A comprehensive prognostic and immunological implications of PFKP in pan-cancer.

BACKGROUND: Phosphofructokinase P (PFKP) is a key rate-limiting enzyme in glycolysis, playing a crucial role in various pathophysiological processes. However, its specific function in tumors remains unclear. This study aims to evaluate the expression and specific role of PFKP across multiple tumor types (Pan-cancer) and to explore its potential clinical significance as a therapeutic target in cancer treatment. METHODS: We analyzed the expression of PFKP, immune cell infiltration, and patient prognosis across various cancers using data from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Additionally, we conducted a series of experiments in lung cancer cells, including Western blot, CCK-8 assay, colony formation assay, transwell migration assay, scratch wound healing assay, LDH release assay, and flow cytometry, to evaluate the impact of PFKP on tumor cells. RESULTS: PFKP was found to be highly expressed in most cancers and identified as a prognostic risk factor. Elevated PFKP expression is associated with poorer clinical outcomes, particularly in lung adenocarcinoma (LUAD). Receiver operating characteristic (ROC) curve analysis indicated that PFKP can effectively differentiate between cancerous and normal tissues. The expression of PFKP in most tumors showed significant correlations with tumor mutational burden (TMB), microsatellite instability (MSI), immune score, and immune cell infiltration. In vitro experiments demonstrated that PFKP overexpression promotes lung cancer cell proliferation and migration while inhibiting apoptosis, whereas PFKP deficiency results in the opposite effects. CONCLUSION: PFKP acts as an oncogene involved in tumorigenesis and may influence the immune microenvironment within the tumor. Our findings suggest that PFKP could serve as a potential biomarker for predicting prognosis and the efficacy of immunotherapy in tumors.

Psychosis Prognosis Predictor: A continuous and uncertainty-aware prediction of treatment outcome in first-episode psychosis.

INTRODUCTION: Machine learning models have shown promising potential in individual-level outcome prediction for patients with psychosis, but also have several limitations. To address some of these limitations, we present a model that predicts multiple outcomes, based on longitudinal patient data, while integrating prediction uncertainty to facilitate more reliable clinical decision-making. MATERIAL AND METHODS: We devised a recurrent neural network architecture incorporating long short-term memory (LSTM) units to facilitate outcome prediction by leveraging multimodal baseline variables and clinical data collected at multiple time points. To account for model uncertainty, we employed a novel fuzzy logic approach to integrate the level of uncertainty into individual predictions. We predicted antipsychotic treatment outcomes in 446 first-episode psychosis patients in the OPTiMiSE study, for six different clinical scenarios. The treatment outcome measures assessed at both week 4 and week 10 encompassed symptomatic remission, clinical global remission, and functional remission. RESULTS: Using only baseline predictors to predict different outcomes at week 4, leave-one-site-out validation AUC ranged from 0.62 to 0.66; performance improved when clinical data from week 1 was added (AUC = 0.66-0.71). For outcome at week 10, using only baseline variables, the models achieved AUC = 0.56-0.64; using data from more time points (weeks 1, 4, and 6) improved the performance to AUC = 0.72-0.74. After incorporating prediction uncertainties and stratifying the model decisions based on model confidence, we could achieve accuracies above 0.8 for ~50% of patients in five out of the six clinical scenarios. CONCLUSION: We constructed prediction models utilizing a recurrent neural network architecture tailored to clinical scenarios derived from a time series dataset. One crucial aspect we incorporated was the consideration of uncertainty in individual predictions, which enhances the reliability of decision-making based on the model's output. We provided evidence showcasing the significance of leveraging time series data for achieving more accurate treatment outcome prediction in the field of psychiatry.

MMGCN: Multi-modal multi-view graph convolutional networks for cancer prognosis prediction.

BACKGROUND AND OBJECTIVE: Accurate prognosis prediction for cancer patients plays a significant role in the formulation of treatment strategies, considerably impacting personalized medicine. Recent advancements in this field indicate that integrating information from various modalities, such as genetic and clinical data, and developing multi-modal deep learning models can enhance prediction accuracy. However, most existing multi-modal deep learning methods either overlook patient similarities that benefit prognosis prediction or fail to effectively capture diverse information due to measuring patient similarities from a single perspective. To address these issues, a novel framework called multi-modal multi-view graph convolutional networks (MMGCN) is proposed for cancer prognosis prediction. METHODS: Initially, we utilize the similarity network fusion (SNF) algorithm to merge patient similarity networks (PSNs), individually constructed using gene expression, copy number alteration, and clinical data, into a fused PSN for integrating multi-modal information. To capture diverse perspectives of patient similarities, we treat the fused PSN as a multi-view graph by considering each single-edge-type subgraph as a view graph, and propose multi-view graph convolutional networks (GCNs) with a view-level attention mechanism. Moreover, an edge homophily prediction module is designed to alleviate the adverse effects of heterophilic edges on the representation power of GCNs. Finally, comprehensive representations of patient nodes are obtained to predict cancer prognosis. RESULTS: Experimental results demonstrate that MMGCN outperforms state-of-the-art baselines on four public datasets, including METABRIC, TCGA-BRCA, TCGA-LGG, and TCGA-LUSC, with the area under the receiver operating characteristic curve achieving 0.827 ± 0.005, 0.805 ± 0.014, 0.925 ± 0.007, and 0.746 ± 0.013, respectively. CONCLUSIONS: Our study reveals the effectiveness of the proposed MMGCN, which deeply explores patient similarities related to different modalities from a broad perspective, in enhancing the performance of multi-modal cancer prognosis prediction. The source code is publicly available at https://github.com/ping-y/MMGCN.

Integrative alternative splicing analysis reveals new prognosis signature in B-cell acute lymphoblastic leukemia.

The dysregulation of alternative splicing (AS) is increasingly recognized as a pivotal player in the pathogenesis, progression, and treatment resistance of B-cell acute lymphoblastic leukemia (B-ALL). Despite its significance, the clinical implications of AS events in B-ALL remain largely unexplored. This study developed a prognostic model based on 18 AS events (18-AS), derived from a meticulous integration of bioinformatics methodologies and advanced machine learning algorithms. The 18-AS signature observed in B-ALL distinctly categorized patients into different groups with significant differences in immune infiltration, V(D)J rearrangement, drug sensitivity, and immunotherapy outcomes. Patients classified within the high 18-AS group exhibited lower immune infiltration scores, poorer chemo- and immune-therapy responses, and worse overall survival, underscoring the model's potential in refining therapeutic strategies. To validate the clinical applicability of the 18-AS, we established an SF-AS regulatory network and identified candidate drugs. More importantly, we conducted in vitro cell proliferation assays to confirm our analysis, demonstrating that the High-18AS cell line (SUP-B15) exhibited significantly enhanced sensitivity to Dasatinib, Dovitinib, and Midostaurin compared to the Low-18AS cell line (REH). These findings reveal AS events as novel prognostic biomarkers and therapeutic targets, advancing personalized treatment strategies in B-ALL management.

The prognostic role of MRI-based radiomics in tongue carcinoma: a multicentric validation study.

PURPOSE: Radiomics is an emerging field that utilizes quantitative features extracted from medical images to predict clinically meaningful outcomes. Validating findings is crucial to assess radiomics applicability. We aimed to validate previously published magnetic resonance imaging (MRI) radiomics models to predict oncological outcomes in oral tongue squamous cell carcinoma (OTSCC). MATERIALS AND METHODS: Retrospective multicentric study on OTSCC surgically treated from 2010 to 2019. All patients performed preoperative MRI, including contrast-enhanced T1-weighted (CE-T1), diffusion-weighted sequences and apparent diffusion coefficient map. We evaluated overall survival (OS), locoregional recurrence-free survival (LRRFS), cause-specific mortality (CSM). We elaborated different models based on clinical and radiomic data. C-indexes assessed the prediction accuracy of the models. RESULTS: We collected 112 consecutive independent patients from three Italian Institutions to validate the previously published MRI radiomic models based on 79 different patients. The C-indexes for the hybrid clinical-radiomic models in the validation cohort were lower than those in the training cohort but remained > 0.5 in most cases. CE-T1 sequence provided the best fit to the models: the C-indexes obtained were 0.61, 0.59, 0.64 (pretreatment model) and 0.65, 0.69, 0.70 (posttreatment model) for OS, LRRFS and CSM, respectively. CONCLUSION: Our clinical-radiomic models retain a potential to predict OS, LRRFS and CSM in heterogeneous cohorts across different centers. These findings encourage further research, aimed at overcoming current limitations, due to the variability of imaging acquisition, processing and tumor volume delineation.

Proteomic landscape profiling of primary prostate cancer reveals a 16-protein panel for prognosis prediction.

Prostate cancer (PCa) is the most common malignant tumor in men. Currently, there are few prognosis indicators for predicting PCa outcomes and guiding treatments. Here, we perform comprehensive proteomic profiling of 918 tissue specimens from 306 Chinese patients with PCa using data-independent acquisition mass spectrometry (DIA-MS). We identify over 10,000 proteins and define three molecular subtypes of PCa with significant clinical and proteomic differences. We develop a 16-protein panel that effectively predicts biochemical recurrence (BCR) for patients with PCa, which is validated in six published datasets and one additional 99-biopsy-sample cohort by targeted proteomics. Interestingly, this 16-protein panel effectively predicts BCR across different International Society of Urological Pathology (ISUP) grades and pathological stages and outperforms the D'Amico risk classification system in BCR prediction. Furthermore, double knockout of NUDT5 and SEPTIN8, two components from the 16-protein panel, significantly suppresses the PCa cells to proliferate, invade, and migrate, suggesting the combination of NUDT5 and SEPTIN8 may provide new approaches for PCa treatment.

Prognosis Prediction in Head and Neck Squamous Cell Carcinoma by Radiomics and Clinical Information.

(1) Background: head and neck squamous cell carcinoma (HNSCC) is a common cancer whose prognosis is affected by its heterogeneous nature. We aim to predict 5-year overall survival in HNSCC radiotherapy (RT) patients by integrating radiomic and clinical information in machine-learning models; (2) Methods: HNSCC radiotherapy planning computed tomography (CT) images with RT structures were obtained from The Cancer Imaging Archive. Radiomic features and clinical data were independently analyzed by five machine-learning algorithms. The results were enhanced through a voted ensembled approach. Subsequently, a probability-weighted enhanced model (PWEM) was generated by incorporating both models; (3) Results: a total of 299 cases were included in the analysis. By receiver operating characteristic (ROC) curve analysis, PWEM achieved an area under the curve (AUC) of 0.86, which outperformed both radiomic and clinical factor models. Mean decrease accuracy, mean decrease Gini, and a chi-square test identified T stage, age, and disease site as the most important clinical factors in prognosis prediction; (4) Conclusions: our radiomic-clinical combined model revealed superior performance when compared to radiomic and clinical factor models alone. Further prospective research with a larger sample size is warranted to implement the model for clinical use.

Feasibility of noninvasive near-infrared spectroscopy monitoring in predicting the prognosis of spontaneous intracerebral hemorrhage.

Objective: This study aimed to assess the impact of multimodal monitoring on predicting the prognosis of patients with spontaneous intracerebral hemorrhage (SICH) and to examine the feasibility of using noninvasive near-infrared spectroscopy (NIRS) for monitoring clinical prognosis. Methods: Clinical data of 38 patients with SICH who underwent surgery in the Department of Neurosurgery of Shaanxi Provincial People's Hospital from May 2022 to December 2022 were retrospectively analyzed. The patients were categorized into two groups based on the Glasgow Outcome Scale (GOS) 3 months after operation: poor outcome group (GOSI-III) and good outcome group (GOSIV and V). Multimodal monitoring included invasive intracranial pressure (ICP), brain temperature (BT), internal jugular venous oxygen saturation (SjvO2), and noninvasive NIRS. NIRS monitoring comprised the assessment of brain tissue oxygen saturation (StO2), blood volume index (BVI), and tissue hemoglobin index (THI). The prognostic differences between the two groups were compared. The predictive values were evaluated using the receiver operating characteristic (ROC) curve and the area under the curve (AUC). Results: ICP, BT, BVI, and THI in the good prognosis group were lower than those in the poor prognosis group. The SjvO2 and StO2 in the group with a good prognosis were higher than those in the group with a poor prognosis. Conclusion: The levels of ICP, BT, SjvO2, StO2, BVI, and THI reflect the changes in brain function and cerebral blood flow and significantly correlate with the prognosis of patients with SICH. NIRS monitoring has a high clinical utility in assessing the prognosis.

The relationship between imaging-based body composition abnormalities and long-term mortality in patients with liver cirrhosis.

BACKGROUND: Emerging evidence on cirrhosis suggests a close correlation between abnormality in body composition characteristics and poor prognosis. This study aimed to evaluate the impact of dynamic changes in body composition on the prognostic outcomes in patients with cirrhosis. METHODS: This retrospective analysis included 158 patients diagnosed as cirrhosis from January 2018 to August 2023. Skeletal muscle mass, muscle quality, visceral and subcutaneous adiposity were evaluated using computed tomography (CT) imaging at the third lumbar vertebra level. Competing risk model was performed four different body composition status (i.e., normal, only sarcopenia, only myosteatosis, and combined status) for liver-related mortality. We also explored the relationship between the dynamic change in body composition and long-term prognosis by applying Gray's test. RESULTS: Of the 158 cirrhotic patients (mean [SD] age, 57.1 [12.6] years), sarcopenia was present in 85 (60.1 %) patients, while 22 (13.9 %) patients had sarcopenic obesity and 68 (43.0 %) had myosteatosis. Patients solely diagnosed with sarcopenia exhibited a higher mortality rate compared to those with normal body composition (Gray's test, P=0.006), while patients solely diagnosed with myosteatosis or with a combination of sarcopenia and myosteatosis did not reach statistical significance (Gray's test, P=0.076; P=0.140). Multivariable analysis also revealed that VSR (HR=1.10 [1.01∼1.20]; P=0.028), sarcopenia (HR=2.73 [1.20∼6.22], P=0.017) and myosteatosis (HR=2.39 [1.10∼5.18], P=0.028) were significant independent predictors of liver-related deaths. Otherwise, patients exhibiting aggravating body composition during follow-up period were associated with a significantly higher mortality risk compared to those with normal or remission body composition status (HR=7.63 [1.12∼51.14]; P=0.036). CONCLUSION: Progressive alterations in body composition status appears to be associated with liver-related mortality in individuals with liver cirrhosis. Focusing on the management of skeletal muscle, along with visceral and subcutaneous adiposity, may contribute to improving the prognosis of cirrhotic patients.

A novel nomogram for predicting overall survival in patients with tongue squamous cell carcinoma using clinical features and MRI radiomics data: a pilot study.

OBJECTIVE: Tongue squamous cell carcinoma (TSCC) accounts for 43.4% of oral cancers in China and has a poor prognosis. This study aimed to explore whether radiomics features extracted from preoperative magnetic resonance imaging (MRI) could predict overall survival (OS) in patients with TSCC. METHODS: The clinical imaging data of 232 patients with pathologically confirmed TSCC at Xiangyang No. 1 People's Hospital were retrospectively analyzed from February 2010 to October 2022. Based on 2-10 years of follow-up, patients were categorized into two groups: control (healthy survival, n = 148) and research (adverse events: recurrence or metastasis-related death, n = 84). A training and a test set were established using a 7:3 ratio and a time node. Radiomics features were extracted from axial T2-weighted imaging, contrast-enhanced T1-weighted imaging, and diffusion-weighted imaging (DWI) sequences. The corresponding radiomics scores were generated using the least absolute shrinkage and selection operator algorithm. Kaplan-Meier and multivariate Cox regression analyses were used to screen for independent factors affecting adverse events in patients with TSCC using clinical and pathological results. A novel nomogram was established to predict the probability of adverse events and OS in patients with TSCC. RESULTS: The incidence of adverse events within 2-10 years after surgery was 36.21%. Kaplan-Meier analysis revealed that hot pot consumption, betel nut chewing, platelet-lymphocyte ratio, drug use, neutrophil-lymphocyte ratio, Radscore, and other factors impacted TSCC survival. Multivariate Cox regression analysis revealed that the clinical stage (P < 0.001), hot pot consumption (P < 0.001), Radscore 1 (P = 0.01), and Radscore 2 (P < 0.001) were independent factors affecting TSCC-OS. The same result was validated by the XGBoost algorithm. The nomogram based on the aforementioned factors exhibited good discrimination (C-index 0.86/0.81) and calibration (P > 0.05) in the training and test sets, accurately predicting the risk of adverse events and survival. CONCLUSION: The nomogram constructed using clinical data and MRI radiomics parameters may accurately predict TSCC-OS noninvasively, thereby assisting clinicians in promptly modifying treatment strategies to improve patient prognosis.

A Simple Prognostic Scoring System for Hepatocellular Carcinoma Treated with DEB-TACE.

Objective: To develop a simple and effective prognostic scoring system to predict the efficacy of drug-eluting bead-transcatheter arterial chemoembolization (DEB-TACE) in the treatment of hepatocellular carcinoma (HCC). Methods: Data were retrospectively collected from 230 patients with HCC who received DEB-TACE treatment at six medical centers between January 2019 and December 2022. We developed a predictive score based on independent risk factors for overall survival (OS), validated the model using a validation cohort, and compared its prognostic accuracy with commonly used HCC staging systems. Results: The number of tumors, albumin-bilirubin levels, alpha-fetoprotein levels, and portal vein thrombus grade were identified as independent factors influencing OS. Based on these factors, we established the DEB-TACE treatment of HCC (DTH) scoring system. The DTH score correlated well with OS, which decreased as the DTH score increased. According to the DTH score, patients were categorized into three risk groups: low-risk (DTH-A, 0-4 points), medium-risk (DTH-B, 5-6 points), and high-risk (DTH-A, 7 points). The OS of each risk group was 18.73±0.62 months, 12.73±0.10 months, and 6.93±0.19 months, respectively (p<0.001). The external cohort validation confirmed the accuracy of the DTH score, demonstrating superior predictive performance compared to other commonly used HCC scoring systems. Conclusion: The DTH-HCC scoring system effectively predicts the outcomes of HCC patients undergoing DEB-TACE as initial treatment. This model can aid in the initial planning and decision-making process for DEB-TACE treatment in HCC patients.

Machine learning algorithm for predicting 30-day mortality in patients receiving rapid response system activation: A retrospective nationwide cohort study.

This study investigated the accuracy of a machine learning algorithm for predicting mortality in patients receiving rapid response system (RRS) activation. This retrospective cohort study used data from the In-Hospital Emergency Registry in Japan, which collects nationwide data on patients receiving RRS activation. The missing values in the dataset were replaced using multiple imputations (mode imputation, BayseRidge sklearn. linear model, and K-nearest neighbor model), and the enrolled patients were randomly assigned to the training and test cohorts. We established prediction models for 30-day mortality using the following four types of machine learning classifiers: Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting, random forest, and neural network. Fifty-two variables (patient characteristics, details of RRS activation, reasons for RRS initiation, and hospital capacity) were used to construct the prediction algorithm. The primary outcome was the accuracy of the prediction model for 30-day mortality. Overall, the data from 4,997 patients across 34 hospitals were analyzed. The machine learning algorithms using LightGBM demonstrated the highest predictive value for 30-day mortality (area under the receiver operating characteristic curve, 0.860 [95 % confidence interval, 0.825-0.895]). The SHapley Additive exPlanations summary plot indicated that hospital capacity, site of incidence, code status, and abnormal vital signs within 24 h were important variables in the prediction model for 30-day mortality.

Matrix metalloproteinase 9 expression and glioblastoma survival prediction using machine learning on digital pathological images.

This study aimed to apply pathomics to predict Matrix metalloproteinase 9 (MMP9) expression in glioblastoma (GBM) and investigate the underlying molecular mechanisms associated with pathomics. Here, we included 127 GBM patients, 78 of whom were randomly allocated to the training and test cohorts for pathomics modeling. The prognostic significance of MMP9 was assessed using Kaplan-Meier and Cox regression analyses. PyRadiomics was used to extract the features of H&E-stained whole slide images. Feature selection was performed using the maximum relevance and minimum redundancy (mRMR) and recursive feature elimination (RFE) algorithms. Prediction models were created using support vector machines (SVM) and logistic regression (LR). The performance was assessed using ROC analysis, calibration curve assessment, and decision curve analysis. MMP9 expression was elevated in patients with GBM. This was an independent prognostic factor for GBM. Six features were selected for the pathomics model. The area under the curves (AUCs) of the training and test subsets were 0.828 and 0.808, respectively, for the SVM model and 0.778 and 0.754, respectively, for the LR model. The C-index and calibration plots exhibited effective estimation abilities. The pathomics score calculated using the SVM model was highly correlated with overall survival time. These findings indicate that MMP9 plays a crucial role in GBM development and prognosis. Our pathomics model demonstrated high efficacy for predicting MMP9 expression levels and prognosis of patients with GBM.

Prognostic significance of soluble PD-L1 in prostate cancer.

Purpose: The aim of this study was to assess the role of sPD-L1 and sPD-1 as potential biomarkers in prostate cancer (PCa). The association of the values of these soluble proteins were correlated to the clinical data: stage of disease, Gleason score, biochemical recurrence etc. For a comprehensive study, the relationship between sPD-L1 and sPD-1 and circulating immune cells was further investigated. Methods: A total of 88 patients with pT2 and pT3 PCa diagnosis and 41 heathy men were enrolled. Soluble sPD-L1 and sPD-1 levels were measured in plasma by ELISA method. Immunophenotyping was performed by flow cytometry analysis. Results: Our study's findings demonstrate that PCa patients had higher levels of circulating sPD-L1 and sPD-1 comparing to healthy controls (p < 0.001). We found a statistically significant (p < 0.05) relationship between improved progression free survival and lower initial sPD-L1 values. Furthermore, patients with a lower sPD-1/sPD-L1 ratio were associated with a higher probability of disease progression (p < 0.05). Additionally, a significant (p < 0.05) association was discovered between higher Gleason scores and elevated preoperative sPD-L1 levels and between sPD-1 and advanced stage of disease (p < 0.05). A strong correlation (p < 0.05), between immunosuppressive CD4+CD25+FoxP3+ regulatory T cells and baseline sPD-L1 was observed in patients with unfavorable postoperative course of the disease, supporting the idea that these elements influence each other in cancer progression. In addition to the postoperative drop in circulating PD-L1, the inverse relationship (p < 0.05), between the percentage of M-MDSC and sPD-L1 in patients with BCR suggests that M-MDSC is not a source of sPD-L1 in PCa patients. Conclusion: Our findings suggest the potential of sPD-L1 as a promising prognostic marker in prostate cancer.

A new, simplified endoscopic scoring system for predicting clinical outcome in gastric low-grade intraepithelial neoplasia: the "e-cout system".

BACKGROUND AND OBJECTIVES: The clinical outcomes of gastric low-grade intraepithelial neoplasia (LGIN) exhibit significant diversity, and the current reliance on endoscopic biopsy for diagnosis poses limitations in devising appropriate treatment strategies for this disease. This study aims to establish a prognostic prediction scoring system (e-Cout system) for gastric LGIN, offering a theoretical foundation for solving this clinical challenge. METHODS: Retrospectively selecting 1013 cases meeting the inclusion and exclusion criteria from over 300,000 cases of upper gastrointestinal endoscopy performed at the Digestive Endoscopy Center of our hospital between 2000 and 2022, the cohort included 484 cases as development cohort and 529 cases for validation. Employing relevant statistical analysis, we used development cohort data to establish the e-Cout system for gastric LGIN, and further used validation cohort data to for internal validation. RESULTS: In the developmental stage, based on accordant regression coefficients, we assigned point values to six risk factors for poor prognosis: 4 points for microvessel (MV) distortion, 3 points for MV thickening, 2 points for ulcer, and 1 point each for lesion size > 2cm, disease duration > 1 year, and hyperemia and redness on the lesion surface. Patients were then categorized into four risk levels: low risk (0-1 point), medium risk (2-3), high risk (4-6), and very high risk (≥7). During the validation stage, significant differences in the three different outcomes of gastric LGIN were observed across all risk levels. The probability of reversal and progression showed a significant decrease and increase, respectively, with escalating of risk levels, and these differences were statistically significant (P< 0.001). CONCLUSIONS: The proposed e-Cout system holds promise in aiding clinicians to predict the probability and risk levels of different clinical outcomes in patients with gastric LGIN. This system is expected to provide an improved foundation and guidance for the selection of clinical strategies for this disease.

Research and application of omics and artificial intelligence in cancer.

Cancer has a high incidence and lethality rate, which is a significant threat to human health. With the development of high-throughput technologies, different types of cancer genomics data have been accumulated, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics. A comprehensive analysis of various omics data is needed to understand the underlying mechanisms of tumor development. However, integrating such a massive amount of data is one of the main challenges today. Artificial intelligence techniques such as machine learning are now becoming practical tools for analyzing and understanding multi-omics data on diseases. Enabling great optimization of existing research paradigms for cancer screening, diagnosis, and treatment. In addition, intelligent healthcare has received widespread attention with the development of healthcare informatization. As an essential part of innovative healthcare, practical, intelligent prognosis analysis and personalized treatment for cancer patients are also necessary. This paper introduces the advanced multi-omics data analysis technology in recent years, presents the cases and advantages of the combination of both omics data and artificial intelligence applied to cancer diseases, and finally briefly describes the challenges faced by multi-omics analysis and artificial intelligence at the current stage, aiming to provide new perspectives for oncology research and the possibility of personalized cancer treatment. .

Natural course and predictors of consciousness recovery in children with prolonged disorder of consciousness.

Prolonged disorder of consciousness (DoC) is a rising challenge. Pediatric data on diagnosis and prognosis of prolonged DoC were too limited and heterogeneous, making it difficult to define the natural course and evaluate the prognosis. The present study explored the emergence from the Minimally Conscious State (eMCS) incidence at different months postinjury drawing the natural course, and detected the predictors of the incidence in children with prolonged DoC. A hospital-based prospective cohort study was conducted. Kaplan-Meier curves, as well as univariate and multivariate COX regression analysis, were performed. The study enrolled 383 pediatric DoC individuals, including 220 males (57.4%), with an average age of 3.9 (1.9-7.3) years. The median duration between onset and rehabilitation is 30.0 (21.0-46.0) days. At enrollment, the ratio of vegetative state/unresponsive wakefulness syndrome (VS/WUS) to MCS is 78.9%-21.1%. Traumatic brain injury and infection are the major etiologies (36.8% and 37.1%, respectively), followed by hypoxia cerebral injury (12.3%). For children with prolonged DoC, the cumulative incidence of eMCS at months 3, 6, 12, and 24 was 0.510, 0.652, 0.731, 0.784 VS 0.290, 0.418, 0.539, 0.603 in the traumatic VS non-traumatic subgroup, respectively. For children in a persistent vegetative state (PVS), the cumulative incidence of emergence at months in 3, 6, 12, 24, 36 and 48 was testified as 0.439, 0.591, 0.683, 0.724, 0.743 and 0.743 in the traumatic subgroup, and 0.204, 0.349, 0.469, 0.534, 0.589 and 0.620 in the non-traumatic subgroup. Participants who exhibit any of the following four demographical and/or clinical characteristics-namely, older than 4 years at onset, accepted rehabilitation within 28 days of onset, remained MCS at enrollment, or with etiology of traumatic brain injuries-had a significantly positive outcome of consciousness recovery (eMCS). Moreover, both prolongation of the central somatosensory conductive time (CCT) (level 2) and absence of N20 (level 3) independently predict a negative outcome. In children with prolonged DoC, we found that 12 months postinjury was critical to eMCS, and a preferred timepoint to define chronic vegetative state (VS). The characteristics including age, etiology, time before rehabilitation, consciousness state, and SEP results were useful predictors of conscious recovery.Trial registration Registered 06/11/2018, the registration number is chiCTR1800019330 (chictr.org.cn). Registered prospectively.

A predictive study of genes related to lactic acid metabolism in cervical carcinoma.

OBJECTIVES: Lactic acid metabolism, a hallmark of carcinogenesis, may play potential roles in cervical carcinoma, assisting the prognosis prediction. MATERIALS AND METHODS: A regression analysis was conducted to identify the ones with the most frequent variation in mutations and CNV changes in lactate metabolism-related (L-related) genes, after which a prognostic nomogram was built based on selected genes and clinical features by machine learning methods. RESULTS: EGLN1, IL1, IL12RB1, ENO1, and 10 other genes had the most frequent changes and prognostic differences in overall survival (OS). The lactated associated risk (LAR) score model can distinguish the patients in OS (p = 0.046, HR = 101.9, 95%CI 1.1-9447.6), and together with clinical features has a higher AUC (AUC = 0.839). Furthermore, CD8+ T, activated CD4+ memory T and resting mast cells were significantly negatively associated with the LAR score. CONCLUSIONS: Lactic acid metabolism is closely related to the prognosis of cervical carcinoma, where the immune microenvironment may play an important role.

TMO-Net: an explainable pretrained multi-omics model for multi-task learning in oncology.

Cancer is a complex disease composing systemic alterations in multiple scales. In this study, we develop the Tumor Multi-Omics pre-trained Network (TMO-Net) that integrates multi-omics pan-cancer datasets for model pre-training, facilitating cross-omics interactions and enabling joint representation learning and incomplete omics inference. This model enhances multi-omics sample representation and empowers various downstream oncology tasks with incomplete multi-omics datasets. By employing interpretable learning, we characterize the contributions of distinct omics features to clinical outcomes. The TMO-Net model serves as a versatile framework for cross-modal multi-omics learning in oncology, paving the way for tumor omics-specific foundation models.

Development of stemness-related signature to optimize prognosis prediction and identify XMD8-85 as a novel therapeutic compound for glioma.

Glioma is a highly invasive and aggressive type of brain cancer with poor treatment response. Stemness-related transcription factors form a regulatory network that sustains the malignant phenotype of gliomas. We conducted an integrated analysis of stemness-related transcription factors using The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) datasets, established the characteristics of stemness-related transcription factors, including Octamer-Binding Protein 4 (OCT4), Meis Homeobox 1 (MEIS1), E2F Transcription Factor 1 (E2F1), Transcription Factor CP2 Like 1 (TFCP2L1), and RUNX Family Transcription Factor 1 (RUNX1). The characteristic of stemness-related transcription factors was identified as an independent prognostic factor for glioma patients. Patients in the high-risk group have a worse prognosis than those in the low-risk group. The glioma microenvironment in the high-risk group exhibited a more active immune status. Single-cell level analysis revealed that stem cell-like cells exhibited stronger intercellular communication than glioma cells. Meanwhile, patients in different risk stratification exhibited varying sensitivities to immunotherapy and small molecule drug therapy. XMD8-85 was more effective in the high-risk group, and its antitumor effects were validated both in vivo and in vitro. Our results indicate that this prognostic feature will assist clinicians in predicting the prognosis of glioma patients, guiding immunotherapy and personalized treatment, as well as the potential clinical application of XMD8-85 in glioma treatment, and helping to develop effective treatment strategies.