Prediction of the Ki-67 expression level in head and neck squamous cell carcinoma with machine learning-based multiparametric MRI radiomics: a multicenter study.

BACKGROUND: This study aimed to develop and validate a machine learning (ML)-based fusion model to preoperatively predict Ki-67 expression levels in patients with head and neck squamous cell carcinoma (HNSCC) using multiparametric magnetic resonance imaging (MRI). METHODS: A total of 351 patients with pathologically proven HNSCC from two medical centers were retrospectively enrolled in the study and divided into training (n = 196), internal validation (n = 84), and external validation (n = 71) cohorts. Radiomics features were extracted from T2-weighted images and contrast-enhanced T1-weighted images and screened. Seven ML classifiers, including k-nearest neighbors (KNN), support vector machine (SVM), logistic regression (LR), random forest (RF), linear discriminant analysis (LDA), naive Bayes (NB), and eXtreme Gradient Boosting (XGBoost) were trained. The best classifier was used to calculate radiomics (Rad)-scores and combine clinical factors to construct a fusion model. Performance was evaluated based on calibration, discrimination, reclassification, and clinical utility. RESULTS: Thirteen features combining multiparametric MRI were finally selected. The SVM classifier showed the best performance, with the highest average area under the curve (AUC) of 0.851 in the validation cohorts. The fusion model incorporating SVM-based Rad-scores with clinical T stage and MR-reported lymph node status achieved encouraging predictive performance in the training (AUC = 0.916), internal validation (AUC = 0.903), and external validation (AUC = 0.885) cohorts. Furthermore, the fusion model showed better clinical benefit and higher classification accuracy than the clinical model. CONCLUSIONS: The ML-based fusion model based on multiparametric MRI exhibited promise for predicting Ki-67 expression levels in HNSCC patients, which might be helpful for prognosis evaluation and clinical decision-making.

The effect of heat treatment on the lactosylation of milk proteins.

Protein lactosylation is a significant modification that occurs during the heat treatment of dairy products, causing changes in proteins' physical-chemical and nutritional properties. Knowledge of the detailed lactosylation information on milk proteins under various heat treatments is important for selecting appropriate thermo-processing and identifying markers to monitor heat load in dairy products. In the present study, we used proteomics techniques to investigate lactosylated proteins under different heating temperatures. We observed a total of 123 lactosylated lysines in 65 proteins, with lactosylation even occurring in raw milk. The number of lactosylated lysines and proteins increased moderately at 75°C to 130°C, but dramatically at 140°C. We found that 6 out of 10, 9 out of 16, 6 out of 12, and 5 out of 15 lysine residues in κ-casein, ß-lactoglobulin, α-lactalbumin, and αS1-casein, respectively, were lactosylated under the applied heating treatment. Moreover, different lactosylation states of individual lysines and proteins can indicate the intensity of heating processes. Lactosylation of K14 in ß-lactoglobulin could distinguish pasteurized and UHT milk, while lactosylation of lactotransferrin can reflect moderate heat treatment of products.

Piperlongumine synergistically enhances the antitumour activity of sorafenib by mediating ROS-AMPK activation and targeting CPSF7 in liver cancer.

Sorafenib, a multikinase inhibitor, is the first-line agent for advanced liver cancer. Sorafenib strongly inhibits both cell proliferation and tumour angiogenesis. However, the development of drug resistance hampers its anticancer efficacy. To improve the antitumour activity of sorafenib, we demonstrate that piperlongumine (PL), an alkaloid isolated from the fruits and roots of Piper longum L., enhances the cytotoxicity of sorafenib in HCCLM3 and SMMC7721 cells using the cell counting kit-8 test. Flow cytometry analysis indicated that PL and sorafenib cotreatment induced robust reactive oxygen species (ROS) generation and mitochondrial dysfunction, thereby increasing the number of apoptotic cells and the ratio of G2/M phase cells in both HCCLM3 and SMMC7721 cells. Furthermore, AMP-protein kinase (AMPK) signalling was activated by excess ROS accumulation and mediated growth inhibition in response to PL and sorafenib cotreatment. RNA-sequencing analysis indicated that PL treatment disrupted RNA processing in HCCLM3 cells. In particular, PL treatment decreased the expression of cleavage and polyadenylation specificity factor 7 (CPSF7), a subunit of cleavage factor I, in a time- and concentration-dependent manner in HCCLM3 and SMMC7721 cells. CPSF7 knockdown using a gene interference strategy promoted growth inhibition of PL or sorafenib monotherapy, whereas CPSF7 overexpression alleviated the cytotoxicity of sorafenib in cultured liver cancer cells. Finally, PL and sorafenib coadministration significantly reduced the weight and volume of HCCLM3 cell xenografts in vivo. Taken together, our data indicate that PL displays potential synergistic antitumour activity in combination with sorafenib in liver cancer.

Identification of critical ferroptosis regulators in lung adenocarcinoma that RRM2 facilitates tumor immune infiltration by inhibiting ferroptotic death.

Ferroptosis is a novel form of cell death characterized by heavy iron accumulation and lipid peroxidation that plays a critical role in the tumor microenvironment. However, promising biomarkers associated with tumor immune cell infiltration and the immunotherapy response to ferroptosis regulators remain to be elucidated in lung adenocarcinoma (LUAD) patients. In this study, we defined ferroptosis regulators in LUAD through database analysis and experimental validation to determine the implementation of genes associated with clinical relevance, immunotherapy response and tumor microenvironment in LUAD patients. Multiomics data analysis was performed to explore the CNV features, molecular mechanisms and immunogenic characteristics of ferroptosis regulators in LUAD patients. Then, univariate and multivariate Cox regression analyses were used to identify three genes (DDIT4, RRM2, and SLC2A1) that were closely associated with the prognosis of LUAD patients. The prognostic model based on the determination of these three genes was an independent prognostic factor (p < 0.05, HR = 2.838), and patients with superior predictive performance and higher prognostic risk were more likely to have poor survival rates than those with lower prognostic risk in the training group (p < 0.001, HR = 3.19) and the test group (p < 0.001, HR = 2.94; p < 0.001, HR = 3.44). Activated immune cells, including T helper cells and activated CD8 T cells, were lower in the high-risk group, while type 2 T cells were higher (p < 0.05). Patients with higher prognostic risk were less likely to benefit from immunotherapy, partly due to low CTLA4 levels and an immunosuppressive microenvironment (p < 0.05). Combined with LUAD tissue samples and mouse trials, RRM2 was found to influence lung cancer progression and affect tumor immune cell infiltration. RRM2 inhibition effectively promoted M1 macrophage polarization and suppressed M2 macrophage polarization in vitro and in vivo. And ferroptosis inhibitor ferrostatin-1 treatment effectively re-blanced macrophage polarization mediated by RRM2 inhibition. Taken together, the results of the multiomics data analysis and experimental validation identified ferroptosis regulators as promising biomarkers and therapeutic targets associated with tumor immune infiltration in LUAD patients.

More than sugar in the milk: human milk oligosaccharides as essential bioactive molecules in breast milk and current insight in beneficial effects.

Human milk is the gold standard for newborn infants. Breast milk not only provides nutrients, it also contains bioactive components that guide the development of the infant's intestinal immune system, which can have a lifelong effect. The bioactive molecules in breast milk regulate microbiota development, immune maturation and gut barrier function. Human milk oligosaccharides (hMOs) are the most abundant bioactive molecules in human milk and have multiple beneficial functions such as support of growth of beneficial bacteria, anti-pathogenic effects, immune modulating effects, and stimulation of intestine barrier functions. Here we critically review the current insight into the benefits of bioactive molecules in mother milk that contribute to neonatal development and focus on current knowledge of hMO-functions on microbiota and the gastrointestinal immune barrier. hMOs produced via genetically engineered microorganisms are now applied in infant formulas to mimic the nutritional composition of breast milk as closely as possible, and their prospects and scientific challenges are discussed in depth.

Prediction of tumor response via a pretreatment MRI radiomics-based nomogram in HCC treated with TACE.

OBJECTIVES: To develop and validate a pre-transcatheter arterial chemoembolization (TACE) MRI-based radiomics model for predicting tumor response in intermediate-advanced hepatocellular carcinoma (HCC) patients. MATERIALS: Ninety-nine intermediate-advanced HCC patients (69 for training, 30 for validation) treated with TACE were enrolled. MRI examinations were performed before TACE, and the efficacy was evaluated according to the mRECIST criterion 3 months after TACE. A total of 396 radiomics features were extracted from T2-weighted pre-TACE images, and least absolute shrinkage and selection operator (LASSO) regression was applied to feature selection and model construction. The performance of the model was evaluated by receiver operating characteristic (ROC) curves, calibration curves, and decision curves. RESULTS: The AFP value, Child-Pugh score, and BCLC stage showed a significant difference between the TACE response (TR) and non-TACE response (nTR) patients. Six radiomics features were selected by LASSO and the radiomics score (Rad-score) was calculated as the sum of each feature multiplied by the non-zero coefficient from LASSO. The AUCs of the ROC curve based on Rad-score were 0.812 and 0.866 in the training and validation cohorts, respectively. To improve the diagnostic efficiency, the Rad-score was further integrated with the above clinical indicators to form a novel predictive nomogram. Results suggested that the AUC increased to 0.861 and 0.884 in the training and validation cohorts, respectively. Decision curve analysis showed that the radiomics nomogram was clinically useful. CONCLUSION: The radiomics and clinical indicator-based predictive nomogram can well predict TR in intermediate-advanced HCC and can further be applied for auxiliary diagnosis of clinical prognosis. KEY POINTS: • The therapeutic outcome of TACE varies greatly even for patients with the same clinicopathologic features. • Radiomics showed excellent performance in predicting the TACE response. • Decision curves demonstrated that the novel predictive model based on the radiomics signature and clinical indicators has great clinical utility.

Human Milk Oligosaccharides Mediate the Crosstalk Between Intestinal Epithelial Caco-2 Cells and Lactobacillus PlantarumWCFS1in an In Vitro Model with Intestinal Peristaltic Shear Force.

BACKGROUND: The intestinal epithelial cells, food molecules, and gut microbiota are continuously exposed to intestinal peristaltic shear force. Shear force may impact the crosstalk of human milk oligosaccharides (hMOs) with commensal bacteria and intestinal epithelial cells. OBJECTIVES: We investigated how hMOs combined with intestinal peristaltic shear force impact intestinal epithelial cells and crosstalk with a commensal bacterium. METHODS: We applied the Ibidi system to mimic intestinal peristaltic shear force. Caco-2 cells were exposed to a shear force (5 dynes/cm2) for 3 d, and then stimulated with the hMOs, 2'-fucosyllactose (2'-FL), 3-FL, and lacto-N-triose II (LNT2). In separate experiments, Lactobacillus plantarumWCFS1 adhesion to Caco-2 cells was studied with the same hMOs and shear force. Effects were tested on gene expression of glycocalyx-related molecules (glypican 1 [GPC1], hyaluronan synthase 1 [HAS1], HAS2, HAS3, exostosin glycosyltransferase 1 [EXT1], EXT2), defensin ß-1 (DEFB1), and tight junction (tight junction protein 1 [TJP1], claudin 3 [CLDN3]) in Caco-2 cells. Protein expression of tight junctions was also quantified. RESULTS: Shear force dramatically decreased gene expression of the main enzymes for making glycosaminoglycan side chains (HAS3 by 43.3% and EXT1 by 68.7%) (P <0.01), but did not affect GPC1 which is the gene responsible for the synthesis of glypican 1 which is a major protein backbone of glycocalyx. Expression of DEFB1, TJP1, and CLDN3 genes was decreased 60.0-94.9% by shear force (P <0.001). The presence of L. plantarumWCFS1 increased GPC1, HAS2, HAS3, and ZO-1 expression by 1.78- to 3.34-fold (P <0.05). Under shear force, all hMOs significantly stimulated DEFB1 and ZO-1, whereas only 3-FL and LNT2 enhanced L. plantarumWCFS1 adhesion by 1.85- to 1.90-fold (P <0.01). CONCLUSIONS: 3-FL and LNT2 support the crosstalk between the commensal bacterium L. plantarumWCFS1 and Caco-2 intestinal epithelial cells, and shear force can increase the modulating effects of hMOs.

The ferroptosis and iron-metabolism signature robustly predicts clinical diagnosis, prognosis and immune microenvironment for hepatocellular carcinoma.

BACKGROUND: In this study, we comprehensively analyzed genes related to ferroptosis and iron metabolism to construct diagnostic and prognostic models and explore the relationship with the immune microenvironment in HCC. METHODS: Integrated analysis, cox regression and the least absolute shrinkage and selection operator (LASSO) method of 104 ferroptosis- and iron metabolism-related genes and HCC-related RNA sequencing were performed to identify HCC-related ferroptosis and iron metabolism genes. RESULTS: Four genes (ABCB6, FLVCR1, SLC48A1 and SLC7A11) were identified to construct prognostic and diagnostic models. Poorer overall survival (OS) was exhibited in the high-risk group than that in the low-risk group in both the training cohort (P < 0.001, HR = 0.27) and test cohort (P < 0.001, HR = 0.27). The diagnostic models successfully distinguished HCC from normal samples and proliferative nodule samples. Compared with low-risk groups, high-risk groups had higher TMB; higher fractions of macrophages, follicular helper T cells, memory B cells, and neutrophils; and exhibited higher expression of CD83, B7H3, OX40 and CD134L. As an inducer of ferroptosis, erastin inhibited HCC cell proliferation and progression, and it was showed to affect Th17 cell differentiation and IL-17 signaling pathway through bioinformatics analysis, indicating it a potential agent of cancer immunotherapy. CONCLUSIONS: The prognostic and diagnostic models based on the four genes indicated superior diagnostic and predictive performance, indicating new possibilities for individualized treatment of HCC patients. Video Abstract.

Non-starch polysaccharides from kidney beans: comprehensive insight into their extraction, structure and physicochemical and nutritional properties.

Kidney beans (Phaseolus vulgaris L.) are an important legume source of carbohydrates, proteins, and bioactive molecules and thus have attracted increasing attention for their high nutritional value and sustainability. Non-starch polysaccharides (NSPs) in kidney beans account for a high proportion and have a significant impact on their biological functions. Herein, we critically update the information on kidney bean varieties and factors that influence the physicochemical properties of carbohydrates, proteins, and phenolic compounds. Furthermore, their extraction methods, structural characteristics, and health regulatory effects, such as the regulation of intestinal health and anti-obesity and anti-diabetic effects, are also summarized. This review will provide suggestions for further investigation of the structure of kidney bean NSPs, their relationships with biological functions, and the development of NSPs as novel plant carbohydrate resources.

Caffeic acid combined with arabinoxylan or ß-glucan attenuates diet-induced obesity in mice via modulation of gut microbiota and metabolites.

Polyphenols and dietary fibers in whole grains are important bioactive compounds to reduce risks for obesity. However, whether the combination of the two components exhibits a stronger anti-obesity effect remains unclear. Caffeic acid is a major phenolic acid in cereals, and arabinoxylan and ß-glucan are biological macromolecules with numerous health benefits. Here, we investigated the effect of caffeic acid combined with arabinoxylan or ß-glucan on glucose and lipid metabolism, gut microbiota, and metabolites in mice fed a high-fat diet (HFD). Caffeic acid combined with arabinoxylan or ß-glucan significantly reduced the body weight, blood glucose, and serum free fatty acid concentrations. Caffeic acid combined with ß-glucan effectively decreased serum total cholesterol levels and hepatic lipid accumulation, modulated oxidative and inflammatory stress, and improved gut barrier function. Compared with arabinoxylan, ß-glucan, and caffeic acid alone, caffeic acid combined with arabinoxylan or ß-glucan exhibited a better capacity to modulate gut microbiota, including increased microbial diversity, reduced Firmicutes/Bacteroidetes ratio, and increased abundance of beneficial bacteria such as Bifidobacterium. Furthermore, caffeic acid combined with ß-glucan reversed HFD-induced changes in microbiota-derived metabolites involving tryptophan, purine, and bile acid metabolism. Thus, caffeic acid and ß-glucan had a synergistic anti-obesity effect by regulating specific gut microbiota and metabolites.

Non-invasive prediction model of axillary lymph node status in patients with early-stage breast cancer: a feasibility study based on dynamic contrast-enhanced-MRI radiomics.

OBJECTIVES: Accurate axillary evaluation plays an important role in prognosis and treatment planning for breast cancer. This study aimed to develop and validate a dynamic contrast-enhanced (DCE)-MRI-based radiomics model for preoperative evaluation of axillary lymph node (ALN) status in early-stage breast cancer. METHODS: A total of 410 patients with pathologically confirmed early-stage invasive breast cancer (training cohort, N = 286; validation cohort, N = 124) from June 2018 to August 2022 were retrospectively recruited. Radiomics features were derived from the second phase of DCE-MRI images for each patient. ALN status-related features were obtained, and a radiomics signature was constructed using SelectKBest and least absolute shrinkage and selection operator regression. Logistic regression was applied to build a combined model and corresponding nomogram incorporating the radiomics score (Rad-score) with clinical predictors. The predictive performance of the nomogram was evaluated using receiver operator characteristic (ROC) curve analysis and calibration curves. RESULTS: Fourteen radiomic features were selected to construct the radiomics signature. The Rad-score, MRI-reported ALN status, BI-RADS category, and tumour size were independent predictors of ALN status and were incorporated into the combined model. The nomogram showed good calibration and favourable performance for discriminating metastatic ALNs (N + (≥1)) from non-metastatic ALNs (N0) and metastatic ALNs with heavy burden (N + (≥3)) from low burden (N + (1-2)), with the area under the ROC curve values of 0.877 and 0.879 in the training cohort and 0.859 and 0.881 in the validation cohort, respectively. CONCLUSIONS: The DCE-MRI-based radiomics nomogram could serve as a potential non-invasive technique for accurate preoperative evaluation of ALN burden, thereby assisting physicians in the personalized axillary treatment for early-stage breast cancer patients. ADVANCES IN KNOWLEDGE: This study developed a potential surrogate of preoperative accurate evaluation of ALN status, which is non-invasive and easy-to-use.

Development and Validation of a Dual-Energy CT-Based Model for Predicting the Number of Central Lymph Node Metastases in Clinically Node-Negative Papillary Thyroid Carcinoma.

RATIONALE AND OBJECTIVES: This study aimed to develop and validate a dual-energy CT (DECT)-based model for preoperative prediction of the number of central lymph node metastases (CLNMs) in clinically node-negative (cN0) papillary thyroid carcinoma (PTC) patients. MATERIALS AND METHODS: Between January 2016 and January 2021, 490 patients who underwent lobectomy or thyroidectomy, CLN dissection, and preoperative DECT examinations were enrolled and randomly allocated into the training (N = 345) and validation cohorts (N = 145). The patients' clinical characteristics and quantitative DECT parameters obtained on primary tumors were collected. Independent predictors of> 5 CLNMs were identified and integrated to construct a DECT-based prediction model, for which the area under the curve (AUC), calibration, and clinical usefulness were assessed. Risk group stratification was performed to distinguish patients with different recurrence risks. RESULTS: More than 5 CLNMs were found in 75 (15.3%) cN0 PTC patients. Age, tumor size, normalized iodine concentration (NIC), normalized effective atomic number (nZeff) and the slope of the spectral Hounsfield unit curve (λHu) in the arterial phase were independently associated with> 5 CLNMs. The DECT-based nomogram that incorporated predictors demonstrated favorable performance in both cohorts (AUC: 0.842 and 0.848) and significantly outperformed the clinical model (AUC: 0.688 and 0.694). The nomogram showed good calibration and added clinical benefit for predicting> 5 CLNMs. The KaplanMeier curves for recurrence-free survival showed that the high- and low-risk groups stratified by the nomogram were significantly different. CONCLUSION: The nomogram based on DECT parameters and clinical factors could facilitate preoperative prediction of the number of CLNMs in cN0 PTC patients.

Machine Learning Radiomics-Based Prediction of Non-sentinel Lymph Node Metastasis in Chinese Breast Cancer Patients with 1-2 Positive Sentinel Lymph Nodes: A Multicenter Study.

RATIONALE AND OBJECTIVES: This study aimed to construct a machine learning radiomics-based model using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) images to evaluate non-sentinel lymph node (NSLN) metastasis in Chinese breast cancer (BC) patients who underwent total mastectomy (TM) and had 1-2 positive sentinel lymph nodes (SLNs). MATERIALS AND METHODS: In total, 494 patients were retrospectively enrolled from two hospitals, and were divided into the training (n = 286), internal validation (n = 122), and external validation (n = 86) cohorts. Features were extracted from DCE-MRI images for each patient and screened. Six ML classifies were trained and the best classifier was evaluated to calculate radiomics (Rad)-scores. A combined model was developed based on Rad-scores and clinical risk factors, then the calibration, discrimination, reclassification, and clinical usefulness were evaluated. RESULTS: 14 radiomics features were ultimately selected. The random forest (RF) classifier showed the best performance, with the highest average area under the curve (AUC) of 0.833 in the validation cohorts. The combined model incorporating RF-based Rad-scores, tumor size, lymphovascular invasion, and proportion of positive SLNs resulted in the best discrimination ability, with AUCs of 0.903, 0.890, and 0.836 in the training, internal validation, and external validation cohorts, respectively. Furthermore, the combined model significantly improved the classification accuracy and clinical benefit for NSLN metastasis prediction. CONCLUSION: A RF-based combined model using DCE-MRI images exhibited a promising performance for predicting NSLN metastasis in Chinese BC patients who underwent TM and had 1-2 positive SLNs, thereby aiding in individualized clinical treatment decisions.

Changes in Lipids and Proteins of Common Carp (Cyprinus carpio) Fillets under Frozen Storage and Establishment of a Radial Basis Function Neural Network (RBFNN).

Storage via freezing remains the most effective approach for fish preservation. However, lipid oxidation and protein denaturation still occur during storage, along with nutritional loss. The extent of lipid alteration and protein denaturation are associated with human health defects. To precisely predict common carp (Cyprinus carpio) nutritional quality change during frozen storage, here, we first determined lipid oxidation and hydrolysis and protein denaturation of common carp fillets during 17 weeks of frozen preservation at 261 K, 253 K, and 245 K. Results showed that the content of thiobarbituric acid reactive substances (TBARS) and free fatty acids (FFA) were significantly increased. However, salt-soluble protein (SSP) content, Ca2+-ATPase activity, and total sulfhydryl (SH) content kept decreasing during frozen storage, with SSP content decreasing by 64.82%, 38.14%, and 11.24%, respectively, Ca2+-ATP enzyme activity decreasing to 12.50%, 18.52%, and 28.57% Piµmol/mg/min, and SH values decreasing by 70.71%, 64.92%, and 56.51% at 261 K, 253 K, and 245 K, respectively. The values at 261 K decreased more than that at 253 K and 245 K (p < 0.05). Ca2+-ATPase activity was positively correlated (r = 0.96) with SH content. Afterwards, based on the results of the above chemical experiments, we developed a radial basis function neural network (RBFNN) to predict the modification of lipid and protein of common carp fillets during frozen storage. Results showed that all the relative errors of experimental and predicted values were within ±10%. In summary, the quality of common carp can be well protected at 245 K, and the established RBFNN could effectively predict the quality of the common carp under frozen conditions at 261-245 K.

Effects of Co-Modification by Extrusion and Enzymatic Hydrolysis on Physicochemical Properties of Black Wheat Bran and Its Prebiotic Potential.

Black wheat bran (BWB) is an important source of dietary fiber (DF) and phenolic compounds and has stronger nutritional advantages than ordinary WB. However, the low content of soluble dietary fiber (SDF) negatively influences its physicochemical properties and nutritive functions. To obtain a higher content of SDF in BWB, we evaluated the impact of co-modification by extrusion and enzymes (cellulase, xylanase, high-temperature α-amylase, and acid protease) on water extractable arabinoxylan (WEAX) in BWB. An optimized co-modification method was obtained through single-factor and orthogonal experiments. The prebiotic potential of co-modified BWB was also evaluated using pooled fecal microbiota from young, healthy volunteers. The commonly investigated inulin served as a positive control. After co-modification, WEAX content was dramatically increased from 0.31 g/100 g to 3.03 g/100 g (p < 0.05). The water holding capacity, oil holding capacity, and cholesterol adsorption capacity (pH = 2.0 and pH = 7.0) of BWB were increased by 100%, 71%, 131%, and 133%, respectively (p < 0.05). Scanning electron microscopy demonstrated a looser and more porous microstructure for co-modified BWB granules. Through in vitro anerobic fermentation, co-modified BWB achieved a higher content of Bifidobacterium and Lactobacillus than inulin fermentation. In addition, co-modified BWB induced the highest butyric acid production, indicating high potential as prebiotics. The results may contribute to improving technologies for developing high-fiber-content cereal products.

Predictive Models for Colon Adenocarcinoma Diagnosis, Prognosis, and Immune Microenvironment Based on 2 Hypoxia-Related Genes: KDM3A and ENO3.

Background: Hypoxia is known to play a critical role in tumor occurrence, progression, prognosis, and therapy resistance. However, few studies have investigated hypoxia markers for diagnosing and predicting prognosis in colon adenocarcinoma (COAD). This study aims to identify a hypoxia genes-based biomarker for predicting COAD patients' prognosis and response to immunotherapy on an individual basis. Methods: Hypoxia-related genes were extracted from the Molecular Signatures Database. Gene expression, clinical data, and mutation data of COAD were collected retrospectively from the Cancer Genome Atlas, the Gene Expression Omnibus, and the International Cancer Genome Consortium databases. Univariate and multivariate cox regression, and the least absolute shrinkage and selection operator method were used to select the genes most associated with the prognosis of COAD patients. Kaplan-Meier survival analysis, receiver operating characteristic curves, calibration curves, and decision curve analyses were performed to validate the efficacy of the signature in predicting the prognosis of COAD patients. EdU incorporation assays, cell survival assays, western blot assays, and trans-well invasion assays were performed to further confirm the function of the screened genes in tumorigenesis. Results: ENO3 and KDM3A were identified as key genes for constructing prognostic and diagnostic signatures, which were found to be independent risk factors for predicting the prognosis and diagnosis of COAD patients. Using these signatures, COAD patients could be stratified into high-risk and low-risk groups, with the latter exhibiting better overall survival outcomes. Moreover, the high-risk group displayed elevated levels of immune checkpoint genes and tumor mutation burden, indicating that these patients may benefit from immune checkpoint inhibitor therapy. Conclusion: The signature developed in this study demonstrates excellent efficacy in prognosticating the outcomes of COAD patients. Moreover, it can serve as a valuable tool for clinicians to identify COAD patients who are suitable for ICI therapy.

Machine Learning Classifier for Preoperative Prediction of Early Recurrence After Bronchial Arterial Chemoembolization Treatment in Lung Cancer Patients.

RATIONALE AND OBJECTIVES: Bronchial arterial chemoembolization (BACE) was deemed as an effective and safe approach for advanced standard treatment-ineligible/rejected lung cancer patients. However, the therapeutic outcome of BACE varies greatly and there is no reliable prognostic tool in clinical practice. This study aimed to investigate the effectiveness of radiomics features in predicting tumor recurrence after BACE treatment in lung cancer patients. MATERIALS AND METHODS: A total of 116 patients with pathologically confirmed lung cancer who received BACE treatment were retrospectively recruited. All patients underwent contrast-enhanced CT within 2 weeks before BACE treatment and were followed up for more than 6 months. We conducted a machine learning-based characterization of each lesion on the preoperative contrast-enhanced CT images. In the training cohort, recurrence-related radiomics features were screened by least absolute shrinkage and selection operator (LASSO) regression. Three predictive radiomics signatures were built with linear discriminant analysis (LDA), support vector machine (SVM) and logistic regression (LR) algorithms, respectively. Univariate and multivariate LR analyses were performed to select the independent clinical predictors for recurrence. The radiomics signature with best predictive performance was integrated with the clinical predictors to form a combined model, which was visualized as a nomogram. The performance of the combined model was assessed by receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA). RESULTS: Nine recurrence-related radiomics features were screened out, and three radiomics signatures (RadscoreLDA, RadscoreSVM and RadscoreLR) were built based on these features. Patients were classified into the low-risk and high-risk groups based on the optimal threshold of three signatures. Progression-free survival (PFS) analysis showed that patients of low-risk group achieved longer PFS than patients of high-risk group (P < 0.05). The combined model including RadscoreLDA and independent clinical predictors (tumor size, carcinoembryonic antigen and pro-gastrin releasing peptide) achieved the best predictive performance for recurrence after BACE treatment. It yields AUCs of 0.865 and 0.867 in the training and validation cohorts, with accuracy (ACC) of 0.804 and 0.750, respectively. Calibration curves indicated that the probability of recurrence predicted by the model fits well with the actual recurrence probability. DCA showed that the radiomics nomogram was clinically useful. CONCLUSION: The radiomics and clinical predictors-based nomogram can predict tumor recurrence after BACE treatment effectively, which allowing oncologists to identify potential recurrence and enable better patient management and clinical decision-making.

Multi-algorithms analysis for pre-treatment prediction of response to transarterial chemoembolization in hepatocellular carcinoma on multiphase MRI.

OBJECTIVES: This study compared the accuracy of predicting transarterial chemoembolization (TACE) outcomes for hepatocellular carcinoma (HCC) patients in the four different classifiers, and comprehensive models were constructed to improve predictive performance. METHODS: The subjects recruited for this study were HCC patients who had received TACE treatment from April 2016 to June 2021. All participants underwent enhanced MRI scans before and after intervention, and pertinent clinical information was collected. Registry data for the 144 patients were randomly assigned to training and test datasets. The robustness of the trained models was verified by another independent external validation set of 28 HCC patients. The following classifiers were employed in the radiomics experiment: machine learning classifiers k-nearest neighbor (KNN), support vector machine (SVM), the least absolute shrinkage and selection operator (Lasso), and deep learning classifier deep neural network (DNN). RESULTS: DNN and Lasso models were comparable in the training set, while DNN performed better in the test set and the external validation set. The CD model (Clinical & DNN merged model) achieved an AUC of 0.974 (95% CI: 0.951-0.998) in the training set, superior to other models whose AUCs varied from 0.637 to 0.943 (p < 0.05). The CD model generalized well on the test set (AUC = 0.831) and external validation set (AUC = 0.735). CONCLUSIONS: DNN model performs better than other classifiers in predicting TACE response. Integrating with clinically significant factors, the CD model may be valuable in pre-treatment counseling of HCC patients who may benefit the most from TACE intervention.

MLL4 Regulates the Progression of Non-Small-Cell Lung Cancer by Regulating the PI3K/AKT/SOX2 Axis.

PURPOSE: Mixed-lineage leukemia protein 4 (MLL4/KMT2D) is a histone methyltransferase, and its mutation has been reported to be associated with a poor prognosis in many cancers, including lung cancer. We investigated the function of MLL4 in lung carcinogenesis. Materials and Methods: RNA sequencing (RNA-seq) in A549 cells transfected with control siRNA or MLL4 siRNA was performed. Also, we used EdU incorporation assay, colony formation assays, growth curve analysis, transwell invasion assays, immunohistochemical staining, and in vivo bioluminescence assay to investigate the function of MLL4 in lung carcinogenesis. RESULTS: We found that MLL4 expression was downregulated in non-small cell lung cancer (NSCLC) tissues compared to adjacent normal tissues and tended to decrease with disease stage progression. We analyzed the transcriptomes in control and MLL4- deficient cells using high-throughput RNA deep sequencing (RNA-seq) and identified a cohort of target genes, such as SOX2, ATF1, FOXP4, PIK3IP1, SIRT4, TENT5B, and LFNG, some of which are related to proliferation and metastasis. Our results showed that low expression of MLL4 promotes NSCLC cell proliferation and metastasis and is required for the maintenance of NSCLC stem cell properties. CONCLUSION: Our findings identify an important role of MLL4 in lung carcinogenesis through transcriptional regulation of PIK3IP1, affecting the PI3K/AKT/SOX2 axis, and suggest that MLL4 could be a potential prognostic indicator and target for NSCLC therapy.

Prediction of placenta accreta spectrum with nomogram combining radiomic and clinical factors: A novel developed and validated integrative model.

OBJECTIVE: To develop and validate a clinicoradiomic nomogram based on sagittal T2WI images to predict placenta accreta spectrum (PAS). METHODS: Between October 2016 and April 2022, women suspected of PAS by ultrasound were enrolled. After taking into account exclusion criteria, 132 women were retrospectively included in the study. The variance threshold SelectKBest and the least absolute shrinkage and selection operator were applied to select radiomic features, which was further used to calculate the Rad-score. Multivariable logistic regression was used to screen clinical factor. RESULTS: Based on 13 radiomic features, five radiomic models were constructed. A clinical factor of intraplacental T2-hypointense bands was obtained by multivariate logistic regression. The area under the curve (AUC) value of the stochastic gradient descent (SGD) radiomic model was 0.82 in the training cohort and 0.78 in the test cohort. After adding clinical factors to the SGD radiomic model, the AUC value of the clinicoradiomic model was significantly increased from 0.82 and 0.78 to 0.84 in both the training and test cohorts. The nomogram of the clinicoradiomic model was constructed, which had good performance verified by calibration and a decision curve. CONCLUSION: The presented nomogram could be useful for predicting PAS.