Study on the active components and mechanism of Atractylodis Macrocephalae Rhizoma for invigorating the spleen and tonifying qi based on spectrum-effect relationship and network pharmacology.

Spleen deficiency can lead to various abnormal physiological functions of the spleen. Atractylodis Macrocephalae Rhizoma (AMR) is a traditional Chinese medicine used to invigorate the spleen and tonify qi. The study aimed to identify the primary active components influencing the efficacy of AMR in strengthening the spleen and replenishing qi through spectrum-effect relationship and chemometrics. Network pharmacology was used to investigate the mechanism by which AMR strengthens the spleen and replenishes qi, with molecular docking utilized for validation purposes. The findings indicated that bran-fried AMR exhibited superior efficacy, with atractylenolides and atractylone identified as the primary active constituents. Atractylenolide II emerged as the most influential component impacting the effectiveness of AMR, while the key target was androgen receptor. Furthermore, crucial pathways implicated included the mitogen-activated protein cascade (MAPK) cascade, RNA polymerase II transcription factor activity, ligand-activated sequence-specific DNA binding, and RNA polymerase II sequence-specific DNA-binding transcription factor binding. In summary, our study has identified the primary active components associated with the efficacy of AMR and has provided an initial exploration of its mechanism of action. This offers a theoretical foundation for future investigations into the material basis and molecular mechanisms underlying the pharmacodynamics of AMR.

Exploring new frontiers: cell surface vimentin as an emerging marker for circulating tumor cells and a promising therapeutic target in advanced gastric Cancer.

BACKGROUND: Circulating tumor cells (CTCs) hold immense promise in guiding treatment strategies for advanced gastric cancer (GC). However, their clinical impact has been limited due to challenges in identifying epithelial-mesenchymal transition (EMT)-CTCs using conventional methods. METHODS: To bridge this knowledge gap, we established a detection platform for CTCs based on the distinctive biomarker cell surface vimentin (CSV). A prospective study involving 127 GC patients was conducted, comparing CTCs enumeration using both EpCAM and CSV. This approach enabled the detection of both regular and EMT-CTCs, providing a comprehensive analysis. Spiking assays and WES were employed to verify the reliability of this marker and technique. To explore the potential inducer of CSV+CTCs formation, a combination of Tandem Mass Tag (TMT) quantitative proteomics, m6A RNA immunoprecipitation-qPCR (MeRIP-qPCR), single-base elongation- and ligation-based qPCR amplification method (SELECT) and RNA sequencing (RNA-seq) were utilized to screen and confirm the potential target gene. Both in vitro and in vivo experiments were performed to explore the molecular mechanism of CSV expression regulation and its role in GC metastasis. RESULTS: Our findings revealed the potential of CSV in predicting therapeutic responses and long-term prognosis for advanced GC patients. Additionally, compared to the conventional EpCAM-based CTCs detection method, the CSV-specific positive selection CTCs assay was significantly better for evaluating the therapeutic response and prognosis in advanced GC patients and successfully predicted disease progression 14.25 months earlier than radiology evaluation. Apart from its excellent role as a detection marker, CSV emerges as a promising therapeutic target for attenuating GC metastasis. It was found that fat mass and obesity associated protein (FTO) could act as a potential catalyst for CSV+CTCs formation, and its impact on the insulin-like growth factor-I receptor (IGF-IR) mRNA decay through m6A modification. The activation of IGF-I/IGF-IR signaling enhanced the translocation of vimentin from the cytoplasm to the cell surface through phosphorylation of vimentin at serine 39 (S39). In a GC mouse model, the simultaneous inhibition of CSV and blockade of the IGF-IR pathway yielded promising outcomes. CONCLUSION: In summary, leveraging CSV as a universal CTCs marker represents a significant breakthrough in advancing personalized medicine for patients with advanced GC. This research not only paves the way for tailored therapeutic strategies but also underscores the pivotal role of CSV in enhancing GC management, opening new frontiers for precision medicine.

Study on the quality of Corydalis Rhizoma in Zhejiang based on multidimensional evaluation method.

ETHNOPHARMACOLOGICAL RELEVANCE: The quality requirements of Corydalis Rhizoma (CR) in different producing areas are uniform, resulting in uneven efficacy. As a genuine producing area, the effective quality control of CR in Zhejiang Province (ZJ) could provide a theoretical basis for the rational application of medicinal materials. AIM OF THE STUDY: The purpose of this study was to effectively distinguish the CR inside and outside ZJ, and provided a theoretical basis for the quality control and material basis research of ZJ CR. MATERIALS AND METHODS: The core components of ZJ CR could be identified by HPLC combined with chemometrics screening, and the quality of CR from different producing areas was evaluated by a genetic algorithm-back propagation (GA-BP) neural network. Chromaticity and near-infrared (NIR) spectroscopy were used to identify CR inside and outside ZJ, and rapid content prediction was realized. The analgesic effect of CR in different regions was compared by a zebrafish analgesic experiment. Analgesic experiments in rats and analysis of the research status of quality components were used to screen the quality control components of ZJ CR. RESULTS: The contents of palmatine hydrochloride (YSBMT), dehydrocorydaline (TQZJJ), tetrahydropalmatine (YHSYS), tetrahydroberberine (SQXBJ), corydaline (YHSJS), stylopine (SQHLJ), and isoimperatorin (YOQHS) in ZJ CR were higher than those in CR from outside ZJ, but the content of protopine (YAPJ) and berberine hydrochloride (YSXBJ) was lower than that in CR from outside ZJ. YHSJS and SQHLJ could be used as the core components to identify ZJ CR. The GA-BP neural network showed that the relative importance of ZJ CR was the strongest. Chroma-content correlation analysis and the NIR qualitative model could effectively distinguish CR from inside and outside of ZJ, and the NIR quantitative model could quickly predict the content of CR from inside and outside of ZJ. Zebrafish experiments showed that ZJ, Shaanxi (SX), Henan (HN), and Sichuan (SC) CR had significant analgesic effects, while Hebei (HB) CR had no significant analgesic effect. Overall comparison, the analgesic effect of ZJ CR was better than that of CR outside ZJ. The comprehensive score of the grey correlation degree between YAPJ, YSBMT, YSXBJ, TQZJJ, YHSYS, YHSJS, SQXBJ, and SQHLJ were higher than 0.9, and the research frequency were extremely high. CONCLUSIONS: The relative importance of the content and origin of most components of ZJ CR was higher than that of CR outside ZJ. The holistic analgesic effect of ZJ CR was better than that of CR outside ZJ, but slightly lower than that of SX CR. YHSJS and SQHLJ could be used as the core components to identify ZJ CR. YAPJ, YSBMT, YSXBJ, TQZJJ, YHSYS, SQXBJ, YHSJS, and SQHLJ could be used as the quality control components of ZJ CR. The multidimensional evaluation method used in this study provided a reference for the quality control and material basis research of ZJ CR.

Analysis of Quality Differences in Radix Dipsaci before and after Processing with Salt Based on Quantitative Control of HPLC Multi-Indicator Components Combined with Chemometrics.

Radix Dipsaci (RD) is the dry root of the Dipsacus asper Wall. ex DC., which is commonly used for tonifying the kidney and strengthening bone. The purpose of this study was to analyze the difference between raw and salt-processed RD from the chemical composition comprehensively. The fingerprints of raw and salt-processed RD were established by HPLC-DAD to determine the contents of loganin (LN), asperosaponin VI (AVI), caffeic acid (CaA), dipsanoside A (DA), dipsanoside B (DB), chlorogenic acid (CA), loganic acid (LA), isochlorogenic acid A (IA), isochlorogenic acid B (IB), and isochlorogenic acid C (IC). The results showed that after processing with salt, the components with increased contents were LA, CaA, DA, and AVI, and the components with decreased contents were CA, LN, IB, IA, IC, and DB. Then, the chemometric methods such as principal component analysis (PCA) and fisher discriminant analysis (FDA) were used to evaluate the quality of raw and salt-processed RD. In the classification of raw and salt-processed RD, the order of importance of each chemical component was LA > DB > IA > IC > IB > LN > CA > DA > AVI > CaA. These integrated methods successfully assessed the quality of raw and salt-processed RD, which will provide guidance for the development of RD as a clinical medication.

Safety, Tolerability, Pharmacokinetics, and Preliminary Efficacy of YK-029A in Treatment-Naive Patients With Advanced NSCLC Harboring EGFR Exon 20 Insertion Mutations: A Phase 1 Trial.

INTRODUCTION: Treatment options for treatment-naive patients with advanced NSCLC harboring EGFR exon 20 insertion (ex20ins) mutations are limited. This study evaluated the safety, tolerability, and pharmacokinetics of YK-029A, a third-generation EGFR tyrosine kinase inhibitor, and the preliminary efficacy of YK-029A in treatment-naive patients with EGFR ex20ins mutation. METHODS: This multicenter, dose-escalation, and dose-expansion phase 1 clinical trial enrolled patients with NSCLC harboring EGFR mutations. During the dose-escalation phase, YK-029A was orally administered using the traditional 3+3 principle at 50, 100, 150, 200, and 250 mg/d. In the dose-expansion phase, treatment-naive patients with EGFR ex20ins mutations were enrolled and administered YK-029A 200 mg/d. The primary end point was safety and tolerability. RESULTS: The safety analysis included 108 patients. No dose-limiting toxicity was observed, and the maximum tolerated dose was not reached. The most common treatment-emergent adverse events were anemia (50.9%), diarrhea (49.1%), and rash (34.3%). There was minimal drug accumulation after multiple doses. A total of 28 treatment-naive patients with EGFR ex20ins mutations were enrolled in the dose-expansion and 26 were included in the efficacy analysis. According to the independent review committee evaluation, the objective response rate was 73.1% (95% confidence interval: 52.21%-88.43%), and the disease control rate was 92.3% (95% confidence interval: 74.87%-99.05%). CONCLUSIONS: YK-029A was found to have manageable safety and be tolerable in patients with NSCLC harboring EGFR mutations and have promising antitumor activity in untreated patients with EGFR ex20ins mutations.

PB-LNet: a model for predicting pathological subtypes of pulmonary nodules on CT images.

OBJECTIVE: To investigate the correlation between CT imaging features and pathological subtypes of pulmonary nodules and construct a prediction model using deep learning. METHODS: We collected information of patients with pulmonary nodules treated by surgery and the reference standard for diagnosis was post-operative pathology. After using elastic distortion for data augmentation, the CT images were divided into a training set, a validation set and a test set in a ratio of 6:2:2. We used PB-LNet to analyze the nodules in pre-operative CT and predict their pathological subtypes. Accuracy was used as the model evaluation index and Class Activation Map was applied to interpreting the results. Comparative experiments with other models were carried out to achieve the best results. Finally, images from the test set without data augmentation were analyzed to judge the clinical utility. RESULTS: Four hundred seventy-seven patients were included and the nodules were divided into six groups: benign lesions, precursor glandular lesions, minimally invasive adenocarcinoma, invasive adenocarcinoma Grade 1, Grade 2 and Grade 3. The accuracy of the test set was 0.84. Class Activation Map confirmed that PB-LNet classified the nodules mainly based on the lungs in CT images, which is in line with the actual situation in clinical practice. In comparative experiments, PB-LNet obtained the highest accuracy. Finally, 96 images from the test set without data augmentation were analyzed and the accuracy was 0.89. CONCLUSIONS: In classifying CT images of lung nodules into six categories based on pathological subtypes, PB-LNet demonstrates satisfactory accuracy without the need of delineating nodules, while the results are interpretable. A high level of accuracy was also obtained when validating on real data, therefore demonstrates its usefulness in clinical practice.

Revealing a causal relationship between gut microbiota and lung cancer: a Mendelian randomization study.

Background: The gut microbiota has been found to be associated with the risk of lung cancer. However, its causal relationship with various types of lung cancer remains unclear. Methods: We conducted a Mendelian randomization (MR) study using the largest genome-wide association analysis of gut microbiota data to date from the MiBioGen consortium, with pooled statistics for various types of lung cancer from the Transdisciplinary Research in Cancer of the Lung, the International Lung Cancer Consortium, and FinnGen Consortium R7 release data. Inverse variance weighted, weighted model, MR-Egger regression, and weighted median were adapted to assess the causal relationship between gut microbiota and various types of lung cancer. Sensitivity analysis was used to test for the presence of pleiotropy and heterogeneity in instrumental variables. A reverse MR analysis was performed on these bacteria to determine their potential role in causing lung cancer. A reverse MR analysis was performed on these bacteria to determine their potential role in causing lung cancer. Multivariable Mendelian randomization (MVMR) was conducted to assess the direct causal impact of gut microbiota on the risk of various types of lung cancer. Results: Using IVW as the primary analytical method, we identified a total of 40 groups of gut microbiota with potential causal associations with various subtypes of lung cancer, of which 10 were associated with lung cancer, 10 with lung adenocarcinoma, 9 with squamous cell lung cancer, and 11 groups of bacteria associated with small cell lung cancer. After performing FDR correction, we further found that there was still a significant causal relationship between Peptococcaceae and lung adenocarcinoma. Sensitivity analyses demonstrated the robustness of these results, with no heterogeneity or pleiotropy found. Conclusions: Our results confirm a causal relationship between specific gut microbiota and lung cancer, providing new insights into the role of gut microbiota in mediating the development of lung cancer.

Research on the effect of Dipsaci Radix before and after salt-processed on kidney yang deficiency syndrome rats and the preliminary mechanism study through the BMP-Smad signaling pathway.

ETHNOPHARMACOLOGICAL RELEVANCE: Dipsaci Radix (DR) is the dry root of Dipsacus asper Wall. ex DC. AIM OF THE STUDY: The purpose of this study was to compare the effects of DR on rats before and after salt-processed with kidney yang deficiency syndrome (KYDS), and we selected the BMP-Smad signaling pathway to explore the mechanism of DR. MATERIALS AND METHODS: The model of KYDS was established by subcutaneous injection of hydrocortisone, the crude DR (CDR) and salt-processed DR (SDR) were given the corresponding dose (2 g/kg, 4 g/kg, and 6 g/kg). The organ index and the contents of adrenocorticotropic hormone (ACTH), cortistatin (CORT), thyroid hormone (T4), tumor necrosis factor-alpha (TNF-α), testosterone (T), estradiol (E2), cyclic adenosine monophosphate (cAMP), cyclic guanosine monophosphate (cGMP), Na+-K+-ATPase, and growth hormone (GH) in serum were measured to evaluate the intervention effect of DR on KYDS rats. The expression of Smad 1, Smad 4, Smad 5, Smad 8, and BMP 7 protein in kidney was determined by immunohistochemistry, quantitative PCR (qPCR) and Western blot analysis. The effects of DR on 5 expression factors in the BMP-Smad signaling pathway were studied. Constituents absorbed into blood were identified by UPLC-Q-TOF/MS. RESULTS: The results showed that compared with the model group, the thymus and kidney index, as well as the contents of ACTH, CORT, cAMP, GH, Na+-K+-ATPase, T, T4, and E2 were significantly increased in the CDR and SDR groups, and the contents of cGMP and TNF-α were significantly decreased. Compared with the CDR high dose group, ACTH, Na+-K+-ATPase, T, and T4 were significantly increased in the SDR high dose group. The results of immunohistochemistry, qPCR, and Western blot analysis showed that compared with the model group, the expression levels of Smad 1, Smad 4, Smad 5, Smad 8 and BMP 7 proteins in the kidney of DR groups were significantly increased. And SDR groups tended to be better than CDR groups. 8 constituents migrating to blood were identified. CONCLUSION: This study showed that both CDR and SDR could have a good therapeutic effect on KYDS, and SDR was better than CDR. This study chose the BMP-Smad signaling pathway to study the mechanism of DR in the treatment of KYDS and provided a scientific basis for the processing mechanism of salt-processed.

Exploring active ingredients of anti-osteoarthritis in raw and wine-processed Dipsaci Radix based on spectrum-effect relationship combined with chemometrics.

ETHNOPHARMACOLOGICAL RELEVANCE: Dipsaci Radix (DR) is the dry root of the Dipsacus asper Wall. ex DC., which has the function of tonifying the liver and kidney, continuing tendons and bones, and regulating blood vessels. However, there are few reports on the main active ingredients. AIM OF THE STUDY: This study aimed to find the main active components of DR in the treatment of osteoarthritis (OA) by spectrum-effect relationship and compare the differences between RDR and WDR. MATERIALS AND METHODS: Firstly, the high-performance liquid chromatography (HPLC) method was used to establish the fingerprint of DR, and 10 peaks of them were determined by UPLC-Q-TOF/MS. Then, the OA rat model was established by injecting sodium iodoacetate to study the effect of DR on OA. The spectrum-effect relationship was analyzed by grey relational analysis (GRA) and Pearson correlation analysis. RESULTS: According to the pharmacological results, compared with the model group, the cartilage score, tumor necrosis factor-α (TNF-α), interleukin-1ß (IL-1ß), and interleukin-6 (IL-6), and Mankin score of rats in low, medium and high dose groups were decreased, and the therapeutic effect of wine-processed DR tended to be better than raw DR at the same dose. Finally, the active components of DR were preliminarily determined as 4 (loganic acid), 6 (chlorogenic acid), 8 (caffeic acid), 14 (dipsanoside B), 16, and 17 (asperosaponin VI) which had a large correlation in GRA and Pearson correlation analysis. CONCLUSION: This study established the spectrum-effect relationship between the raw and wine-processed DR for the first time, which provided a theoretical basis for the study of the pharmacodynamic substance basis of DR before and after processing. This research provided a reference for the subsequent study of DR.

SMARCA4: Current status and future perspectives in non-small-cell lung cancer.

SMARCA4, also known as transcription activator, is an ATP-dependent catalytic subunit of SWI/SNF (SWItch/Sucrose NonFermentable) chromatin-remodeling complexes that participates in the regulation of chromatin structure and gene expression by supplying energy. As a tumor suppressor that has aberrant expression in ∼10% of non-small-cell lung cancers (NSCLCs), SMARCA4 possesses many biological functions, including regulating gene expression, differentiation and transcription. Furthermore, NSCLC patients with SMARCA4 alterations have a weak response to conventional chemotherapy and poor prognosis. Therefore, the mechanisms of SMARCA4 in NSCLC development urgently need to be explored to identify novel biomarkers and precise therapeutic strategies for this subtype. This review systematically describes the biological functions of SMARCA4 and its role in NSCLC development, metastasis, functional epigenetics and potential therapeutic approaches for NSCLCs with SMARCA4 alterations. Additionally, this paper explores the relationship and regulatory mechanisms shared by SMARCA4 and its mutually exclusive catalytic subunit SMARCA2. We aim to provide innovative treatment strategies and improve clinical outcomes for NSCLC patients with SMARCA4 alterations.

Identification of the Mechanism of Matrine Combined with Glycyrrhizin for Hepatocellular Carcinoma Treatment through Network Pharmacology and Bioinformatics Analysis.

Matrine and glycyrrhizin are representative active ingredients of traditional Chinese medicine (TCM) used in clinical practice. Studies have demonstrated that matrine has antitumor pharmacological effects and that glycyrrhizin protects liver function. However, the potential bioactive compounds and mechanisms remain unknown, as well as whether they have synergistic effects in killing cancer cells and protecting liver cells. To investigate the synergistic effects and mechanism of matrine combined with glycyrrhizin in hepatocellular carcinoma (HCC) treatment, we used both network pharmacology and bioinformatics analyses. First, the chemical gene interaction information of matrine and glycyrrhizin was obtained from the PubChem database. The pathogenic genes of HCC were accessed from five public databases. The RNA sequencing data and clinical information of HCC patients were downloaded from The Cancer Genome Atlas (TCGA). Next, the overlapping genes among the potential targets of matrine and glycyrrhizin and HCC-related targets were determined using bioinformatics analysis. We constructed the drug-target interaction network. Prognosis-associated genes were acquired through the univariate Cox regression model and Lasso-Cox regression model. The results were verified by the International Cancer Genome Consortium (ICGC) database. Finally, we predicted the immune function of the samples. The drug-target interaction network consisted of 10 matrine and glycyrrhizin targets. We selected a Lasso-Cox regression model consisting of 3 differentially expressed genes (DEGs) to predict the efficacy of the combination in HCC. Subsequently, we successfully predicted the overall survival of HCC patients using the constructed prognostic model and investigated the correlation of the immune response. Matrine and glycyrrhizin have synergistic effects on HCC. The model we obtained consisted of three drug-target genes by Lasso-Cox regression analysis. The model independently predicted the combined effect of matrine and glycyrrhizin in HCC treatment and OS, which will be helpful for guiding clinical treatment. The prognostic model was correlated with the immune cells and immune checkpoints of patients, which had an adjuvant effect on HCC immunotherapy. Matrine and glycyrrhizin can have therapeutic effects on HCC by promoting the production or enhancing the core gene activity in the drug network and improving the immune system function of patients.

miR-3168 promotes hepatocellular carcinoma progression via downregulating p53.

Hepatocellular carcinoma (HCC) is a highly malignant disease with poor prognosis, which is partly due to the presence of liver cancer stem cells (CSCs). CSCs participate in tumor recurrence, metastasis, and chemoresistance. However, the mechanisms underlying liver CSC regulation are unknown. In this study, we found that miR-3168 expression is increased in HCC and that it predicts poor prognosis. Functional assays showed that miR-3168 promotes HCC cells' proliferation and facilitates liver CSC self-renewal and tumorigenicity. Mechanistically, bioinformatics and the luciferase reporter assay demonstrated that miR-3168 targets the 3'UTR of the p53 mRNA. MiR-3168 expression was negatively correlated with p53 mRNA in HCC tissue samples. Rescue assays demonstrated that p53 knockdown abrogates the discrepancies in proliferation, self-renewal, and tumorigenicity between miR-3168 knockdown HCC cells and control HCC cells. Furthermore, miR-3168 expression was negatively correlated with p53 in HCC tissues. The combined HCC panels exhibited a worse prognostic value for HCC patients than any of these components alone. Moreover, miR-3168 expression was increased in cisplatin-resistant HCC cells and patient-derived xenografts. Clinical cohort analysis revealed that HCC patients with low miR-3168 levels have a superior survival rate when treated with postoperative transcatheter arterial chemoembolization compared with that of patients with high miR-3168 levels. In conclusion, our study uncovered a novel mechanism of liver CSC regulation and provided a potential therapeutic target for liver CSCs.

Exploring potential targets of Actinidia chinensis Planch root against hepatocellular carcinoma based on network pharmacology and molecular docking and development and verification of immune-associated prognosis features for hepatocellular carcinoma.

Background: Hepatocellular carcinoma (HCC) is one of the malignant tumors with the highest morbidity and mortality worldwide, and its prognosis remains a challenge. Actinidia chinensis Planch (ACP) root has good efficacy against HCC. This study aimed to explore the link between ACP and potential targets of HCC, and to develop a novel immune-based gene signature to predict HCC patient survival. Methods: Transcriptome data and clinical information on HCC were obtained from The Cancer Genome Atlas (TCGA; HCC: 374, normal: 50) and International Cancer Genome Consortium (ICGC) database (HCC: 243, normal: 202). Combined with the 2,483 immune-related genes from the Immport database, we used the least absolute shrinkage and selection operator (LASSO) to construct a prognostic model. Patients were divided into high-risk and low-risk groups by the median of the risk scores of the TCGA cohort. Kaplan-Meier survival analysis and receiver operating characteristic (ROC) curves were used to estimate the predictability of the model in HCC prognosis, and carried out external validation based on ICGC cohort. We analyzed the correlation of this model with immune cells and immune checkpoint genes. Finally, molecular docking of these genes and the corresponding ACP components. Results: We constructed a prognostic model composed of 3 immune-related genes [epidermal growth factor (EGF), baculoviral inhibitor of apoptosis repeat-containing protein 5 (BIRC5), and secreted phosphoprotein 1 (SPP1)]. And the high-risk group had a lower overall survival (OS) rate compared to the low-risk group (TCGA cohort: P=1.761e-05, ICGC cohort: P=8.716e-04). The outcomes of the AUC of ROC of prognostic risk model to predict for 1-, 2-, and 3-year OS: TCGA cohort: 0.749, 0.710, and 0.653 and ICGC cohort: 0.698, 0.736, and 0.753. Molecular docking results showed that quercetin had good binding activities with SPP1, BIRC5, and EGF, and ursolic acid (UA) and BIRC5 also had this feature. Conclusions: Our study speculates that ACP root anti-HCC may be involved in the immune regulation of the body by targeting EGF, BIRC5 and SPP1, which possess great potential and value as early warning molecules for HCC. This model may provide a reference for individualized diagnosis and treatment for HCC patients.

A deep learning-based system for survival benefit prediction of tyrosine kinase inhibitors and immune checkpoint inhibitors in stage IV non-small cell lung cancer patients: A multicenter, prognostic study.

Background: For clinical decision making, it is crucial to identify patients with stage IV non-small cell lung cancer (NSCLC) who may benefit from tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs). In this study, a deep learning-based system was designed and validated using pre-therapy computed tomography (CT) images to predict the survival benefits of EGFR-TKIs and ICIs in stage IV NSCLC patients. Methods: This retrospective study collected data from 570 patients with stage IV EGFR-mutant NSCLC treated with EGFR-TKIs at five institutions between 2010 and 2021 (data of 314 patients were from a previously registered study), and 129 patients with stage IV NSCLC treated with ICIs at three institutions between 2017 and 2021 to build the ICI test dataset. Five-fold cross-validation was applied to divide the EGFR-TKI-treated patients from four institutions into training and internal validation datasets randomly in a ratio of 80%:20%, and the data from another institution was used as an external test dataset. An EfficientNetV2-based survival benefit prognosis (ESBP) system was developed with pre-therapy CT images as the input and the probability score as the output to identify which patients would receive additional survival benefit longer than the median PFS. Its prognostic performance was validated on the ICI test dataset. For diagnosing which patient would receive additional survival benefit, the accuracy of ESBP was compared with the estimations of three radiologists and three oncologists with varying degrees of expertise (two, five, and ten years). Improvements in the clinicians' diagnostic accuracy with ESBP assistance were then quantified. Findings: ESBP achieved positive predictive values of 80·40%, 75·40%, and 77·43% for additional EGFR-TKI survival benefit prediction using the probability score of 0·2 as the threshold on the training, internal validation, and external test datasets, respectively. The higher ESBP score (>0·2) indicated a better prognosis for progression-free survival (hazard ratio: 0·36, 95% CI: 0·19-0·68, p<0·0001) in patients on the external test dataset. Patients with scores >0·2 in the ICI test dataset also showed better survival benefit (hazard ratio: 0·33, 95% CI: 0·18-0·55, p<0·0001). This suggests the potential of ESBP to identify the two subgroups of benefiting patients by decoding the commonalities from pre-therapy CT images (stage IV EGFR-mutant NSCLC patients receiving additional survival benefit from EGFR-TKIs and stage IV NSCLC patients receiving additional survival benefit from ICIs). ESBP assistance improved the diagnostic accuracy of the clinicians with two years of experience from 47·91% to 66·32%, and the clinicians with five years of experience from 53·12% to 61·41%. Interpretation: This study developed and externally validated a preoperative CT image-based deep learning model to predict the survival benefits of EGFR-TKI and ICI therapies in stage IV NSCLC patients, which will facilitate optimized and individualized treatment strategies. Funding: This study received funding from the National Natural Science Foundation of China (82001904, 81930053, and 62027901), and Key-Area Research and Development Program of Guangdong Province (2021B0101420005).

RBMS1 promotes gastric cancer metastasis through autocrine IL-6/JAK2/STAT3 signaling.

Metastasis is the most important reason for the poor prognosis of gastric cancer (GC) patients, and the mechanism urgently needs to be clarified. Here, we explored a prognostic model for the estimation of tumor-associated mortality in GC patients and revealed the RNA-binding protein RBMS1 as a candidate promoter gene for GC metastasis by analyzing GOBO and Oncomine high-throughput sequencing datasets for 408 GC patients. Additionally, RBMS1 was observed with overexpression in 85 GC patient clinical specimens by IHC staining and further be verified its role in GC metastasis via inducing EMT process both in in vitro and in vivo experiments. Moreover, we identified that IL-6 was predicted to be one of the most significant upstream cytokines in the RBMS1 overexpression gene set based on the Ingenuity Pathway Analysis (IPA) algorithm. Most importantly, we also revealed that RBMS1 could promote migration and invasion through IL6 transactivation and JAK2/STAT3 downstream signaling pathway activation by influencing histone modification in the promoter regions after binding with the transcription factor MYC in the HGC-27 and SGC-7901 GC cell lines. Hence, we shed light on the potential molecular mechanisms of RBMS1 in the promotion of GC metastasis, which suggests that RBMS1 may be a potential therapeutic target for GC patients.

Automatic pulmonary ground-glass opacity nodules detection and classification based on 3D neural network.

PURPOSE: Pulmonary ground-glass opacity (GGO) nodules are more likely to be malignant compared with solid solitary nodules. Due to indistinct boundaries of GGO nodules, the detection and diagnosis are challenging for doctors. Therefore, designing an automatic GGO nodule detection and classification scheme is significantly essential. METHODS: In this paper, we proposed a two-stage 3D GGO nodule detection and classification framework. First, we used a pretrained 3D U-Net to extract lung parenchyma. Second, we adapted the architecture of Mask region-based convolutional neural networks (RCNN) to handle 3D medical images. The 3D model was then applied to detect the locations of GGO nodules and classify lesions (benign or malignant). The class-balanced loss function was also used to balance the number of benign and malignant lesions. Finally, we employed a novel false positive elimination scheme called the feature-based weighted clustering (FWC) to promote the detection accuracy further. RESULTS: The experiments were conducted based on fivefold cross-validation with the imbalanced data set. Experimental results showed that the mean average precision could keep a high level (0.5182) in the phase of detection. Meanwhile, the false positive rate was effectively controlled, and the competition performance metric (CPM) reached 0.817 benefited from the FWC algorithm. The comparative statistical analyses with other deep learning methods also proved the effectiveness of our proposed method. CONCLUSIONS: We put forward an automatic pulmonary GGO nodules detection and classification framework based on deep learning. The proposed method locate and classify nodules accurately, which could be an effective tool to help doctors in clinical diagnoses.

Trends in the application of deep learning networks in medical image analysis: Evolution between 2012 and 2020.

PURPOSE: To evaluate the general rules and future trajectories of deep learning (DL) networks in medical image analysis through bibliometric and hot spot analysis of original articles published between 2012 and 2020. METHODS: Original articles related to DL and medical imaging were retrieved from the PubMed database. For the analysis, data regarding radiological subspecialties; imaging techniques; DL networks; sample size; study purposes, setting, origins and design; statistical analysis; funding sources; authors; and first authors' affiliation was manually extracted from each article. The Bibliographic Item Co-Occurrence Matrix Builder and VOSviewer were used to identify the research topics of the included articles and illustrate the future trajectories of studies. RESULTS: The study included 2685 original articles. The number of publications on DL and medical imaging has increased substantially since 2017, accounting for 97.2% of all included articles. We evaluated the rules of the application of 47 DL networks to eight radiological tasks on 11 human organ sites. Neuroradiology, thorax, and abdomen were frequent research subjects, while thyroid was under-represented. Segmentation and classification tasks were the primary purposes. U-Net, ResNet, and VGG were the most frequently used Convolutional neural network-derived networks. GAN-derived networks were widely developed and applied in 2020, and transfer learning was highlighted in the COVID-19 studies. Brain, prostate, and diabetic retinopathy-related studies were mature research topics in the field. Breast- and lung-related studies were in a stage of rapid development. CONCLUSIONS: This study evaluates the general rules and future trajectories of DL network application in medical image analyses and provides guidance for future studies.

Fibrinogen-like protein 1 (FGL1): the next immune checkpoint target.

Immune checkpoint therapy has achieved significant efficacy by blocking inhibitory pathways to release the function of T lymphocytes. In the clinic, anti-programmed cell death protein 1/programmed cell death ligand 1 (PD-1/PD-L1) monoclonal antibodies (mAbs) have progressed to first-line monotherapies in certain tumor types. However, the efficacy of anti-PD-1/PD-L1 mAbs is still limited due to toxic side effects and de novo or adaptive resistance. Moreover, other immune checkpoint target and biomarkers for therapeutic response prediction are still lacking; as a biomarker, the PD-L1 (CD274, B7-H1) expression level is not as accurate as required. Hence, it is necessary to seek more representative predictive molecules and potential target molecules for immune checkpoint therapy. Fibrinogen-like protein 1 (FGL1) is a proliferation- and metabolism-related protein secreted by the liver. Multiple studies have confirmed that FGL1 is a newly emerging checkpoint ligand of lymphocyte activation gene 3 (LAG3), emphasizing the potential of targeting FGL1/LAG3 as the next generation of immune checkpoint therapy. In this review, we summarize the substantial regulation mechanisms of FGL1 in physiological and pathological conditions, especially tumor epithelial to mesenchymal transition, immune escape and immune checkpoint blockade resistance, to provide insights for targeting FGL1 in cancer treatment.

Neurolytic Splanchnic Nerve Block and Pain Relief, Survival, and Quality of Life in Unresectable Pancreatic Cancer: A Randomized Controlled Trial.

BACKGROUND: Neurolytic splanchnic nerve block is used to manage pancreatic cancer pain. However, its impact on survival and quality of life remains controversial. The authors' primary hypothesis was that pain relief would be better with a nerve block. Secondarily, they hypothesized that analgesic use, survival, and quality of life might be affected. METHODS: This randomized, double-blind, parallel-armed trial was conducted in five Chinese centers. Eligible patients suffering from moderate to severe pain conditions were randomly assigned to receive splanchnic nerve block with either absolute alcohol (neurolysis) or normal saline (control). The primary outcome was pain relief measured on a visual analogue scale. Opioid consumption, survival, quality of life, and adverse effects were also documented. Analgesics were managed using a protocol common to all centers. Patients were followed up for 8 months or until death. RESULTS: Ninety-six patients (48 for each group) were included in the analysis. Pain relief with neurolysis was greater for the first 3 months (largest at the first month; mean difference, 0.7 [95% CI, 0.3 to 1.0]; adjusted P < 0.001) compared with placebo injection. Opioid consumption with neurolysis was lower for the first 5 months (largest at the first month; mean difference, 95.8 [95% CI, 67.4 to 124.1]; adjusted P < 0.001) compared with placebo injection. There was a significant difference in survival (hazard ratio, 1.56 [95% CI, 1.03 to 2.35]; P = 0.036) between groups. A significant reduction in survival in neurolysis was found for stage IV patients (hazard ratio, 1.94 [95% CI, 1.29 to 2.93]; P = 0.001), but not for stage III patients (hazard ratio, 1.08 [95% CI, 0.59 to 1.97]; P = 0.809). No differences in quality of life were observed. CONCLUSIONS: Neurolytic splanchnic nerve block appears to be an effective option for controlling pain and reducing opioid requirements in patients with unresectable pancreatic cancer.

DenseCapsNet: Detection of COVID-19 from X-ray images using a capsule neural network.

At present, the global pandemic as it relates to novel coronavirus pneumonia is still a very difficult situation. Due to the recent outbreak of novel coronavirus pneumonia, novel chest X-ray (CXR) images that can be used for deep learning analysis are very rare. To solve this problem, we propose a deep learning framework that integrates a convolutional neural network and a capsule network. DenseCapsNet, a new deep learning framework, is formed by the fusion of a dense convolutional network (DenseNet) and the capsule neural network (CapsNet), leveraging their respective advantages and reducing the dependence of convolutional neural networks on a large amount of data. Using 750 CXR images of lungs of healthy patients as well as those of patients with other pneumonia and novel coronavirus pneumonia, the method can obtain an accuracy of 90.7% and an F1 score of 90.9%, and the sensitivity for detecting COVID-19 can reach 96%. These results show that the deep fusion neural network DenseCapsNet has good performance in novel coronavirus pneumonia CXR radiography detection.