Personalized Treatment Strategies Research Articles

Exploring the molecular profile of localized colon cancer: insights from the AIO Colopredict Plus registry.

Understanding the mutational landscape of colon cancer (CC) is crucial for targeted therapy development. Microsatellite instability (MSI-H), rat sarcoma (RAS), and B-Raf proto-oncogene, serine/threonine kinase (BRAF) mutations (MT) are pivotal markers. Further investigation into clinicopathological features of RAS and BRAF MT in microsatellite stable (MSS) and MSI-H tumors is warranted. A retrospective analysis of 4883 localized CC patients (pts.) was conducted. Molecular profiling assessed MSI, KRAS, NRAS, and BRAF MT. Correlation with clinicopathological data employed ANOVA and Chi-square tests. Disease-free survival (DFS) and overall survival (OS) were analyzed adjusting for age, gender, sidedness, UICC stage, Charlson Comorbidity Index (CCI). A Cox model incorporated all variables as covariates. This analysis included 4883 pts. (2302 female/2572 male, 3865 (79.2%) MSS, 1018 (20.8%) MSI-H). MSS pts. had more All-Wild Type (WT), KRAS MT, and NRAS MT tumors vs. MSI-H pts. (42.1% vs. 21.1%; 39.8% vs. 15.4%; 3.6% vs. 0.7%; p<0.001 for each). BRAF MT tumors (95.5% BRAF V600E MT) were more prevalent in MSI-H individuals (62.8% vs. 8.1%, p<0.001). KRAS and BRAF MT tumors were more frequently right-sided, while BRAF MT tumors were associated with female gender, advanced disease stage, lymph node positivity, and poorer differentiation in the MSS subset (p<0.001). Common KRAS mutations included p.G12D (30.44%) and p.G12V (21.3%) in MSS and p.G13D (28.9%) and p.G12D (22.37%) in MSI-H. NRAS MT tumors were dominated by codon 61 mutations (51.7%). Survival analysis revealed worst prognosis in BRAF MT MSS tumors (DFS: HR 1.74 (95% CI 1.15-2.62, p=0.009; OS: HR 1.61 (95% CI 0.99-2.6), p=0.055). The 3-years DFS and 5-years OS rates were lowest in this subset (61.6% and 57.7% respectively). These findings highlight the complex interplay between molecular subtypes, clinicopathological features, and survival outcomes in early CC. Further research is needed to elucidate underlying mechanisms and develop personalized treatment strategies.

UNDERSTANDING THE PAIN IN KNEE OSTEOARTHRITIS: INSIGHTS FROM THE SEMI-QUANTITATIVE MRI OSTEOARTHRITIS KNEE SCORE ANALYSIS

This study aimed to analyze and predict various types of knee pain in knee osteoarthritis (KOA) patients based on Semi-Quantitative MRI Osteoarthritis Knee Score (SQ-MOAKS) features. The goal was to identify structural abnormalities in the knee joint associated with different types of pain, thereby aiding in the development of personalized treatment plans. Subjects were selected from the Osteoarthritis Initiative (OAI) database, including 297 patients who underwent both SQ-MOAKS assessment on knee MRI images and WOMAC knee pain assessment. Univariate and multivariate logistic regression analyses were used for feature selection, followed by the construction of multi-risk factor models and corresponding nomograms for each pain type. The predictive values of the models were evaluated using ROC curves and related metrics (AUC, accuracy, sensitivity, specificity), and the DeLong test was used to assess the significance of differences between various ROCs and AUCs. Significant associations were found between specific SQ-MOAKS features and different types of knee pain. Bone marrow lesions (BML) and cartilage morphology (CM) features were crucial in predicting the walking pain and rest pain, while osteophytes (OS) were more associated with nocturnal pain and pain during stair climbing. The models demonstrated high predictive accuracy, particularly for walking pain and rest pain. This study demonstrated that SQ-MOAKS features could effectively predict different types of knee pain, revealing the structural basis of knee pain in KOA patients. These results prompted the use of SQ-MOAKS in developing personalized treatment strategies, enhancing clinical decision-making, and improving patient quality of life.

Transcriptomics-Based Liquid Biopsy for Early Detection of Recurrence in Locally Advanced Gastric Cancer.

The study presents a transcriptomics-based liquid biopsy approach for early recurrence detection in locally advanced gastric cancer (LAGC). Four mRNA biomarkers (AGTR1, DNER, EPHA7, and SUSD5) linked to recurrence are identified through transcriptomic data analysis. A Risk Stratification Assessment (RSA) model combining these biomarkers with clinical features showed superior predictive accuracy for postoperative recurrence, with AUCs of 0.919 and 0.935 in surgical and liquid biopsy validation cohorts, respectively. Functional studies using human gastric cancer cell lines AGS and HGC-27 demonstrated that silencing the identified mRNA panel genes impaired cell migration, invasion, and proliferation. In vivo experiments further showed reduced tumor growth, metastasis, and lymphangiogenesis in mice, possibly mediated by the cAMP signaling pathway. This non-invasive approach offers significant potential for enhancing recurrence detection and enabling personalized treatment strategies, thereby improving patient outcomes in the management of LAGC.

Recommendations for Research to Develop a Patient-Centered Clinical Follow-Up Protocol for Oral Epithelial Dysplasia.

Oral epithelial dysplasia (OED) is the primary histological marker for assessing the progression of oral potentially malignant disorders (OPMDs) to cancer. Despite challenges in grading and low inter-pathologist reproducibility, OED severity remains the key predictor of malignant transformation. However, globally accepted guidelines for OED monitoring are lacking, despite calls for individualized management based on host and lesion characteristics. The proposed research protocol involves acquiring high-quality intraoral images, assessing oral hygiene and periodontal status, eliminating chronic mechanical irritation and Candida infections, and applying adjunctive diagnostic methods like toluidine blue staining, optical evaluation, and brush cytology. Tailored follow-up regimens based on individual risk assessments are emphasized, with frequent monitoring for high-grade dysplasia or patients at higher risk of progression. Therefore, effective OED management should consider the patient's immune status, dietary habits, and oral microbiota, aiming to develop personalized treatment strategies that optimize patient-centered care.

Isolation and identification of patient-derived liver cancer stem cells and development of personalized treatment strategies

BackgroundLiver cancer stem cells (LCSCs) are thought to drive the metastasis and recurrence, however, the heterogeneity of molecular markers of LCSCs has hindered the development of effective methods to isolate them.MethodsThis study introduced an effective approach to isolate and culture LCSCs from human primary liver cancer (HPLC), leveraging mouse embryonic fibroblasts (MEFs) as feeder cells in conjunction with using defined medium. Isolated LCSCs were further characterized by multiple approaches. Transcriptome sequencing data analysis was conducted to identify highly expressed genes in LCSCs and classify different subtypes of liver cancers.ResultsTotal sixteen cell strains were directly isolated from 24 tissues of three types of HPLC without sorting, seven of which could be maintained long-term culture as colony growth on MEFs, which is unique characteristics of stem cells. Even 10 of cloned cells formed the tumors in immunodeficient mice, indicating that those cloned cells were tumorgenic. The histologies and gene expression pattern of human xenografts were very similar to those of HPLC where these cloned cells were isolated. Moreover, putative markers of LCSCs were further verified to all express in cloned cells, confirming that these cells were LCSCs. These cloned LCSCs could be cryopreserved, and still maintained the feature of colony growth on MEFs after the recovery. Compared to suspension culture as conventional approach to culture LCSCs, our approach much better maintained stemness of LCSCs for a long time. To date, these cloned cells could be cultured on MEFs over 12 passages. Moreover, bioinformatics analysis of sequencing data revealed the gene expression profiles in LCSCs, and liver cancers were classified into two subtypes C1 and C2 based on genes associated with the prognosis of LCSCs. Patients of the C2 subtype, which is closely related to the extracellular matrix, were found to be sensitive to treatments such as Cisplatin, Axitinib, JAK1 inhibitors, WNT-c59, Sorafenib, and RO-3306.ConclusionIn summary, this effective approach offers new insights into the molecular landscape of human liver cancers, and the identification of the C2 subtype and its unique response to the treatment pave the way for the creation of more effective, personalized therapeutic strategies.

Integrated single-cell analysis reveals heterogeneity and therapeutic insights in osteosarcoma

Osteosarcoma (OSA) is a primary bone malignancy characterized by its aggressive nature and high propensity for metastasis. Despite advancements in multimodal therapies, the clinical outcomes for OSA patients remain suboptimal, necessitating deeper molecular insights for improved therapeutic strategies. Here, we employed single-cell RNA sequencing (scRNA-seq) to elucidate the cellular heterogeneity and transcriptional dynamics of OSA tumors. Our study identified eleven distinct tumor cell subpopulations, including osteoblastic, chondroblastic, and myeloid lineages, each exhibiting unique transcriptional profiles associated with disease progression and metastasis. Epithelial-mesenchymal transition (EMT) emerged as a critical process driving aggressive phenotypes, supported by gene set enrichment analyses (GSVA) and transcription factor regulatory network analyses. Integration of copy number variation (CNV) data highlighted genomic alterations in osteoblastic and chondroblastic cells, implicating potential therapeutic targets. Furthermore, immune cell infiltration analyses revealed distinct immune profiles across OSA subtypes, correlating with tumor mutational burden (TMB) and clinical outcomes. Our findings underscore the complexity of OSA biology and provide a foundation for developing personalized treatment strategies targeting tumor heterogeneity and immune interactions.

The impact of diet on the inflammatory bowel disease course – a literature review

Introduction and Objective. Inflammatory bowel disease (IBD), including ulcerative colitis (UC) and Crohn’s disease (CD), is a chronic gastrointestinal disorder marked by relapsing symptoms. Current treatments mainly involve immunosuppressive therapies, which are effective but may have significant side effects. Diet as an adjunctive treatment is appealing due to its accessibility, low cost, and minimal adverse effects. This review explores the role of diet in managing IBD, focusing on dietary strategies during both active disease and remission. Methods. A literature review was conducted using PubMed and Google Scholar with search terms like "inflammatory bowel disease", "Crohn’s disease", "ulcerative colitis", "diet", "IBD treatment", "anti-inflammatory food" and related variations. Articles published within the last five years were prioritized. Brief description of the state of Knowledge. Ulcerative colitis and Crohn’s disease cause gastrointestinal inflammation. Crohn’s disease symptoms include abdominal pain, fistulas and rectal lesions, while UC is characterized by diarrhea, severe discomfort, and blood in the stool. Certain foods often exacerbate IBD symptoms. Proper nutrition during disease stages can improve patients’ quality of life. Diets high in processed foods, sugar, and red meat may increase IBD risk, while fiber and vitamin D3 are associated with fewer flare-ups. Regular malnutrition screening is essential for all IBD patients. Conclusions. Understanding the dietary components that influence inflammation in IBD could lead to better treatment outcomes and longer-lasting symptom relief. Integrating nutrition into personalized treatment strategies should become standard practice for managing IBD.

A Microfluidic-Based Sensing Platform for Rapid Quality Control on Target Cells from Bioreactors.

We investigated the design and characterization of a Lab-On-a-Chip (LoC) cell detection system primarily designed to support immunotherapy in cancer treatment. Immunotherapy uses Chimeric Antigen Receptors (CARs) and T Cell Receptors (TCRs) to fight cancer, engineering the response of the immune system. In recent years, it has emerged as a promising strategy for personalized cancer treatment. However, it requires bioreactor-based cell culture expansion and manual quality control (QC) of the modified cells, which is time-consuming, labour-intensive, and prone to errors. The miniaturized LoC device for automated QC demonstrated here is simple, has a low cost, and is reliable. Its final target is to become one of the building blocks of an LoC for immunotherapy, which would take the place of present labs and manual procedures to the benefit of throughput and affordability. The core of the system is a commercial, on-chip-integrated capacitive sensor managed by a microcontroller capable of sensing cells as accurately measured charge variations. The hardware is based on standardized components, which makes it suitable for mass manufacturing. Moreover, unlike in other cell detection solutions, no external AC source is required. The device has been characterized with a cell line model selectively labelled with gold nanoparticles to simulate its future use in bioreactors in which labelling can apply to successfully engineered CAR-T-cells. Experiments were run both in the air-free drop with no microfluidics-and in the channel, where the fluid volume was considerably lower than in the drop. The device showed good sensitivity even with a low number of cells-around 120, compared with the 107 to 108 needed per kilogram of body weight-which is desirable for a good outcome of the expansion process. Since cell detection is needed in several contexts other than immunotherapy, the usefulness of this LoC goes potentially beyond the scope considered here.

A delphi-based model for prognosis of femoral head collapse in osteonecrosis: a multi-factorial approach

BackgroundOsteonecrosis of the femoral head (ONFH) is a progressive and debilitating condition characterized by the death of bone tissue due to inadequate blood supply. Despite advances in diagnostic imaging and treatment strategies, predicting the risk of femoral head collapse remains a significant clinical challenge. This study seeks to address this gap by developing a robust prognostic model that integrates clinical, imaging, and laboratory data to improve early diagnosis and guide therapeutic decision-making.MethodsWe conducted a qualitative systematic review and employed the Delphi method to select key prognostic factors from clinical data, imaging findings, and laboratory indicators. The study included ONFH patients treated from January 2014 to December 2021. We used univariate and multivariate Cox regression analyses to develop a nomogram for predicting the risk of femoral head collapse. The model’s performance was evaluated using the concordance index (C-index), calibration plots, and decision curve analysis (DCA).ResultsThe study included 297 patients (454 hips) with ONFH. Key prognostic factors identified included pain presence (p < 0.001, RR = 0.185, 95% CI: 0.11–0.31), JIC classification (C1: p < 0.001, RR = 0.096, 95% CI: 0.054–0.171; C2: p < 0.001, RR = 0.323, 95% CI: 0.215–0.487), necrotic area (3 < MNAI < 6: p < 0.001, RR = 0.107, 95% CI: 0.061–0.190; MNAI ≥ 6: p < 0.001, RR = 0.466, 95% CI: 0.314–0.692), weight-bearing reduction (p < 0.001, RR = 0.466, 95% CI: 0.323–0.672), preservation of the anterolateral pillar (p < 0.001, RR = 0.223, 95% CI: 0.223–0.473), and subchondral bone fracture on CT (p < 0.001, RR = 0.32, 95% CI: 0.217–0.472). The nomogram demonstrated a high C-index of 0.88, indicating excellent predictive accuracy. Calibration plots showed good agreement between predicted and observed outcomes, and DCA confirmed the model’s clinical utility.ConclusionsThe prognostic model developed in this study provides a reliable tool for predicting femoral head collapse in ONFH patients. It allows for early identification of high-risk patients, guiding personalized treatment strategies to improve patient outcomes and reduce the need for invasive surgical procedures.

HLA-G high-expressor 3’UTR markers are linked to gastric cancer development and survival

Gastric cancer ranks fifth in both world prevalence and lethality, with a 5-year survival of less than 30%. HLA-G, a non-classical class I HLA gene, has emerged as a potential marker for cancer susceptibility and prognosis due to its immunomodulatory properties. Its level of expression is regulated by polymorphisms in the 3’ untranslated region (3’UTR) polymorphisms, which form various combined haplotypes (UTR-1 to -9). In this study, we examined HLA-G 3’UTR polymorphisms in paired tissue samples from 111 patients with gastric adenocarcinoma and 119 healthy controls. Polymorphism analysis was performed using PCR and Sanger sequencing, followed by statistical analysis using SNPStats software. Survival analysis was conducted using Kaplan–Meier curves and multivariate Cox regression models. High-expressor HLA-G 3’UTR haplotypes (UTR-1 and UTR-6) were significantly associated with gastric cancer susceptibility, indicating a potential role in tumor immune evasion. Additionally, the 14 base pair insertion/deletion polymorphism (14 bp I/D) emerged as a prognostic marker, with D/D genotype carriers showing lower survival rates compared to I/D and I/I genotype carriers. Our study highlights the clinical relevance of HLA-G polymorphisms in gastric cancer, suggesting their potential as prognostic markers and therapeutic targets. Further elucidation of HLA-G-related pathways could lead to personalized treatment strategies and improved patient outcomes in gastric cancer.

Elevated ETV4 expression in cholangiocarcinoma is linked to poor prognosis and may guide targeted therapies.

Cholangiocarcinoma (CCA), a highly aggressive bile duct cancer, is associated with late-stage diagnosis and limited treatment options, leading to poor patient outcomes. Early detection and personalized treatment strategies are crucial. The study by Wang et al highlights the prognostic potential of the PEA3 subfamily genes (ETV1, ETV4, and ETV5) in CCA, identifying ETV4 as a particularly promising biomarker. Their bioinformatic analysis revealed that elevated ETV4 expression correlates with poorer survival, positioning it as a strong indicator of disease progression. These findings suggest that ETV4 could enhance prognostic precision and guide personalized therapies, although further validation through large-scale clinical trials is essential. Challenges in clinical application include the need for comprehensive experimental validation and addressing the tumor heterogeneity in CCA. Future research should focus on validating these biomarkers in diverse cohorts and developing targeted therapies, especially in regions where CCA is endemic.

Triglyceride-glucose index and risk of all-cause and cardiovascular mortality in patients with cardiovascular disease: Analysis from 1999 to 2018 NHANES data.

This research seeks to examine the correlation between the triglyceride-glucose index and the hazard of all-cause and cardiovascular death in individuals with cardiovascular disease (CVD). By evaluating the index, we can better anticipate and assess the risk and prognosis of CVD patients, and provide precise and individualized guidance for clinical treatment and management. Demographic and clinical data of 2185 CVD patients from 10 cycles of the National Health and Nutrition Examination Survey database from 1999 to 2018 were extracted for analysis. Employed the 3-level quantile method to group data, and a multivariate Cox proportional hazard model along with subgroup analysis to study the correlation between index and both mortalities. Additionally, restricted cubic spline examination was employed to assess the correlation, aiming to enhance the comprehension of their interrelation. Over the course of an average post-observation duration of 89.5 months involving 2185 CVD individuals, 607 patients suffered from all-cause mortality and 313 patients suffered from CVD-related mortality. An inverted U-shaped correlation was identified through restricted cubic spline analysis. During the multivariate COX regression analysis, it was found that individuals in the T2 and T3 had a dramatically lower hazard of both mortalities as opposed to those in the T1. The results were overall consistent across subgroup analyses according to age, gender, race, body mass index, diabetes, and hypertension, the interaction between these characteristics and the index was not remarkable (P > .05). Studies conducted on CVD individuals in the US have revealed a U-shaped correlation between triglyceride-glucose index and hazard of both all-cause and CVD-related death. However, further investigations are required to examine the particular function of index in forecasting the prognosis of CVD individuals. This will be helpful in accurately evaluating the risk and prognosis of CVD patients, and ultimately in developing more precise and personalized treatment and management strategies.

Functional Outcome Prediction of Acute Ischemic Stroke Based on the Oral and Gut Microbiota.

Although several studies have identified a distinct gut microbiota in individuals with acute ischemic stroke (AIS), there is a limited amount of research that has simultaneously investigated alterations in the oral and intestinal microbiota in AIS patients and their correlation with clinical prognosis. This was a prospective and observational single-center cohort study in which we included 160 AIS patients who were admitted within 24h after a stroke event. We collected oral and rectal swab samples for analysis using 16S rRNA high-throughput sequencing. Our study revealed that patients with unfavorable outcomes after AIS showed early disruptions in their oral and intestinal microbiota. Rectal swabs showed increased levels of facultatively anaerobic bacteria in patients with a poor prognosis, while the oral cavity exhibited higher levels of anaerobic and opportunistic pathogenic bacteria. By employing machine learning analysis, we found that the microbiota composition at both rectal and oral sites could predict early and long-term outcomes. Moreover, patients with a poor prognosis displayed increased oral bacterial colonization in the rectal microbiota and altered interactions between the oral and gut microbiota. This study reveals distinct rectal and oral bacteria that could predict unfavorable outcomes for AIS patients. Monitoring the microbiota of various body sites during the early stages after admission may hold prognostic value and inform personalized treatment strategies. The presence of oral bacteria colonizing the intestines during the acute phase of stroke could serve as an early indication of poor outcomes for AIS patients.

Subtyping colorectal cancer based on septic shock-associated genes: prognosis and immune characteristics.

Sepsis and colorectal cancer (CRC) are leading causes of death. Given their mutual dependence for susceptibility, we used bioinformatics to explore potential connections between septic shock (SS) and CRC. We identified 452 co-expressed genes between SS-related differential expression genes (SS-DEGs) and CRC patient-expressed genes (TCGA-CRC genes). CRC samples were categorized into two cluster subgroups through hierarchical clustering. We then compared the prognosis and immune landscapes of the two cluster subgroups through survival analysis, immune microenvironment analysis, and immune therapy response evaluation. Clustering analysis of the 452 CRC patient-expressed SS-DEGs identified two subtypes: SS-like CRC (SL-CRC) and non-SS-like CRC (NSL-CRC). There were no significant differences in overall survival between the CRC subtypes. However, the subtypes displayed significant differences in immune score, stromal score, and ESTIMATE score. Based on immune therapy databases, there were also significant differences in responses to anti-CTLA-4 and anti-PD-1 immune checkpoint inhibitors between the subtypes. Our study reveals significant differences in the immune microenvironment and immune therapy responses between SL-CRC and NSL-CRC subtypes. These findings provide a foundation for identifying new therapeutic targets and developing personalized treatment strategies for specific CRC subtypes.

EEG and brain network analysis in the early diagnosis of Alzheimer's disease

Abstract. The early diagnosis of Alzheimers Disease (AD) is critical for effective intervention and improved patient outcomes. This paper explores the role of electroencephalography (EEG) and brain network analysis in the early detection of AD, with a particular focus on neurofeedback training as a therapeutic intervention. We review the characteristic changes in EEG signals, such as decreased alpha power and increased theta power, and their implications for identifying early cognitive decline. Functional connectivity analysis using coherence and phase synchronization methods reveals disruptions in key brain networks, providing valuable biomarkers for early AD. We also discuss quantitative EEG (qEEG) techniques, including power spectral density and fractal dimension, which enhance diagnostic accuracy. Additionally, mathematical models like the Kuramoto model offer insights into network dynamics and predict disease progression. Integrating EEG with other diagnostic tools, such as MRI and PET, creates a comprehensive multimodal approach. This integrative framework enhances early diagnosis and informs personalized treatment strategies, representing a significant advancement in the field of AD research and clinical practice.

Investigating the diagnostic and prognostic significance of genes related to fatty acid metabolism in hepatocellular carcinoma

BackgroundHepatocellular carcinoma (HCC) is one of the most prevalent and lethal cancers worldwide, with death rates increasing by approximately 2–3% per year. The high mortality and poor prognosis of HCC are primarily due to inaccurate early diagnosis and lack of monitoring when liver transplantation is not feasible. Fatty acid (FA) metabolism is a critical metabolic pathway that provides energy and signaling factors in cancer, particularly in HCC, and promotes malignancy. Therefore, it is essential to explore specific FA metabolism-related diagnostic and prognostic signatures that can enable the effective early diagnosis and monitoring of HCC.MethodsIn this study, we used genes associated with FA metabolism pathway and weighted gene co-expression network analysis (WGCNA) to establish a gene co-expression network and identify hub genes related to HCC (disease WGCNA) and FA clusters (cluster WGCNA). A diagnostic model was constructed using data downloaded from the Gene Expression Omnibus database (GSE25097), and a prognostic model was established using The Cancer Genome Atlas cohort, in which Univariate Cox regression analysis, multivariate Cox risk model, and LASSO Cox regression analysis were applied. The immune infiltration of HCC cells was evaluated using CIBERSORT. The function of the key SLC22A1 gene was experimentally verified in vitro and in vivo.ResultsTwelve overlapping genes (CPEB3, ASPDH, DEPDC7, ETFDH, UGT2B7, GYS2, F11, ANXA10, CYP2C8, GLYATL1, C6, and SLC22A1) from disease and cluster WGCNA were identified as key genes and used in the construction of the diagnostic and prognostic models. The RF model had the highest area under the ROC curve (AUC) of 0.994 was identified as the most effective for distinguishing patients with HCC with different features. The top five important genes (C6, UGT2B7, SLC22A1, F11, and CYP2C8) from the RF model were selected as diagnostic genes for further analysis (ROC curves: AUC value = 0.986, 95% confidence interval [95% CI] = 0.967–0.999). Moreover, a risk score formula consisting of four genes (GYS2, F11, ANXA10 and SLC22A1) was established and its independent prognostic ability was further demonstrated (univariate Cox regression analysis: hazard ratio [HR] = 3.664%, 95% CI = 2.033–6.605, P < 0.001; multivariate Cox regression analysis: HR = 2.801%, 95% CI = 1.553–5.049, P < 0.001). Additionally, in vitro and in vivo experiments demonstrated that SLC22A1 inhibits HCC tumor development, suggesting it may be a potential therapeutic target for HCC.ConclusionsThese findings indicate a considerable value of specific FA metabolism-related genes in the diagnostic and prognostic evaluation of HCC, which provide novel insights into the disease’s management, as well as has potential implications for personalized treatment strategies. However, further investigation of the effects of these model genes on HCC is required.

Clinical Subtype Trajectories in Sepsis Patients Admitted to the ICU: A Secondary Analysis of an Observational Study.

Sepsis is an evolving process and proposed subtypes may change over time. We hypothesized that previously established sepsis subtypes are dynamic, prognostic of outcome, and trajectories are associated with host response alterations. A secondary analysis of two observational critically ill sepsis cohorts: the Molecular diAgnosis and Risk stratification of Sepsis (MARS) and the Medical Information Mart for Intensive Care-IV (MIMIC-IV). ICUs in the Netherlands and United States between 2011-2014 and 2008-2019, respectively. Patient admission fulfilling the Sepsis-3 criteria upon ICU admission adjudicated to one of four previously identified subtypes, comprising 2,416 admissions in MARS and 10,745 in MIMIC-IV. Subtype stability and the changes per subtype on days 2, 4 and 7 of ICU admission were assessed. Next, the associated between change in clinical subtype and outcome and host response alterations. In MARS, upon ICU admission, 6% (n = 150) of the patient admissions were α-type, 3% (n = 70) β-type, 55% (n = 1317) γ-type, and 36% (n = 879) δ-type; in MIMIC-IV, this was α = 22% (n = 2398), β = 22% (n = 2365), γ = 31% (n = 3296), and δ = 25% (2686). Overall, prevalence of subtypes was stable over days 2, 4, and 7. However, 28-56% (MARS/MIMIC-IV) changed from α on ICU admission to any of the other subtypes on day 2, 33-71% from β, 57-32% from γ, and 50-48% from δ. On day 4, overall subtype persistence was 33-36%. γ or δ admissions remaining in, or transitioning to, subtype γ on days 2, 4, and 7 exhibited lower mortality rates compared with those remaining in, or transitioning to, subtype δ. Longitudinal host response biomarkers reflecting inflammation, coagulation, and endothelial dysfunction were most altered in the δ-δ group, followed by the γ-δ group, independent of the day or biomarker domain. In two large cohorts, subtype change to δ was associated with worse clinical outcome and more aberrant biomarkers reflecting inflammation, coagulation, and endothelial dysfunction. These findings underscore the importance of monitoring sepsis subtypes and their linked host responses for improved prognostication and personalized treatment strategies.

Improved prognostication of overall survival after radiotherapy in lung cancer patients by an interpretable machine learning model integrating lung and tumor radiomics and clinical parameters.

Accurate prognostication of overall survival (OS) for non-small cell lung cancer (NSCLC) patients receiving definitive radiotherapy (RT) is crucial for developing personalized treatment strategies. This study aims to construct an interpretable prognostic model that combines radiomic features extracted from normal lung and from primary tumor with clinical parameters. Our model aimed to clarify the complex, nonlinear interactions between these variables and enhance prognostic accuracy. We included 661 stage III NSCLC patients from three multi-national datasets: a training set (N = 349), test-set-1 (N = 229), and test-set-2 (N = 83), all undergoing definitive RT. A total of 104 distinct radiomic features were separately extracted from the regions of interest in the lung and the tumor. We developed four predictive models using eXtreme gradient boosting and selected the top 10 features based on the Shapley additive explanations (SHAP) values. These models were the tumor radiomic model (Model-T), lung radiomic model (Model-L), a combined radiomic model (Model-LT), and an integrated model incorporating clinical parameters (Model-LTC). Model performance was evaluated through Harrell's concordance index, Kaplan-Meier survival curves, time-dependent area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis. Interpretability was assessed using the SHAP framework. Model-LTC exhibited superior performance, with notable predictive accuracy (C-index: training set, 0.87; test-set-2, 0.76) and time-dependent AUC above 0.75. Complex nonlinear relationships and interactions were evident among the model's variables. The integration of radiomic and clinical factors within an interpretable framework significantly improved OS prediction. The SHAP analysis provided insightful interpretability, enhancing the model's clinical applicability and potential for aiding personalized treatment decisions.

Machine learning-based prediction of one-year mortality in ischemic stroke patients.

Accurate prediction of mortality following an ischemic stroke is essential for tailoring personalized treatment strategies. This study evaluates the effectiveness of machine learning models in predicting one-year mortality after an ischemic stroke. Five machine learning models were trained using data from a national stroke registry, with logistic regression demonstrating the highest performance. The SHapley Additive exPlanations (SHAP) analysis explained the model's outcomes and defined the influential predictive factors. Analyzing 8183 ischemic stroke patients, logistic regression achieved 83% accuracy, 0.89 AUC, and an F1 score of 0.83. Significant predictors included stroke severity, pre-stroke functional status, age, hospital-acquired pneumonia, ischemic stroke subtype, tobacco use, and co-existing diabetes mellitus (DM). The model highlights the importance of predicting mortality in enhancing personalized stroke care. Apart from pneumonia, all predictors can serve the early prediction of mortality risk which supports the initiation of early preventive measures and in setting realistic expectations of disease outcomes for all stakeholders. The identified tobacco paradox warrants further investigation. This study offers a promising tool for early prediction of stroke mortality and for advancing personalized stroke care. It emphasizes the need for prospective studies to validate these findings in diverse clinical settings.

Analysis of immune status and prognostic model incorporating lactic acid metabolism-associated genes

BackgroundCancer development is intricately linked with metabolic dysregulation, including lactic acid metabolism, which plays a pivotal role in tumor progression and immune evasion. However, its specific implications in gastric adenocarcinoma (STAD) remain unclear. This study introduces a novel methodology to evaluate lactic acid metabolism comprehensively in STAD, aiming to elucidate its prognostic significance and impact on immunotherapy efficacy. Targeted therapies directed at key lactic acid metabolism genes (LMGs) identified within the tumor microenvironment (TME) hold promise for personalized treatment strategies.MethodsLactic acid metabolism patterns were assessed in 415 STAD patients using a panel of 21 LMGs. Cox regression and Lasso regression analyses were employed to develop a predictive risk model based on differentially expressed genes (DEGs). Validation of the model was conducted using independent cohorts from the GEO and TCGA databases, as well as additional datasets focused on immunotherapy responses. Further investigations into TME dynamics of lactic acid metabolism included functional assays targeting SLC16A3, a pivotal gene identified through our analyses.ResultsPatients were stratified into distinct risk groups based on their lactic acid metabolism profiles. Low-risk patients exhibited attenuated lactic acid metabolism, correlating with favorable clinical outcomes characterized by prolonged survival and enhanced responsiveness to immunotherapy. Notably, tumor cells within the TME demonstrated heightened levels of active lactic acid metabolism, particularly impacting tumor-infiltrating lymphocytes such as CD8 + T cells and regulatory T cells. Mechanistically, SLC16A3 emerged as a critical regulator promoting STAD cell proliferation, invasion, and migration while modulating the metabolic landscape.ConclusionThis study underscores the prognostic value of a lactic acid metabolism-based model in STAD, providing insights into its potential as a predictive biomarker for patient stratification and therapeutic targeting. The findings highlight SLC16A3 as a promising candidate for therapeutic intervention aimed at modulating lactic acid metabolism in the TME, thereby advancing personalized treatment strategies in gastric cancer management.