combined with survival data, univariate Cox regression, LASSO, and multivariate Cox regression were used to establish a prognostic risk model for ccRCC. This model was subsequently evaluated through ROC analysis, risk factor correlation analysis, independent prognostic factor analysis, and intergroup differential analysis. The ssGSEA model was employed to explore immune heterogeneity in ccRCC, and the performance of the model in predicting patient prognosis was evaluated using box plots and the oncoPredict software package. ResultsIn the TCGA-KIRC cohort, TLR4 expression was notably elevated in ccRCC samples compared to normal samples, correlating with improved survival in the high-expression group. The study identified distinct TLR4-related differential genes and categorized ccRCC into three subtypes with varied survival outcomes. A risk prognosis model based on overlapping differential genes was established, showing significant associations with immune cell infiltration and key immune checkpoints (PD-1, PD-L1, CTLA4). Additionally, drug sensitivity differences were observed between risk groups. ConclusionIn the TCGA-KIRC cohort, the expression of TLR4 in ccRCC samples exhibited significant heterogeneity. Through clustering analysis, we identified that the primary immune cells across subtypes are myeloid-derived suppressor cells, central memory CD4 T cells, and regulatory T cells. Furthermore, we successfully constructed a prognostic risk model for ccRCC composed of 17 genes. This model provides valuable references for the prognosis prediction and treatment of ccRCC patients.

The Expression and Prognostic Value of Co-stimulatory Molecules in Clear Cell Renal Cell Carcinoma (CcRcc).

Renal cell carcinoma (RCC) was one of the most common malignant cancers in the urinary system. Clear cell carcinoma (ccRCC) is the most common pathological type, accounting for approximately 80% of RCC. The lack of accurate and effective prognosis prediction methods has been a weak link in ccRCC treatment. Co-stimulatory molecules played the main role in increasing anti-tumor immune response, which determined the prognosis of patients. Therefore, the main objective of the present study was to explore the prognostic value of co-stimulatory molecules genes in ccRCC patients. The TCGA database was used to get gene expression and clinical characteristics of patients with ccRCC. A total of 60 co-stimulatory molecule genes were also obtained from TCGAccRCC, including 13 genes of the B7/ CD28 co-stimulatory molecules family and 47 genes of the TNF family. In the TCGA cohort, the least absolute shrinkage and selection operator (LASSO) Cox regression model was used to generate a multigene signature. R and Perl programming languages were used for data processing and drawing. Real-time PCR was used to verify the expression of differentially expressed genes. The study's initial dataset included 539 ccRCC samples and 72 normal samples. The 13 samples have been eliminated. According to FDR<0.05, there were differences in the expression of 55 co-stimulatory molecule genes in ccRCC and normal tissues. LASSO Cox regression analysis results indicated that 13 risk genes were optimally used to construct a prognostic model of ccRCC. The patients were divided into a high-risk group and a low-risk group. Those in the high-risk group had significantly lower OS (Overall Survival rate) than patients in the low-risk group. Receiver operating characteristic (ROC) curve analysis confirmed the predictive value of the prognosis model of ccRCC (AUC>0.7). There are substantial differences in immune cell infiltration between high and low-risk groups. Functional analysis revealed that immune-related pathways were enriched, and immune status was different between the two risk groups. Real-time PCR results for genes were consistent with TCGA DEGs. By stratifying patients with all independent risk factors, the prognostic score model developed in this study may improve the accuracy of prognosis prediction for patients with ccRCC.

Optimised stacked machine learning algorithms for genomics and genetics disorder detection in the healthcare industry.

With recent advances in precision medicine and healthcare computing, there is an enormous demand for developing machine learning algorithms in genomics to enhance the rapid analysis of disease disorders. Technological advancement in genomics and imaging provides clinicians with enormous amounts of data, but prediction is still mostly subjective, resulting in problematic medical treatment. Machine learning is being employed in several domains of the healthcare sector, encompassing clinical research, early disease identification, and medicinal innovation with a historical perspective. The main objective of this study is to detect patients who, based on several medical standards, are more susceptible to having a genetic disorder. A genetic disease prediction algorithm was employed, leveraging the patient's health history to evaluate the probability of diagnosing a genetic disorder. We developed a computationally efficient machine learning approach to predict the overall lifespan of patients with a genomics disorder and to classify and predict patients with a genetic disease. The SVM, RF, and ETC are stacked using two-layer meta-estimators to develop the proposed model. The first layer comprises all the baseline models employed to predict the outcomes based on the dataset. The second layer comprises a component known as a meta-classifier. Results from the experiment indicate that the model achieved an accuracy of 90.45% and a recall score of 90.19%. The area under the curve (AUC) for mitochondrial diseases is 98.1%; for multifactorial diseases, it is 97.5%; and for single-gene inheritance, it is 98.8%. The proposed approach presents a novel method for predicting patient prognosis in a manner that is unbiased, accurate, and comprehensive. The proposed approach outperforms human professionals using the current clinical standard for genetic disease classification in terms of identification accuracy. The implementation of stacked will significantly improve the field of biomedical research by improving the anticipation of genetic diseases.

Dynamic Prognosis Prediction for Patients on DAPT After Drug-Eluting Stent Implantation: Model Development and Validation.

The rapid evolution of artificial intelligence (AI) in conjunction with recent updates in dual antiplatelet therapy (DAPT) management guidelines emphasizes the necessity for innovative models to predict ischemic or bleeding events after drug-eluting stent implantation. Leveraging AI for dynamic prediction has the potential to revolutionize risk stratification and provide personalized decision support for DAPT management. We developed and validated a new AI-based pipeline using retrospective data of drug-eluting stent-treated patients, sourced from the Cerner Health Facts data set (n=98 236) and Optum's de-identified Clinformatics Data Mart Database (n=9978). The 36 months following drug-eluting stent implantation were designated as our primary forecasting interval, further segmented into 6 sequential prediction windows. We evaluated 5 distinct AI algorithms for their precision in predicting ischemic and bleeding risks. Model discriminative accuracy was assessed using the area under the receiver operating characteristic curve, among other metrics. The weighted light gradient boosting machine stood out as the preeminent model, thus earning its place as our AI-DAPT model. The AI-DAPT demonstrated peak accuracy in the 30 to 36 months window, charting an area under the receiver operating characteristic curve of 90% [95% CI, 88%-92%] for ischemia and 84% [95% CI, 82%-87%] for bleeding predictions. Our AI-DAPT excels in formulating iterative, refined dynamic predictions by assimilating ongoing updates from patients' clinical profiles, holding value as a novel smart clinical tool to facilitate optimal DAPT duration management with high accuracy and adaptability.

Exploration of a hypoxia-immune-related microenvironment gene signature and prediction model for hepatitis C-induced early-stage fibrosis

BackgroundLiver fibrosis contributes to significant morbidity and mortality in Western nations, primarily attributed to chronic hepatitis C virus (HCV) infection. Hypoxia and immune status have been reported to be significantly correlated with the progression of liver fibrosis. The current research aimed to investigate the gene signature related to the hypoxia-immune-related microenvironment and identify potential targets for liver fibrosis.MethodSequencing data obtained from GEO were employed to assess the hypoxia and immune status of the discovery set utilizing UMAP and ESTIMATE methods. The prognostic genes were screened utilizing the LASSO model. The infiltration level of 22 types of immune cells was quantified utilizing CIBERSORT, and a prognosis-predictive model was established based on the selected genes. The model was also verified using qRT-PCR with surgical resection samples and liver failure samples RNA-sequencing data.ResultsElevated hypoxia and immune status were linked to an unfavorable prognosis in HCV-induced early-stage liver fibrosis. Increased plasma and resting NK cell infiltration were identified as a risk factor for liver fibrosis progression. Additionally, CYP1A2, CBS, GSTZ1, FOXA1, WDR72 and UHMK1 were determined as hypoxia-immune-related protective genes. The combined model effectively predicted patient prognosis. Furthermore, the preliminary validation of clinical samples supported most of the conclusions drawn from this study.ConclusionThe prognosis-predictive model developed using six hypoxia-immune-related genes effectively predicts the prognosis and progression of liver fibrosis. The current study opens new avenues for the future prediction and treatment of liver fibrosis.

Development of macrophage-associated genes prognostic signature predicts clinical outcome and immune infiltration for sepsis

Sepsis is a major global health problem, causing a significant burden of disease and death worldwide. Risk stratification of sepsis patients, identification of severe patients and timely initiation of treatment can effectively improve the prognosis of sepsis patients. We procured gene expression datasets for sepsis (GSE54514, GSE65682, GSE95233) from the Gene Expression Omnibus and performed normalization to mitigate batch effects. Subsequently, we applied weighted gene co-expression network analysis to categorize genes into modules that exhibit correlation with macrophage activity. To pinpoint macrophage-associated genes (MAAGs), we executed differential expression analysis and single sample gene set enrichment analysis. We then established a prognostic model derived from four MAAGs that were significantly differentially expressed. Functional enrichment analysis and immune infiltration assessments were instrumental in deciphering the biological mechanisms involved. Furthermore, we employed principal component analysis and conducted survival outcome analyses to delineate molecular subgroups within sepsis. Four novel MAAGs—CD160, CX3CR1, DENND2D, and FAM43A—were validated and used to create a prognostic model. Subgroup classification revealed distinct molecular profiles and a correlation with 28-day survival outcomes. The MAAGs risk score was developed through univariate Cox, LASSO, and multivariate Cox analyses to predict patient prognosis. Validation of the risk score upheld its prognostic significance. Functional enrichment implicated ribonucleoprotein complex biogenesis, mitochondrial matrix, and transcription coregulator activity in sepsis, with an immune infiltration analysis indicating an association between MAAGs risk score and immune cell populations. The four MAAGs exhibited strong diagnostic capabilities for sepsis. The research successfully developed a MAAG-based prognostic model for sepsis, demonstrating that such genes can significantly stratify risk and reflect immune status. Although in-depth mechanistic studies are needed, these findings propose novel targets for therapy and provide a foundation for future precise clinical sepsis management.

The systemic inflammation indexes after admission predict in-hospital mortality in patients with extensive burns

PurposeTo explore the clinical value of various complete blood count (CBC)-derived inflammation indicators to predict in-hospital mortality in patients with extensive burns. MethodsSystemic inflammation indexes, including lymphocyte-platelet ratio (LPR), neutrophil-lymphocyte ratio (NLR), neutrophil-monocyte ratio (NMR), monocyte-lymphocyte ratio (MLR), neutrophil-to-lymphocyte * platelet (NLPR), systemic inflammation index (SII), and systemic inflammation response index (SIRI) on days 1, 3, and 7 after admission were calculated in 135 patients with extensive burns. ResultsWe included 135 patients with extensive burns, including 97 survivors and 38 non-survivors. After adjusting for confounders, only the LPR on day 1, NLPR on days 3 and 7 were significantly associated with survival (OR= 1.237, 1.097, 1.104; 95 % CI: 1.055–1.451, 1.002–1.202, 1.005–1.212; respectively) in the analysis of multivariate logistic regression. The optimum cutoff values of the LPR on day 1 and NLPR on day 3 were 6.37 and 8.06, and the area under the curves (AUC) were 0.695 and 0.794, respectively. The AUC of NLPR on day 7 had the highest value, 0.814, and the optimum cut-off value was 3.84. The efficacy of LPR on day 1, NLPR on days 3 and 7 combined with the burn prognostic score index in predicting the prognosis of patients was higher than that of the burn index alone, and the three composite inflammatory indexes combined with PBI had the highest efficacy in predicting the prognosis (AUC = 0.994). Kaplan-Meier survival analysis showed poor prognosis in patients with higher LPR on day 1 and higher NLPR on days 3 and 7 (log-rank χ2 =9.623,31.564, 20.771, respectively; P < 0.01). ConclusionsLPR on day 1 and NLPR on days 3 and 7 after admission are reliable predictors of prognosis in patients with severe extensive burns. The combination of the burn prognostic score index, LPR on day 1, and NLPR on days 3 and 7 was superior to the burn indexes alone in predicting a patient's prognosis.

Association between haemoglobin, albumin, lymphocytes, and platelets and mortality in patients with heart failure.

The combination of haemoglobin, albumin, lymphocytes, and platelets (HALP) is a new metric used to assess patient prognosis in many diseases. This study aimed to assess the relationship between HALP and short- and long-term mortality in patients with heart failure. This retrospective cohort study included adult patients with heart failure who were hospitalized between 2019 and 2021. The primary outcomes were 1-month mortality and 1-year mortality. The multivariable logistic regression analysis was used to evaluate the association between HALP and the risk of mortality. Stratified analyses were conducted based on New York Heart Association functional classification (NYHA) stage (II/III, IV) and left ventricular ejection fraction (LVEF, <50%, ≥50%). The area under the receiver operating characteristic curve (AUC) was used to evaluate the ability of HALP, prognostic nutritional index (PNI), C-reactive protein (CRP), and the Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC-HF) risk score in predicting mortality in patients with heart failure. A total of 730 patients with heart failure were included, of whom 61 (8.36%) died within 1month and 77 (10.55%) died within 1year. High HALP scores were associated with a reduced risk of 1-month mortality (odds ratio (OR)=0.978, 95% confidence interval (CI): 0.963-0.992, P=0.003) and 1-year mortality (OR=0.987, 95% CI: 0.977-0.997, P=0.009) in patients with heart failure. In patients with different NYHA stages or LVEF levels, high HALP scores were correlated with a reduced risk of 1-year mortality in patients with NYHA stage II/III (OR=0.978, 95% CI: 0.957-1.000, P=0.045) or LVEF ≥50% (OR=0.970, 95% CI: 0.945-0.996, P=0.024). The AUC for HALP, PNI, CRP, and MAGGIC-HF to predict 1-year mortality in patients with heart failure were 0.677 (95% CI: 0.619-0.735), 0.666 (95% CI: 0.608-0.723), 0.638 (95% CI: 0.572-0.704), and 0.654 (95% CI: 0.591-0.717), respectively. HALP may be a potential marker for predicting mortality in patients with heart failure. Further exploration based on HALP may yield better clinical predictors of prognosis in patients with heart failure.

Metabolic-Related Gene Prognostic Index for Predicting Prognosis, Immunotherapy Response, and Candidate Drugs in Ovarian Cancer.

Ovarian cancer (OC) is a highly heterogeneous disease, with patients at different tumor staging having different survival times. Metabolic reprogramming is one of the key hallmarks of cancer; however, the significance of metabolism-related genes in the prognosis and therapy outcomes of OC is unclear. In this study, we used weighted gene coexpression network analysis and differential expression analysis to screen for metabolism-related genes associated with tumor staging. We constructed the metabolism-related gene prognostic index (MRGPI), which demonstrated a stable prognostic value across multiple clinical trial end points and multiple validation cohorts. The MRGPI population had its distinct molecular features, mutational characteristics, and immune phenotypes. In addition, we investigated the response to immunotherapy in MRGPI subgroups and found that patients with low MRGPI were prone to benefit from anti-PD-1 checkpoint blockade therapy and exhibited a delayed treatment effect. Meanwhile, we identified four candidate therapeutic drugs (ABT-737, crizotinib, panobinostat, and regorafenib) for patients with high MRGPI, and we evaluated the pharmacokinetics and safety of the candidate drugs. In summary, the MRGPI was a robust clinical feature that could predict patient prognosis, immunotherapy response, and candidate drugs, facilitating clinical decision making and therapeutic strategy of OC.

Venous-to-arterial carbon-dioxide tension difference as a useful predictor of patient prognosis after major surgery

PurposeChange in venous-to-arterial carbon dioxide partial pressure difference[P(v-a)CO2] could be a useful marker to assess tissue perfusion status. Herein, we assessed the predictive values of postoperative P(v-a)CO2 measurements for mortality in critically ill patients after major surgery. The correlation between P(v-a)CO2 values and other conventional parameters of patient prognosis was also evaluated. MethodsPatients admitted to the intensive care unit(ICU) after abdominal surgery were enrolled. Arterial and venous blood gas analyses were performed within 1 h(T0) and after 24 h(T1) of admission to the ICU, respectively. The relationship between P(v-a)CO2 levels at T1 and other conventional parameters were assessed using a Bland-Altman plot. Logistic regression analysis was performed to examine the predisposing factors of mortality after surgery. ResultsA total of 231 patients were finally analyzed. We divided the participants into the high PvaCO2 group[P(v-a)CO2 ≥ 8.6] and the low PvaCO2 group[P(v-a)CO2 < 8.6]. Seven-day-, 28-day, and in-hospital mortality were significantly higher in the high PvaCO2 group than in the low PvaCO2 group. There was significant agreement between P(v-a)CO2 values at T1 and APACHE II scores, lactate levels at T1 and total SOFA scores at T1. In multivariate logistic analysis, an increased P(v-a)CO2 value at T1 was the only significant risk factor of 7-day mortality after surgery. [odds ratio:1.341, 95%confidence interval: 1.050–1.714, p=0.019]. ConclusionP(v-a)CO2 measurements could be not only a significant predictor of postoperative prognosis, but also a useful surveillance parameter to maintain tissue perfusion after abdominal surgery in patients with a potential risk of fatal complication-related tissue hypoperfusion.

ID2 Promotes Lineage Transition of Prostate Cancer through FGFR and JAK-STAT Signaling.

The use of androgen receptor pathway inhibitors (ARPIs) has led to an increase in the proportion of AR-null prostate cancer, including neuroendocrine prostate cancer (NEPC) and double-negative prostate cancer (DNPC), but the mechanism underlying this lineage transition has not been elucidated. We found that ID2 expression was increased in AR-null prostate cancer. In vitro and in vivo studies confirmed that ID2 promotes PCa malignancy and can confer resistance to enzalutamide in PCa cells. We generated an ID2 UP50 signature, which is capable of determining resistance to enzalutamide and is valuable for predicting patient prognosis. Functional experiments showed that ID2 could activate stemness-associated JAK/STAT and FGFR signaling while inhibiting the AR signaling pathway. Our study indicates a potentially strong association between ID2 and the acquisition of a stem-like phenotype in adenocarcinoma cells, leading to resistance to androgen deprivation therapy (ADT) and next-generation ARPIs in prostate cancer.

Construction of an abnormal glycosylation risk model and its application in predicting the prognosis of patients with head and neck cancer

Head and neck squamous cell carcinoma (HNSCC) is the most common malignant tumor of the head and neck, and the incidence rate is increasing year by year. Protein post-translational modification, recognized as a pivotal and extensive form of protein modification, has been established to possess a profound association with tumor occurrence and progression. This study employed bioinformatics analysis utilizing transcriptome sequencing data, patient survival data, and clinical data from HNSCC to establish predictive markers of genes associated with glycosylation as prognostic risk markers. The R procedure WGCNA was employed to construct a gene co-expression network using the gene expression profile and clinical characteristics of HNSCC samples. Multiple Cox Proportional Hazards Regression Model (Cox regression) and LASSO analysis were conducted to identify the key genes exhibiting the strongest association with prognosis. A risk score, known as the glycosylation-related genes risk score (GLRS), was subsequently formulated utilizing the aforementioned core genes. This scoring system facilitated the classification of samples into high-risk and low-risk categories, thereby enabling the prediction of patient prognosis. The association between GLRS and clinical variables was examined through both univariate and multivariate Cox regression analysis. The validation of six core genes was accomplished using quantitative real-time polymerase chain reaction (qRT-PCR). The findings demonstrated noteworthy variations in risk scores among subgroups, thereby affirming the efficacy of GLRS in prognosticating patient outcomes. Furthermore, a correlation has been observed between the risk-scoring model and immune infiltration. Moreover, significant disparities exist in the expression levels of diverse immune checkpoints, epithelial-mesenchymal transition genes, and angiogenic factors between the high and low-risk groups.

Alcohol-related cancer morbidity and mortality are stratified using modified albumin platelet product

Alcohol abuse is associated with several diseases, such as hepatocellular carcinoma, cirrhosis, and extrahepatic malignancies. Recently, we reported albumin platelet product (APP) and modified APP (mAPP) as novel indices of liver fibrosis staging and prognosis in patients without alcoholic liver diseases. This retrospective cohort study aimed to extend application of APP and mAPP in prognosis prediction of patients with alcoholic liver diseases. We enrolled 222 patients with alcoholic liver diseases based on their medical records. Cut-off values of APP = 4.349 and mAPP = 2.484 were adopted based on a past report. Hazard ratios of APP and mAPP were compared to those of albumin-bilirubin score and fibrosis-4 index. The primary and secondary endpoints were carcinogenesis and death, respectively. Thus, APP = 4.349 and mAPP = 2.484 significantly differentiated cancer-free survival and overall survival in univariate analysis. Hazard ratios of mAPP = 2.484 were greater than those of the albumin-bilirubin score of -2.270 and fibrosis-4 index of 3.25. Multivariate analysis revealed mAPP = 2.484 as an independent risk factor for carcinogenesis and overall death. In conclusion, mAPP is a simple index to stratify patient’s risk for carcinogenesis and death.

Predictive Panel for Immunotherapy in Low-Grade Glioma

BackgroundThe main treatment of lower-grade glioma (LGG) is still surgical resection followed by radiotherapy and/or chemotherapy, which has certain limitations, including side effects and drug resistance. Immunotherapy is a promising treatment for LGG, but it is generally hindered by the tumor microenvironment with the limited expression of tumor antigens. MethodsWe integrated RNA sequencing data sets and clinical information and conducted consistent cluster analysis to explore the most suitable patients for immune checkpoint therapy. Gene set enrichment analysis, UMAP analysis, mutation correlation analysis, TIMER analysis and TIDE analysis were used to identify the immune characteristics of three immune subtypes and the feasibility of five antigens as immune checkpoint markers. ResultsWe analyzed the isolation and mutation of homologous recombination repair genes（HRR） of the three immune subtypes, and the HRR genes of the three subtypes were obviously segregated. Among them, IS2 subtype has a large number of HRR gene mutations, which increases the immunogenicity of tumors, which is consistent with the results of tumor mutation load analysis of three immune subtypes. Then we evaluated the immune cell infiltration of immune subtypes and found that IS2 and IS3 subtypes were rich in immune cells. It is worth noting that there are many Treg cells and NK cells in IS1 subtype. In addition, when analyzing the immune checkpoint gene expression of the three subtypes, we found that they were up-regulated most in IS2 subtypes compared with other subtypes. Then when we further confirmed the role of immune-related genes in LGG, through TIDE analysis and TISIDB analysis, we obtained five markers that can predict the efficacy of ICB in patients with LGG. In addition, we confirmed that they were associated with poor prognosis through survival analysis. ConclusionsWe obtained three reliable immune subtypes, and patients with the IS2 subtype are suitable for immunotherapy, in which NAMPT, SLC11A1, TNC, VIM, and SPP1 are predictive panel markers for ICB in the LGG group. Our findings provide a rationale for immunotherapy selection and prediction of patient prognosis in LGG patients.

Development and evaluation of the Newstage system: integrating tumor regression grade and lymph node status for improved prognostication in neoadjuvant treatment of gastric cancer

BackgroundThe predictive correlation of tumor depth of invasion changes after neoadjuvant therapy, and the 8th American Joint Committee on Cancer (AJCC) ypTNM system for gastric cancer may not accurately predict patient prognosis following neoadjuvant therapy.MethodsA retrospective analysis was conducted on a total of 258 patients who underwent radical surgery for gastric cancer after neoadjuvant therapy. The Newstage system was established based on tumor regression grade and pathological lymph node status. The 3-year survival rates of patients classified by the Newstage system were compared with those classified by the AJCC ypTNM system.ResultsIn a cohort of 258 patients, the 3-year overall survival rates based on the Newstage system were: (I) 94.6%, (II) 79.3%, (III) 54.5%, and (IV) 30.2%. The Newstage system exhibited a lower Akaike information criterion value (902.57 vs. 912.03). Additionally, the area under the ROC curve (0.756 vs. 0.733) and the C-index (0.731 vs. 0.718) was higher than the AJCC ypTNM system. Furthermore, a multivariate analysis indicated that the Newstage system was an independent prognostic factor (p = 0.001).ConclusionThe Newstage system exhibits superior predictive performance in estimating survival rates for neoadjuvant therapy in gastric cancer. It also functions as an independent prognostic factor.

Loss of the epithelial marker CDX1 predicts poor prognosis in early-stage CRC patients

BackgroundWe have previously shown that non-curative chemotherapy imposes fetal conversion and high metastatic capacity to cancer cells. From the set of genes differentially expressed in Chemotherapy Resistant Cells, we obtained a characteristic fetal intestinal cell signature that is present in a group of untreated tumors and is sufficient to predict patient prognosis. A feature of this fetal signature is the loss of CDX1. MethodsWe have analyzed transcriptomic data in public datasets and performed immunohistochemistry analysis of paraffin embedded tumor samples from two cohorts of colorectal cancer patients. ResultsWe demonstrated that low levels of CDX1 are sufficient to identify patients with poorest outcome at the early tumor stages II and III. Presence tumor areas that are negative for CDX1 staining in stage I cancers is associated with tumor relapse. ConclusionsOur results reveal the actual possibility of incorporating CDX1 immunostaining as a valuable biomarker for CRC patients.

Comprehensive Bioinformatic Analysis Reveals an Autophagy-related Gene Signature for Predicting Outcome, Immune Status, and Drug Sensitivity in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is one of the most common malignancies in the world, but molecular complexity and tumor heterogeneity make predictive models for HCC prognosis ineffective. Many recent studies have suggested that autophagy is important in tumor progression. Using autophagy-related genes (ARGs), we attempted to create a novel signature for individual prognosis prediction in patients with HCC. Differentially expressed ARGs (DE-ARGs) in HCC and normal samples were screened using TCGA datasets. Univariate Cox and multivariate Cox regression analyses were performed to determine ARGs related to survival in HCC. An autophagy-based signature was constructed using LASSO, and its correlation with the prognosis and the immune infiltration of HCC patients was explored. In this study, we screened 32 survival-related DE-ARGs by analyzing TCGA datasets. Functional enrichment indicated that the 32 DE-ARGs may play important functional and regulatory roles in cellular autophagy, the regulation of multiple signaling pathways, as well as in the context of cancer and neurological diseases. Based on PPI Network, we identified several hub genes. LASSO Cox regression analysis selected five genes (CASP8, FOXO1, PRKCD, SPHK1, and SQSTM1) for a novel prognostic model. AUCs of 0.752, 0.686, and 0.665 in the TCGA whole set indicated that the model accurately predicted 1-, 3-, and 5-year overall survival, respectively. Cox regression analysis showed that the five-gene signature is an independent and robust predictor in patients with HCC. The high-risk group demonstrated higher levels of immune cell infiltration and exhibited a strong correlation with the immune microenvironment and tumor stem cells. In addition, we further identified PRKCD and SQSTM1 as critical regulators involved in HCC progression. The expression levels of PRKCD and SQSTM1 genes play a crucial role in chemotherapy drug sensitivity and resistance. We introduce here a novel ARG-based predictive feature for HCC patients. Effective use of this signature will aid in determining a patient's prognosis and may lead to novel approaches to clinical decision-making and therapy.

A zinc metabolism-related gene signature for predicting prognosis and characteristics of breast cancer.

Breast cancer is one of the most serious and prevalent malignancies. Zinc is commonly known to play a crucial role in the development and progression of breast cancer; however, the detailed mechanisms underlying this role are not well understood. This study aimed to develop a zinc metabolism-related gene (ZMRG) signature based on a multi-database study to predict patient prognosis and investigate the relationship between drug therapy response and immune enrichment. Data for breast cancer samples from The Cancer Genome Atlas and Gene Expression Omnibus databases were screened for zinc metabolism-related genes using the Molecular Signature Database. Cox and Least Absolute Shrinkage and Selection Operator regressions were performed to construct a ZMRG signature. To assess the predictive performance of the gene signature, Kaplan-Meier analysis and receiver operating characteristic curves were used. Additionally, we utilised single-sample gene set enrichment analysis, the Tumour Immune Estimation Resource, the Genomics of Drug Sensitivity in Cancer database, and the Cancer Therapeutics Response Portal to investigate the association between the tumour microenvironment and drug sensitivity. Quantitative PCR was used to assess the expression of each gene in the signature in breast cancer cell lines and patient samples. Five ZMRGs were identified (ATP7B, BGLAP, P2RX4, SLC39A11, and TH) and a risk profile was constructed for each. Two risk groups, high- and low-risk, were identified in this way, and the high-risk score subgroups were found to have worse prognosis. This risk profile was validated using the GSE42568 dataset. Tumour microenvironment and drug sensitivity analyses showed that the expression of these five ZMRGs was significantly associated with immune response. The high-risk group showed substantial immune cell infiltration and enrichment of immune pathways, and patients were more sensitive to drugs commonly used in breast cancer. The ZMRG signature represents a new prognostic predictor for patients with breast cancer, and may also provide new insights into individualised treatment of breast cancer.

Prediction of prognosis in patients with nontraumatic intracranial hemorrhage using blood urea nitrogen-to-creatinine ratio on admission: a retrospective cohort study based on data from the medical information Mart for intensive care-IV database.

The blood urea nitrogen-to-creatinine ratio (BUNCR) has been proposed as a potential biomarker for critical illness-induced catabolism. However, its specific relevance and significance in the context of non-traumatic intracranial hemorrhage (NTIH) remains unclear. As such, the primary objective of this study was to determine the role of BUNCR in the prognosis of patients with NTIH. All data were sourced from the Medical Information Mart for Intensive Care-IV 2.0 (MIMIC-IV) database. Study outcomes included 30-day and 1-year mortality rates. Univariate and multivariate logistic regression analyses were used to calculate adjusted odds ratio with corresponding 95% confidence interval, and generalized additive model were used to identify both linear and non-linear relationships between BUNCR and mortality rates. A two-piecewise regression model was performed to calculate the saturation effect. Subgroup analyses were performed to evaluate outcome stability in various groups. A retrospective study of 3,069 patients with NTIH revealed a U-shaped relationship between BUNCR levels and 30-day/1-year mortality. The two-piecewise regression model showed that the inflection points for 30-day and 1-year mortality were 10.455 and 16.25, respectively. On the left side of the inflection point, the 30-day and 1-year mortality rate decreased by 17.7% (OR = 0.823, 95%CI: 0.705-0.960; p = 0.013) and 5.3% (OR = 0.947, 95%CI: 0.899-0.999; p = 0.046), respectively, per 1 unit increment of BUNCR. On the right side of the inflection point, the 30-day and 1-year mortality rate increased by 1.6% (OR = 1.016, 95%CI: 1.000-1.031; p = 0.046) and 3.6% (OR = 1.036, 95%CI:1.019-1.054; p < 0.001) per 1 unit decrement of BUNCR. Subgroup analyses revealed consistent results across different strata. This study identified a nonlinear relationship between BUNCR and mortality in patients with NTIH, indicating that BUNCR may be valuable prognostic marker for early identification and proactive management.

Transcriptomic insights into UTUC: role of inflammatory fibrosis and potential for personalized treatment

BackgroundUpper tract urothelial carcinoma (UTUC) is a rare disease, belonging to the same category of urothelial cancers as bladder cancer (BC). Despite sharing similar non-surgical treatment modalities, UTUC demonstrates a higher metastasis propensity than BC. Furthermore, although both cancers exhibit similar molecular disease emergence mechanisms, sequencing data reveals some differences. Our study investigates the transcriptomic distinctions between UTUC and BC, explores the causes behind UTUC's heightened metastatic tendency, constructs a model for UTUC metastasis and prognosis, and propose personalized treatment strategies for UTUC.MethodsIn our research, we utilized differential gene expression analysis, interaction networks, and Cox regression to explore the enhanced metastatic propensity of UTUC. We formulated and validated a prognostic risk model using diverse techniques, including cell co-culture, reverse transcription quantitative polymerase chain reaction (rt-qPCR), western blotting, and transwell experiments. Our methodological approach also involved survival analysis, risk model construction, and drug screening leveraging the databases of CTRPv2, PRISM and CMap. We used the Masson staining technique for histological assessments. All statistical evaluations were conducted using R software and GraphPad Prism 9, reinforcing the rigorous and comprehensive nature of our research approach.ResultsScreening through inflammatory fibrosis revealed a reduction of extracellular matrix and cell adhesion molecules regulated by proteoglycans in UTUC compared with BC, making UTUC more metastasis-prone. We demonstrated that SDC1, LUM, VEGFA, WNT7B, and TIMP3, are critical in promoting UTUC metastasis. A risk model based on these five molecules can effectively predict the risk of UTUC metastasis and disease-free survival time. Given UTUC's unique molecular mechanisms distinct from BC, we discovered that UTUC patients could better mitigate the issue of poor prognosis associated with UTUC's easy metastasis through tyrosine kinase inhibitors (TKIs) alongside the conventional gemcitabine and cisplatin chemotherapy regimen.ConclusionsThe poor prognosis of UTUC because of its high metastatic propensity is intimately tied to inflammatory fibrosis induced by the accumulation of reactive oxygen species. The biological model constructed using the five molecules SDC1, LUM, VEGFA, WNT7B, and TIMP3 can effectively predict patient prognosis. UTUC patients require specialized treatments in addition to conventional regimens, with TKIs exhibiting significant potential.