Predictor Of Survival In Patients Research Articles

Comparison of different 2D muscle indexes measured at the level of the 3rd lumbar vertebra in survival prediction in patients with renal cell carcinoma.

Low computed tomography (CT)-determined muscle mass, commonly determined with height-adjusted muscle indexes (MIs), predicts worse survival in several cancers and has been suggested as a prognostic assessment tool. Although several MIs measured at the level of the 3rd lumbar vertebra (L3) are commonly used, it remains unestablished how different L3-determined MIs perform in survival prognostication compared to each other. The objective of this study was to investigate the performance of different MIs for survival prognostication in renal cell carcinoma (RCC). We retrospectively enrolled 214 consecutive patients with RCC. We determined three L3-MIs (psoas muscle index (PMI), psoas muscle index and erector spinae index (PMI+ESI), and whole skeletal muscle index (SMI)) from preoperative CT scans. Categorization of those with low and normal muscle mass was based on the Youden Index sex-specific MI cut-offs. We determined sensitivity, specificity, and accuracy metrics for predicting 1-year, 5-year, and overall survival (OS) using Cox regression models. Low PMI, PMI+ESI, and SMI significantly predicted decreased 1-year, 5-year, and OS in uni- and multivariate models. PMI+ESI and SMI were more accurate than PMI in males, and PMI and PMI+ESI were more accurate than SMI in females in the prediction of 1-year survival. However, there were no differences in accuracies between MIs in 5-year and OS prediction. PMI+ESI performed well overall in short-term prognostication, but there were no differences between the MIs in long-term prognostication. We recommend the use of PMI+ESI for muscle evaluation, particularly when SMI cannot be evaluated.

Stellae-123 gene expression signature improved risk stratification in taiwanese acute myeloid leukemia patients

The European Leukemia Net recommendations provide valuable guidance in treatment decisions of patients with acute myeloid leukemia (AML). However, the genetic complexity and heterogeneity of AML are not fully covered, notwithstanding that gene expression analysis is crucial in the risk stratification of AML. The Stellae-123 score, an AI-based model that captures gene expression patterns, has demonstrated robust survival predictions in AML patients across four western-population cohorts. This study aims to evaluate the applicability of Stellae-123 in a Taiwanese cohort. The Stellae-123 model was applied to 304 de novo AML patients diagnosed and treated at the National Taiwan University Hospital. We find that the pretrained (BeatAML-based) model achieved c-indexes of 0.631 and 0.632 for the prediction of overall survival (OS) and relapse-free survival (RFS), respectively. Model retraining within our cohort further improve the cross-validated c-indexes to 0.667 and 0.667 for OS and RFS prediction, respectively. Multivariable analysis identify both pretrained and retrained models as independent prognostic biomarkers. We further show that incorporating age, Stellae-123, and ELN classification remarkably improves risk stratification, revealing c-indices of 0.73 and 0.728 for OS and RFS, respectively. In summary, the Stellae-123 gene expression signature is a valuable prognostic tool for AML patients and model retraining can improve the accuracy and applicability of the model in different populations.

THOC6 is a novel biomarker of glioma and a target of anti-glioma drugs: An analysis based on bioinformatics and molecular docking.

Glioma is a typical malignant tumor of the nervous system. It is of great significance to identify new biomarkers for accurate diagnosis of glioma. In this context, THOC6 has been studied as a highly diagnostic prognostic biomarker, which contributes to improve the dilemma in diagnosing gliomas. We used online databases and a variety of statistical methods, such as Wilcoxon rank sum test, Dunn test and t test. We analyzed the mutation, location and expression profile of THOC6, revealing the network of THOC6 interaction with disease. Wilcoxon rank sum test showed that THOC6 is highly expressed in gliomas (P < 0.001). Dunn test, Wilcoxon rank sum test and t test showed that THOC6 expression was correlated with multiple clinical features. Logistic regression analysis further confirmed that THOC6 gene expression was a categorical dependent variable related to clinical features of poor prognosis. Kaplan-Meier survival analysis showed that the overall survival (OS) of glioma patients with high expression of THOC6 was poor (P < 0.001). Both univariate (P < 0.001) and multivariate (P = 0.04) Cox analysis confirmed that THOC6 gene expression was an independent risk factor for OS in patients with glioma. ROC curve analysis showed that THOC6 had a high diagnostic value in glioma (AUC = 0.915). Based on this, we constructed a nomogram to predict patient survival. Enrichment analysis showed that THOC6 expression was associated with multiple signal pathways. Immuno-infiltration analysis showed that the expression of THOC6 in glioma was closely related to the infiltration level of multiple immune cells. Molecular docking results showed that THOC6 might be the target of anti-glioma drugs. THOC6 is a novel diagnostic factor and prognostic biomarker of glioma.

Individualized survival prediction and surgery recommendation for patients with glioblastoma

BackgroundThere is a lack of individualized evidence on surgical choices for glioblastoma (GBM) patients.AimThis study aimed to make individualized treatment recommendations for patients with GBM and to determine the importance of demographic and tumor characteristic variables in the selection of extent of resection.MethodsWe proposed Balanced Decision Ensembles (BDE) to make survival predictions and individualized treatment recommendations. We developed several DL models to counterfactually predict the individual treatment effect (ITE) of patients with GBM. We divided the patients into the recommended (Rec.) and anti-recommended groups based on whether their actual treatment was consistent with the model recommendation.ResultsThe BDE achieved the best recommendation effects (difference in restricted mean survival time (dRMST): 5.90; 95% confidence interval (CI), 4.40–7.39; hazard ratio (HR): 0.71; 95% CI, 0.65–0.77), followed by BITES and DeepSurv. Inverse probability treatment weighting (IPTW)-adjusted HR, IPTW-adjusted OR, natural direct effect, and control direct effect demonstrated better survival outcomes of the Rec. group.ConclusionThe ITE calculation method is crucial, as it may result in better or worse recommendations. Furthermore, the significant protective effects of machine recommendations on survival time and mortality indicate the superiority of the model for application in patients with GBM. Overall, the model identifies patients with tumors located in the right and left frontal and middle temporal lobes, as well as those with larger tumor sizes, as optimal candidates for SpTR.

Role of invasive hemodynamics monitoring in sepsis intensive care

The objective was to study the effect of early planned use of transpulmonary thermodilution (TPTD) and therapeutic measures to stabilize blood circulation on the clinical outcome of sepsis.Materials and methods. The cohort study involved 132 patients with abdominal sepsis with SOFA &gt;7 and blood lactate &gt;1.6 mmol/L. Septic shock was diagnosed in 56 % of patients. TPTD in the early periods of intensive care was began in 53.8 % of patients. Logistic regression and ROC-analysis were used to process the data.Results. Early use of invasive monitoring (OR 2,3715, 95 % CI 1,1107–5,0635, p=0,026, AUC 0.655) and infusion volume &gt;43 ml/kg per day (OR 1.0313, 95 % CI 1.0073–1.0558, p=0.01, AUC 0.677) were predictors of survival in patients with abdominal sepsis. The use of TPTD compared to patients of group II was accompanied by an increase in the daily infusion volume (53.7 [38.1–63.5] vs 38.2 [29.9–47.2], ml/kg per day, p = 0.0001), more frequent use of inotropic drugs (39.4 vs 16.4 %, p = 0.004), and higher level of the inotropic scale (0 [0–4.7] vs 0 [0–0], p = 0.01). There were no differences in the frequency of prescription (57.7 vs 65.5 %, p = 0.376) and dosages (0.2 [0.1–0.4] vs 0.3 [0.2–0.4] μg/kg/min, p = 0.554) of norepinephrine. Twenty-eight-day mortality in groups I and II was 31 and 50.8 % (p = 0.022), hospital mortality was 32.9 and 54.0 % (p = 0.014).Conclusion. When assessed by SOFA &gt; 7 points and lactatemia &gt; 1.6 mmol/L, the onset of TPTD and infusion volume &gt; 43 mL/kg/day increase the likelihood of survival of patients with abdominal sepsis, as a result, 28-day and hospital mortality decrease by 1.6 times. The use of invasive monitoring of central hemodynamics in this clinical situation is accompanied by an increase in the prescription of inotropes by 2.4 times with an unchanged intensity of norepinephrine use.

Endocrine Immune-Related Adverse Events Are Independent Predictors of Survival in Patients with Lung Cancer.

Lung cancer (LC) is a serious health problem worldwide. Survival outcomes have improved over time due to the widespread use of novel therapeutic agents, including immune checkpoint inhibitors (ICIs). Endocrine immune-related adverse events (e-irAEs) are common in LC patients treated with ICIs. We performed a retrospective study of patients with LC who received treatment with ICIs at a tertiary referral center between January 2014 and October 2023. In total, 983 LC patients were included in the study. E-irAEs presented at a median time of 4.1 months and included hypothyroidism (15.6%), hyperthyroidism (4.3%), adrenal insufficiency (0.4%), hypophysitis (0.4%), and diabetes mellitus (0.2%). These toxicities were not related to the duration of treatment or the type of ICIs. Most (97.6%) e-irAEs were mild (grade 1-2). Median overall survival (OS) was higher in LC patients who experienced e-irAEs (31.6 months) compared to those who did not (10.8 months). The difference remained statistically significant in the 3-month (HR: 0.42) and 6-month landmark analysis (HR: 0.51). The OS advantage was observed in both patients with NSCLC (HR: 0.36) and SCLC (HR: 0.27). Additional research is needed to validate the role of e-irAEs as an independent predictor of survival outcomes in patients with LC.

Comprehensive analysis of immune-related lncRNAs in AML patients uncovers potential therapeutic targets and prognostic biomarkers

PurposeThe objective of this study was to provide theoretically feasible strategies by understanding the relationship between the immune microenvironment and the diagnosis and prognosis of AML patients. To this end, we built a ceRNA network with lncRNAs as the core and analyzed the related lncRNAs in the immune microenvironment by bioinformatics analysis. MethodsAML transcriptome expression data and immune-related gene sets were obtained from TCGA and ImmPort. Utilizing Pearson correlation analysis, differentially expressed immune-related lncRNAs were identified. Then, the LASSO-Cox regression analysis was performed to generate a risk signature consisting immune-related lncRNAs. Accuracy of signature in predicting patient survival was evaluated using univariate and multivariate analysis. Next, GO and KEGG gene enrichment and ssGSEA were carried out for pathway enrichment analysis of 183 differentially expressed genes, followed by drug sensitivity and immune infiltration analysis with pRRophetic and CIBERSORT, respectively. Cytoscape was used to construct the ceRNA network for these lncRNAs. Results816 common lncRNAs were selected to acquire the components related to prognosis. The final risk signature established by multivariate Cox and stepwise regression analysis contained 12 lncRNAs engaged in tumor apoptotic and metastatic processes: LINC02595, HCP5, AC020934.2, AC008770.3, LINC01770, AC092718.4, AL589863.1, AC131097.4, AC012368.1, C1RL-AS1, STARD4-AS1, and AC243960.1. Based on this predictive model, high-risk patients exhibited lower overall survival rates than low-risk patients. Signature lncRNAs showed significant correlation with tumor-infiltrating immune cells. In addition, significant differences in PD-1/PD-L1 expression and bleomycin/paclitaxel sensitivity were observed between risk groups. ConclusionLncRNAs related to immune microenvironment were prospective prognostic and therapeutic options for AML.

Individualized prediction of conditional survival for colorectal signet-ring cell carcinoma patients.

Conditional survival (CS) considers the time already survived after surgery and may provide additional survival information. The authors sought to construct and validate novel conditional survival nomograms for the prediction of conditional overall survival (OS) and cancer-specific survival (CSS) of colorectal signet-ring cell carcinoma (SRCC) patients. Patients diagnosed with stage I-III SRCC between 2010 and 2019 were identified from the Surveillance, Epidemiology, and End Results database. The formula calculating CS was: CS(x|y) = S(x+y)/S(x), where S(x) represents the survival at x years. CS nomograms were then constructed to predict the 5-year conditional OS and CSS, followed by internal validation. A total of 944 colorectal SRCC patients were finally identified in this study. The 5-year OS and CSS improved gradually with additional survival time. Univariate and multivariate Cox regression analysis conducted in training set revealed that age, race, T stage, LNR, and perineural invasion were independent risk factors for both OS and CSS. Two nomograms with considerable predictive ability were successfully constructed [area under the curve (AUC) for OS: 0.788; AUC for CSS: 0.847] and validated (AUC for OS: 0.773; AUC for CSS: 0.799) for the prediction of 5-year OS and CSS, based on the duration of 1-4 years post-surgery survival. The probability of achieving 5-year OS and 5-year CSS in colorectal SRCC patients improved gradually with additional time. Conditional nomograms considering survival time will be more reliable and informative for risk stratification and postoperative follow-up.

ECG-surv: A deep learning-based model to predict time to 1-year mortality from 12-lead electrocardiogram

Electrocardiogram (ECG) abnormalities have demonstrated potential as prognostic indicators of patient survival. However, the traditional statistical approach is constrained by structured data input, limiting its ability to fully leverage the predictive value of ECG data in prognostic modeling. This study aims to introduce and evaluate a deep-learning model to simultaneously handle censored data and unstructured ECG data for survival analysis. We herein introduce a novel deep neural network called ECG-surv, which includes a feature extraction neural network and a time-to-event analysis neural network. The proposed model is specifically designed to predict the time to 1-year mortality by extracting and analyzing unique features from 12-lead ECG data. ECG-surv was evaluated using both an independent test set and an external set, which were collected using different ECG devices. The performance of ECG-surv surpassed that of the Cox proportional model, which included demographics and ECG waveform parameters, in predicting 1-year all-cause mortality, with a significantly higher concordance index (C-index) in ECG-surv than in the Cox model using both the independent test set (0.860 [95% CI: 0.859- 0.861] vs. 0.796 [95% CI: 0.791- 0.800]) and the external test set (0.813 [95% CI: 0.807- 0.814] vs. 0.764 [95% CI: 0.755- 0.770]). ECG-surv also demonstrated exceptional predictive ability for cardiovascular death (C-index of 0.891 [95% CI: 0.890- 0.893]), outperforming the Framingham risk Cox model (C-index of 0.734 [95% CI: 0.715-0.752]). ECG-surv effectively utilized unstructured ECG data in a survival analysis. It outperformed traditional statistical approaches in predicting 1-year all-cause mortality and cardiovascular death, which makes it a valuable tool for predicting patient survival.

Association analysis of FXYD5 with prognosis and immunological characteristics across pan-cancer

BackgroundThe FXYD domain-containing ion transport regulator 5 (FXYD5) gene is a cancer promoter. However, evidence for an association between FXYD5 and various types of cancer is still lacking. Using multi-omics bioinformatics, our study aimed to reveal the expression distribution, prognostic value, immune infiltration correlation, and molecular functions of FXYD5. MethodsUsing pan-cancer multi-omics data (including The Cancer Genome Atlas, PrognoScan, Gene Expression Profiling Interactive Analysis, cBioPortal, Gene Expression Omnibus, TIMER and scTIME Portal), we assessed the differences in the expression and prognostic value of FXYD5 in malignant tumors. Furthermore, at the single-cell level, we analyze the expression distribution of FXYD5 across different cell types within the tumor microenvironment, and its relationship with the immune microenvironment. Finally, focusing on ovarian cancer, we conducted preliminary validation of the above findings using cell and molecular biology techniques. ResultsOur results indicated that FXYD5 was up-regulated in various tumor types and was positively associated with tumor progression. We also revealed that FXYD5 was ubiquitously expressed in microenvironmental cells at the single-cell level, and its upregulation was associated with enhanced immune infiltration, cancer-associated fibroblast infiltration, and dysfunction of tumor-infiltrating cytotoxic T lymphocyte. Additionally, its expression was positively correlated with immune checkpoint genes, DNA mismatch repair genes, MSI (microsatellite instability) and TMB (tumor mutational burden) across various cancers. Its higher expression in cytotoxic T lymphocytes attenuated its ability to predict patient survival with PD-L1 (programmed death-ligand 1) blockade therapy, and FXYD5 was found to be a potential regulator of tumor immune escape and resistance to cancer immunotherapies. Based on GSEA (gene set enrichment analysis) and experimental verification, FXYD5 activated TGF‐β/SMAD signaling and drove EMT (epithelial-mesenchymal transition) to promote ovarian cancer progression. ConclusionIn summary, our study revealed that FXYD5-TGFβ axis may coregulate the interaction between tumors, CAFs (carcinoma-associated fibroblasts) and immune cells to reshape the tumor immune microenvironment and promote tumorigenesis and tumor progression. Thus, FXYD5 could be used as an immune-related biomarker for diagnosing and predicting the prognosis of multiple cancer types. Therefore, our findings suggest that targeting FXYD5 in TME (tumor microenvironment) may be a promising therapeutic strategy.

Grading Cytological Epithelial Atypia in Pancreatic Mucinous Cysts Predicts Patient Survival: Correlation with Histological, Molecular, and Clinical Follow-Up

Cytological examination of epithelial cells in cyst fluids from pancreatic mucinous cysts is the optimal method for identifying high-grade atypia (HGA), which may represent histological high-grade dysplasia (HGD) or invasive carcinoma, and thereby classify the cyst as high-risk, warranting surgical resection. Cytological features of HGA were previously described at our institution in 2013 and implemented thereafter, but performance of grading with these criteria has not yet been reported. 1322 pancreatic cyst fluid specimens (2014-2021) were identified; all pathology reports and relevant clinical data were reviewed in detail. 230 unique cysts (217 patients) contained neoplastic mucinous epithelium. Of the 230 cysts, 178 had low-grade atypia (LGA), and 52 had HGA. 97 cysts had histologic follow-up: 77 (79%) were resections and 20 (21%) were diagnostic surgical biopsies only. 92 (95%) were confirmed neoplastic mucinous cysts, 3 were adenocarcinomas, and 2 were benign entities. Among histologically confirmed neoplastic mucinous cysts, 58 had low-grade dysplasia (LGD); 34 had HGD, of which 14 also had invasive carcinoma. A significantly higher proportion of cysts with HGA (63%) demonstrated at least HGD on follow-up compared to those with LGA (26%, p<0.001). The sensitivity and specificity of HGA for accurately classifying a high-risk cyst were 54% and 81%, respectively. 146/230 (64%) cysts had corresponding next-generation sequencing results. 31% of HGA cysts harbored a high-risk mutation (TP53, CDKN2A, and/or SMAD4) vs. 7% of LGA cysts (p<0.001). Among cysts without histologic confirmation, 25% of HGA cysts had a high-risk mutation vs. 7% of LGA cysts. The grade of cytological atypia was predictive of overall survival and recurrence-free survival (p<0.001 and p=0.020, respectively). Implementation of cytological criteria for HGA in pancreatic mucinous cysts has relatively low sensitivity but modest specificity for classifying a high-risk cyst. Though high-risk mutations were more commonly found in cysts with HGA, their frequency is overall low. Thus, evaluating the degree of cytologic atypia, which is predictive of patient survival, provides significant value and informs patient outcomes.

Screening of immune-related lncRNAs in lung adenocarcinoma and establishing a survival prognostic risk prediction model.

This study aimed to improve lung adenocarcinoma (LUAD) prognosis prediction based on a signature of immune-related long non-coding RNAs (lncRNAs). LUAD samples from the TCGA database were divided into the immunity_H group and the immunity_L group. Differentially expressed RNAs (DERs) between the two groups were identified. Optimized immune-related lncRNAs combination was obtained using LASSO Cox regression. A prognostic risk prediction (RS) model was built and further validated in the training and validation datasets. A network among lncRNAs in the RS model, their co-expressed DERs, and the related KEGG pathways were established. Critical lncRNAs were validated in LUAD tissue samples. In total, 255 DERs were obtained, and 11 immune-related lncRNAs were significantly related to prognosis. Six lncRNAs were demonstrated as an optimal combination for building the RS model, including LINC00944, LINC00930, LINC00607, LINC00582, LINC00543, and LINC00319. The KM curve and ROC curve revealed the RS model to be a reliable indicator for LUAD prognosis. LINC00944 and LINC00582 showed a co-expression relationship with the MS4A1. LINC00944, LINC00582, and MS4A1 were successfully validated in LUAD samples. We have established a promising LUAD patient survival prediction model based on six immune-related lncRNAs. For LUAD patients, this prognostic model could guide personalized treatment.

Cardiac Disease Diagnosis Using K-Nearest Neighbor Algorithm: A Study on Heart Failure Clinical Records Dataset

This article introduces an approach to diagnose heart diseases utilizing the K-Nearest Neighbor algorithm and diverse correlation filters for selecting the most pertinent attributes. Results high- light that meticulous filter selection enhances survival predictions in patients with heart diseases. Employing K = 5 and correlation filter CF = 0.1, key attributes for classification were identified as anemia, high blood pressure, serum creatinine, and sex. Omitting the 'time' attribute led to information loss but was crucial to prevent biases and generalize predictions across various clinical scenarios. Utilizing these classification parameters, we designed an Android mobile application called “Heart Info System”, functioning as an artificial intelligence service. It employs the K-Nearest Neighbor algorithm with optimal parameters to evaluate the probability of survival in the progression of heart disease. The main activity of the application retrieves data from a Firebase database. While the study results show promise, the accuracy of the application may be influenced by inaccurate or incomplete input data. Nevertheless, this application has the potential to improve the early detection of heart diseases, paving the way for life-saving interventions.

Dynamic change in the peritoneal cancer index based on CT after chemotherapy in the overall survival prediction of gastric cancer patients with peritoneal metastasis

PurposeThe purpose of this research was to investigate the efficacy of the CT-based peritoneal cancer index (PCI) to predict the overall survival of patients with peritoneal metastasis in gastric cancer (GCPM) after two cycles of chemotherapy.MethodsThis retrospective study registered 112 individuals with peritoneal metastasis in gastric cancer in our hospital. Abdominal and pelvic enhanced CT before and after chemotherapy was independently analyzed by two radiologists. The PCI of peritoneal metastasis in gastric cancer was evaluated according to the Sugarbaker classification, considering the size and distribution of the lesions using CT. Then we evaluated the prognostic performance of PCI based on CT, clinical characteristics, and imaging findings for survival analysis using multivariate Cox proportional hazard regression.ResultsThe PCI change ratio based on CT after treatment (ΔPCI), therapy lines, and change in grade of ascites were independent factors that were associated with overall survival (OS). The area under the curve (AUC) value of ΔPCI for predicting OS with 0.773 was higher than that of RECIST 1.1 with 0.661 (P < 0.05). Patients with ΔPCI less than −15% had significantly longer OS.ConclusionCT analysis after chemotherapy could predict OS in patients with GCPM. The CT-PCI change ratio could contribute to the determination of an appropriate strategy for gastric cancer patients with peritoneal metastasis.

Identification of PANoptosis-related subtypes, construction of a prognosis signature, and tumor microenvironment landscape of hepatocellular carcinoma using bioinformatic analysis and experimental verification.

Hepatocellular carcinoma (HCC) is one of the most lethal malignancies worldwide. PANoptosis is a recently unveiled programmed cell death pathway, Nonetheless, the precise implications of PANoptosis within the context of HCC remain incompletely elucidated. We conducted a comprehensive bioinformatics analysis to evaluate both the expression and mutation patterns of PANoptosis-related genes (PRGs). We categorized HCC into two clusters and identified differentially expressed PANoptosis-related genes (DEPRGs). Next, a PANoptosis risk model was constructed using LASSO and multivariate Cox regression analyses. The relationship between PRGs, risk genes, the risk model, and the immune microenvironment was studies. In addition, drug sensitivity between high- and low-risk groups was examined. The expression profiles of these four risk genes were elucidate by qRT-PCR or immunohistochemical (IHC). Furthermore, the effect of CTSC knock down on HCC cell behavior was verified using in vitro experiments. We constructed a prognostic signature of four DEPRGs (CTSC, CDCA8, G6PD, and CXCL9). Receiver operating characteristic curve analyses underscored the superior prognostic capacity of this signature in assessing the outcomes of HCC patients. Subsequently, patients were stratified based on their risk scores, which revealed that the low-risk group had better prognosis than those in the high-risk group. High-risk group displayed a lower Stromal Score, Immune Score, ESTIMATE score, and higher cancer stem cell content, tumor mutation burden (TMB) values. Furthermore, a correlation was noted between the risk model and the sensitivity to 56 chemotherapeutic agents, as well as immunotherapy efficacy, in patient with. These findings provide valuable guidance for personalized clinical treatment strategies. The qRT-PCR analysis revealed that upregulated expression of CTSC, CDCA8, and G6PD, whereas downregulated expression of CXCL9 in HCC compared with adjacent tumor tissue and normal liver cell lines. The knockdown of CTSC significantly reduced both HCC cell proliferation and migration. Our study underscores the promise of PANoptosis-based molecular clustering and prognostic signatures in predicting patient survival and discerning the intricacies of the tumor microenvironment within the context of HCC. These insights hold the potential to advance our comprehension of the therapeutic contribution of PANoptosis plays in HCC and pave the way for generating more efficacious treatment strategies.

Stratification and prognostic evaluation of breast cancer subtypes defined by obesity-associated genes

ObjectiveBreast cancer was the most common type of cancer among women worldwide, significantly impacting their quality of life and survival rates. And obesity has been widely accepted as an important risk factor for breast cancer. However, the specific mechanisms by which obesity affects breast cancer were still unclear. Therefore, studying the impact mechanisms of obesity as a risk factor for breast cancer was of utmost importance.MethodsThis study was based on TCGA breast cancer RNA transcriptomic data and the GeneCard obesity gene set. Through single and multiple factor Cox analysis and LASSO coefficient screening, seven hub genes were identified. The independent mechanisms of these seven hub genes were evaluated from various aspects, including survival data, genetic mutation data, single-cell sequencing data, and immune cell data. Additionally, the risk prognosis model and the neural network diagnostic model were established to further investigate these seven hub genes. In order to achieve precision treatment for breast cancer (BRCA), based on the RNA transcriptomic data of the seven genes, 1226 BRCA patients were divided into two subtypes: BRCA subtype 1 and BRCA subtype 2. By studying and comparing the immune microenvironment, investigating the mechanisms of differential gene expression, and exploring the mechanisms of subnetworks, we aim to explore the clinical differences in the presentation of BRCA subtypes and achieve precision treatment for BRCA. Finally, qRT-PCR experiments were conducted to validate the conclusions of the bioinformatics analysis.ResultsThe 7 hub genes showed good diagnostic independence and can serve as excellent biomarkers for molecular diagnosis. However, they do not perform well as independent prognostic molecular markers for BRCA patients. When predicting the survival of BRCA patients, their AUC values at 1 year, 3 years, and 5 years are mostly below 0.5. Nevertheless, through the establishment of the risk prognosis model considering the combined effect of the seven hub genes, it was found that the survival prediction of BRCA patients can be significantly improved. The risk prognosis model, compared to the independent use of the seven hub genes as prognostic markers, achieved higher timeROC AUC values at 1 year, 3 years, and 5 years, with values of 0.651, 0.669, and 0.641 respectively. Additionally, the neural network diagnostic model constructed from the 7 genes performs well in diagnosing BRCA, with an AUC value of 0.94, accurately identifying BRCA patients. The two subtypes identified by the seven hub genes exhibited significant differences in survival period, with subtype 1 having a poor prognosis. The differential mechanisms between the two subtypes mainly originate from regulatory differences in the immune microenvironment. Finally, the results of this study’s bioinformatics analysis were validated through qRT-PCR experiments.Conclusion7 hub genes serve as excellent independent biomarkers for molecular diagnosis, and the neural network diagnostic model can accurately distinguish BRCA patients. In addition, based on the expression levels of these seven genes in BRCA patients, two subtypes can be reliably identified: BRCA subtype 1 and BRCA subtype 2, and these two subtypes showed significant differences in BRCA patient survival prognosis, proportion of immune cells, and expression levels of immune cells. Among them, patients with subtype 1 of BRCA had a poor prognosis.

Utility of Initial Tumor Reduction as a Prognostic Factor in Esophageal Squamous Cell Cancer Patients Undergoing Neoadjuvant Chemotherapy Followed by Surgery : A Retrospective Cohort Study.

While a neoadjuvant chemotherapy regimen using docetaxel, cisplatin, and 5-fluorouracil (NAC-DCF) is considered the standard treatment for locally advanced esophageal cancer (EC) in Japan, a reliable marker for early prediction of treatment efficacy remains unclear. We investigated the utility of the tumor response after a first course of NAC-DCF as a post-surgery survival predictor in patients with EC. We enrolled 150 consecutive patients who underwent NAC-DCF followed by surgery for EC between September 2009 and January 2019. The initial tumor reduction (ITR), defined as the percentage decrease in the shorter diameter of the tumor after the first course of NAC-DCF, was evaluated using computed tomography. We analyzed the relationship between ITR, clinicopathological parameters, and survival. The median ITR was 21.07% (range -11.45 to 50.13%). The optimal cut-off value for ITR for predicting prognosis was 10% (hazard ratio [HR] 3.30, 95% confidence interval [CI] 1.98-5.51), based on univariate logistic regression analyses for recurrence-free survival (RFS). Compared with patients with ITR <10%, patients with ITR ≥10% showed a significantly higher proportion of ypM0 (80.0% vs. 92.5%) and responders in terms of overall clinical response (50.0% vs. 80.8%). Multivariate analysis for RFS revealed that ypN2-3 (HR 2.78, 95% CI 1.67-4.62), non-response in terms of overall clinical response (HR 1.87, 95% CI 1.10-3.18), and ITR <10% (HR 2.48, 95% CI 1.42-4.32) were independent prognostic factors. Tumor response after the first course of NAC-DCF may be a good predictor of survival in patients with EC who underwent NAC-DCF plus surgery.

Do Presenting Symptoms Predict Treatment Decisions and Survival in Glioblastoma? -Real World Data from 1458 patients in the Swedish Brain Tumour Registry

Abstract Background Glioblastoma is the most common malignant brain tumour in adults. Non-invasive clinical parameters could play a crucial role in treatment planning and serve as predictors of patient survival. Our register-based real-life study aimed to investigate the prognostic value of presenting symptoms. Methods Data on presenting symptoms and survival, as well as known prognostic factors, were retrieved for all glioblastoma patients in Sweden registered in the Swedish Brain Tumour Registry between 2018 and 2021. The prognostic impact of different presenting symptoms was calculated using the Cox proportional hazard model. Results Data from 1458 adults with pathologically verified IDH wild-type glioblastoma were analysed. Median survival time was 345 days. The two-year survival rate was 21.5%. Registered presenting symptoms were focal neurological deficits, cognitive dysfunction, headache, epilepsy, signs of raised intracranial pressure and cranial nerve symptoms, with some patients having multiple symptoms. Patients with initial cognitive dysfunction had significantly shorter survival than patients without; 265 days (245-285) vs. 409 days (365-453) (p&amp;lt;0.001). The reduced survival remained after Cox regression adjusting for known prognostic factors. Patients presenting with seizures and patients with headaches had significantly longer overall survival compared to patients without these symptoms, but the difference was not retained in multivariate analysis. Patients with cognitive deficits were less likely to have radical surgery and to receive extensive anti-neoplastic nonsurgical treatment. Conclusion This extensive real-life study reveals that initial cognitive impairment acts as an independent negative predictive factor for treatment decisions and adversely affects survival outcomes in glioblastoma patients.

A 4-gene prognostic index for enhancing acute myeloid leukaemia survival prediction.

Despite advancements in utilizing genetic markers to enhance acute myeloid leukaemia (AML) outcome prediction, significant disease heterogeneity persists, hindering clinical management. To refine survival predictions, we assessed the transcriptome of non-acute promyelocytic leukaemia chemotherapy-treated AML patients from five cohorts (n = 975). This led to the identification of a 4-gene prognostic index (4-PI) comprising CYP2E1, DHCR7, IL2RA and SQLE. The 4-PI effectively stratified patients into risk categories, with the high 4-PI group exhibiting TP53 mutations and cholesterol biosynthesis signatures. Single-cell RNA sequencing revealed enrichment for leukaemia stem cell signatures in high 4-PI cells. Validation across three cohorts (n = 671), including one with childhood AML, demonstrated the reproducibility and clinical utility of the 4-PI, even using cost-effective techniques like real-time quantitative polymerase chain reaction. Comparative analysis with 56 established prognostic indexes revealed the superior performance of the 4-PI, highlighting its potential to enhance AML risk stratification. Finally, the 4-PI demonstrated to be potential marker to reclassified patients from the intermediate ELN2017 category to the adverse category. In conclusion, the 4-PI emerges as a robust and straightforward prognostic tool to improve survival prediction in AML patients.

Survival prediction in patients with gynecological cancer irradiated for brain metastases.

This large population-based, retrospective, single-center study aimed to identify prognostic factors in patients with brain metastases (BM) from gynecological cancers. One hundred and forty four patients with BM from gynecological cancer treated with radiotherapy (RT) were identified. Primary cancer diagnosis, age, performance status, number of BM, presence of extracranial disease, and type of BM treatment were assessed. Overall survival (OS) was calculated using the Kaplan-Meier method and the Cox proportional hazards regression model was used for multivariable analysis. A prognostic index (PI) was developed based on scores from independent predictors of OS. Median OS for the entire study population was 6.2 months. Forty per cent of patients died within 3 months after start of RT. Primary cancer with the origin in cervix or vulva (p = 0.001), Eastern Cooperative Oncology Group (ECOG) 3-4 (p < 0.001), and the presence of extracranial disease (p = 0.001) were associated with significantly shorter OS. The developed PI based on these factors, categorized patients into three risk groups with a median OS of 13.5, 4.0, and 2.4 months for the good, intermediate, and poor prognosis group, respectively. Patients with BM from gynecological cancers carry a poor prognosis. We identified prognostic factors and developed a scoring tool to select patients with better or worse prognosis. Patients in the high-risk group have a particular poor prognosis, and omission of RT could be considered.