Prognostic Model Research Articles

Integrating Single-cell and Bulk RNA-seq to Construct a Metastasis-related Model for Evaluating Immunotherapy and Chemotherapy in Uveal Melanoma.

Metastasis is a major cause of death in UM, highlighting the need to use highly specific and sensitive prognostic markers to identify patients with a risk of developing metastasis. The aim of this study was to improve the current precision treatment for patients with metastatic uveal melanoma (UM). The objective of this work was to investigate the heterogeneity between primary human UM and metastatic UM at the single-cell level and to discover potential molecules regulating UM metastasis. Seurat R toolkit was employed to analyze single-cell sequencing data of UM and to identify differentially expressed genes (DEGs) between primary and metastatic UM. Least absolute shrinkage and selection operator (LASSO) and Cox regression analyses were performed on the DEGs from the bulk RNA-seq cohort to develop a prognostic model. Based on the model, patients were divided into high and low groups. The correlations among the risk score, immune indicators, immune checkpoint blockade (ICB) therapy, and anti-tumor drug therapy were analyzed. Cell types in primary UM and metastatic UM tumors include B/plasma cells, endothelial cells, melanocytes, monocytes/macrophages, photoreceptor cells, and T cells. Among 157 DEGs between the two tumor types, S100A4, PDE4B, CHCHD10, NSG1, and C4orf48 were selected to construct a prognostic model. The model could accurately and independently predict response to ICB treatment and sensitivity to antineoplastic drugs for UM patients as well as their immune infiltration levels, risk of death, and metastasis possibility. This study analyzed the tumor ecosystem of primary and metastatic UM, providing a metastasis-related model that could be used to evaluate the prognosis, risk of metastasis, immunotherapy, and efficacy of antineoplastic drug treatment of UM.

A Novel Prognostic Risk Score Model Based on RNA Editing Level in Lower-Grade Glioma

BackgroundLower-grade glioma (LGG) refers to WHO grade 2 and 3 gliomas. Surgery combined with radiotherapy and chemotherapy can significantly improve the prognosis of LGG patients, but tumor progression is still unavoidable. As a form of posttranscriptional regulation, RNA editing (RE) has been reported to be involved in tumorigenesis and progression and has been intensively studied recently. MethodsSurvival data and RE data were subjected to univariate and multivariate Cox regression analysis and lasso regression analysis to establish an RE risk score model. A nomogram combining the risk score and clinicopathological features was built to predict the 1-, 3-, and 5-year survival probability of patients. The relationship among ADAR1, SOD2 and SOAT1 was verified by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) ResultsA risk model associated with RE was constructed and patients were divided into different risk groups based on risk scores. The model demonstrated strong prognostic capability, with the area under the ROC curve (AUC) values of 0.882, 0.938, and 0.947 for 1-, 3-, and 5-year survival predictions, respectively. Through receiver operating characteristic curve (ROC) curves and calibration curves, it was verified that the constructed nomogram had better performance than age, grade, and risk score in predicting patient survival probability. Apart from this functional analysis, the results of correlation analyses between risk differentially expressed genes (RDEGs) and RE help us to understand the underlying mechanism of RE in LGG. ADAR may regulate the expression of SOD2 and SOAT1 through gene editing. ConclusionIn conclusion, this study establishes a novel and accurate 17-RE model and a nomogram for predicting the survival probability of LGG patients. ADAR may affect the prognosis of glioma patients by influencing gene expression.

Development of a Combined Oxidative Stress and Endoplasmic Reticulum Stress-Related Prognostic Signature for Hepatocellular Carcinoma.

Oxidative stress and endoplasmic reticulum stress are important components of the cellular stress process, which plays a critical role in tumor initiation and progression. First, the correlation between oxidative stress and endoplasmic reticulum stress was detected in 68 human hepatocellular carcinoma (HCC) tissue microarray samples by immunohistochemistry. Differentially expressed oxidative stress- and endoplasmic reticulum stressrelated genes (OESGs) then were screened in HCC. Next, an OESGs prognostic signature was constructed for HCC in the training cohort (TCGA-LIHC from The Cancer Genome Atlas), by least absolute shrinkage and selection operator Cox and stepwise Cox regression analyses, and was verified in the external cohort (GSE14520 from the Gene Expression Omnibus). The MCP counter was employed to evaluate immune cell infiltration. The C-index was used to evaluate the predictive power of prognostic signature. Finally, a prognostic nomogram model was constructed to predict the survival probability of patients with HCC based on the results of Cox regression analysis. We demonstrated a positive correlation between oxidative stress and endoplasmic reticulum stress in human HCC samples. We then identified five OESGs as a prognostic signature consisting of IL18RAP, ECT2, PPARGC1A, STC2, and NQO1 for HCC. Related risk scores correlated with tumor stage, grade, and response to transcatheter arterial chemoembolization therapy, and the higher risk score group had less T cells, CD8+ T cells, cytotoxic lymphocytes and natural killer cell infiltration. The C-index of our OESGs prognostic signature was superior to four previously published signatures. Furthermore, we developed a nomogram based on the OESGs prognostic signature and clinical parameters for patients with HCC that is an effective quantitative analysis tool to predict patient survival. The OESGs signature showed excellent performance in predicting survival and therapeutic responses for patients with HCC.

Unraveling the Biological Functions of UCEC: Insights from a Prognostic Signature Model

BackgroundUterine corpus endometrial carcinoma (UCEC) is a prevalent gynecological tumor with a bleak prognosis. Anomalous glycosylation plays a pivotal role in tumorigenesis. Currently, there is a lack of prognostic signatures based on glycosylation-related genes for UCEC. Thus, our research aims to construct a predictive model and validate the correlation between relevant genes and biological functions. MethodsUsing the TCGA database, we developed prognostic models and explored their relationships with survival outcomes. We further selected key genes to verify their expression in tissues and assess their impact on cellular behavior. ResultsThe clinical prognosis of the high-risk group was significantly worse than that of the low-risk group. The nomogram model accurately predicted UCEC patient prognosis. Additionally, we identified OLFML1 as a unique signature gene that can inhibit UCEC progression and reduce radiation resistance in vitro. ConclusionsOur model, which is based on glycosylation-related genes in UCEC, effectively identifies high-risk patients and provides valuable prognostic information. In addition, OLFML1 acts as a tumor suppressor in UCEC and enhances radiosensitivity, suggesting a new potential target for improving therapeutic efficacy.

Radiomic features of primary retroperitoneal sarcomas: a prognostic study

BackgroundRisk-stratification of patients with retroperitoneal sarcomas (RPS) relies on validated nomograms, such as Sarculator. This retrospective study investigated whether radiomic features extracted from computed tomography (CT) imaging could i) enhance the performance of Sarculator and ii) identify G3 dedifferentiated liposarcoma (DDLPS) or leiomyosarcoma (LMS), which are currently consider in a randomized clinical trial testing neoadjuvant chemotherapy. MethodsPatients with primary localized RPS treated with curative-intent surgery (2011-2015) and available pre-operative CT imaging were included. Regions of interest (ROIs) were manually annotated on both unenhanced and portal venous phase acquisitions. Top performing radiomic features were selected with outcome-specific random forest models, through generation of replicative experiments (contexts) where patients were split into training and testing sets. Endpoints were overall and disease-free survival (OS, DFS).Prognostic models for DFS and OS included the top five selected radiomic features and the Sarculator nomogram score.Models accuracy was assessed with Harrell’s Concordance (C-)index. ResultsThe study included 112 patients, with a median follow-up of 77 months (IQR 65-92 months).Sarculator alone achieved a C-index of 0.622 and 0.686 for DFS and OS, respectively. Radiomic features only marginally enhanced the prediction accuracy of Sarculator for OS (C-index=0.726, C-index gain: 0.04) or DFS (C-index=0.639, C-index gain: 0.017). Finally, radiomic features identified patients with G3 DDLPS or LMS with an accuracy of 0.806. ConclusionRadiomic features marginally improved the performance of Sarculator in RPS.However, they accurately identified G3 DDLPS or LMS at diagnosis, potentially improving patients selection for neoadjuvant treatments.

Hypoxia-related bioinformatic signatures associated with prognosis and tumor microenvironment of pancreatic cancer: Current status, concerns, and future perspectives

Pancreatic cancer (PC), a highly lethal tumor with nearly identical incidence and mortality rates, has become the sixth leading cause of cancer-related deaths. Hypoxia is an important malignant factor in PC, as it regulates angiogenesis, metabolic reprogramming, tumor progression, and metastasis. Disrupting the hypoxic microenvironment can enhance the efficacy of antitumor therapy and improve the prognosis of patients with PC. With the advent of bioinformatics, hypoxia-related PC models have emerged in recent years. They provide a reference for estimating the prognosis and immune microenvironment of patients with PC and identify potential biomarkers for targeting hypoxic microenvironment. However, these findings based on bioinformatic analysis may not be completely reliable without further experimental evidence and clinical cohort validation. The application of these models and biomarkers in clinical practice to predict survival time and develop anti hypoxic therapeutic strategies for patients with PC remains in its infancy. In this editorial, we review the current status of hypoxia-related prognostic models in PC, analyze their similarities and differences, discuss several existing challenges, and provide potential solutions and directions for further studies. This editorial will facilitate the optimization, validation, and determination of the molecular mechanisms of related models.

Development and validation of the antibody-dependent cellular phagocytosis-based signature: A prognostic risk model of gastric cancer

Development and validation of the antibody-dependent cellular phagocytosis-based signature: A prognostic risk model of gastric cancer

Construction of molecular subtype and prognostic model for gastric cancer based on nucleus-encoded mitochondrial genes

Construction of molecular subtype and prognostic model for gastric cancer based on nucleus-encoded mitochondrial genes

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

Osteonecrosis 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. We 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). The 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. The 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.

Immune-oncology targets and therapeutic response of cell pyroptosis-related genes with prognostic implications in neuroblastoma.

Construction of a neuroblastoma (NB) prognostic predictive model based on pyroptosis-related genes (PRGs) to improve individualized management of NB patients. The NB cohort GSE49711 was obtained from the Gene Expression Omnibus (GEO) database, and a total of 498 patients were enrolled into the study, which were randomized into a training set and a test set at a ratio of 1:1, with 250 patients in the training set and 248 patients in the test set. A risk prediction model was constructed using the training set, and the GSE49711 cohort and test set were used as internal validation to verify the reliability of the model. Independent predictors associated with prognosis were screened using univariate and multivariate COX regression analyses, and risk score models were constructed. Single-cell gene set enrichment analysis (ssGSEA) was used to assess the relationship between PRGs and the tumor immune microenvironment. Nomograms were constructed to extend the clinical usability of the model and the reliability of the model was verified using ROC curves and calibration curves. Protein interaction networks of risk genes were mapped using the String database, and the expression of PRGs in NB cell lines was staged using the CCLE database. A prognostic model was first developed with the training set: the risk score formula was (-0.30 × GSDMB) + (-0.46 × IL-18) + (-0.21 × NLRP3) + (0.56 × AIM2). Patients were categorized into high- and low-risk groups based on the median risk score value. Survival analysis showed that NB patients in the high-risk group had a significantly lower survival rate than those in the low-risk group (P < 0.001). In both the GSE49711 overall cohort and the test cohort, survival analyses showed that patients in the high-risk group had significantly lower survival than those in the low-risk group (P < 0.001). Single-cell gene set enrichment analysis was used to assess the relationship between PRGs and the tumor immune microenvironment. Time-dependent ROC curves assessed the predictive performance of the nomogram in 5-, 7.5-, and 10-year survival with areas under the curve (AUC) of 0.843, 0.802 and 0.797, respectively. The calibration curves show good clinical predictive performance for nomograms. The results suggest that PRGs may serve as a novel prognostic marker for NB patients to provide new immunotherapeutic targets for the clinical treatment of NB patients.

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

Hepatocellular 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. In 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. Twelve 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. These 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.

Maximizing Survival in Pediatric Congenital Cardiac Surgery Using Machine Learning, Explainability, and Simulation Techniques

Background: Pediatric and congenital heart surgery (PCHS) is highly risky. Complications associated with this surgical procedure are mainly caused by the severity of the disease or the unnecessary, late, or premature execution of the procedure, which can be fatal. In this context, prognostic models are crucial to reduce the uncertainty of the decision to perform surgery; however, these models alone are insufficient to maximize the probability of success or to reverse a future scenario of patient death. Method: A new approach is proposed to reverse the prognosis of death in PCHS through the use of (1) machine learning (ML) models to predict the outcome of surgery; (2) an explainability technique (ET) to determine the impact of main risk factors; and (3) a simulation method to design health scenarios that potentially reverse a negative prognosis. Results: Accuracy levels of 96% in the prediction of mortality and survival were achieved using a dataset of 565 patients undergoing PCHS and assessing 10 risk factors. Three case studies confirmed that the ET known as LIME provides explanations that are consistent with the observed results, and the simulation of one real case managed to reverse the initial prognosis of death to one of survival. Conclusions: An innovative method that integrates ML models, ETs, and Simulation has been developed to reverse the prognosis of death in patients undergoing PCHS. The experimental results validate the relevance of this approach in medical decision-making, demonstrating its ability to reverse negative prognoses and provide a solid basis for more informed and personalized medical decisions.

Survival prediction in patients with head and neck squamous cell carcinoma and novel mechanistic insights of S100A8/A9.

S100A8/A9, an innate immune protein, significantly regulates inflammatory processes and immune responses. While S100A8/A9 has been linked to various diseases, its association with head and neck squamous cell carcinoma (HNSCC) remains unclear. Samples from the Cancer Genome Atlas (TCGA) were categorized into groups with low and high expression of S100A8/A9. R software, Sangerbox, UALCAN, GEPIA2, STRING, Cytoscape, TCGC Data Portal, miRcode, OncomiR and ENCORI databases were used to comprehensively study the expression, interactome and mutational profiles of S100A8/A9 and associated mechanism in HNSCC. The proteins S100A8/A9 were found to be associated with processes of 'epithelium development', 'regulating pluripotency of stem cells' and 'regulation of immune system'. The most frequent mutation observed in the S100A8 protein was E93K (3/37, MU4401889), while for the S100A9 protein, it was R10C (4/37, MU4633862). Furthermore, the group expressing high levels of S100A8/A9 showed increased infiltration by dendritic cells and neutrophils, but decreased infiltration by M2 macrophages, compared to the group with low S100A8/A9 expression. S100A8/A9 was also found to interact with a variety of mRNA transcripts, several of which were involved in initiation and progression of HNSCC. Through LASSO-Cox method, 20 genes (CALML5, MSX1, FZD3, STC2, SLC2A3, TMEM198B, DYNC1I1, SPHK2, ALMS1.IT1, SPPL2B, PDGFA, BCORL1, TEX101, SGCE, DNAJC12, RRN3P1, HOXB9, TMEM150C, METTL7B and PSPN) were screening to construct a prognostic model to distinguish favorable and poor prognosis of HNSCC patients. Besides, our prognostic model was also validated in GSE41613 cohort. S100A8/A9 may be a promising marker for the diagnostic and prognostic assessment of the HNSCC patients. Based on these insights, we have devised a new classification model for HNSCC, which has the potential to enhance the management and personalized treatment of HNSCC patients. The model should also be further optimized through the expansion of sample size and implemented experimental studies in future research.

Prognostic model development using novel genetic signature associated with adenosine metabolism and immune status for patients with hepatocellular carcinoma.

The high mortality rate of hepatocellular carcinoma (HCC) is partly due to advanced diagnosis, emphasizing the need for effective predictive tools in HCC treatment. The aim of this study is to propose a novel prognostic model for HCC based on adenosine metabolizing genes and explore the potential relationship between them. Regression analysis was performed to identify differentially expressed genes associated with adenosine metabolism in HCC patients using RNA sequencing data obtained from a public database. Adenosine metabolism-related risk score (AMrisk) was derived using the least absolute shrinkage and selection operator (LASSO) Cox regression and verified using another database. Changes in adenosine metabolism in HCC were analyzed using functional enrichment analysis and multiple immune scores. The gene expression levels in patient samples were validated using quantitative reverse transcription polymerase chain reaction. Thirty adenosine metabolism-related differentially expressed genes were identified in HCC, and six genes (ADA, P2RY4, P2RY6, RPIA, SLC6A3, and VEGFA) were used to calculate the AMrisk score; the higher the risk scores, the lower the overall survival. Moreover, immune infiltration activation and immune checkpoints were considerably higher in the high-risk group. Additional in vitro experiments validated the enhanced expression of these six genes in HCC. The established predictive model demonstrated that adenosine metabolism-related genes was significantly associated with prognosis in HCC patients.

Textbook oncological outcome of locally advanced gastric cancer patients with preoperative sarcopenia: a multicenter clinical study.

The impact of postoperative sarcopenia on the Textbook Oncological Outcome (TOO) in locally advanced gastric cancer (LAGC) remains uncertain. This study investigates the relationship between sarcopenia and TOO, explores its long-term prognostic value, and develops a prognostic model incorporating sarcopenia and TOO for survival prediction. We performed a retrospective analysis of clinical and pathological data from patients with LAGC who underwent radical surgery at two Chinese tertiary referral hospitals. Sarcopenia was defined as an SMI < 36.4 cm2/m2 in males and < 28.4 cm2/m2 in females. TOO was defined as the addition of perioperative chemotherapy to the textbook outcomes (TO). A nomogram was developed to predict postoperative overall survival (OS) and recurrence-free survival (RFS) in LAGC patients. The study included 972 patients with LAGC. The overall TOO achievement rate was 67.1%. The TOO achievement rate was significantly higher in patients non-sarcopenia compared to those with sarcopenia (68.9% vs. 61.1%, P = 0.031). Logistic regression revealed that age ≥ 65, high ASA score, and sarcopenia were independent risk factors for TOO failure. Cox regression analysis identified TOO, sarcopenia, tumor size, differentiation, vascular invasion, pT stage, and pN stage as independent predictors of OS and RFS. Nomogram models based on sarcopenia and TOO accurately predicted the 3-year and 5-year OS and RFS. Preoperative sarcopenia was an independent predictor of TOO implementation. A prognostic prediction model that integrates preoperative sarcopenia and TOO, which outperforms the current staging system, can aid clinicians in effectively assessing the prognosis of patients with LAGC.

Development of a prognostic signature for overall survival using peripheral blood biomarkers in head and neck squamous cell carcinoma treated with immune checkpoint inhibitors.

We previously reported in recurrent/metastatic head and neck squamous cell carcinoma (R/M HNSCC) treated with immune checkpoint inhibitors (ICIs), pretreatment higher lactate dehydrogenase (LDH) and absolute (abx) neutrophils as well as lower percent (%) lymphocytes correlated with worse overall survival (OS). In this study we aimed to develop a prognostic signature for HNSCC treated with ICIs using these peripheral blood biomarkers (PBBMs). Adults with R/M HNSCC treated with ICIs at our institution from 08/2012 to 03/2021 with pretreatment PBBMs were included. Follow-up continued until 02/15/2022. The cohort (n = 151) was randomly split into training (n = 100) and testing (n = 51) datasets. A prognostic score incorporating LDH, % lymphocytes, and abx neutrophils was developed from the training dataset using Cox proportional hazards regression. In the training dataset, a grid search identified the optimal cutpoints classifying patients into high, medium, and low-risk groups (trichotomized signature) as well as high vs. low-risk groups (dichotomized signature). The prognostic score, dichotomized and trichotomized signatures were then validated in the testing dataset. Training and testing datasets showed no clinically meaningful differences in clinicodemographic characteristics or PBBMs. An OS prognostic model was developed from the training dataset: Risk score = 1.24*log10(LDH) - 1.95*log10(% lymphocytes) + 0.47*log10(abx neutrophils). Optimal risk score cutpoints for the dichotomized and trichotomized signatures were defined in the training dataset, and Kaplan-Meier curves for both dichotomized and trichotomized signatures showed good separation between risk groups. Risk scores were calculated in the testing dataset, where the trichotomized signature demonstrated overlap between low and medium-risk groups but good separation from the high-risk group while the dichotomized signature showed clear separation between low and high-risk groups. Higher risk score correlated with worse OS (HR 2.08, [95%CI 1.17-3.68], p = 0.012). Progression-free survival Kaplan-Meier curves likewise showed excellent separation between dichotomized risk groups in the training and testing datasets. We developed a prognostic signature for OS based on 3 previously identified PBBMs for HNSCC treated with ICIs and identified a high-risk group of patients least likely to have survival benefit from ICIs. This signature may improve ICI patient selection and warrants validation in an independent cohort as well as correlation with CPS.

Transcriptome-based network analysis related to regulatory T cells infiltration identified RCN1 as a potential biomarker for prognosis in clear cell renal cell carcinoma.

Regulatory T cells (Tregs) play a critical role in shaping the immunosuppressive microenvironment within tumors. Investigating the role of Tregs in Clear cell renal cell carcinoma (ccRCC) is crucial for identifying prognostic markers and therapeutic targets for ccRCC. Weighted gene co-expression network analysis (WGCNA) was utilized to pinpoint modules related to Treg infiltration in TCGA-KIRC samples. Following this, consensus clustering was employed to derive two clusters associated with Treg infiltration in ccRCC. A prognostic model was then developed using the gene module associated with Treg infiltration. We then evaluated the ability of the prognostic model to predict ccRCC overall survival and demonstrated that RCN1 can be used as a target to predict ccRCC prognosis. We deduce that the two clusters associated with Treg infiltration exhibit distinct compositions of the immune microenvironment, pathway activations, prognosis, and drug sensitivities commonly utilized in ccRCC treatment. Furthermore, a 7-gene model risk score, developed based on ccRCC Treg infiltration, proved to be a reliable prognostic marker in both training and validation cohorts. Additionally, survival analysis indicated that RCN1 serves as a reliable prognostic factor for ccRCC. Single-cell sequencing analysis revealed that RCN1 is predominantly expressed in tumor cells. A pan-cancer analysis highlighted that RCN1 is linked with poor prognosis and the activation of inflammatory response pathways across various cancers. We developed a prognostic model associated with Treg infiltration, which facilitates the clinical categorization of ccRCC progression. Moreover, our findings underscore the significant potential of RCN1 as a ccRCC biomarker.

Neutrophil-centric analysis of gastric cancer: prognostic modeling and molecular insights.

Gastric cancer remains a significant global health concern with poor prognosis. This study investigates the role of neutrophils in gastric cancer progression and their potential as prognostic indicators. Using multi-omics approaches, including Weighted Gene Co-expression Network Analysis (WGCNA), machine learning, and single-cell analysis, we identified neutrophil-associated gene signatures and developed a robust prognostic model. Our findings reveal distinct gastric cancer subtypes based on neutrophil-associated genes, with one subtype showing increased neutrophil infiltration and poorer prognosis. Single-cell analysis uncovered neutrophil-associated alterations in cell composition, gene expression profiles, and intercellular communication within the tumor microenvironment. Additionally, we explored the relationship between neutrophil-associated genes, microbiota composition, and alternative splicing events in gastric cancer. Furthermore, we identified QKI as a key regulator of alternative splicing and demonstrated its role in promoting malignant phenotypes and enhancing TGF-beta signaling and epithelial-mesenchymal transition in gastric cancer cells by wet experiment. Lastly, the role of QKI in the association with drug resistance and the identification of specific agents for treating QKI-associated drug resistance were also explored. This comprehensive study provides novel insights into the complex interplay between neutrophils, the tumor microenvironment, microbiota, alternative splicing and gastric cancer progression, offering potential new targets for therapeutic intervention.

Predictors of outcomes after gastric peroral endoscopic myotomy for refractory gastroparesis: a systematic review

Background: Gastroparesis is a debilitating gastroduodenal disorder for which gastric peroral endoscopic myotomy (GPOEM) has emerged as an efficacious treatment option. However, response to GPOEM varies between 50-80%, such that preoperative predictors of treatment success are needed to guide patient selection. Methods: We performed a systematic review to identify predictors of clinical and functional response to GPOEM among adult patients with gastroparesis (PROSPERO: CRD42023457359). MEDLINE, Embase, and CENTRAL databases were searched systematically for studies reporting outcomes after GPOEM in September 2023. A narrative synthesis of predictive factors on univariable and multivariable analysis was performed with consideration of response rates via meta-analysis, and evaluation of discrimination if prognostic models were developed. ROBINS-E was used for risk of bias assessment. Results: Of 1899 articles reviewed, 30 were included. The GPOEM response rate was 63.1% (95% confidence interval 56.3%-69.5%) with most studies defining clinical success on the basis of improvement in gastroparesis cardinal symptom index (87%, 26/30). Older age, shorter duration of gastroparesis, non-diabetic etiology, lower body mass index, and response to intrapyloric botulinum toxin were associated with positive response to GPOEM on multivariable analyses. Predictors on physiological tests such as EndoFLIP or gastric emptying scintigraphy were inconsistent. No prognostic models underwent external validation. Conclusion: Currently, there are limited reproducible predictors of response to GPOEM among patients with refractory gastroparesis. Robust prospective studies investigating scalable, reproducible, and actionable biomarkers of treatment response are required.

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.