Background and Objective:Cancer pain, a common and debilitating symptom among patients with advanced malignancies, often necessitates sustained, individualized management that extends beyond the clinic. Home-based management is therefore a natural setting for continuous assessment and timely intervention. Traditional home-based pain management approaches serve various functions but fall short in terms of sufficient timeliness, intelligence and individualization. Recent advances in telehealth are reshaping this landscape by enabling proactive, individualized, and scalable support. In this context, this review was conducted to elucidate the recent progress on application of telehealth in home-based management of cancer pain, providing new insights and general principles for both providers and patients. Methods:A comprehensive literature search was performed on PubMed, Cochrane Library, and Cumulative Index to Nursing and Allied Health Literature (CINAHL). The search included randomized and non-randomized trials, cohort or pre-post studies, and systematic reviews that evaluated telehealth interventions aimed at home management of cancer pain. Search terms encompassed relevant keywords for cancer pain, home-based, pain management and telehealth. Key Content and Findings:Telehealth has empowered cancer pain management through remote symptom monitoring, real-time assessment, personalized intervention, smart pain management and provider-patient empowerment. It facilitates the identification and documentation of pain intensity fluctuations, improves patient-reported outcomes (PROs) and may reduce safety risks such as opioid overdose. Owing to its advantages in transcending time and space, telehealth has gained broad acceptance among both providers and patients. However, the successful implementation of telehealth in the home setting depends on seamless integration with multiple factors and support from different dimensions while overcoming numerous challenges and limitations. This may entail enormous efforts and consideration from different groups. Looking ahead, the greatest opportunities lie in artificial intelligence (AI) driven predictive analytics, systems or platforms to forecast pain fluctuations, personalize treatment and proactively reduce safety risks. Another promising direction is hybrid healthcare models that combine remote monitoring and in‑person interventions. Conclusions:This narrative review combs through the promising empowerment of telehealth in the home-based cancer pain management, discussing its multifaceted benefits, characterizing current challenges and limitations while providing a general principle for future research and application directions in telehealth empowered home-based management of cancer pain.

Objective:This study aimed to evaluate the effectiveness of the Information-Knowledge-Attitude-Practice (IKAP) model in breastfeeding management for pregnant women with high hepatitis B virus (HBV) DNA loads (≥2 × 105 IU/mL). Methods:A prospective quasi-experimental study enrolled 136 eligible women between January 2023 and October 2023. Based on compliance, 68 who received the full IKAP-based intervention were assigned to the experimental group, and 68 who received routine health education served as controls. The experimental group received systematic, individualised IKAP-model education from the second trimester through 12 months postpartum, progressing through information, knowledge, attitude and practice stages. The control group received routine education (eg, prenatal classes, outpatient consultations). Primary outcomes (exclusive breastfeeding rate and self-efficacy at 42 days postpartum) and neonatal HBV transmission blockade outcomes at 7-8 months were compared. Results:The exclusive breastfeeding rate at 42 days was significantly higher in the intervention group (85.29%) than in the control group (67.65%) (absolute risk difference = 17.64%, relative risk = 1.26, p < 0.05). Postpartum breastfeeding self-efficacy scores were also significantly higher in the intervention group (38.2 ± 4. 1 vs 32.5 ± 5.3; mean difference = 5.7, p < 0.05). Neonatal breastfeeding initiation success was 100% in both groups (p > 0.05). Conclusion:The phased IKAP model demonstrated superior outcomes compared with conventional education, safely increasing exclusive breastfeeding rates by approximately 17% and enhancing feeding confidence in high-risk mothers. Despite limitations, such as a non-randomised, single-centre design, it provides an effective and scalable framework for real-world application in supporting this special population.

The early diagnosis rate of gastric cancer is low, and most patients are already at an advanced stage by the time they are diagnosed, posing significant challenges for treatment and exhibiting high recurrence rates, which notably diminish patients’ survival time and quality of life. Therefore, there is an urgent need to identify methods that can enhance treatment efficacy. Nanomedicine, distinguished by its small size, high targeting specificity, and strong biological compatibility, is particularly well-suited to address the toxic side effects associated with current diagnostic and therapeutic approaches for gastric cancer. Consequently, the application of nanomedicine and delivery systems in the diagnosis and treatment of gastric cancer has garnered increasing interest from researchers. This review provides an overview of recent advancements in the use of nanomaterials as drugs or drug delivery systems in gastric cancer research, encompassing their applications in diagnosis, chemotherapy, radiotherapy, surgery, and phototherapy, and explores the promising prospects of nanomedicine in the treatment of gastric cancer.

BackgroundHigh body mass index (BMI) is a well-established risk factor for ovarian and uterine cancer. However, the global, regional, and national burden of these cancers attributable to high BMI remains underexplored. This study quantifies the trends and disparities in the burden of ovarian and uterine cancer due to high BMI from 1990 to 2021 using the Global Burden of Disease (GBD) 2021 dataset.MethodsWe extracted data from GBD 2021 to estimate the mortality, incidence, and disability-adjusted life years (DALYs) attributable to high BMI for ovarian and uterine cancer across different locations and time periods. We focused on the burden of ovarian and uterine cancers among women aged 20-49. Age-standardized rates (ASRs) were calculated, and temporal trends were analyzed using the estimated annual percentage change (EAPC). Regional and national disparities were assessed using sociodemographic index (SDI) classifications. Forecasts employed the exponential smoothing (ES) and autoregressive integrated moving average (ARIMA) models.ResultsGlobally, the burden of ovarian and uterine cancer attributable to high BMI increased substantially from 1990 to 2021, with variations across regions and countries. High-income and upper-middle-income regions exhibited the highest ASRs, whereas low-SDI countries showed increasing trends in recent years. The EAPC analysis indicated a growing burden in developing regions, reflecting the rising prevalence of obesity. Age-stratified analysis revealed that middle-aged and older adults bear the highest burden.ConclusionsThe global burden of ovarian and uterine cancer attributable to high BMI has increased significantly over the past three decades. Targeted interventions, including obesity prevention and cancer screening, are crucial for mitigating this burden, particularly in emerging high-risk regions. These findings underscore the need for urgent public health strategies to address obesity-related cancer risks worldwide.

Purpose To build a Multy-Task Learning (MTL)-based Artificial Intelligence (AI) model that can precisely predict clinical stage, histology, grade and LNM for cervical cancer simultaneously.Methods A retrospective database with preoperative clinical information of cervical cancer was used as training set. A prospective database was collected as independent validation set. The ANN algorithm was used to build a MTL based AI model. The accuracy and weighted F1 score were calculated as evaluation indicators. The performance between MTL model and Single-Task Learning (STL) model was compared. Furthermore, the Turing test was performed by gynecologists and compared with this AI model.Results A total of retrospectively 223 and 58 prospectively cervical cancer cases were enrolled into the training set and independent validation set. The accuracy of this cervical cancer AI model constructed with ANN algorithm in predicting stage, histology, grade and LNM were 75%, 95%, 86% and 76%, respectively. And the corresponding weighted F1 score were 70%, 94%, 86%, and 76%, respectively. The average time consumption of AI simultaneously, doctor, and doctor with AI predicting stage, histology, grade and LNM for cervical cancer was 0.01s (95%CI: 0.01–0.01) ,581.1s (95%CI: 300.0-900.0) ,and 534.8s (95%CI: 255.0-720.0) per 20 patients, respectively. Except for LNM, both the accuracy and F-score of the AI model were significantly better than STL AI, doctors and AI-assisted doctors in predicting stage, grade and histology. (P < 0.05) The time consumption of AI was significantly less than that of doctors’ prediction and AI-assisted doctors’ results. (P < 0.05)Conclusions A multi-task learning AI model can simultaneously predict stage, histology, grade and LNM for cervical cancer preoperatively with minimal time consumption.

Circular RNAs (circRNAs) are involved in tumor growth and metastasis. The aim of this study was to elucidate the possible role of has_circ_0002454 in cervical cancer (CC) and its potential molecular mechanism. has_circ_0002454, miR-654-3p, and SDC4 mRNA levels were determined by RT-qPCR, and SDC4 protein was by Western Blot. Cell proliferation was detected by MTT, colony formation, and wound healing. Invasion and migration capacities of cells were assessed by Transwell assays. The targeting relationship between miR-654-3p and has_circ_0002454 or SDC4 was confirmed by dual-luciferase reporter gene and RIP assay. Xenograft tumor model was constructed to determine the role of has_circ_0002454 in CC. In CC tissues and cell lines, has_circ_0002454 was associated with significantly higher SDC4 levels and significantly lower miR-654-3p expression. Down-regulation of has_circ_0002454 promoted miR-654-3p expression while decreasing SDC4 expression levels. has_circ_0002454 competitively bound miR-654-3p, and elevated miR-654-3p expression rescued the effects of has_circ_0002454 silencing on cancer cell proliferation, apoptosis, migration, and invasion. miR-654-3p directly targeted SDC4, and overexpression of SDC4 reversed the effect of has_circ_0002454 knockdown on cancer cell function. In addition, down-regulation of has_circ_0002454 effectively suppressed tumor growth in vivo. has_circ_0002454 reduction weakens the cancer phenotype of CC cells by modulating the miR-654-3p/SDC4 axis.

Abstract Purpose To build an Mult-Task Learning (MTL) based Artificial Intelligence(AI) model that can simultaneously predict clinical stage, histology, grade and LNM for cervical cancer before surgery. Methods This retrospective and prospective cohort study was conducted from January 2001 to March 2014 for the training set and from January 2018 to November 2021 for the validation set at Beijing Chaoyang Hospital, Capital Medical University. Preoperative clinical information of cervical cancer patients was used. An Artificial Neural Network (ANN) algorithm was used to build the MTL-based AI model. Accuracy and weighted F1 scores were calculated as evaluation indicators. The performance of the MTL model was compared with Single-Task Learning (STL) models. Additionally, a Turing test was performed by 20 gynecologists and compared with this AI model. Results A total of 223 cervical cancer cases were retrospectively enrolled into the training set, and 58 cases were prospectively collected as independent validation set. The accuracy of this cervical cancer AI model constructed with ANN algorithm in predicting stage, histology, grade and LNM were 75%, 95%, 86% and 76%, respectively. And the corresponding weighted F1 score were 70%, 94%, 86%, and 76%, respectively. The average time consumption of AI simultaneously predicting stage, histology, grade and LNM for cervical cancer was 0.01s (95%CI: 0.01–0.01) per 20 patients. The mean time consumption doctor and doctor with AI were 581.1s (95%CI: 300.0-900.0) per 20 patients and 534.8s (95%CI: 255.0-720.0) per 20 patients, respectively. Except for LNM, both the accuracy and F-score of the AI model were significantly better than STL AI, doctors and AI-assisted doctors in predicting stage, grade and histology. (P < 0.05) The time consumption of AI was significantly less than that of doctors’ prediction and AI-assisted doctors’ results. (P < 0.05 Conclusion A multi-task learning AI model can simultaneously predict stage, histology, grade, and LNM for cervical cancer preoperatively with minimal time consumption. To improve the conditions and use of the beneficiaries, the model should be integrated into routine clinical workflows, offering a decision-support tool for gynecologists. Future studies should focus on refining the model for broader clinical applications, increasing the diversity of the training datasets, and enhancing its adaptability to various clinical settings. Additionally, continuous feedback from clinical practice should be incorporated to ensure the model’s accuracy and reliability, ultimately improving personalized patient care and treatment outcomes.

Bone metastasis of liver cancer leads to a worse prognosis with no appropriate treatment clinically. Exosomes are associated with tumor bone metastasis. This study aimed to investigate the effects of liver cancer cell-derived exosomes on bone metastasis. Exosomes were isolated from Hep3B cells, and the effects of osteoclast differentiation were assessed using TRAP assay. The expression of OPG and RANKL was assessed using qRT-PCR. The interaction of miR-574-5p and BMP2 was analyzed using luciferase reporter analysis, RNA pull-down, and qRT-PCR. We found that Hep3B cells promoted osteoclast differentiation of RANKL-induced Raw264.7 cells by secreting exosomes, with decreased OPG and increased RANKL expression. The exosomes were isolated from Hep3B cells, which promoted osteoclast differentiation. Exosomal miR-574-5p promoted osteoclastogenesis by targeting BMP2. Moreover, exosomes facilitated osteoclast differentiation, promoting bone metastasis by regulating miR-574-3p in vivo. In conclusion, liver cancer cell-derived exosomal miR-574-5p promoted osteoclastogenesis by regulating BMP2, thereby promoting bone metastasis in vivo. The findings suggest that liver cancer cell-released exosomes are the potential therapeutic approach for bone metastatic liver cancer. DATA AVAILABILITY STATEMENT: The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

Current diagnosis of glioma types requires combining both histological features and molecular characteristics, which is an expensive and time-consuming procedure. Determining the tumor types directly from whole-slide images (WSIs) is of great value for glioma diagnosis. This study presents an integrated diagnosis model for automatic classification of diffuse gliomas from annotation-free standard WSIs. Our model is developed on a training cohort ( n = 1362) and a validation cohort ( n = 340), and tested on an internal testing cohort ( n = 289) and two external cohorts ( n = 305 and 328, respectively). The model can learn imaging features containing both pathological morphology and underlying biological clues to achieve the integrated diagnosis. Our model achieves high performance with area under receiver operator curve all above 0.90 in classifying major tumor types, in identifying tumor grades within type, and especially in distinguishing tumor genotypes with shared histological features. This integrated diagnosis model has the potential to be used in clinical scenarios for automated and unbiased classification of adult-type diffuse gliomas.

BACKGROUND:Ureteral injury is common during gynaecological laparoscopic surgery. Real-time auto-segmentation can assist gynaecologists in identifying the ureter and reduce intraoperative injury risk. METHODS:A deep learning segmentation model was crafted for ureter recognition in surgical videos, utilising 3368 frames from 11 laparoscopic surgeries. Class activation maps enhanced the model's interpretability, showing its areas. The model's clinical relevance was validated through an End-User Turing test and verified by three gynaecological surgeons. RESULTS:The model registered a Dice score of 0.86, a Hausdorff 95 distance of 22.60, and processed images in 0.008 s on average. In complex surgeries, it pinpointed the ureter's position in real-time. Fifty five surgeons across eight institutions found the model's accuracy, specificity, and sensitivity comparable to human performance. Yet, artificial intelligence experience influenced some subjective ratings. CONCLUSIONS:The model offers precise real-time ureter segmentation in laparoscopic surgery and can be a significant tool for gynaecologists to mitigate ureteral injuries.

To investigate whether radiomic features extracted from dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) can improve the prediction of the molecular subtypes of adult diffuse gliomas, and to further develop and validate a multimodal radiomic model by integrating radiomic features from conventional and perfusion MRI. We extracted 1197 radiomic features from each sequence of conventional MRI and DSC-PWI, respectively. The Boruta algorithm was used for feature selection and combination, and a three-class random forest method was applied to construct the models. We also constructed a combined model by integrating radiomic features and clinical metrics. The models’ diagnostic performance for discriminating the molecular subtypes (IDH wild type [IDHwt], IDH mutant and 1p/19q-noncodeleted [IDHmut-noncodel], and IDH mutant and 1p/19q-codeleted [IDHmut-codel]) was compared using AUCs in the validation set. We included 272 patients (training set, n = 166; validation set, n = 106) with grade II–IV gliomas (mean age, 48.7 years; range, 19–77 years). The proportions of the molecular subtypes were 66.2

Purpose To build a machine learning model to predict histology (type I and type II), stage, and grade preoperatively for endometrial carcinoma to quickly give a diagnosis and assist in improving the accuracy of the diagnosis, which can help patients receive timely, appropriate, and effective treatment. Materials and Methods This study used a retrospective database of preoperative examinations (tumor markers, imaging, diagnostic curettage, etc.) in patients with endometrial carcinoma. Three algorithms (random forest, logistic regression, and deep neural network) were used to build models. The AUC and accuracy were calculated. Furthermore, the performance of machine learning models, doctors’ prediction, and doctors with the assistance of models were compared. Results A total of 329 patients were included in this study with 16 features (age, BMI, stage, grade, histology, etc.). A random forest algorithm had the highest AUC and Accuracy. For histology prediction, AUC and accuracy was 0.69 (95% CI=0.67-0.70) and 0.81 (95%CI=0.79-0.82). For stage they were 0.66 (95% CI=0.64-0.69) and 0.63 (95% CI=0.61-0.65) and for differentiation grade 0.64 (95% CI=0.63-0.65) and 0.43 (95% CI=0.41-0.44). The average accuracy of doctors for histology, stage, and grade was 0.86 (with AI) and 0.79 (without AI), 0.64 and 0.53, 0.5 and 0.45, respectively. The accuracy of doctors’ prediction with AI was higher than that of Random Forest alone and doctors’ prediction without AI. Conclusion A random forest model can predict histology, stage, and grade of endometrial cancer preoperatively and can help doctors in obtaining a better diagnosis and predictive results.

Circulating leukocytes are an important part of the immune system. The aim of this work is to explore the role of preoperative circulating leukocytes in serous ovarian carcinoma and investigate whether they can be used to predict survival prognosis. Routine blood test results and clinical information of patients with serous ovarian carcinoma were retrospectively collected. And to predict survival according to the blood routine test result the decision tree method was applied to build a machine learning model.The results showed that the number of preoperative white blood cells (p = 0.022), monocytes (p < 0.001), lymphocytes (p < 0.001), neutrophils (p < 0.001), and eosinophils (p < 0.001) and the monocyte to lymphocyte (MO/LY) ratio in the serous ovarian cancer group were significantly different from those in the control group. These factors also showed a correlation with other clinicopathological characteristics. The MO/LY was the root node of the decision tree, and the predictive AUC for survival was 0.69. The features involved in the decision tree were the MO/LY, differentiation status, CA125 level, neutrophils (NE,) ascites cytology, LY% and age.In conclusion, the number and percentage of preoperative leukocytes in patients with ovarian cancer is changed significantly compared to those in the normal control group, as well as the MO/LY. A decision tree was built to predict the survival of patients with serous ovarian cancer based on the CA125 level, white blood cell (WBC) count, presence of lymph node metastasis (LNM), MO count, the MO/LY ratio, differentiation status, stage, LY%, ascites cytology, and age.

BACKGROUND:According to published guidelines, gynecologic surgical patients should be stratified into different risk level groups to receive prophylaxis for venous thromboembolism (VTE), but the applicability of available risk assessment models (RAMs) in common gynecologic surgical patients remained to be confirmed. We aimed to validate the use of the Caprini RAM and gynecologic Caprini (G-Caprini) RAM for assessing postoperative VTE risk in gynecologic surgical patients.METHODS:The database of a randomized controlled trial (RCT) was used to select patients who underwent gynecologic surgeries for benignant and malignant diseases in five institutions in China between 2011 and 2018. The Caprini RAM version recommended by the American College of Chest Physicians (ACCP) was adopted. Participants were divided into four risk levels based on the Caprini and G-Caprini scores. For each risk level group, the incidence of VTE was calculated. The correlation between VET incidence and risk levels was assessed by Spearman's rank correlation coefficient (RS) value.RESULTS:As a result, 800 patients in the data base were analyzed with an overall VTE incidence of 5.8%. Caprini RAM showed that the percentage of patients at very low risk, low risk, moderate risk, and high risk were 0%, 4.3%, 44.4%, and 51.4%, respectively, and the VTE incidence was 2.9%, 2.3%, and 9.0%, respectively. RS value between the risk stratification and VTE incidence was 0.500 (P=0.667). G-Caprini RAM showed that the percentage of patients at very low risk, low risk, moderate risk, and high risk were 7.8%, 28.0%, 32.0%, and 32.3%, respectively, and the VTE incidence was 0.0%, 2.9%, 2.3%, and 9.0%, respectively. RS value between the risk stratification and VTE incidence was 1.000 (P<0.01).CONCLUSIONS:The G-Caprini RAM was as suitable as the Caprini RAM for VTE risk assessment in gynecologic surgical patients. The gynecologic model has the advantages of ease of use and more accurate identification of low-risk groups.

Abstract Background Serous ovarian carcinoma is the most common type of ovarian carcinoma. Tumor-associated macrophages (TAMs) promote ovarian cancer progression. Most macrophages are generated by monocyte differentiation. Lysophosphatidic acid (LPA) levels are high in blood, tissues and ascites of patients with ovarian cancer. This study investigated whether human monocytes can directly differentiate into TAMs in the serous ovarian carcinoma microenvironment. Methods Human monocytes were isolated and purified from umbilical cord blood. A serous ovarian carcinoma-like microenvironment was generated by coculturing monocytes and SKOV3 cells in 0.4-μm-pore-size Transwell chambers. Additionally, the effect of LPA was assessed. The two cultured cell types and supernatants were evaluated. Results The morphology and function of monocytes cocultured with SKOV3 cells and/or stimulated with LPA were significantly changed compared with those of non-stimulated monocytes. The CD14 + CD163 + and CD206 + phenotype indicated that stimulated cells were TAMs. The induced cells promoted SKOV3 cell proliferation and invasion, further proving that they were TAMs. The level of the cytokine interleukin-6R in the supernatant was significantly elevated in the treatment groups compared to the control monocyte group. Pathway enrichment analysis of ELISA results showed a strong influence of interleukin-6 family signaling, especially the JAK-STAT signaling pathway, further confirming the importance of IL-6R. Conclusion Monocytes can differentiate into TAMs under coculture with SKOV3 cells and/or LPA stimulation. The induced TAMs promote SKOV3 cell proliferation and invasion. The cytokine receptor IL-6sR and the JAK-STAT signaling pathway play an important role in the differentiation of monocytes into TAMs.

The WHO (2021) Classification classified a group of pediatric-type high-grade gliomas as IDH wildtype, H3 wildtype but as of currently, they are characterized only by negative molecular features of IDH and H3. We recruited 35 cases of pediatric IDH wildtype and H3 wildtype hemispheric glioblastomas. We evaluated them with genome-wide methylation profiling, targeted sequencing, RNAseq, TERT promoter sequencing, and FISH. The median survival of the cohort was 27.6 months. With Capper et al.’s36 methylation groups as a map, the cases were found to be epigenetically heterogeneous and were clustered in proximity or overlay of methylation groups PXA-like (n = 8), LGG-like (n = 10), GBM_MYCN (n = 9), GBM_midline (n = 5), and GBM_RTKIII (n = 3). Histology of the tumors in these groups was not different from regular glioblastomas. Methylation groups were not associated with OS. We were unable to identify groups specifically characterized by EGFR or PDGFRA amplification as proposed by other authors. EGFR, PDGFRA, and MYCN amplifications were not correlated with OS. 4/9 cases of the GBM_MYCN cluster did not show MYCN amplification; the group was also enriched for EGFR amplification (4/9 cases) and the two biomarkers overlapped in two cases. Overall, PDGFRA amplification was found in only four cases and they were not restricted to any groups. Cases in proximity to GBM_midline were all hemispheric and showed loss of H3K27me3 staining. Fusion genes ALK/NTRK/ROS1/MET characteristic of infantile glioblastomas were not identified in 17 cases successfully sequenced. BRAF V600E was only found in the PXA group but CDKN2A deletion could be found in other methylation groups. PXA-like cases did not show PXA histological features similar to findings by other authors. No case showed TERT promoter mutation. Mutations of mismatch repair (MMR) genes were poor prognosticators in single (p ≤ 0.001) but not in multivariate analyses (p = 0.229). MGMT had no survival significance in this cohort. Of the other common biomarkers, only TP53 and ATRX mutations were significant poor prognosticators and only TP53 mutation was significant after multivariate analyses (p = 0.024). We conclude that IDH wildtype, H3 wildtype pediatric hemispheric glioblastomas are molecularly heterogeneous and in routine practice, TP53, ATRX, and MMR status could profitably be screened for risk stratification in laboratories without ready access to methylation profiling.

This study aimed to further investigate the effect of PLD1 on the biological characteristics of human cervical cancer (CC) cell line, CASKI and the potential related molecular mechanism. CRISPR/Cas9 genome editing technology was used to knock out the PLD1 gene in CASKI cells. Cell function assays were performed to evaluate the effect of PLD1 on the biological function of CASKI cells in vivo and in vitro. A PLD1-overexpression rescue experiment in these knockout cells was performed to further confirm its function. Two PLD1-knockout CASKI cell lines (named PC-11 and PC-40, which carried the ins1/del4 mutation and del1/del2/ins1 mutation, respectively), were constructed by CRISPR/Cas9. PLD1 was overexpressed in these knockout cells (named PC11-PLD1 and PC40-PLD1 cells), which rescued the expression of PLD1 by approximately 71.33% and 74.54%, respectively. In vivo, the cell function assay results revealed that compared with wild-type (WT)-CASKI cells, the ability of PC-11 and PC-40 cells to proliferate, invade and migrate was significantly inhibited. The expression of H-Ras and phosphorylation of Erk1/2 (p-Erk1/2) was decreased in PC-11 and PC-40 cells compared with WT-CASKI cells. PC-11 and PC-40 cells could sensitize CASKI cells to cisplatin. More importantly, the proliferation, migration and invasion of PC11-PLD1 and PC40-PLD1 cells with PLD1 overexpression were significantly improved compared with those of the two types of PLD1 knockout cells. The sensitivity to cisplatin was decreased in PC11-PLD1 and PC40-PLD1 cells compared with PC-11 and PC-40 cells. In vivo, in the PC-11 and PC-40 tumour groups, tumour growth was significantly inhibited and tumour weight (0.95 ± 0.27 g and 0.66 ± 0.43 g vs. 1.59 ± 0.67 g, p = 0.0313 and 0.0108) and volume (1069.41 ± 393.84 and 1077.72 mm3  ± 815.07 vs. 2142.94 ± 577.37 mm3 , p = 0.0153 and 0.0128) were significantly reduced compared to those in the WT-CASKI group. Tumour differentiation of the PC-11 and PC40 cells was significantly better than that of the WT-CASKI cells. The immunohistochemistry results confirmed that the expression of H-Ras and p-Erk1/2 was decreased in PC-11 and PC-40 tumour tissues compared with WT-CASKI tumour tissues. PLD1 promotes CC progression by activating the RAS pathway. Inhibition of PLD1 may serve as an attractive therapeutic modality for CC.

A Correction to this paper has been published: https://doi.org/10.1038/s41379-021-00802-0

Gliomas can be classified into five molecular groups based on the status of IDH mutation, 1p/19q codeletion, and TERT promoter mutation, whereas they need to be obtained by biopsy or surgery. Thus, we aimed to use MRI-based radiomics to noninvasively predict the molecular groups and assess their prognostic value. We retrospectively identified 357 patients with gliomas and extracted radiomic features from their preoperative MRI images. Single-layered radiomic signatures were generated using a single MR sequence using Bayesian-regularization neural networks. Image fusion models were built by combing the significant radiomic signatures. By separately predicting the molecular markers, the predictive molecular groups were obtained. Prognostic nomograms were developed based on the predictive molecular groups and clinicopathologic data to predict progression-free survival (PFS) and overall survival (OS). The results showed that the image fusion model incorporating radiomic signatures from contrast-enhanced T1-weighted imaging (cT1WI) and apparent diffusion coefficient (ADC) achieved an AUC of 0.884 and 0.669 for predicting IDH and TERT status, respectively. cT1WI-based radiomic signature alone yielded favorable performance in predicting 1p/19q status (AUC = 0.815). The predictive molecular groups were comparable to actual ones in predicting PFS (C-index: 0.709 vs. 0.722, P = 0.241) and OS (C-index: 0.703 vs. 0.751, P = 0.359). Subgroup analyses by grades showed similar findings. The prognostic nomograms based on grades and the predictive molecular groups yielded a C-index of 0.736 and 0.735 in predicting PFS and OS, respectively. Accordingly, MRI-based radiomics may be useful for noninvasively detecting molecular groups and predicting survival in gliomas regardless of grades.