Low-risk Group Research Articles

Predictive MINT Pathologic Risk Score for Adjuvant Chemotherapy in Resected Cholangiocarcinoma: A Propensity Score-Matched Multicenter Study in Thailand.

This study aims to clarify the benefit of adjuvant chemotherapy (AC) in resectable cholangiocarcinoma (CCA) and develop a predictive risk score for treatment selection. Patients with resected CCA undergoing curative surgery, with or without AC, were identified from three centers in Thailand. Patients with R2 resection and 30 days postoperative death were excluded. Using the largest center as the discovery cohort, we generated propensity score matching (PSM). A predictive model for overall survival (OS) was identified, and a predictive risk score was developed from the PSM discovery cohort, classifying patients into high- and low-risk groups. The proposed risk score was validated in the other two centers. In the discovery cohort, 493 patients were identified. After PSM, 328 patients were categorized into surgery (n = 164) and surgery + AC (n = 164) groups. The baseline characteristics in the PSM discovery cohort were well-balanced. In the validation cohort (n = 83), patients with positive lymph node 1 received AC more frequently than those under observation (47% v 18%; P = .02). A MINT pathologic risk score was developed from multivariate analysis for OS. The score includes margin, perineural invasion, pathologic nodal status, and pathologic tumor size. In the PSM discovery cohort, for the low-risk score group, the surgery group had significantly longer OS compared with the surgery + AC group (49.4 v 31.5 months; hazard ratio [HR], 1.78 [95% CI, 1.11 to 2.86]; P = .016). Conversely, for the high-risk score group, the surgery + AC group had better OS than the surgery group (18.8 v 8 months; HR, 0.60 [95% CI, 0.46 to 0.79]; P < .001). The results were comparable in the validation cohort. Patients with resected CCA with a high-risk MINT pathologic risk score were likely to benefit from AC, whereas those with a low-risk score were not. Further validation in a larger prospective cohort is warranted.

The relationship between sarcopenia and metabolic parameters of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) and the prognostic value of sarcopenia in early-stage non-small cell lung cancer.

Patients with lung cancer face a heightened risk of developing sarcopenia. Despite this known risk, the impact of sarcopenia on the long-term prognosis of lung cancer patients, specifically concerning progression-free survival (PFS) and overall survival (OS), remains unclear. The primary objective of our study was to examine the correlation between metabolic parameters derived from 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) and sarcopenia, as well as the prognostic value of sarcopenia in patients with surgically resected early-stage non-small cell lung cancer (NSCLC). In this retrospective cross-sectional study, we analyzed 187 NSCLC patients who underwent 18F-FDG PET/CT at the First Affiliated Hospital of Soochow University between March 2019 and October 2023. Patients were divided into two groups based on the presence (n=46) or absence (n=141) of sarcopenia. The correlation between sarcopenia, metabolic parameters, and patient characteristics was evaluated using chi-square and Mann-Whitney U tests. Survival analyses, including PFS and OS, were conducted using Kaplan-Meier analysis and Cox proportional hazards regression. Based on sarcopenia, metabolic parameters and patient characteristics, patients were classified into high-risk (n=28), intermediate-risk (n=121), and low-risk (n=38) groups. Our analysis identified gender, body mass index (BMI), psoas Hounsfield unit (HU), and maximum standardized uptake value of the psoas major muscle (SUVmax-Muscle) as independent predictors of sarcopenia (P<0.05 for all). A nomogram model, utilizing these parameters, was constructed to predict sarcopenia. Survival analysis further demonstrated that total lesion glycolysis [hazard ratio (HR) =2.499; 95% confidence interval (CI): 2.014-3.267; P=0.016], sarcopenia (HR =3.323; 95% CI: 1.748-6.316; P<0.001), and programmed death ligand-1 (PD-L1) expression (HR =0.093; 95% CI: 0.012-0.698; P=0.021) emerged as independent predictors of OS in early-stage NSCLC. Notably, patients categorized as high-risk, characterized by elevated total lesion glycolysis, presence of sarcopenia, and PD-L1 positivity, exhibited a significantly poorer prognosis compared to the intermediate-risk (P<0.05) and low-risk groups (P<0.05). Our findings indicated an inverse relationship between SUVmax-Muscle or psoas HU with the incidence of sarcopenia in NSCLC patients. Additionally, total lesion glycolysis, sarcopenia, and PD-L1 expression were identified as independent prognostic factors for OS in early-stage NSCLC. The risk stratification model, incorporating total lesion glycolysis, sarcopenia, and PD-L1 expression, assumed a pivotal role in guiding personalized therapy decisions and post-treatment monitoring.

Clinical outcomes analysis of image-guided brachytherapy as definitive treatment for inoperable endometrial cancer

ObjectivesThis study evaluates the efficacy and toxicity of image-guided brachytherapy combined with or without external beam radiotherapy (IGBT ± EBRT) as definitive treatment for patients with inoperable endometrial cancer (IOEC), in addition to establishing a risk classification to predict prognosis.MethodsFifty-one IOEC patients who underwent IGBT ± EBRT at Peking Union Medical College Hospital from January 2012 to December 2021 were retrospectively analyzed, of which 42 patients (82.4%) were treated with IGBT + EBRT and 9 patients (17.6%) with IGBT alone. Establishing risk classification based on FIGO 2009 staging and biopsy pathology, stage III/IV, non-endometrioid, or Grade 3 endometrioid cancer were included in the high-risk group (n = 25), and stage I/II with Grade 1–2 endometrioid cancer was included in the low-risk group (n = 26).ResultsThe median follow-up time was 58.0 months (IQR, 37.0–69.0). Clinical complete remission (CR) was achieved in 92.2% of patients after radiotherapy (n = 47). The cumulative incidences of locoregional and distant failure were 19.6% (n = 10) and 7.8% (n = 4), respectively. A total of 20 patients died (39.2%), including 10 cancer-related deaths (19.6%) and 10 comorbidity-related deaths (19.6%). The 5-year locoregional control (LRC), time to progression (TTP), overall survival (OS), and cancer-specific survival (CSS) were 76.9%, 71.2%, 59.4%, and 77.0%, respectively. No Grade 3 or above acute or late toxicities were reported. In univariate analysis, LRC, TTP, and CSS were significantly higher in the low-risk group than in the high-risk group (P < 0.05). After adjusting for age, number of comorbidities, radiotherapy modality, and chemotherapy, the low-risk group was still significantly better than the high-risk group in terms of LRC (HR = 6.10, 95% CI: 1.18–31.45, P = 0.031), TTP (HR = 8.07, 95% CI: 1.64–39.68, P = 0.010) and CSS (HR = 6.29, 95% CI: 1.19–33.10, P = 0.030).ConclusionsIGBT ± EBRT is safe and effective as definitive treatment for IOEC patients, achieving satisfactory locoregional control, favorable survival outcomes, and low toxicity. Risk classification based on FIGO 2009 staging and biopsy pathology is an independent prognostic factor for LRC, TTP, and CSS.

Two simple modifications to the World Falls Guidelines algorithm improves its ability to stratify older people into low, intermediate and high fall risk groups.

We conducted a secondary analysis of a cohort study to examine the World Falls Guidelines algorithm's ability to stratify older people into sizable fall risk groups or whether minor modifications were necessary to achieve this. Six hundred and ninety-three community-living people aged 70-90years (52.4% women) were stratified into low, intermediate and high fall risk groups using the original algorithm and a modified algorithm applying broader Timed Up and Go test screening with a >10-s cut point (originally >15s). Prospective fall rates and physical and neuropsychological performance among the three groups were compared. The original algorithm was not able to identify three sizable groups, i.e. only five participants (0.7%) were classified as intermediate risk. The modified algorithm classified 349 participants (50.3%) as low risk, 127 participants (18.3%) as intermediate risk and 217 participants (31.3%) as high risk. The sizable intermediate-risk group had physical and neuropsychological characteristics similar to the high-risk group, but a fall rate similar to the low-risk group. The high-risk group had a significantly higher rate of falls than both the low- [incidence rate ratio (IRR) = 2.52, 95% confidence interval (CI) = 1.99-3.20] and intermediate-risk groups (IRR = 2.19, 95% CI = 1.58-3.03). A modified algorithm stratified older people into three sizable fall risk groups including an intermediate group who may be at risk of transitioning to high fall rates in the medium to long term. These simple modifications may assist in better triaging older people to appropriate and tailored fall prevention interventions.

An immune-related gene pair signature predicts the prognosis and immunotherapeutic response in glioblastoma

BackgroundGlioblastoma (GBM) has the feature of aggressive growth and high rates of recurrence. Immunotherapy was not included in standard therapy for GBM due to lacking the predictive biomarkers. In the present study, we performed an immune-related gene pair (IRGP) signature to predict the prognosis and immunotherapy response of GBM. MethodsA total of 160 GBM patients from TCGA were included. ssGSEA was conducted to evaluate the immune infiltration level. Univariate Cox, LASSO regression analysis, ROC analysis, and Kaplan-Meier survival analysis were applied to construct and evaluate the risk model. Moreover, the association between immune infiltration and the risk score was assessed. Finally, the expression of immune checkpoints between different risk groups was explored. ResultsAccording to the normal/tumor, high-/low-immunity group, we identified 125 differentially expressed immune-related genes. Subsequently, a prognostic model including 22 IRGPs was established. The area under the ROC curve to predict 1, 3, and 5-year was 0.811, 0.958, and 0.99 respectively. According to the optimal cut-off value of the 3-year ROC curve, patients were classified into high- and low-risk groups. The Kaplan-Meier analysis result indicated that patients in the low-risk group have longer survival time. The risk score was an independent prognostic predictor (P < 0.001). Moreover, PDCD1 was positively correlated with the risk score (P < 0.01). We also found that patients with high PDCD1 expression had worse survival. ConclusionsThe IRGP signature was built to predict the prognosis of GBM patients. This signature can serve as a tool to predict the response to immunotherapy in GBM.

Association of various RAS codon mutations and prognostic outcomes of patients with colorectal liver metastases after hepatectomy.

The prognostic and predictive value of RAS mutations in patients with colorectal liver metastases (CRLM) who have undergone hepatectomy holds substantial importance. The present study aimed to investigate the impact of different RAS codon mutations on long-term survival in CRLM patients. A retrospective analysis was conducted on clinicopathological data from 399 CRLM patients with RAS mutations who underwent hepatectomy between January 2000 and December 2020. The RAS mutation gene status was assessed in KRAS codons (G12, G13, Q61, and A146) and NRAS codons (G12, G13, and Q61). Survival curves were generated using the Kaplan-Meier plotter and compared using the log-rank test. Univariate and multivariate analyses were performed to analyze the clinicopathological data. In the entire cohort, patients with KRAS G12 mutations exhibited the most favorable prognosis (p = 0.018). Comparatively, patients harboring KRAS Q61 mutations experienced poorer overall survival (OS) with a median of 15 months versus 33 months (p = 0.011) when compared to those with KRAS G12 mutations. Moreover, patients with NRAS Q61 mutations also showed decreased OS with a median of 26 months versus 33 months (p = 0.020) in comparison to KRAS G12 mutation patients. The results of multivariate analysis showed that both KRAS Q61 mutation (HR 2.130; 95% CI 1.088-4.168; p = 0.027) and NRAS Q61 mutation (HR 2.877; 95% CI 1.398-5.922; p = 0.004) were independent influencing factors of OS. Based on all identified risk factors, patients with RAS mutation were divided into high-risk and low-risk groups. Notably, in the high-risk group, the incorporation of postoperative chemotherapy was associated with longer OS, while it did not improve the survival of patients in the low-risk group. KRAS Q61 and NRAS Q61 mutations are promising predictors for OS in CRLM patients after hepatectomy. Postoperative chemotherapy may significantly benefit CRLM patients with RAS mutations, particularly those identified as high-risk.

Predictive utility of a combination of the modified caprini risk assessment score and D-dimer for the evaluation and management of lower extremity venous thrombosis after lung cancer surgery

ObjectiveThe objective of this study was to examine the utility of a combination of the modified Caprini score and D-dimer levels for the evaluation and management of lower extremity venous thrombosis following lung cancer surgery. The purpose was to offer insights for developing clinical intervention programs.MethodsThe study sample consisted of 224 patients who underwent surgery for lung cancer at the First Central Hospital of Baoding City. General patient data and D-dimer levels on the first day post-surgery were collected. The modified Caprini risk assessment score was calculated. All patients underwent ultrasonography of the lower limb veins before and after surgery to identify venous thrombosis in the lower limb veins. Differences in lower extremity venous thrombosis and D-dimer levels among patients in various modified Caprini score groups were compared and analyzed.ResultsBased on the modified Caprini risk assessment score, all patients were categorized into three groups: the low-risk, medium-risk, and high-risk groups. The groups did not differ significantly in terms of age, but the differences in the rates of lower extremity venous thrombosis in the low, intermediate, and high-risk Caprini risk groups (16.5%, 19.2%, and 37.1%, respectively) were statistically significant. Out of the total 224 patients, 47 (21%) were diagnosed with venous thromboembolisms post-surgery, and all of them had thrombosis of the intermuscular veins of the lower extremity. The difference in the modified Caprini risk assessment score between patients with and without lower extremity venous thrombosis was statistically significant (P = 0.035), as were the postoperative D-dimer levels (1.28 ± 1.64 vs. 2.69 ± 2.77, respectively; P < 0.05) between these two groups of patients. The modified Caprini risk assessment score showed an association with lower extremity venous thrombosis (r = 0.15, P = 0.56) with an area under the receiver operating characteristic curve (AUC) of 0.59.ConclusionIn this study, we found that combining the modified Caprini risk assessment score with D-dimer measurements enhanced the accuracy of assessing the severity of deep vein thrombosis (DVT). This combination can be beneficial in evaluating thrombosis risk post-lung cancer surgery and holds significant clinical utility.

Predicting Survival Among Colorectal Cancer Patients: Development and Validation of Polygenic Survival Score.

Colorectal cancer is the second leading cause of cancer-related death in the United States. A multi-omics approach has contributed in identifying various cancer-specific mutations, epigenetic alterations, and cells response to chemotherapy. This study aimed to determine the factors associated with colorectal cancer survival and develop and validate a polygenic survival scoring system (PSS) using a multi-omics approach. Data were obtained from the Cancer Genome Atlas (TCGA). Colon Adenocarcinoma (TCGA-COAD) data were used to develop a survival prediction model and PSS, whereas rectal adenocarcinoma (TCGA-READ) data were used to validate the PSS. Cox proportional hazards regression analysis was conducted to examine the association between the demographic characteristics, clinical variables, and mRNA gene expression. Overall accuracy of PSS was also evaluated. The median overall survival for TCGA-COAD patients was 7 years and for TCGA-READ patients was 5 years. The multivariate Cox proportional hazards model identified age, cancer stage, and expression of nine genes as predictors of colon cancer survival. Based on the median PSS of 0.38, 48% of TCGA-COAD patients had high mortality risk. Patients in the low risk group had significantly higher 5-year survival rates than those in the high group (p <0.0001). The PSS demonstrated a high overall accuracy in predicting colorectal cancer survival. This study integrated clinical and transcriptome data to identify survival predictors in patients with colorectal cancer. PSS is an accurate and valid measure for estimating colorectal cancer survival. Thus, it can serve as an important tool for future colorectal cancer research.

Association between polygenic risk scores combined with clinical characteristics and antidepressant efficacy

BackgroundWhile millions of people suffer from major depressive disorder (MDD), research has shown that individual differences in antidepressant efficacy exist, potentially attributable to various factors. Polygenic risk scores (PRSs) carry clinical potential, but associations with treatment response are seldom reported. Here, we examined whether PRSs for MDD and schizophrenia (SCZ) are associated with antidepressant effectiveness and the influence of other factors. MethodsA total of 999 patients were included, and the PRSs for the MDD and SCZ were calculated. The main outcome was a change in the 17-item Hamilton Depression Rating Scale (HAMD17) scores from before to after 2-week treatment. The Mann-Whitney test, Spearman correlation analysis, multiple stepwise linear regression analysis, and interaction analysis were used for statistical analysis. ResultsIn the 912 subjects passing quality control, a difference in the HAM-D17 score reduction rate between the MDD phenotype PRS (MDD-PRS) high-risk and the low-risk groups was discovered (P = 0.009), and a correlation was found between the MDD-PRS and the HAM-D17 score reduction rate (r = −0.075, P = 0.024). Moreover, antidepressant efficacy was related to MDD-PRS (β = −4.086, P = 0.039), the Snaith-Hamilton Pleasure Scale-total score (β = −0.009, P = 0.005), and non-first episode (β = −0.039, P < 0.001). However, the result of the interaction analysis was nonsignificant. LimitationsThe main limitation was that only 1309 targeted genes were selected based on pathways known to be involved in MDD and/or antidepressant effects. ConclusionThese findings suggest a difference in antidepressant efficacy between patients in different MDD-PRS groups. Moreover, the MDD-PRS combined with clinical characteristics partially explained inter-individual differences in antidepressant efficacy.

Prostate Cancer Risk Stratification in NRG Oncology Phase III Randomized Trials Using Multimodal Deep Learning With Digital Histopathology.

Current clinical risk stratification methods for localized prostate cancer are suboptimal, leading to over- and undertreatment. Recently, machine learning approaches using digital histopathology have shown superior prognostic ability in phase III trials. This study aims to develop a clinically usable risk grouping system using multimodal artificial intelligence (MMAI) models that outperform current National Comprehensive Cancer Network (NCCN) risk groups. The cohort comprised 9,787 patients with localized prostate cancer from eight NRG Oncology randomized phase III trials, treated with radiation therapy, androgen deprivation therapy, and/or chemotherapy. Locked MMAI models, which used digital histopathology images and clinical data, were applied to each patient. Expert consensus on cut points defined low-, intermediate-, and high-risk groups on the basis of 10-year distant metastasis rates of 3% and 10%, respectively. The MMAI's reclassification and prognostic performance were compared with the three-tier NCCN risk groups. The median follow-up for censored patients was 7.9 years. According to NCCN risk categories, 30.4% of patients were low-risk, 25.5% intermediate-risk, and 44.1% high-risk. The MMAI risk classification identified 43.5% of patients as low-risk, 34.6% as intermediate-risk, and 21.8% as high-risk. MMAI reclassified 1,039 (42.0%) patients initially categorized by NCCN. Despite the MMAI low-risk group being larger than the NCCN low-risk group, the 10-year metastasis risks were comparable: 1.7% (95% CI, 0.2 to 3.2) for NCCN and 3.2% (95% CI, 1.7 to 4.7) for MMAI. The overall 10-year metastasis risk for NCCN high-risk patients was 16.6%, with MMAI further stratifying this group into low-, intermediate-, and high-risk, showing metastasis rates of 3.4%, 8.2%, and 26.3%, respectively. The MMAI risk grouping system expands the population of men identified as having low metastatic risk and accurately pinpoints a high-risk subset with elevated metastasis rates. This approach aims to prevent both overtreatment and undertreatment in localized prostate cancer, facilitating shared decision making.

The m6A and immune regulatory gene signature predicts the prognosis and correlates with immune infiltration of head and neck squamous cell carcinoma

Recent investigations have underscored the epigenetic modulation of the immune response; however, the interplay between RNA N6-methyladenosine (m6A) modification and immunomodulation in head and neck squamous cell carcinoma (HNSC) remains relatively unexplored. To bridge this knowledge gap, we undertook an extensive examination of the potential contributions of m6A modification and immunomodulation in HNSC. We amalgamated and deduplicated 27 m6A -related genes (m6AGs) and 1342 immune regulation-related genes (IMRGs), resulting in a comprehensive dataset encompassing 1358 genes. This dataset was scrutinized for m6A modification and immunomodulatory patterns within HNSC specimens. Employing Cox regression analysis and the Least Absolute Shrinkage and Selection Operator (LASSO) technique, we developed a prognostic risk model for m6A regulator-mediated methylation modification and immunomodulation-related differentially expressed genes (m6A&IMRDEGs). Our differential expression analysis delineated 29 m6A&IMRDEGs, and Weighted Gene Co-expression Network Analysis (WGCNA) elucidated two module genes (IL11 and MMP13) subjected to correlation analysis. The prognostic prediction models revealed that the clinical predictive efficacy peaked for 1-year forecasts, followed sequentially by 3-year and 5-year predictions. The risk scores derived from the model adeptly categorized HNSC patients into high- and low-risk cohorts, with the high-risk group exhibiting a more unfavorable prognosis. Protein-Protein Interaction (PPI) analysis identified 7 hub genes implicated in m6A and immune regulation, namely BPIFB1, BPIFB2, GP2, MUC5B, MUC7, PIP, and SCGB3A1. Furthermore, we noted marked disparities in the expression profiles of 18 immune cell types between the high- and low-risk groups. Our results substantiate that the clustering subpopulations and risk models associated with m6A and immune regulatory genes portend a poor prognosis in HNSC. The risk score emerges as a potent prognostic biomarker and predictive metric for HNSC patients. A thorough assessment of m6A and immune regulatory genes in HNSC will augment our comprehension of the tumor immune microenvironment and facilitate the advancement of HNSC therapeutics.

Nomogram based on burn characteristics and the national early warning score to predict survival in severely burned patients

BackgroundExtensive burns are associated with a high mortality rate. Early prediction and action can reduce mortality. The National Early Warning Score (NEWS) is considered the best early warning score for predicting mortality. However, there has been no assessment conducted on the clinical prognostic significance of NEWS in individuals suffering from severe burns. The objective of this research was to establish a nomogram based on burn characteristics and the NEWS to predict survival in severely burned patients. MethodsA retrospective analysis was performed on 335 patients diagnosed with extensive burns from 2005 to 2021 in the Department of Burn Surgery of XX Hospital. Univariate and multivariate analyses were used to determine independent prognostic factors. A nomogram was developed using these prognostic factors and its internal validity was assessed through bootstrap resampling. ResultsThe results of multivariate analysis showed that the independent factors affecting the prognosis of severe burn patients were age, full-thickness burn, creatinine, absorptive injury, tracheotomy, and the NEWS, all of which were identified to create the nomogram. The Akaike Information Criterion and Bayesian Information Criterion values of the nomogram demonstrated superior goodness-of-fit in predicting severe burns compared to NEWS, with lower scores (195.21 vs. 201.24; 221.91 vs. 224.12, respectively). The bootstrap-adjusted concordance index (C-index) of the nomogram yielded a higher value of 0.923 (95% CI 0.892–0.953), compared to NEWS which had a C-index of 0.699 (95% CI 0.628–0.770). The calibration curves demonstrated excellent agreement between predicted probabilities and observed outcomes in the nomogram analysis. Furthermore, decision curve analysis indicated promising clinical utility for the proposed nomogram model. By applying an appropriate cutoff value derived from receiver operating characteristics curve analysis, it was observed that the high-risk group identified by the nomogram exhibited a significantly higher mortality rate than the low-risk group. ConclusionThis study introduces an innovative nomogram that predicts the survival rate of individuals with severe burn injuries by combining clinical attributes and laboratory examinations, demonstrating superior efficacy compared to conventional NEWS systems.

Development and validation of a clinic-radiomics model based on intratumoral habitat imaging for progression-free survival prediction of patients with clear cell renal cell carcinoma: A multicenter study

PurposeTo develop and validate a clinicoradiomics model based on intratumoral habitat imaging for preoperatively predicting of progression-free survival (PFS) of clear cell renal cell carcinoma (ccRCC) and analyzing progression-associated genes expression. MethodsThis retrospective study included 691 ccRCC patients from multicenter databases. Entire tumor segmentation was performed with handcrafted process to generate habitat subregions based on a pixel-wise gray-level co-occurrence matrix analysis. Cox regression models for PFS prediction were constructed using conventional volumetric radiomics features (Radiomics), habitat subregions-derived radiomics (Rad-Habitat), and an integration of habitat radiomics and clinical characteristics (Hybrid Cox). Training (n = 393) and internal validation (n = 118) was performed in a Nanjing cohort, external validation was performed in a Wuhan and Zhejiang cohort (n = 227) and in a TCGA-KIRC (n =71) with imaging-genomic correlation. Statistical analysis included the area-under-ROC curve analysis, C-index, decision curve analysis (DCA) and Kaplan-Meier survival analysis. ResultsHybrid Cox model resulted in a C-index of 0.83 (95% CI, 0.73–0.93) in internal validation and 0.79 (95% CI, 0.74–0.84) in external validation for PFS prediction, higher than Radiomics and Rad-Habitat model. Patients stratified by Hybrid Cox model presented with significant difference survivals between high-risk and low-risk group in 3 data sets (all P < 0.001 at Log-rank test). TCGA-KIRC data analysis revealed 37 upregulated and 81 downregulated genes associated with habitat imaging features of ccRCC. Differentially expressed genes likely play critical roles in protein and mineral metabolism, immune defense, and cellular polarity maintenance.

Exploring Shared Genetic Features and Molecular Mechanisms between Non-Alcoholic Steatohepatitis and Hepatocellular Carcinoma through Bioinformatics.

Hepatocellular Carcinoma (HCC) is one of the most common malignant tumors in the world, characterized by high incidence, high malignancy, and low survival rate. Currently, 1/4 of adults in the world suffer from Non-Alcoholic Fatty Liver Disease (NAFLD), with an incidence rate of 27% in Asia. We used TCGA and GEO public database data sets to conduct weighted gene coexpression network analysis to identify relevant gene modules, defined the intersection of tumorigenesis-related modules and NASH development-related modules as shared genes, and then used single-factor Cox, LASSO, and multivariate Cox regression analysis screened out core shared genes and verified their prognostic value. We further investigated the relationship between core shared genes and immune infiltration, tumor mutational load, and drug sensitivity. Finally, RT-qPCR was used to verify its mRNA expression in different cell lines. We identified Karyopherin α 2 (KPNA2) as the core shared gene between NASH and HCC. Patients were divided into low-risk groups and high-risk groups based on the expression of KPNA2. The prognosis of the low-risk group was significantly better than that of the highrisk group. Furthermore, we found significant differences in tumor immune cell infiltration, somatic mutations, microsatellite instability, and drug sensitivity between different expression groups. There are very few studies on the molecular mechanism of the relationship between NAFLD and HCC. Our study demonstrates that KPNA2 is a potential therapeutic target and immune-related biomarker for patients with NAFLD and HCC.

A Four-Gene Autophagy-Related Prognostic Model Signature and Its Association With Immune Phenotype in Lung Squamous Cell Carcinoma.

In the era of immunotherapy, there is a critical need for effective biomarkers to improve outcome prediction and guide treatment decisions for patients with lung squamous cell carcinoma (LUSC). We hypothesized that the immune contexture of LUSC may be influenced by tumor intrinsic events, such as autophagy. We aimed to develop an autophagy-related risk signature and assess its predictive value for immune phenotype. Expression profiles of autophagy-related genes (ARGs) in LUSC samples were obtained from the TCGA and GEO databases. Survival analyses were conducted to identify survival-related ARGs and construct a risk signature using the Random Forest algorithm. Four ARGs (CFLAR, RGS19, PINK1, and CTSD) with the most significant prognostic value were selected to construct the risk signature. Patients in the high-risk group exhibited worse prognosis than those in the low-risk group (p < 0.0001 in TCGA; p < 0.01 in GEO) and the risk score was identified as an independent prognostic factor. We observed that the high-risk group displayed an immune-suppressive status and showed higher levels of infiltrating regulatory T cells and macrophages, which are associated with poorer outcomes. Additionally, the risk score exhibited a significantly positive correlation with the expression of PD-1 and CTLA4, as well as the estimate score and immune score. This study provided an effective autophagy-related prognostic signature, which could also predict the immune phenotype.

Living donor liver transplantation for hepatocellular carcinoma using expanded criteria and alpha-fetoprotein threshold of 1000 ng/mL

BackgroundNew guidelines propose a minimum 5-year survival of 60% for hepatocellular carcinoma (HCC) with living donor liver transplantation (LDLT). This study aimed to evaluate the 5- and 10-year survival rates after transplantation for the expanded criteria for HCC. MethodsThis single-center retrospective cohort study included 208 patients who underwent LDLT for the expanded criteria (the largest tumor diameter of ≤10 cm, any tumor number, and alpha-fetoprotein [AFP] level of <1000 ng/mL) and analyzed 5- and 10-year overall survival (OS) and recurrence risk (RR) rates. ResultsWith a median follow-up of 65.1 months (IQR, 19.1–80.2), the 5- and 10-year OS and RR rates were 67.0% and 61.0% and 20.5% and 22.5%, respectively. The largest tumor diameter of >6 cm (hazard ratio [HR], 3.7; 95% CI, 1.7–8.2; P = .001) and AFP level of >400 ng/mL (HR, 4.0; 95% CI, 1.8–9.0; P = .001) were predictors of recurrence. Patients outside the Milan criteria (MC) were grouped into low- and high-risk HCC based on tumor size and AFP level. For low-risk HCC (tumor size of <6 cm, any tumor number, and AFP level of <400 ng/mL), the 5-year RR was comparable to the MC and increased the transplant pool by 35.7% (P > .5). The median number of tumors and the rate of microvascular invasion in the high-risk group, low-risk group, and MC were 2.0 (1.0–3.2), 4.0 (2.0–5.0), and 1.0 (1.0–2.0) (P < .001) and 72.2% (13/18), 44.0% (22/50), and 22.8% (32/140) (P < .001), respectively. ConclusionThe expanded criteria met the benchmark for 5-year survival. LDLT for the low-risk HCC in the expanded criteria was associated with an acceptable RR.

Unlocking the Potential of Disulfidptosis-Related LncRNAs in Lung Adenocarcinoma: A Promising Prognostic LncRNA Model for Survival and Immunotherapy Prediction.

Disulfidptosis was stimulated in high SLC7A11 expression cells starving to glucose. We attempted to identify disulfidptosis-related lncRNAs (DRLs), built a prognostic model to predict survival, and analyzed the tumor microenvironment. The TCGA database was utilized to procure the pertinent data. By utilizing both Cox regression and the least absolute shrinkage and selection operator (LASSO) method, a risk model based on DRLs was formulated for prognostic evaluation. The ability of survival prediction was validated by multiple approaches. The biological functions were screened through GO, KEGG, and GSEA. Various methods were employed to evaluate the tumor immune environment, which included ESTIMATE, tumor mutation burden (TMB) score, CIBERSORT algorithm, and tumor immune dysfunction and exclusion (TIDE) score. Ninety-one DRLs were recognized, and lncRNA AC092718.4, AL365181.2, AL606489.1, EMSLR, and ENTPD3-AS1 were involved in the risk model. The GEO database was used to verify the influence of these lncRNAs on survival. The following analyses showed that survival could be predicted excellently by the DRLs risk model. The results of enrichment analyses pointed toward the involvement of the cell cycle and IgA production pathways. In the low-risk patient group, there was a notable surge in stromal, immune, and ESTIMATE scores, while the TMB scores took a tumble. Conversely, the high-risk patient group displayed a converse trend. Notably, the group of patients with lower risk scores and higher TMB scores showed the most favorable survival outcomes, underscoring the importance of considering both risk score and TMB in predicting the response to immune checkpoint blockade therapy. Furthermore, patients classified as high-risk might display resistance to both chemotherapy and targeted therapy. Cellular biological experiments proved that lncRNA AC092718.4 promoted invasion, migration, and proliferation abilities invitro. These results provided valuable insights into the role of DRLs in LUAD and presented a possible effective treatment approach for LUAD. We developed a disulfidptosis-related risk model with 5 lncRNAs that enables survival prediciton for LUAD patients and aids cilinical decisions by forecasting the TME, TMB, and drug sensitivity, making it a valuable tool for outcomes prediction.

Preoperative risk-stratified analysis: External versus internal pancreatic stents in pancreatoduodenectomy

BackgroundSeveral risk-stratified studies have compared the outcomes of external and internal pancreatic stents in pancreatoduodenectomy (PD), but no resolute standard for a fistula-mitigation strategy exists. The study investigated the efficacy of these stents in a preoperative risk-stratified setting. MethodsData from 285 patients who underwent PD with pancreaticojejunostomy using an external or internal stent from 2015 to 2023 were analyzed. The preoperative pancreatic fistula score (preFRS) was used to classify patients into low-risk (preFRS: 0–5) and high-risk (preFRS: 6–8) groups. ResultsPreFRS accurately predicted the risk of clinically relevant postoperative pancreatic fistula (CR-POPF) as 0% and >40% in patients with preFRS ≤1 and ≥7, respectively. Although no significant difference was observed in postoperative outcomes in low-risk patients, the external stent significantly reduced CR-POPF (21% vs 44%, P = .024) and postpancreatectomy hemorrhage (PPH, 0% vs 19%, P = .02) in high-risk patients, leading to the superiority of the external stent in the entire cohort in terms of CR-POPF (12% vs 24%, P = .033) and PPH (1% vs 11%, P = .013). There were no significant differences in stent-related complications or pancreatic dysfunction. External stent malfunction occurred in 14% and significantly affected CR-POPF development in both low- (20% vs 0%, P < .01) and high-risk groups (60% vs 14%, P = .021). ConclusionThe external pancreatic stent showed a more beneficial effect on CR-POPF and PPH, especially in high-risk patients, without increasing other complications. Risk-stratified strategy and improving stent management might enhance postoperative outcomes.

Characterization and proteomic analysis of plasma-derived small extracellular vesicles in locally advanced rectal cancer patients.

Neoadjuvant chemoradiotherapy (nCRT) stands as a pivotal therapeutic approach for locally advanced rectal cancer (LARC), yet the absence of a reliable biomarker to forecast its efficacy remains a challenge. Thus, this study aimed to assess whether the proteomic compositions of small extracellular vesicles (sEVs) might offer predictive insights into nCRT response among patients with LARC, while also delving into the proteomic alterations within sEVs post nCRT. Plasma samples were obtained from LARC patients both pre- and post-nCRT. Plasma-derived sEVs were isolated utilizing the TIO2-based method, followed by LC-MS/MS-based proteomic analysis. Subsequently, pathway enrichment analysis was performed to the Differentially Expressed Proteins (DEPs). Additionally, ROC curves were generated to evaluate the predictive potential of sEV proteins in determining nCRT response. Public databases were interrogated to identify sEV protein-associated genes that are correlated with the response to nCRT in LARC. A total of 16 patients were enrolled. Among them, 8 patients achieved a pathological complete response (good responders, GR), while the remaining 8 did not achieve a complete response (poor responders, PR). Our analysis of pretreatment plasma-derived sEVs revealed 67 significantly up-regulated DEPs and 9 significantly down-regulated DEPs. Notably, PROC (AUC: 0.922), F7 (AUC: 0.953) and AZU1 (AUC: 0.906) demonstrated high AUC values and significant differences (P value < 0.05) in discriminating between GR and PR patients. Furthermore, a signature consisting of 5 sEV protein-associated genes (S100A6, ENO1, MIF, PRDX6 and MYL6) was capable of predicting the response to nCRT, yielding an AUC of 0.621(95% CI: 0.454-0.788). Besides, this 5-sEV protein-associated gene signature enabled stratification of patients into low- and high-risk group, with the low-risk group demonstrating a longer overall survival in the testing set (P = 0.048). Moreover, our investigation identified 11 significantly up-regulated DEPs and 31 significantly down-regulated DEPs when comparing pre- and post-nCRT proteomic profiles. GO analysis unveiled enrichment in the regulation of phospholipase A2 activity. Differential expression of sEV proteins distinguishes between GR and PR patients and holds promise as predictive markers for nCRT response and prognosis in patients with LARC. Furthermore, our findings highlight substantial alterations in sEV protein composition following nCRT.

Unraveling the Biological Functions of UCEC: Insights from a Prognostic Signature Model

BackgroundUterine corpus endometrial carcinoma (UCEC) is a prevalent gynecological tumor with a bleak prognosis. Anomalous glycosylation plays a pivotal role in tumorigenesis. Currently, there is a lack of prognostic signatures based on glycosylation-related genes for UCEC. Thus, our research aims to construct a predictive model and validate the correlation between relevant genes and biological functions. MethodsUsing the TCGA database, we developed prognostic models and explored their relationships with survival outcomes. We further selected key genes to verify their expression in tissues and assess their impact on cellular behavior. ResultsThe clinical prognosis of the high-risk group was significantly worse than that of the low-risk group. The nomogram model accurately predicted UCEC patient prognosis. Additionally, we identified OLFML1 as a unique signature gene that can inhibit UCEC progression and reduce radiation resistance in vitro. ConclusionsOur model, which is based on glycosylation-related genes in UCEC, effectively identifies high-risk patients and provides valuable prognostic information. In addition, OLFML1 acts as a tumor suppressor in UCEC and enhances radiosensitivity, suggesting a new potential target for improving therapeutic efficacy.