Oncolytic adenovirus in treating malignant ascites: A phase II trial and longitudinal single-cell study.

Malignant ascites is a common complication resulting from the peritoneal spread of malignancies, and currently lacks effective treatments. We conducted a phase II trial (NCT04771676) to investigate the efficacy and safety of oncolytic adenovirus H101 and virotherapy-induced immune response in 25 patients with malignant ascites. Oncolytic virotherapy achieved an increased median time to repeat paracentesis of 45 days (95% confidence interval 16.5-73.5 days), compared with the preset control value of 13 days. Therapy was well-tolerated, with pyrexia, fatigue, nausea, and abdominal pain as the most common toxicities. Longitudinal single-cell profiling identified marked oncolysis, early virus replication, and enhanced CD8+ T cells-macrophages immune checkpoint crosstalk, especially in responsive patients. H101 also triggered a proliferative burst of CXCR6+ and GZMK+CD8+ T cells with promoted tumor-specific cytotoxicity. Further establishment of oncolytic virus-induced T cell expansion signature (OiTE) implicated the potential benefits for H101-responsive patients from subsequent anti-PD(L)1 therapy. Patients with upregulated immune-signaling pathways in tumor cells and a higher proportion of CLEC10A+ dendritic cells and GZMK+CD8+ T cells at baseline showed a superior response to H101 treatment. Our study demonstrates promising clinical responses and tolerability of oncolytic adenovirus in treating malignant ascites and provides insights into the relevant cellular processes following oncolytic virotherapy.

BAGAIL: Multi-modal imitation learning from imbalanced demonstrations.

Expert demonstrations in imitation learning often contain different behavioral modes, e.g., driving modes such as driving on the left, keeping the lane, and driving on the right in the driving tasks. Although most existing multi-modal imitation learning methods allow learning from demonstrations of multiple modes, they have strict constraints on the data of each mode, generally requiring a near data ratio of all modes. Otherwise, it tends to fall into a mode collapse or only learn the data distribution of the mode that has the largest data volume. To address the problem, an algorithm that balances real-fake loss and classification loss by modifying the output of the discriminator, referred to as BAlanced Generative Adversarial Imitation Learning (BAGAIL), is proposed. With this modification, the generator is only rewarded for generating real trajectories with correct modes. BAGAIL is therefore able to deal with imbalanced expert demonstrations and carry out efficient learning for each mode. The learning process of BAGAIL is divided into a pre-training stage and an imitation learning stage. During the pre-training stage, BAGAIL initializes the generator parameters by means of conditional Behavioral Cloning, laying the foundation for the direction of parameter optimization. During the imitation learning stage, BAGAIL optimizes the parameters by using the adversary between the generator and the modified discriminator so that the finally obtained policy can successfully learn the distribution of imbalanced expert data. The experiments showed that BAGAIL accurately distinguished different behavioral modes with imbalanced demonstrations. What is more, the learning result of each mode is close to the expert standard and more stable than other multi-modal imitation learning methods.

Anoikis-related signature predicts prognosis and characterizes immune landscape of ovarian cancer.

Ovarian cancer (OV) is the most lethal gynecological malignancy worldwide, with high recurrence rates. Anoikis, a newly-acknowledged form of programmed cell death, plays an essential role in cancer progression, though studies focused on prognostic patterns of anoikis in OV are still lacking. We filtered 32 potential anoikis-related genes (ARGs) among the 6406 differentially expressed genes (DEGs) between the 180 normal controls and 376 TCGA-OV samples. Through the LASSO-Cox analysis, a 2-gene prognostic signature, namely AKT2, and DAPK1, was finally distinguished. We then demonstrated the promising prognostic value of the signature through the K-M survival analysis and time-dependent ROC curves (p-value < 0.05). Moreover, based on the signature and clinical features, we constructed and validated a nomogram model for 1-year, 3-year, and 5-year overall survival, with reliable prognostic values in both TCGA-OV training cohort (p-value < 0.001) and ICGC-OV validation cohort (p-value = 0.030). We evaluated the tumor immune landscape through the CIBERSORT algorithm, which indicated the upregulation of resting Myeloid Dendritic Cells (DCs), memory B cells, and naïve B cells and high expression of key immune checkpoint molecules (CD274 and PDCD1LG2) in the high-risk group. Interestingly, the high-risk group exhibited better sensitivity toward immunotherapy and less sensitivity toward chemotherapies, including Cisplatin and Bleomycin. Especially, based on the IHC of tissue microarrays among 125 OV patients at our institution, we reported that aberrant upregulation of DAPK1 was related to poor prognosis. Conclusively, the anoikis-related signature was a promising tool to evaluate prognosis and predict therapy responses, thus assisting decision-making in the realm of OV precision medicine.

Comprehensive machine learning-based preoperative blood features predict the prognosis for ovarian cancer.

PURPOSE: Significant advancements in improving ovarian cancer (OC) outcomes have been limited over the past decade. To predict prognosis and improve outcomes of OC, we plan to develop and validate a robust prognosis signature based on blood features. METHODS: We screened age and 33 blood features from 331 OC patients. Using ten machine learning algorithms, 88 combinations were generated, from which one was selected to construct a blood risk score (BRS) according to the highest C-index in the test dataset. RESULTS: Stepcox (both) and Enet (alpha = 0.7) performed the best in the test dataset with a C-index of 0.711. Meanwhile, the low RBS group possessed observably prolonged survival in this model. Compared to traditional prognostic-related features such as age, stage, grade, and CA125, our combined model had the highest AUC values at 3, 5, and 7 years. According to the results of the model, BRS can provide accurate predictions of OC prognosis. BRS was also capable of identifying various prognostic stratifications in different stages and grades. Importantly, developing the nomogram may improve performance by combining BRS and stage. CONCLUSION: This study provides a valuable combined machine-learning model that can be used for predicting the individualized prognosis of OC patients.

CSGALNACT2 restricts ovarian cancer migration and invasion by modulating MAPK/ERK pathway through DUSP1.

PURPOSE: Ovarian cancer is one of the leading causes of cancer-related death among women. CSGALNACT2 is a vital Golgi transferase and is related to a variety of human diseases. However, its expression pattern and function in ovarian cancer remain uncertain. METHODS: The Cancer Genome Atlas and GEPIA databases were used to assess the expression of CSGALNACT2 in ovarian cancer patients. RNA-seq, qRT-PCR, and IHC were used to verify the expression of CSGALNACT2 in ovarian cancer tissues. Then, in vivo and in vitro experiments were conducted to evaluate the role of CSGALNACT2 in the progression of ovarian cancer. RNA-seq and GSEA were used to reveal the potential biological function and oncogenic pathways of CSGALNACT2. RESULTS: We demonstrated that the mRNA expression and protein level of CSGALNACT2 were significantly downregulated in ovarian cancer and ovarian cancer metastatic tissues. CSGALNACT2 can significantly inhibit the migration, invasion, and clonogenic growth of ovarian cancer in vitro and is progressively lost during ovarian cancer progression in vivo. CSGALNACT2 suppresses ovarian cancer migration and invasion via DUSP1 modulation of the MAPK/ERK pathway through RNA-seq, KEGG analysis, and Western blotting. Moreover, CSGALNACT2 expression was correlated with immune cell infiltration and had prognostic value in different immune cell-enriched or decreased ovarian cancer. In addition, patients with CSGALNACT2 downregulation are less likely to benefit from immunotherapy. CONCLUSION: As an ovarian cancer suppressor gene, CSGALNACT2 inhibits the development of ovarian cancer, and it might be used as a prognostic biomarker in patients with ovarian cancer.

Clinical significance and immune infiltration analyses of a novel coagulation-related signature in ovarian cancer.

Ovarian cancer (OV) is the most lethal gynecological malignancies worldwide. The coagulation cascade could induce tumor cell infiltration and contribute to OV progression. However, coagulation-related gene (CRG) signature for OV prognosis hasn't been determined yet. In this study, we evaluated the prognostic value of coagulation scores through receiver operating characteristics (ROC) analysis and K-M curves, among OV patients at our institution. Based on the transcriptome data of TCGA-OV cohort, we stratified two coagulation-related subtypes with distinct differences in prognosis and tumor immune microenvironment (p < 0.05). Moreover, from the 6406 differentially-expressed genes (DEGs) between the GTEx (n = 180) and TCGA-OV cohorts (n = 376), we identified 138 potential CRGs. Through LASSO-Cox algorithm, we finally distinguished a 3-gene signature (SERPINA10, CD38, and ZBTB16), with promising prognostic ability in both TCGA (p < 0.001) and ICGC cohorts (p = 0.040). Stepwise, we constructed a nomogram based on the clinical features and coagulation-related signature for overall survival prediction, with the C-index of 0.6761, which was evaluated by calibration curves. Especially, based on tissue microarrays analysis, Quantitative real-time fluorescence PCR (qRT-PCR), and Western Blot, we found that aberrant upregulation of CRGs was related to poor prognosis in OV at both mRNA and protein level (p < 0.05). Collectively, the coagulation-related signature was a robust prognostic biomarker, which could provide therapeutic benefits for chemotherapy/immunotherapy and assist clinical decision in OV patients.

MtDNA Copy Number in Oral Epithelial Cells Serves as a Potential Biomarker of Mitochondrial Damage by Neonicotinoid Exposure: A Cross-Sectional Study.

As the mitochondrial DNA copy number (mtDNAcn) has been reported to be a biomarker for mtDNA damage in honeybees when exposed to sublethal neonicotinoids, the feasibility of using human mitochondria as a predictor upon neonicotinoid exposure remains elusive. This study investigated the association between the urinary neonicotinoid and the relative mtDNAcn (RmtDNAcn) of oral epithelial cells collected in a cross-sectional study with repeated measurements over 6 weeks. The molecular mechanism underlying neonicotinoid-caused mitochondrial damage was also examined by in vitro assay. Herein, the average integrated urinary neonicotinoid (IMIRPF) concentration ranged from 8.01 to 13.70 µg/L (specific gravity-adjusted) during the sampling period. Concomitantly, with an increase in the urinary IMIRPF, the RmtDNAcn significantly increased from 1.20 (low group) to 1.93 (high group), indicating potential dose-dependent mitochondrial damage. Furthermore, the linear regression analysis confirmed the significant correlation between the IMIRPF and RmtDNAcn. Results from in vitro assays demonstrated that neonicotinoid exposure led to the inhibition of the genes encoding mitochondrial oxidative phosphorylation (OXPHOS) complexes I and III (e.g., ND2, ND6, CytB, and CYC1), accompanied by increased reactive oxygen species production in SH-SY5Y cells. Conjointly, neonicotinoid exposure led to mitochondrial dysfunction and a resulting increase in the RmtDNAcn, which may serve as a plausible biomarker in humans.

Development of QuEChERS coupled with UHPLC-MS/MS for simultaneous determination of eight neonicotinoid pesticides in breast milk.

A speedy and hypersensitive method was built to detect eight neonicotinoid insecticides (neonics) in breast milk by ultrahigh-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS). The breast milk was extracted with a mixture of acetonitrile and water and purified with primary secondary amine (PSA) and C18. The recovery of the method ranged from 74.3 to 105.9% with relative standard deviations (RSDs) of less than 10%, and the limit of detection ranged from 0.05 to 0.18 ng/mL. Among 32 samples obtained from women 1 month postpartum, thiamethoxam and imidacloprid were the most frequently detected neonics. Moreover, thiacloprid and imidaclothiz were not detected in any samples. The concentrations of neonics in breast milk ranged from 1.90 to 149.95 ng/mL. Considering the toxic effects on mammals and even humans, infants who are exposed to neonics through ingestion of breast milk should receive extensive attention in future studies.

A novel autophagy-related gene signature associated with prognosis and immune microenvironment in ovarian cancer.

Ovarian cancer (OV), the most fatal gynecological malignance worldwide, has high recurrence rates and poor prognosis. Recently, emerging evidence supports that autophagy, a highly regulated multi-step self-digestive process, plays an essential role in OV progression. Accordingly, we filtered 52 potential autophagy-related genes (ATGs) among the 6197 differentially expressed genes (DEGs) identified in TCGA-OV samples (n = 372) and normal controls (n = 180). Based on the LASSO-Cox analysis, we distinguished a 2-gene prognostic signature, namely FOXO1 and CASP8, with promising prognostic value (p-value < 0.001). Together with corresponding clinical features, we constructed a nomogram model for 1-year, 2-year, and 3-year survival, which was validated in both in training (TCGA-OV, p-value < 0.001) and validation (ICGC-OV, p-value = 0.030) cohorts. Interestingly, we evaluated the immune infiltration landscape through the CIBERSORT algorithm, which indicated the upregulation of 5 immune cells, including CD8 + T cells, Tregs, and Macrophages M2, and high expression of critical immune checkpoints (CTLA4, HAVCR2, PDCD1LG2, and TIGIT) in high-risk group. Stepwise, high-risk group exhibited better sensitivity towards chemotherapies of Bleomycin, Sorafenib, Veliparib, and Vinblastine, though less sensitive to immunotherapy. Especially, based on the IHC of tissue microarrays among 125 patients in our institution, we demonstrated that aberrant upregulation of FOXO1 in OV was related to metastasis and poor prognosis. Moreover, FOXO1 could significantly promote tumor invasiveness, migration, and proliferation in OV cell lines, which was assessed through the Transwell, wound-healing, and CCK-8 assay, respectively. Briefly, the autophagy-related signature was a reliable tool to evaluate immune responses and predict prognosis in the realm of OV precision medicine.

The distribution and human health risk assessment of eight neonicotinoid residues in agricultural soils from four provinces, south China.

The widespread use of neonicotinoid (neonic) insecticides in China's agricultural sector has led to high residual concentrations in the agroecosystem. Since soil is the primary source of direct pesticide exposure, soil contamination is a significant concern, particularly in regions with extensive agricultural production. This study aims to determine the spatial distribution of neonics in farmlands from four southern provinces that are home to China's crucial commercial grain bases. By combining eight neonics into imidacloprid-equivalent total neonics (IMIRPF) using the relative potency factor method, the ecological risks to humans were also assessed. The results showed that imidacloprid had the highest detection rate (96%-100%), followed by thiamethoxam and clothianidin, which ranged from 44% to 64%. Maximum and average IMIRPF values in soil samples from Zhejiang Province were 277.02 and 46.05 µg kg-1 (dry weight), respectively. Guangdong (maximum = 191.62 µg kg-1, mean = 39.70 µg kg-1) and Jiangxi (maximum = 199.13 µg kg-1, mean = 28.95 µg kg-1) had comparable IMIRPF while Jiangsu had the lowest level of total neonics, with a maximum of 86.07 µg kg-1 and a mean of 19.49 µg kg-1. A significant positive correlation between IMIRPF and total organic carbon in soils was also found. The average daily doses of neonics from soil-borne exposure through food intake, soil ingestion, inhalation, and dermal contact calculated for adults and children in each province were all lower than the reference dose (RfD, 57 µg kg-1 d-1) of imidacloprid. However, the potential health risk to human health cannot be disregarded, given their increasing use and pervasiveness in the environment. Our results help to raise concerns about the safety of the agroecological environment under neonic exposure in the major agricultural provinces of southern China.

The partitioning and distribution of neonicotinoid insecticides in human blood.

The burden of neonicotinoid insecticides (neonics) in humans has attracted widespread attention in recent years due to the potential adverse effects. Nonetheless, information on the partitioning behavior and distribution in human blood is still limited. Herein, we obtained 115 adult whole blood and plasma specimens for analysis of eight neonics to better understand neonics' partitioning and distribution in human blood. At least one neonic was detected in 49.6% of the red blood cells and 55.7% of the plasma. In red blood cells, the highest detection rate and concentration was thiamethoxam (THI) with 19.1% and 3832 ng/L, respectively. Imidacloprid had the highest detection rate with 26.1% in the plasma. The mass fraction (Fp) of neonics detected indicates that thiacloprid, imidacloprid, and dinotefuran are mostly resided in plasma upon entering into human blood, while thiamethoxam is mostly present in red blood cells. The distribution of clothianidin and acetamiprid between plasma and red blood cells is similar. The mass fraction (Fp) values for THI were significantly different compared to other neonics, and the effect of age and gender on THI partitioning concluded that there may not be significant variability in the distribution of THI in the sampled population. Overall, this study was the first to investigate neonics residuals in red blood cells and provided fundamental information on the partitioning and distribution of neonics in human blood.

Stereoselective metabolic disruption of cypermethrin by remolding gut homeostasis in rat.

Cypermethrin (CYP), a prototypical synthetic pyrethroid, reportedly causes metabolic disruption, while its stereoselective impact remains elusive. This study initially revealed that only α-CYP caused significant weight loss at 8.5 mg/(kg•day) in rats. All three CYP isomers caused the accumulation of hepatic glycogen, and hyperlipemia phenotype as the increment of total triglyceride. Rats treated with α-CYP had markedly high blood glucose levels and homeostasis model assessment of insulin resistance index. The systematic inflammation of θ-CYP group rats was evidenced by high lipopolysaccharide-binding protein levels and abnormalities of leukocytes indices. By examining the gut microbiome, we found that α-CYP-treated rats had low contents of Firmicutes and high levels of Verrucomicrobia while Elusimicrobia was enriched in the ß-CYP group. The increasing alpha diversity in the θ-CYP group may be due to the dominance of pathogenic bacteria and the increase of probiotics to counteract adverse effects. Exclusively, the α-CYP group enriched total short-chain fatty acids (SCFAs), whereas most SCFAs depleted in the θ-CYP group. The correlation analysis further found Firmicutes, an energy storage modulator, was positive to body weight (BW), while SCFAs exerted the opposite, confirming the low BW in α-CYP. Blood glucose that correlated well with SCFAs and Verrucomicrobia can be accounted for the discrepancy between α-CYP and θ-CYP. Overall, the three isomers exerted stereoselective glycolipid disruption in rats, and gut homeostasis acted as vital indicators.

The stereoselective metabolic disruption of cypermethrin on rats by a sub-acute study based on metabolomics.

Due to the massive application of cypermethrin (CYP) for pest control in China, the adverse effects on non-target organisms have aroused great attention. However, comparative studies between its different stereoisomers remain scarce, especially for metabolism perturbations. Herein, the rats were administered α-CYP, ß-CYP, and θ-CYP by gavage at doses of 8.5, 29.2, and 25.0 mg/kg/day, respectively, for 28 consecutive days. By blood examination, significant changes in liver and renal function parameters were observed in rats exposed to all three CYPs. The stereoisomeric selectivity in metabolic disturbances was assessed based on a metabolomic strategy via multivariate analysis and pathway analysis. The results demonstrated that amino acid and glycolipid metabolism were disrupted in all CYP groups. Among them, the most significant changes in the metabolic phenotype were observed in the θ-CYP group, with 56 differential metabolites enriched in 9 differential metabolic pathways. At the same time, the endogenous metabolite trimethylamine oxide (TMAO), which is closely linked to the gut microbiota, was also significantly elevated in this group. Gender differences were found in α- and θ-CYP-exposed rats, with perturbations in amino acid and glucose metabolism of greater concern in females and lipid metabolism of greater concern in males. Overall, ß-CYP exhibited a lower risk of metabolic perturbations than α-CYP or θ-CYP, which helps to screen suitable agrochemical products for green agricultural development.

Artificial intelligence-based preoperative prediction system for diagnosis and prognosis in epithelial ovarian cancer: A multicenter study.

Background: Ovarian cancer (OC) is the most lethal gynecological malignancy, with limited early screening methods and poor prognosis. Artificial intelligence technology has made a great breakthrough in cancer diagnosis. Purpose: We aim to develop a specific interpretable machine learning (ML) prediction model for the diagnosis and prognosis of epithelial ovarian cancer (EOC) based on a variety of biomarkers. Methods: A total of 521 patients with EOC and 144 patients with benign gynecological diseases were enrolled including derivation datasets and an external validation cohort. The predicted information was acquired by 9 supervised ML methods, through 34 parameters. Behind predicted reasons for the best ML were improved by using the SHapley Additive exPlanations (SHAP) algorithm. In addition, the prognosis of EOC was analyzed by unsupervised clustering and Kaplan-Meier (KM) survival analysis. Results: ML technology was superior to conventional logistic regression in predicting EOC diagnosis and XGBoost performed best in the external validation datasets. The AUC values of distinguishing EOC and benign disease patients, determining pathological type, grade and clinical stage were 0.958 (0.926-0.989), 0.792 (0.701-0.8834), 0.819 (0.687-0.950) and 0.68 (0.573-0.788) respectively. For negative CA-125 EOC patients, the AUC performance of XGBoost model was 0.835(0.763-0.907). We used unsupervised cluster analysis to identify EOC subgroups with significantly poor overall survival (p-value <0.0001) and recurrence-free survival (p-value <0.0001). Conclusions: Based on the preoperative characteristics, we proved that ML algorithm can provide an acceptable diagnosis and prognosis prediction model for EOC patients. Meanwhile, SHAP analysis can improve the interpretability of ML models and contribute to precision medicine.

Intraperitoneal oncolytic virotherapy for patients with malignant ascites: Characterization of clinical efficacy and antitumor immune response.

Oncolytic viruses mediate antitumor responses through direct tumor cell lysis and induction of host antitumor immunity. However, the therapeutic efficacy of oncolytic viruses against malignant ascites has rarely been explored. This study aimed to evaluate the efficacy, safety, and immunomodulatory effect of an intraperitoneal injection of human type 5 recombinant adenovirus (called H101) against malignant ascites. Forty patients with malignant ascites were recruited and treated with intraperitoneal H101 in the Fudan University Shanghai Cancer Center. The 4-week clinical responses were determined by an objective assessment of ascites volume change. The ascites response rate and ascites control rate were 40% (16/40) and 75% (30/40), respectively. The major adverse events following intraperitoneal H101 administration were mild-to-moderate abdominal pain (8/40, 20.0%) and fever (11/40, 27.5%); no grade III/IV adverse events were observed. Mass cytometry and immunocytological analysis at baseline, and days 7 and 14 post-treatment showed that intraperitoneally injected H101 led to marked tumor cell depletion, increased dendritic cell and CD8+ T cell densities. H101-meditated tumor-specific immune activation on day 14 post-treatment was further identified by enzyme-linked immunospot assay. In conclusion, intraperitoneal H101 administration was well tolerated and effective in treating malignant ascites; thus, its immune activation ability may be a promising tool in combination with immunotherapy.

Integrated assessment of endocrine disrupting potential of four novel brominated flame retardants.

Novel brominated flame retardants (NBFRs) have emerged as alternatives to the legacy BFRs due to BFRs' persistence, bioaccumulation and evidence of adverse health effects. The increasing production of NBFRs has led to the frequent detection in environmental media and even in organisms. Thus the potential health risks of these novel NBFRs need to be taken into account. Herein, the endocrine disrupting effects of the four NBFRs (α/ß-TBCO, PBEB, EHTBB and BEHTBP) were evaluated by constructing an estrogen receptor (ERα), glucocorticoid receptor (GR), and mineralocorticoid receptor (MR) mediated dual-luciferase reporter gene assays on the CHO cells, in combination with steroid experiments on the H295R cells and molecular docking. The results revealed that α/ß-TBCO, PBEB and EHTBB induced anti-estrogenic activity at certain concentrations while none of the four NBFRs was agonistic to ERα. For reporter gene assay, only PBEB exhibited GR antagonistic effects. Notably, none of the four NBFRs possess neither agonistic nor antagonistic activity of MR. The molecular docking results were generally consistent with the reporter gene assay, which showed the different binding affinities between NBFRs and the receptors. For steroidogenesis, α/ß-TBCO, PBEB, and EHTBB all upregulated genes encoding for steroid synthesis enzymes, including 17ßHSD, CYP11B1 and CYP17. Altogether, the data clarified that NBFRs may pose risks of endocrine disruption.

Response of bacterial community to iron oxide nanoparticles during agricultural waste composting and driving factors analysis.

The succession of bacterial communities and their function, and the core microorganisms for water soluble organic carbon (WSC) and organic matter (OM) changes during agricultural waste composting with addition of iron oxide nanomaterials (FeONPs, Fe2O3 NPs and Fe3O4 NPs) were investigated. Moreover, driving factors for bacterial composition and metabolism were analyzed. Results showed that FeONPs treatments increased the relative abundance of thermophilic microorganisms for OM degradation. Most of the core genera were responsible for decomposition of OM and synthesis of WSC. Additionally, FeONPs promoted the metabolism of amino acids. The most significant factors for dominant genera in control, Fe2O3 NPs and Fe3O4 NPs group were moisture (62.1%), moisture (62.0%) and OM (58.2%), respectively. For metabolism, the most significant factors in control, Fe2O3 NPs and Fe3O4 NPs group were temperature (57.2%), NO3--N (60.5%), NO3--N (62.6%), respectively. The relationships between compost properties, bacterial community and metabolism were changed by FeONPs.

Exploring prognostic indicators in the pathological images of ovarian cancer based on a deep survival network.

Background: Tumor pathology can assess patient prognosis based on a morphological deviation of tumor tissue from normal. Digitizing whole slide images (WSIs) of tissue enables the use of deep learning (DL) techniques in pathology, which may shed light on prognostic indicators of cancers, and avoid biases introduced by human experience. Purpose: We aim to explore new prognostic indicators of ovarian cancer (OC) patients using the DL framework on WSIs, and provide a valuable approach for OC risk stratification. Methods: We obtained the TCGA-OV dataset from the NIH Genomic Data Commons Data Portal database. The preprocessing of the dataset was comprised of three stages: 1) The WSIs and corresponding clinical data were paired and filtered based on a unique patient ID; 2) a weakly-supervised CLAM WSI-analysis tool was exploited to segment regions of interest; 3) the pre-trained model ResNet50 on ImageNet was employed to extract feature tensors. We proposed an attention-based network to predict a hazard score for each case. Furthermore, all cases were divided into a high-risk score group and a low-risk one according to the median as the threshold value. The multi-omics data of OC patients were used to assess the potential applications of the risk score. Finally, a nomogram based on risk scores and age features was established. Results: A total of 90 WSIs were processed, extracted, and fed into the attention-based network. The mean value of the resulting C-index was 0.5789 (0.5096-0.6053), and the resulting p-value was 0.00845. Moreover, the risk score showed a better prediction ability in the HRD + subgroup. Conclusion: Our deep learning framework is a promising method for searching WSIs, and providing a valuable clinical means for prognosis.

Circulating EVs long RNA-based subtyping and deconvolution enable prediction of immunogenic signatures and clinical outcome for PDAC.

Identification of clinically applicable molecular subtypes of pancreatic ductal adenocarcinoma (PDAC) is crucial to improving patient outcomes. However, the traditional tissue-dependent transcriptional subtyping strategies are invasive and not amenable to routine clinical evaluation. In this study, we developed a circulating extracellular vesicle (cEV) long RNA (exLR)-based PDAC subtyping method and provided exLR-derived signatures for predicting immunogenic features and clinical outcomes in PDAC. We enrolled 426 individuals, among which 227 PDACs served as an internal cohort, 118 PDACs from two other medical centers served as an independent validation cohort, and 81 healthy individuals served as the control. ExLR sequencing was performed on all plasma samples. We found that PDAC could be categorized into three subtypes based on plasma exLR profiles. Each subpopulation showed its own molecular features and was associated with patient clinical prognosis. The immunocyte-derived cEV fractions were altered among PDAC subtypes and interconnected with tumor-infiltrating lymphocytes in cancerous tissue. Additionally, we found a significant concordance of immunoregulators between tissue and blood EVs, and we harvested potential PDAC therapeutic targets. Most importantly, we constructed a nine exLR-derived, tissue-applicable signature for prognostic assessment of PDAC. The circulating exLR-based features may offer an attractive platform for personalized treatment and predicting patient outcomes in multiple types of cancer.

Significance of intratumoral infiltration of B cells in cancer immunotherapy: From a single cell perspective.

Immunotherapy for cancer has provided new treatment approaches for malignant tumors, but there are low rates of response and high rates of resistance. The most recent sequencing method which is called single-cell RNA sequencing(scRNA-seq) determines the transcriptome at the single cell level, which allows high-resolution dynamic monitoring of the tumor microenvironment (TME) during immunotherapy. As an important part of humoral immunity, tumor-infiltrated B cells have been reported to have distinct functions in anti-tumor immunity, which are characterized by their RNA transcriptome, membrane surface receptors, and immunoglobulin secretion, suggesting great immunotherapeutic effects. On the basis of the important roles of B cells in immunotherapy reported in recent publications, we discuss the tumor-infiltrated B cells' subpopulations, differentiation trajectory, and interactions with other cells in the TME in this review, hoping to illustrate its significance in potential clinical application as biomarkers and therapeutic targets.