Cadonilimab with chemotherapy in HER2-negative gastric or gastroesophageal junction adenocarcinoma: the phase 1b/2 COMPASSION-04 trial.

Treatment with anti-programmed cell death protein 1 (PD-1) therapy and chemotherapy prolongs the survival of patients with unresectable advanced or metastatic gastric or gastroesophageal junction (GEJ) adenocarcinoma. The benefit from anti-PD-1 therapy is enriched in patients with programmed cell death 1 ligand 1 (PD-L1) combined positive score (CPS)-positive or CPS-high tumors compared with patients with PD-L1 CPS-negative or CPS-low tumors. In this phase 1b/2 study, we evaluated the efficacy and safety of cadonilimab, a bispecific antibody targeting PD-1 and cytotoxic T-lymphocyte antigen-4, plus chemotherapy as first-line treatment in patients with human epidermal growth factor receptor 2-negative unresectable advanced or metastatic gastric or GEJ adenocarcinoma. The primary endpoint was the recommended phase 2 dose (RP2D) for phase 1b and the objective response rate for phase 2. Secondary endpoints included disease control rate, duration of response, time to response, progression-free survival, overall survival (OS) and safety. The primary endpoint was met. No dose-limiting toxicities were observed during dose escalation in phase 1b; the recommended phase 2 dose was determined as 6 mg kg-1 every 2 weeks. The objective response rate was 52.1% (95% confidence interval (CI) = 41.6-62.5), consisting of complete and partial responses in 4.3% and 47.9% of patients, respectively. The median duration of response, progression-free survival and OS were 13.73 months (95% CI = 7.79-19.12), 8.18 months (95% CI = 6.67-10.48) and 17.48 months (95% CI = 12.35-26.55), respectively. The median OS in patients with a PD-L1 CPS ≥ 5 was 20.32 months (95% CI = 4.67-not estimable); in patients with a PD-L1 CPS < 1, the median OS reached 17.64 months (95% CI = 11.63-31.70). The most common treatment-related grade 3 or higher adverse events were decreased neutrophil count (19.1%), decreased platelet count (16.0%), anemia (12.8%) and decreased leukocyte count (8.5%). No new safety signal was identified. The current regimen showed promising clinical activity and manageable safety in patients with gastric or GEJ adenocarcinoma regardless of PD-L1 expression. Chinadrugtrials.org.cn registration: CTR20182027.

Microbial response and recovery strategy of the anammox process under ciprofloxacin stress from pure strain and consortia perspectives.

Ciprofloxacin (CIP) poses a high risk of resistance development in water environments. Therefore, comprehensive effects and recovery strategies of CIP in anaerobic ammonia oxidation (anammox) process were systematically elucidated from consortia and pure strains perspectives. The anammox consortia was not significantly affected by the stress of 10 mg L-1 CIP, while the higher concentration (20 mg L-1) of CIP caused a dramatic reduction in the nitrogen removal performance of anammox system. Simultaneously, the abundances of dominant functional bacteria and corresponding genes also significantly decreased. Such inhibition could not be mitigated by the recovery strategy of adding hydrazine and hydroxylamine. Reducing nitrogen load rate from 5.1 to 1.4 kg N m-3 d-1 promoted the restoration of three reactors. In addition, the robustness and recovery of anammox systems was evaluated using starvation and shock strategies. Simultaneously, antibiotic resistance genes and key metabolic pathways of anammox consortia were upregulated, such as carbohydrate and energy metabolisms. In addition, 11 pure stains were isolated from the anammox system and identified through phylogenetic analysis, 40 % of which showed multidrug resistance, especially Pseudomonas. These findings provide deep insights into the responding mechanism of anammox consortia to CIP stress and promote the application of anammox process for treating wastewater containing antibiotics.

A novel transformer autoencoder for multi-modal emotion recognition with incomplete data.

Multi-modal signals have become essential data for emotion recognition since they can represent emotions more comprehensively. However, in real-world environments, it is often impossible to acquire complete data on multi-modal signals, and the problem of missing modalities causes severe performance degradation in emotion recognition. Therefore, this paper represents the first attempt to use a transformer-based architecture, aiming to fill the modality-incomplete data from partially observed data for multi-modal emotion recognition (MER). Concretely, this paper proposes a novel unified model called transformer autoencoder (TAE), comprising a modality-specific hybrid transformer encoder, an inter-modality transformer encoder, and a convolutional decoder. The modality-specific hybrid transformer encoder bridges a convolutional encoder and a transformer encoder, allowing the encoder to learn local and global context information within each particular modality. The inter-modality transformer encoder builds and aligns global cross-modal correlations and models long-range contextual information with different modalities. The convolutional decoder decodes the encoding features to produce more precise recognition. Besides, a regularization term is introduced into the convolutional decoder to force the decoder to fully leverage the complete and incomplete data for emotional recognition of missing data. 96.33%, 95.64%, and 92.69% accuracies are attained on the available data of the DEAP and SEED-IV datasets, and 93.25%, 92.23%, and 81.76% accuracies are obtained on the missing data. Particularly, the model acquires a 5.61% advantage with 70% missing data, demonstrating that the model outperforms some state-of-the-art approaches in incomplete multi-modal learning.

ADFCNN: Attention-Based Dual-Scale Fusion Convolutional Neural Network for Motor Imagery Brain-Computer Interface.

Convolutional neural networks (CNNs) have been successfully applied to motor imagery (MI)-based brain-computer interface (BCI). Nevertheless, single-scale CNN fail to extract abundant information over a wide spectrum from EEG signals, while typical multi-scale CNNs cannot effectively fuse information from different scales with concatenation-based methods. To overcome these challenges, we propose a new scheme equipped with attention-based dual-scale fusion convolutional neural network (ADFCNN), which jointly extracts and fuses EEG spectral and spatial information at different scales. This scheme also provides novel insight through self-attention for effective information fusion from different scales. Specifically, temporal convolutions with two different kernel sizes identify EEG µ and ß rhythms, while spatial convolutions at two different scales generate global and detailed spatial information, respectively, and the self-attention mechanism performs feature fusion based on the internal similarity of the concatenated features extracted by the dual-scale CNN. The proposed scheme achieves the superior performance compared with state-of-the-art methods in subject-specific motor imagery recognition on BCI Competition IV dataset 2a, 2b and OpenBMI dataset, with the cross-session average classification accuracies of 79.39% and significant improvements of 9.14% on BCI-IV2a, 87.81% and 7.66% on BCI-IV2b, 65.26% and 7.2% on OpenBMI dataset, and the within-session average classification accuracies of 86.87% and significant improvements of 10.89% on BCI-IV2a, 87.26% and 8.07% on BCI-IV2b, 84.29% and 5.17% on OpenBMI dataset, respectively. What is more, ablation experiments are conducted to investigate the mechanism and demonstrate the effectiveness of the dual-scale joint temporal-spatial CNN and self-attention modules. Visualization is also used to reveal the learning process and feature distribution of the model.

An EEG Channel Selection Framework for Driver Drowsiness Detection via Interpretability Guidance.

Drowsy driving has a crucial influence on driving safety, creating an urgent demand for driver drowsiness detection. Electroencephalogram (EEG) signal can accurately reflect the mental fatigue state and thus has been widely studied in drowsiness monitoring. However, the raw EEG data is inherently noisy and redundant, which is neglected by existing works that just use single-channel EEG data or full-head channel EEG data for model training, resulting in limited performance of driver drowsiness detection. In this paper, we are the first to propose an Interpretability-guided Channel Selection (ICS) framework for the driver drowsiness detection task. Specifically, we design a two-stage training strategy to progressively select the key contributing channels with the guidance of interpretability. We first train a teacher network in the first stage using full-head channel EEG data. Then we apply the class activation mapping (CAM) to the trained teacher model to highlight the high-contributing EEG channels and further propose a channel voting scheme to select the top N contributing EEG channels. Finally, we train a student network with the selected channels of EEG data in the second stage for driver drowsiness detection. Experiments are designed on a public dataset, and the results demonstrate that our method is highly applicable and can significantly improve the performance of cross-subject driver drowsiness detection.

Double-tract reconstruction is superior to esophagogastrostomy in controlling reflux esophagitis and enhancing quality of life after proximal gastrectomy: Results from a prospective randomized controlled clinical trial in China.

Objective: The aim of this study was to prospectively compare double-tract reconstruction (DTR) and esophagogastrostomy (EG) after proximal gastrectomy (PG) regarding the incidence of reflux esophagitis, quality of life (QOL), nutritional status and surgical safety. Methods: This study was a randomized controlled trial. Patients eligible for PG were enrolled and randomly assigned to the EG group and DTR group. The characteristics of patients, parameters for surgical safety, incidence of reflux esophagitis, nutrition status and QOL were collected and compared between the two groups. Univariate analysis and multivariate analysis were performed to determine the significant factors affecting the incidence of reflux esophagitis after PG. Results: Thirty-seven patients of the EG group and 36 patients of the DTR group were enrolled. The incidence of reflux esophagitis was significantly lower in the DTR group than in the EG group (8.3% vs. 32.4%, P=0.019). The DTR group demonstrated a more favorable QOL than the EG group after PG. The nutritional status was balanced within the EG group and the DTR group. The operation time was longer in the DTR group than in the EG group (191 min vs. 221 min, P=0.001), while surgical safety was similar in the two groups. Conclusions: Our research demonstrated that DTR is superior to EG after PG in terms of the incidence of reflux esophagitis and provides a more satisfactory QOL without increasing surgical complications or sacrificing nutritional status.

A Practical Nomogram for Predicting the Prognosis of Elderly Patients with Gastric Adenocarcinoma After Gastrectomy.

PURPOSE: To establish a pragmatic prognostic nomogram for predicting the survival of elderly patients undergoing gastrectomy for gastric adenocarcinoma. PATIENTS AND METHODS: Data of elderly patients undergoing gastrectomy for gastric adenocarcinoma between 2004 and 2015 were obtained from the Surveillance, Epidemiology, and End Results database. Prognostic factors were identified by the Kaplan-Meier method and the Cox proportional hazards model. Based on these factors, we developed a nomogram to predict the overall survival (OS) and gastric cancer-specific survival (GCSS). Concordance index (C-index) and calibration curve are employed to assess the predictive accuracy of the model. Decision curve analysis (DCA) and receiver operating characteristic curve (ROC) analysis are applied to further appraise the clinical utility of the model. RESULTS: A total of 8401 cases were incorporated into this research. After univariate and multivariate analyses, nine prognostic factors of OS were identified, including age (P < 0.001), race (P < 0.001), marital status (P < 0.001), tumor site (P < 0.001), tumor size (P = 0.024), differentiation (P < 0.001), T stage (P < 0.001), N stage (P < 0.001), and M stage (P < 0.001); ten prognostic factors of GCSS were identified, including age (P < 0.001), race (P < 0.001), tumor site (P < 0.001), tumor size (P = 0.002), differentiation (P < 0.001), T stage (P < 0.001), N stage (P < 0.001), M stage (P < 0.001), radiotherapy (P < 0.001) and chemotherapy (P < 0.001). The C-index of the constructed nomogram for OS was 0.708 (95% CI: 0.701-0.715) while for GCSS was 0.745 (95% CI: 0.737-0.753). The calibration curves of the nomogram predictions and actual observations displayed good agreement for the 3- and 5-year OS and GCSS probabilities. The results of DCA and the area under the curve calculated by ROC analysis showed that the developed model was superior than TNM stage. CONCLUSION: The nomogram we established could accurately predict the prognosis of individual elderly patients who underwent gastrectomy for gastric adenocarcinoma.

Multi-View Spatial-Temporal Graph Convolutional Networks With Domain Generalization for Sleep Stage Classification.

Sleep stage classification is essential for sleep assessment and disease diagnosis. Although previous attempts to classify sleep stages have achieved high classification performance, several challenges remain open: 1) How to effectively utilize time-varying spatial and temporal features from multi-channel brain signals remains challenging. Prior works have not been able to fully utilize the spatial topological information among brain regions. 2) Due to the many differences found in individual biological signals, how to overcome the differences of subjects and improve the generalization of deep neural networks is important. 3) Most deep learning methods ignore the interpretability of the model to the brain. To address the above challenges, we propose a multi-view spatial-temporal graph convolutional networks (MSTGCN) with domain generalization for sleep stage classification. Specifically, we construct two brain view graphs for MSTGCN based on the functional connectivity and physical distance proximity of the brain regions. The MSTGCN consists of graph convolutions for extracting spatial features and temporal convolutions for capturing the transition rules among sleep stages. In addition, attention mechanism is employed for capturing the most relevant spatial-temporal information for sleep stage classification. Finally, domain generalization and MSTGCN are integrated into a unified framework to extract subject-invariant sleep features. Experiments on two public datasets demonstrate that the proposed model outperforms the state-of-the-art baselines.

Construction and Validation of a Risk-Scoring Model that Preoperatively Predicts Lymph Node Metastasis in Early Gastric Cancer Patients.

OBJECTIVE: The aim of this study was to create a risk-scoring model to preoperatively predict the incidence of lymph node metastasis (LNM) in early gastric cancer (EGC) patients to guide treatment. METHODS: To construct the risk-scoring model, we retrospectively analyzed a primary cohort of 548 EGC patients. Univariate analysis and logistic regression were performed. A risk-scoring model for predicting LNM in EGC patients was developed based on preoperative factors, and another cohort of 73 patients was then analyzed to validate the model. RESULTS: In the primary cohort, LNM was pathologically confirmed in 72 (13.1%) patients. In the multivariate analysis, the presence of ulceration and tumor size on gastroscopy, undifferentiated histological type, and presence of enlarged lymph nodes on computed tomography or endoscopic ultrasonography were independent risk factors for LNM. A 17-point risk-scoring model was developed to predict LNM risk. The cut-off score of the model was 8, and the area under the receiver operating characteristic curve (AUC) of the model was 0.835 [95% confidence interval (CI) 0.784-0.886]. In the validation cohort, the AUC of the model was 0.829 (95% CI 0.699-0.959). CONCLUSIONS: We developed and validated an effective 17-point risk-scoring model that could preoperatively predict LNM for EGC patients.

Phase II trial of prophylactic hyperthermic intraperitoneal chemotherapy in patients with locally advanced gastric cancer after curative surgery.

BACKGROUND: HIPEC is an emerging procedure to treat peritoneal metastasis of gastric cancer. Data about HIPEC in locally advanced gastric cancer is scarce. The purpose of this trial is to evaluate the safety and toxicity of prophylactic HIPEC with cisplatin for patients with locally advanced gastric cancer. METHODS: From March 2015 to November 2016, a prospective, randomized phase II trial was conducted. After radical gastrectomy, patients in the experimental group underwent HIPEC with cisplatin followed by adjuvant chemotherapy with SOX regime. Patients in the other group were treated with SOX regime alone. Postoperative complications and patient survival were compared. RESULTS: In total, 50 patients were eligible for analyses. No significant difference was found in the incidence of postoperative complications including anastomotic/intestinal leakage, liver dysfunction, bone marrow suppression, wound infection and ileus (P > 0.05). Mean duration of hospitalization after radical gastrectomy was 11.7 days. 12.2 days in experimental group and 10.8 days in control group respectively (P = 0.255). The percentage of patients with elevated tumor markers was 12.1% in experimental group, which was significantly lower than 41.2% in control group (P = 0.02). 3-year RFS of patients who treated with or without prophylactic HIPEC were 84.8 and 88.2% respectively (P = 0.986). In the multivariate analysis, pathological T stage was the only independent risk factor for the RFS of patients (P = 0.012, HR =15.071). CONCLUSION: Additional intraoperative HIPEC with cisplatin did not increase postoperative complications for locally advanced gastric cancer after curative surgery. Prophylactic HIPEC with cisplatin was safe and tolerable, while it did not reduce the risk of peritoneal recurrence in this trial, supporting further studies to validate the efficacy of it. TRIAL REGISTRATION: Chinese Clinical Trial Registry, ChiCTR2000038331. Registered 18 September 2020 - Retrospectively registered, http://www.chictr.org.cn/showproj.aspx?proj=59692 .

Double Tract Reconstruction Reduces Reflux Esophagitis and Improves Quality of Life after Radical Proximal Gastrectomy for Patients with Upper Gastric or Esophagogastric Adenocarcinoma.

PURPOSE: The aim of the present study was to compare the difference between double tract reconstruction and esophagogastrostomy. MATERIALS AND METHODS: Patients who underwent radical proximal gastrectomy with esophagogastrostomy or double tract reconstruction were included in this study. RESULTS: Sixty-four patients were included in this study and divided into two groups according to reconstruction method. The two groups were well balanced in perioperative safety and 3-year overall survival (OS). The rates of postoperative reflux esophagitis in the double tract reconstruction group and esophagogastrostomy group were 8.0% and 30.8%, respectively (p=0.032). Patients in the double tract reconstruction group had a better global health status (p < 0.001) and emotional functioning (p < 0.001), and complained less about nausea and vomiting (p < 0.001), pain (p=0.039), insomnia (p=0.003), and appetite loss (p < 0.001) based on the European Organization for Research and Treatment of Cancer (EORTC) QLQ-C30 questionnaire. Regarding the EORTC QLQ-STO22 questionnaire, patients in the double tract reconstruction group complained less about dysphagia (p=0.030), pain (p=0.008), reflux (p < 0.001), eating (p < 0.001), anxiety (p < 0.001), dry mouth (p=0.007), and taste (p=0.001). The multiple linear regression analysis showed that reconstruction method, postoperative complications, reflux esophagitis, and operation duration had a linear relationship with the global health status score. CONCLUSION: Double tract reconstruction could better prevent reflux esophagitis and improve quality of life without scarifying perioperative safety or 3-year OS.

Association between mental health and community support in lockdown communities during the COVID-19 pandemic: Evidence from rural China.

Governments worldwide have taken unprecedented social distancing and community lockdown measures to halt the COVID-19 epidemic, leaving millions of people restrained in locked-down communities and their mental well-being at risk. This study examines Chinese rural residents' mental health risk under emergency lockdown during the COVID-19 pandemic. It investigates how the environmental, socioeconomic, and behavioral dimensions of community support affect mental health in this emergency context. We also explore whether community support's effectiveness depends on the strictness of lockdown measures implemented and the level of individual perceived COVID-19 infection risk. We collect self-reported mental health risk, community support, and demographics information through a cross-sectional survey of 3892 Chinese rural residents living in small towns and villages. Ordinary least square regressions are employed to estimate the psychological effects of community support. The results suggest that the COVID-19 epidemic and lockdown policies negatively affect psychological well-being, especially for rural females. The capacity for community production has the largest impact on reducing mental health risks, followed by the stability of basic medical services, community cohesion, housing condition, the stability of communications and transportation supply, and the eco-environment. The effectiveness of different community support dimensions depends on the level of lockdown policy implemented and the levels of one's perceived risk of COVID-19 infection. Our study stresses the psychological significance of a healthy living environment, resilient infrastructure and public service system, and community production capacity during the lockdown in rural towns and villages.

The Value of Perioperative Chemotherapy for Patients With Hepatoid Adenocarcinoma of the Stomach Undergoing Radical Gastrectomy.

BACKGROUND: Hepatoid adenocarcinoma of the stomach (HAS) is a rare type of gastric cancer, but the role of perioperative chemotherapy is still poorly understood. The aim of this retrospective study was to investigate the associations between perioperative chemotherapy and prognosis of HAS. METHOD: We retrospectively analyzed patients with locally advanced HAS who received radical surgery in Peking University Cancer Hospital between November 2009 and October 2020. Patients were divided into neoadjuvant chemotherapy-first (NAC-first) group and surgery-first group. The relationships between perioperative chemotherapy and prognosis of HAS were analyzed using univariate, multivariate survival analyses and propensity score matching analysis (PSM). RESULTS: A total of 100 patients were included for analysis, including 29 in the NAC-first group and 71 in the surgery-first group. The Her-2 amplification in HAS patients was 22.89% (19/83). For NAC-first group, 4 patients were diagnosed as tumor recession grade 1 (TRG1), 4 patients as TRG 2, and 19 patients as TRG 3. No significant difference in prognosis between the surgery-first group and the NAC-first group (P=0.108) was found using PSM analysis. In the surgery-first group, we found that the survival rate was better in group of ≥6 cycles of adjuvant chemotherapy than that of <6 cycles (P=0.013). CONCLUSION: NAC based on platinum and fluorouracil may not improve the Overall survival (OS) and Disease-free survival time (DFS) of patients with locally advanced HAS. Patients who received ≥6 cycles of adjuvant chemotherapy had better survival. Therefore, the combination treatment of radical gastrectomy and sufficient adjuvant chemotherapy is recommended for patients with locally advanced HAS.

ASB16-AS1 up-regulated and phosphorylated TRIM37 to activate NF-κB pathway and promote proliferation, stemness, and cisplatin resistance of gastric cancer.

BACKGROUND: Long non-coding RNA (lncRNA) ASB16 antisense RNA 1 (ASB16-AS1) is recognized as an oncogene in several cancer types, but its relation to GC is unknown. Tripartite motif containing 37 (TRIM37) has been proven to accelerate the development of gastric cancer (GC), whereas the molecular mechanism assisted ASB16-AS1 and TRIM37 in regulating GC progression remains unclear. METHODS: Differentially expressed lncRNAs in GC samples were analyzed based on Gene Expression Omnibus (GEO) data. CCK-8 and colony formation assays were applied to determine the proliferative ability of GC cells. Stem cell-like phenotype of GC cells was assessed by sphere formation assay and flow cytometry analysis. Luciferase reporter assay, RNA immunoprecipitation (RIP), pulldown, and co-immunoprecipitation (Co-IP) were performed to verify the interplay of RNA molecules. RESULTS: ASB16-AS1 was upregulated in GC samples according to GEO data and qRT-PCR analysis. ASB16-AS1 strengthened the proliferative ability and stem cell-like characteristics in GC cells. More importantly, ASB16-AS1 encouraged GC cell growth in vivo. Mechanistically, ASB16-AS1 strengthened TRIM37 expression by sequestering miR-3918 and miR-4676-3p. ASB16-AS1 activated NF-kappa B (NF-κB) pathway by cooperating with ATM serine/threonine kinase (ATM) to induce TRIM37 phosphorylation. CONCLUSION: In summary, ASB16-AS1 exerted oncogenic functions in GC through modulating TRIM37 expression at both mRNA and protein levels.

Representation based on ordinal patterns for seizure detection in EEG signals.

EEG signals carry rich information about brain activity and play an important role in the diagnosis and recognition of epilepsy. Numerous algorithms using EEG signals to detect seizures have been developed in recent decades. However, most of them require well-designed features that highly depend on domain-specific knowledge and algorithm expertise. In this study, we introduce the unigram ordinal pattern (UniOP) and bigram ordinal pattern (BiOP) representations to capture the different underlying dynamics of time series, which only assumes that time series derived from different dynamics can be characterized by repeated ordinal patterns. Specifically, we first transform each subsequence in a time series into the corresponding ordinal pattern in terms of the ranking of values and consider the distribution of ordinal patterns of all subsequences as the UniOP representation. Furthermore, we consider the distribution of the cooccurrence of ordinal patterns as the BiOP representation to characterize the contextual information for each ordinal pattern. We then combine the proposed representations with the nearest neighbor algorithm to evaluate its effectiveness on three publicly available seizure datasets. The results on the Bonn EEG dataset demonstrate that this method provides more than 90% accuracy, sensitivity, and specificity in most cases and outperforms several state-of-the-art methods, which proves its ability to capture the key information of the underlying dynamics of EEG time series at healthy, seizure-free, and seizure states. The results on the second dataset are comparable with the state-of-the-art method, showing the good generalization ability of the proposed method. All performance metrics on the third dataset are approximately 89%, which demonstrates that the proposed representations are suitable for large-scale datasets.

Refined nonuniform embedding for coupling detection in multivariate time series.

State-space reconstruction is essential to analyze the dynamics and internal interactions of complex systems. However, it is difficult to estimate high-dimensional conditional mutual information and select the optimal time delays in most existing nonuniform state-space reconstruction methods. Therefore, we propose a nonuniform embedding method framed in information theory for state-space reconstruction. We use a low-dimensional approximation of conditional mutual information criterion for time delay selection, which is effectively solved by the particle swarm optimization algorithm. The obtained embedded vector has relatively strong independence and low redundancy, which better characterizes multivariable complex systems and detects coupling within complex systems. In addition, the proposed nonuniform embedding method exhibits good performance in coupling detection of linear stochastic, nonlinear stochastic, chaotic systems. In the actual application, the importance of small airports that cause delay propagation has been demonstrated by constructing the delay propagation network.

Patient-derived tumor-like cell clusters for drug testing in cancer therapy.

Several patient-derived tumor models emerged recently as robust preclinical drug-testing platforms. However, their potential to guide clinical therapy remained unclear. Here, we report a model called patient-derived tumor-like cell clusters (PTCs). PTCs result from the self-assembly and proliferation of primary epithelial, fibroblast, and immune cells, which structurally and functionally recapitulate original tumors. PTCs enabled us to accomplish personalized drug testing within 2 weeks after obtaining the tumor samples. The defined culture conditions and drug concentrations in the PTC model facilitate its clinical application in precision oncology. PTC tests of 59 patients with gastric, colorectal, or breast cancers revealed an overall accuracy of 93% in predicting their clinical outcomes. We implemented PTC to guide chemotherapy selection for a patient with mucinous rectal adenocarcinoma who experienced recurrence with metastases after conventional therapy. After three cycles of a nonconventional therapy identified by the PTC, the patient showed a positive response. These findings need to be validated in larger clinical trials, but they suggest that the PTC model could be prospectively implemented in clinical decision-making for therapy selection.

Identification of key long non-coding RNAs in gastric adenocarcinoma.

BACKGROUND: Gastric cancer is the third leading cause of cancer-related deaths worldwide. OBJECTIVE: The present study aims to identify key long non-coding RNAs (lncRNAs) and their potential roles in the pathogenesis of gastric adenocarcinoma. METHODS: The lncRNA and mRNA expression profile between gastric adenocarcinoma and adjacent non-tumor tissues were obtained from The Cancer Genome Atlas (TCGA). Differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs) between gastric adenocarcinoma and adjacent non-tumor tissues were identified after bioinformatics analysis. DElncRNA-DEmRNA co-expression network and DElncRNA-nearby DEmRNA interaction network were constructed, respectively. Functional annotation for DEmRNAs interacted with DElncRNAs was performed. Receiver operating characteristic (ROC) analysis of selected DElncRNAs was conducted. RESULTS: Based on TCGA, the mRNA and lncRNA expression profiles of 375 gastric adenocarcinoma and 32 adjacent non-tumor tissues were downloaded. A total of 1502 DEmRNAs and 928 DElncRNAs between gastric adenocarcinoma and adjacent non-tumor tissues were identified. HOXC-AS3 might involve with gastric adenocarcinoma by regulating a set of HOX genes (HOXC8, HOXC9, HOXC10, HOXC11, HOXC12 and HOXC13) with cis-effect. AC115619.1-APOA4/APOB and AP006216.2-APOA1/APOA4 integrations might play roles in gastric adenocarcinoma through regulating pathways of Fat digestion and absorption and Vitamin digestion and absorption. Six lncRNAs including (HOTAIR, C20orf166-AS1, PGM5-AS1, HOXC-AS3, HOXC-AS2 and AC012531.1) have excellent diagnostic value for gastric adenocarcinoma. CONCLUSIONS: This study identifies key lncRNAs in gastric adenocarcinoma which provides clues for exploring the pathogenesis and developing potential biomarkers for gastric adenocarcinoma.

The 8th edition of the American Joint Committee on Cancer tumor-node-metastasis staging system for gastric cancer is superior to the 7th edition: results from a Chinese mono-institutional study of 1663 patients.

BACKGROUND: We investigated the superiority of the 8th edition of the tumor-node-metastasis (TNM) system for patients in China with gastric cancer. METHODS: The survival outcomes of 1663 patients with gastric cancer undergoing radical resection were analyzed. RESULTS: In the 8th edition system, homogeneous 5-year survival rates among different pathological TNM (pTNM) categories belonging to the same stage were observed. However, in the 7th edition system, the differences of 5-year survival rate among pTNM categories belonging to the same stage were observed in stages IIB (P = 0.010), IIIB (P = 0.004), and IIIC (P < 0.001). For patients in the pT1-3 (P < 0.001) and pT4a (P < 0.001) categories, there were significant differences in survival between patients in the pN3a and pN3b categories. Furthermore, partial cases (pT4bN0M0/T4aN2M0) of stage IIIB were downstaged to stage IIIA in the 8th edition system, and the 5-year survival rate of these patients was significantly better than that of patients in stage IIIB in the 8th edition system. Similarly, the 5-year survival rate of patients in p4bN2M0/T4aN3aM0 downstaged from stage IIIC to IIIB was significantly better than that of patients in stage IIIC. Compared with the 7th edition system, the 8th edition system had a higher likelihood ratio and linear trend chi-squared score and a smaller Akaike information criteria value. CONCLUSIONS: The 8th edition system is superior to the 7th edition system in terms of homogeneity, discriminatory ability, and monotonicity of gradients for Chinese patients with gastric cancer.