Classification and prediction of chemoradiotherapy response and survival from esophageal carcinoma histopathology images.

Whole slide imaging (WSI) of Hematoxylin and Eosin-stained biopsy specimens has been used to predict chemoradiotherapy (CRT) response and overall survival (OS) of esophageal squamous cell carcinoma (ESCC) patients. This retrospective study collected 279 specimens in 89 non-surgical ESCC patients through endoscopic biopsy between January 2010 and January 2019. These patients were divided into a CRT response group (CR + PR group) and a CRT non-response group (SD + PD group). The WSIs have segmented approximately 1,206,000 non-overlapping patches. Two experienced pathologists manually delineated the eight types of tissues on 32 WSIs, including esophagus tumor cell (TUM), cancer-associated stroma (CAS), normal epithelium layer (NEL), smooth muscle (MUS), lymphocytes (LYM), Red cells (RED), debris (DEB), uneven areas (UNE). The chemoradiotherapy response prediction models were built using maximum relevance-minimum redundancy (MRMR) feature selection and least absolute shrinkage and selection operator (LASSO) regression. However, pathological features with p < 0.1 were selected and integrated to be further screened using a LASSO Cox regression model to build a multivariate Cox proportional hazards model for predicting the OS. The testing accuracy of the tissue classification model was 91.3 %. The pathological model created using two CAS in-depth features and eight TUM in-depth features performed best for the prediction of treatment response and achieved an AUC of 0.744. For the prediction of OS, the testing AUC of this model at one year and three years were 0.675 and 0.870, respectively. The TUM model showed the highest AUC at one year (0.712). With its high accuracy rate, the deep learning model has the potential to transform from bench to bedside in clinical practice, improve patient's quality of life, and prolong the OS rate.

Optimal sequence of LT for symptomatic BM in EGFR-mutant NSCLC: a comparative study of first-line EGFR-TKIs with/without upfront LT.

BACKGROUND: The third-generation epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) can penetrate blood-brain barrier and are effective for brain metastases (BMs). There is no consensus on the optimal sequence of local therapy (LT) and EGFR-TKIs for symptomatic BM patients because patients suffering neurological symptoms were not enrolled in most clinical trials. METHODS: Non-small cell lung cancer (NSCLC) patients with EGFR mutation (EGFRm) and symptomatic BM receiving first-line osimertinib and aumolertinib from two medical centers were collected. All participants were allocated into the third-generation EGFR-TKIs (TKIs) group and the upfront LT (uLT) plus third-generation EGFR-TKIs (TKIs + uLT) group. Demographic data, survival outcomes, treatment failure patterns, and adverse events were evaluated between the two groups. We also conducted subgroup analyses to explore the impact of BM number on survival outcomes. RESULTS: 86 patients were enrolled, 44 in the TKIs group and 42 in the TKIs + uLT group. There were no significant differences in the short-term response between the groups. TKIs + uLT was associated with significantly longer overall survival (OS) (43 vs. 28 months; hazard ratio [HR], 0.36, 95% confidence interval [CI], 0.17-0.77; p = .011). No differences in progression-free survival (PFS), intracranial PFS (iPFS), failure patterns, or safety were observed. In subgroup analyses of oligo-BM patients, TKIs + uLT could prolong OS (43 vs. 31 months; HR 0.22; 95% CI 0.05-0.92; p = .015). CONCLUSIONS: EGFRm NSCLC patients with symptomatic BM might benefit from uLT, particularly oligo-BM patients. However, larger prospective cohort studies should be carried out to confirm the responses of the TKIs + uLT scheme.

Cuproptosis-related signature for clinical prognosis and immunotherapy sensitivity in hepatocellular carcinoma.

BACKGROUND: Copper homeostasis imbalance has been implicated in tumor progression, aggressiveness, and treatment response. However, the precise roles of cuproptosis-related genes (CRGs) in hepatocellular carcinoma (HCC) remain poorly understood. METHODS: In this study, we employed a consensus clustering algorithm to identify distinct molecular subtypes. We then performed Kaplan-Meier analysis and univariate Cox regression analysis to identify prognostic differentially expressed genes. The expression of these genes was subsequently validated using qPCR on fresh-frozen tissues obtained from HCC patients. Moreover, leveraging the TCGA-HCC cohort, we constructed a CRGs-related risk prediction model using the LASSO and multivariate Cox regression analysis. RESULTS: By analyzing the data, we successfully established a CRGs risk prognostic model for HCC patients, comprising five differential genes (CAD, SGCB, TXNRD1, KDR, and MTND4P20). Cox regression analysis revealed that the CRGs risk score could serve as an independent prognostic factor for overall survival (hazard ratio [HR] = 1.308, 95% confidence interval [CI] = 1.200 - 1.426, P < 0.001). The area under the curve (AUC) values of the CRGs-score for predicting 1-year, 3-year, and 5-year survival rates were 0.785, 0.724, and 0.723, respectively. Notably, the expression levels of immune checkpoints (including PD-1, PD-L1, and CTLA4) significantly differed between the low- and high-risk score groups. Furthermore, the low-risk score group displayed increased sensitivity to sorafenib, cisplatin, cyclopamine, nilotinib, salubrinal, and gemcitabine, whereas the high-risk score group exhibited heightened sensitivity to lapatinib, erlotinib, and gefitinib. CONCLUSIONS: Our findings highlight the potential of the CRGs risk score as an independent and promising biomarker for clinical prognosis and immunotherapy sensitivity in HCC patients.

Serum Lipoprotein(a) and High-Density Lipoprotein Cholesterol Associate with Diabetic Nephropathy: Evidence from Machine Learning Perspectives.

Purpose: Diabetic nephropathy (DN) is a common complication of type 2 diabetes mellitus (T2DM) that significantly impacts the quality of life for affected patients. Dyslipidemia is a known risk factor for developing cardiovascular complications in T2DM patients. However, the association between serum lipoprotein(a) (Lp(a)) and high-density lipoprotein cholesterol (HDL-C) with DN requires further investigation. Patients and Methods: For this cross-sectional study, we randomly selected T2DM patients with nephropathy (DN, n = 211) and T2DM patients without nephropathy (T2DM, n = 217) from a cohort of 142,611 patients based on predefined inclusion and exclusion criteria. We collected clinical data from the patients to identify potential risk factors for DN using binary logistic regression and machine learning. After obtaining the feature importance score of clinical indicators by building a random forest classifier, we examined the correlations between Lp(a), HDL-C and the top 10 indicators. Finally, we trained decision tree models with top 10 features using training data and evaluated their performance with independent testing data. Results: Compared to the T2DM group, the DN group had significantly higher serum levels of Lp(a) (p < 0.001) and lower levels of HDL-C (p = 0.028). Lp(a) was identified as a risk factor for DN, while HDL-C was found to be protective. We identified the top 10 indicators that were associated with Lp(a) and/or HDL-C, including urinary albumin (uALB), uALB to creatinine ratio (uACR), cystatin C, creatinine, urinary ɑ1-microglobulin, estimated glomerular filtration rate (eGFR), urinary ß2-microglobulin, urea nitrogen, superoxide dismutase and fibrinogen. The decision tree models trained using the top 10 features and with uALB at a cut-off value of 31.1 mg/L showed an average area under the receiver operating characteristic curve (AUC) of 0.874, with an AUC range of 0.870 to 0.890. Conclusion: Our findings indicate that serum Lp(a) and HDL-C are associated with DN and we have provided a decision tree model with uALB as a predictor for DN.

Clinical Diagnostic and Prognostic Potential of NDRG1 and NDRG2 in Hepatocellular Carcinoma Patients.

Background: Primary liver cancer is still the most common lethal malignancy. The N-myc downstream-regulated gene family (NDRG1-4) is a group of multifunctional proteins associated with carcinogenesis. However, systematic evaluation of the diagnostic and prognostic values of NDRG1 or NDRG2 expression in liver cancer is poorly investigated. Method: The gene expression matrix of liver hepatocellular carcinoma (LIHC) was comprehensively analyzed by the "limma" and "Dseq2" R packages. The Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA) were used to identify the biological functional differences. A single-sample GSEA (ssGSEA) was conducted to quantify the extent of immune cell infiltration. Finally, the clinical and prognostic information of LIHC patients was systematically investigated using Kaplan-Meier analysis and logistic and Cox regression analysis. Results: Compared with normal tissues, NDRG1 expression was higher, whereas NDRG2 expression was lower in tumor tissues (P <0.001). The area under the receiver operator characteristic curve (AUROC) of NDRG1 and NDRG2 for LIHC was 0.715 and 0.799, respectively. Kaplan-Meier analysis revealed that NDRG1 and NDRG2 were independent clinical prognostic biomarkers for the overall survival (OS, P = 0.001 and 2.9e-06), progression-free interval (PFI, P = 0.028 and 0.005) and disease-specific survival (DSS, P = 0.027 and P <0.001). The C-indexes and calibration plots of the nomogram suggest that NDRG1 and NDRG2 have an effective predictive performance for OS (C-index: 0.676), DSS (C-index: 0.741) and PFI (C-index: 0.630) of liver cancer patients. The mutation rate of NDRG1 in liver cancer reached up to 14%, and DNA methylation levels of NDRG1 and NDRG2 promoters correlated significantly with clinical prognosis. Conclusions: The mRNA expression and DNA methylation of NDRG superfamily members have the potential for LIHC diagnosis and prognosis via integrative analysis from multiple cohorts.

Identification and characterization of a novel indigenous algicidal bacterium Chryseobacterium species against Microcystis aeruginosa.

Harmful Microcystis aeruginosa blooms occurred frequently in many eutrophic lakes and rivers with resultant serious global environmental consequences. Algicidal bacteria may play an important role in inhibiting the growth of Microcystis aeruginosa and are considered as an effective method for preventing the appearance of blooms. In order to counteract the harmful effects of Microcystis aeruginosa, a critical step is to identify, isolate and characterize indigenous algicidal bacteria. This study aimed to isolate a novel indigenous algicidal bacterium identified as Chryseobacterium species based upon its 16S rDNA sequence analysis, and determine whether this bacterium was effective in lysing Microcystis aeruginosa FACHB 905. The influence of environmental factors including temperature, pH, quantity of Chryseobacterium species as well as Microcystis aeruginosa concentration were examined with respect to algae-lysing properties of this bacterial strain. Data demonstrated that the highest algae-lysing activity of 80% against Microcystis aeruginosa FACHB 905 occurred within 72 hr. In addition, the algae-lysing activities of Chryseobacterium species cells were significantly higher than those of cell-free supernatant. In conclusion, data showed the algicidal bacterium Chryseobacterium species exhibited potent Microcystis aeruginosa-lysing activities and attacked Microcystis aeruginosa directly suggesting this algicidal bacterium may be potentially useful for reducing the number of harmful Microcystis aeruginosa blooms.