Prognostic Model Research Articles

Identification of a PANoptosis-related prognostic model in triple-negative breast cancer, from risk assessment, immunotherapy, to personalized treatment

BackgroundTriple-negative breast cancer is a breast cancer subtype characterized by its challenging prognosis, and establishing prognostic models aids its clinical treatment. PANoptosis, a recently identified type of programmed cell death, influences tumor growth and patient outcomes. Nonetheless, the precise impact of PANoptosis-related genes on the prognosis of triple-negative breast cancer has yet to be determined. MethodsClinical information for the triple-negative breast cancer samples was collected from the Gene Expression Omnibus and The Cancer Genome Atlas databases, while 19 PANoptosis-related genes were sourced from previous studies. We first categorized PANoptosis-related subtypes and determined the differentially expressed genes between them. Subsequently, we developed and validated a PANoptosis-associated predictive model using LASSO and Cox multivariate regression analyses. Statistical evaluations were conducted using R software, and the mRNA expression levels of the genes were quantified using real-time PCR. ResultsUsing consensus clustering analysis, we divided triple-negative breast cancer patients into two clusters based on PANoptosis-related genes and identified 1054 differentially expressed genes between these clusters. Prognostic-related genes were subsequently selected to re-cluster patients, validating their predictive ability. A prognostic model was then constructed based on four genes: BTN2A2, CACNA1H, PIGR, and S100B. The expression and enriched cell types of these genes were examined and the expression levels were validated in vitro. Furthermore, the model was validated, and a nomogram was created to enhance personalized risk assessment. The risk score, proven to be an independent prognostic indicator for triple-negative breast cancer, showed a positive correlation with both age and disease stage. Immune infiltration and drug sensitivity analyses suggested appropriate therapies for different risk groups. Mutation profiles and pathway enrichment were analyzed, providing insights into potential therapeutic targets. ConclusionA PANoptosis-related prognostic model was successfully developed for triple-negative breast cancer, offering a novel approach for predicting patient prognosis and guiding treatment strategies.

Retrospective Analysis of Radiofrequency Ablation in Patients with Small Solitary Hepatocellular Carcinoma: Survival Outcomes and Development of a Machine Learning Prognostic Model.

The effectiveness of radiofrequency ablation (RFA) in improving long-term survival outcomes for patients with a solitary hepatocellular carcinoma (HCC) measuring 5 cm or less remains uncertain. This study was designed to elucidate the impact of RFA therapy on the survival outcomes of these patients and to construct a prognostic model for patients following RFA. This study was performed using the Surveillance, Epidemiology, and End Results (SEER) database from 2004 to 2017, focusing on patients diagnosed with a solitary HCC lesion ≤5 cm in size. We compared the overall survival (OS) and cancer-specific survival (CSS) rates of these patients with those of patients who received hepatectomy, radiotherapy, or chemotherapy or who were part of a blank control group. To enhance the reliability of our findings, we employed stabilized inverse probability treatment weighting (sIPTW) and stratified analyses. Additionally, we conducted a Cox regression analysis to identify prognostic factors. XGBoost models were developed to predict 1-, 3-, and 5-year CSS. The XGBoost models were evaluated via receiver operating characteristic (ROC) curves, calibration plots, decision curve analysis (DCA) curves and so on. Regardless of whether the data were unadjusted or adjusted for the use of sIPTWs, the 5-year OS (46.7%) and CSS (58.9%) rates were greater in the RFA group than in the radiotherapy (27.1%/35.8%), chemotherapy (32.9%/43.7%), and blank control (18.6%/30.7%) groups, but these rates were lower than those in the hepatectomy group (69.4%/78.9%). Stratified analysis based on age and cirrhosis status revealed that RFA and hepatectomy yielded similar OS and CSS outcomes for patients with cirrhosis aged over 65 years. Age, race, marital status, grade, cirrhosis status, tumor size, and AFP level were selected to construct the XGBoost models based on the training cohort. The areas under the curve (AUCs) for 1, 3, and 5 years in the validation cohort were 0.88, 0.81, and 0.79, respectively. Calibration plots further demonstrated the consistency between the predicted and actual values in both the training and validation cohorts. RFA can improve the survival of patients diagnosed with a solitary HCC lesion ≤5 cm. In certain clinical scenarios, RFA achieves survival outcomes comparable to those of hepatectomy. The XGBoost models developed in this study performed admirably in predicting the CSS of patients with solitary HCC tumors smaller than 5 cm following RFA.

Machine learning-driven survival prediction in gestational trophoblastic neoplasms: a focus on PSTT and ETT prognosis.

The clinicopathological characteristics and prognosis of placental site trophoblastic tumor (PSTT) and epithelioid trophoblastic tumor (ETT) have not been well summarized. Consequently, we conducted the largest to date series of samples of both types and employed machine learning (ML) to assess treatment effectiveness and develop accurate prognostic models for patients with GTN. Gestational choriocarcinoma (GCC) was used as the control group to show the clinical features of PTSS and ETT. The Surveillance, Epidemiology, and End Results (SEER) database provided the data used for this study's analysis. To identify the prognostic variables, we conducted Cox regression analysis and constructed prognostic models using five ML algorithms to predict the 5-year survival. A validation method incorporating the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to validate the accuracy and reliability of the ML models. We also investigated the role of multiple therapeutic options using the Kaplan-Meier survival analysis. The study population comprised 725 patients. Among them, 139 patients had ETT, 107 had PSTT, and 479 had GCC. There were no significant differences in survival between the different tumor groups. Multivariate Cox regression analysis revealed that metastasis was a significant prognostic factor for GCC, while older age and radiotherapy were significant prognostic factors for PTSS and ETT. ML models revealed that the Gradient Boosting classifier accurately predicted the outcomes, followed by the random forest classifier, K-Nearest Neighbors, Logistic Regression, and multilayer perceptron models. The most significant contributing factors were tumor size, year of diagnosis, age, and race. Our study provides a method for treatment and prognostic assessment of patients with GTN. The ML we developed can be used as a convenient individualized tool to facilitate clinical decision making.

Reproducible and Interpretable Machine Learning-Based Radiomic Analysis for Overall Survival Prediction in Glioblastoma Multiforme

Purpose: To develop and validate an MRI-based radiomic model for predicting overall survival (OS) in patients diagnosed with glioblastoma multiforme (GBM), utilizing a retrospective dataset from multiple institutions. Materials and Methods: Pre-treatment MRI images of 289 GBM patients were collected. From each patient’s tumor volume, 660 radiomic features (RFs) were extracted and subjected to robustness analysis. The initial prognostic model with minimum RFs was subsequently enhanced by including clinical variables. The final clinical–radiomic model was derived through repeated three-fold cross-validation on the training dataset. Performance evaluation included assessment of concordance index (C-Index), integrated area under curve (iAUC) alongside patient stratification into low and high-risk groups for overall survival (OS). Results: The final prognostic model, which has the highest level of interpretability, utilized primary gross tumor volume (GTV) and one MRI modality (T2-FLAIR) as a predictor and integrated the age variable with two independent, robust RFs, achieving moderately good discriminatory performance (C-Index [95% confidence interval]: 0.69 [0.62–0.75]) with significant patient stratification (p = 7 × 10−5) on the validation cohort. Furthermore, the trained model exhibited the highest iAUC at 11 months (0.81) in the literature. Conclusion: We identified and validated a clinical–radiomic model for stratification of patients into low and high-risk groups based on OS in patients with GBM using a multicenter retrospective dataset. Future work will focus on the use of deep learning-based features, with recently standardized convolutional filters on OS tasks.

Comprehensive Analysis of a Platelet- and Coagulation-Related Prognostic Gene Signature Identifies CYP19A1 as a Key Tumorigenic Driver of Colorectal Cancer.

The intricate interplay between the platelet-coagulation system and the progression of malignant tumors has profound therapeutic implications. However, a thorough examination of platelet and coagulation markers specific to colorectal cancer (CRC) is conspicuously absent in the current literature. Consequently, there is an urgent need for further exploration into the mechanistic underpinnings of these markers and their potential clinical applications. By integrating RNA-seq data and clinicopathological information from patients with CRC in the cancer genome atlas, we identified genes related to the platelet-coagulation system using weighted gene co-expression networks and univariate Cox analysis. We established a prognostic risk model based on platelet- and coagulation-related genes using Lasso Cox regression analysis and validated the model in two independent CRC cohorts. We explored potential biological functional disparities between high-risk and low-risk groups through comprehensive bioinformatics analysis. Our findings indicate that colorectal cancer patients classified as high-risk generally exhibit poorer prognoses. Moreover, the model's risk scores were associated with the differential composition of the immune tumor microenvironment, suggesting its applicability to infer immunotherapy responsiveness. Cellular functional experiments and animal experiments indicated that CYP19A1 expression in CRC influences malignant phenotype and platelet activation. In summary, we present a novel platelet- and coagulation-related risk model for prognostic assessment of patients with CRC and confirm the important role of CYP19A1 in promoting malignant progression of CRC.

Prognostic prediction of breast cancer patients using machine learning models: a retrospective analysis.

Breast cancer is a common and complex disease, with various clinical features affecting prognosis. Accurate prediction of prognosis is essential for guiding personalized treatment strategies. This study aimed to develop machine learning models for predicting prognosis in breast cancer patients using retrospective data. A total of 6,477 patients from Affiliated Sir Run Run Shaw Hospital were included, and their electronic medical records (EMRs) were thoroughly examined to identify 15 clinical features significantly associated with breast cancer survival. We employed eight different machine learning algorithms, including Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost), to develop and evaluate the predictive performance of the models. In addition, to investigate the sensitivity of different training/testing set radio to model performance, we examined five sets of ratios: 50:50, 60:40, 70:30, 80:20, 90:10. Among these models, XGBoost demonstrated the highest performance with receiver operating characteristic (ROC) area under the curve (AUC) of 0.813, accuracy of 0.739, sensitivity of 0.815, and specificity of 0.735. Further statistical analysis identified several significant predictors of prognosis, including age, tumor size, lymph node status, and hormone receptor status. The XGBoost model was found to exhibit superior predictive power compared to established prognostic models such as the Nottingham Prognostic Index (NPI) and Predict Breast. Based on the successful performance of the XGBoost model, we developed a prognosis prediction tool specifically designed for breast cancer, providing valuable insights to clinicians, and aiding them in making informed treatment decisions tailored to individual patients. Our study highlights the potential of machine learning models in accurately predicting prognosis for breast cancer patients, ultimately facilitating personalized treatment strategies. Further research and validation are warranted to fully integrate these models into clinical practice.

Biomechanics cerebral and carotid arteries: aneurysm development and atherosclerotic plaque detachment in the presence of combined pathologies

Today, the main cause of mortality in the world remains cardiovascular diseases. In this case, the combined pathology of the neck and head arteries, in which atherosclerosis affects the carotid arteries and there is a change in blood flow in the vertebral arteries or in the circle of Willis due to aplasia of the communicating artery, significantly increases the risk of aneurysms formation and their subsequent rupture. Assessing the risk of complications in this type of pathological condition may allow the physician to initiate timely preventive measures in specific clinical cases. The paper presents the results of modelling various variants of combined pathologies in the arteries of the neck and head. The segment under consideration includes sections of the common carotid, external and internal carotid arteries, as well as vessels of the circle of Willis and the basilar artery. The main idea of the study was to create a prognostic model to assess the risks of aneurysm formation in the circle of Willis and atherosclerotic plaque rupture in the carotid arteries. Such a system may be useful for physicians in routine practice when planning treatment tactics in patients with combined pathologies of the arterial channel. Based on the literature analysis, a number of pathologies of the cerebral and carotid arteries, most frequently occurring in various combinations, were identified: stenoses of the ICA (30 % and 70 %) and aplasia (absence) of one of the connecting arteries were considered from the point of view of geometrical features. In addition, the character of blood flow in the basilar artery, which can change its direction in steal-syndrome, was taken into account. Based on the analysis of blood flow and wall shear stress, two degrees of risk of aneurysm formation were distinguished. High risk corresponds to cases for which both of these values were statistically significantly different from the norm. Medium risk corresponds to cases for which only one of the values was statistically significantly different from the norm. For models containing atherosclerotic plaques, data on shear and normal stresses on the surfaces of atherosclerotic plaques were tabulated. The risk of plaque rupture on each side was determined for each case by analysing the result sets 11 combinations of pathological conditions with increased risk were indicated.

Matrix stiffness-related extracellular matrix signatures and the DYNLL1 protein promote hepatocellular carcinoma progression through the Wnt/β-catenin pathway

BackgroundIn hepatocellular carcinoma (HCC) treatment, first-line targeted therapy in combination with immune checkpoint inhibitors (ICIs) has improved patient prognosis, but the 5-year survival rate is far from satisfactory. Studies have shown that the extracellular matrix (ECM) is an essential part of the tumour microenvironment (TME) and participates in the progression of malignant tumours. ECM remodelling can enhance matrix stiffness in cirrhosis patients, induce an immunosuppressive microenvironment network, and affect the efficacy of targeted therapies and ICIs for treating HCC. However, the exact mechanism is still unclear.MethodsWe downloaded data from public databases, selected differentially expressed ECM proteins associated with matrix stiffness, constructed and validated a prognostic model of HCC using Lasso Cox regression, and investigated the roles and mechanism of one of the ECM proteins, dynein light chain LC8-type 1 (DYNLL1), in HCC proliferation, migration, and apoptosis via in vitro experiments.ResultsIn this study, the risk score of the matrix stiffness-related ECM protein model effectively predicted the prognosis of HCC patients. The high- and low-risk subgroups of the model also showed differences in immune cells, immune functions, and drug sensitivity. DYNLL1 promoted HCC cell progression and migration and inhibited HCC cell apoptosis through the Wnt/β-catenin pathway in vitro.ConclusionThe expression of matrix stiffness-related ECM proteins could be an independent predictor of HCC prognosis. DYNLL1, an oncogenic gene in HCC, has the potential to be a new target for HCC treatment.

Development and validation of a novel liquid-liquid phase separation gene signature for bladder cancer

Bladder carcinoma (BLCA) represents a common urinary tract malignancy, characterized by aggressive behavior and high recurrence rates. The biological response regulation during tumor proliferation and metastasis is intimately associated with liquid-liquid phase separation (LLPS). For the purpose of enhancing early detection and treatment, this study employed transcriptomic data to examine the prognostic implications of LLPS-associated genes and formulate a predictive model. Clinical and transcriptomic data of bladder cancer patients were sourced from the GEO and TCGA databases. This study applied a clustering algorithm using non-negative matrix factorization (NMF) to classify samples, which were systematically compared based on their liquid-liquid phase separation characteristics. Prognostic models were developed using multivariate Cox regression and the Least Absolute Shrinkage Selection Operator (LASSO) algorithm to establish risk formulas for nine genes. The gene signature’s validity was tested across the entire TCGA cohort (406 cases), the TCGA testing cohort (120 cases), and the external validation dataset GSE13507. The predictive accuracy of the signature system was assessed using receiver operating characteristic (ROC) and Kaplan-Meier curves. Additionally, decision curve analysis incorporating clinicopathological parameters and the genetic signature was employed to predict individual survival. This study identified two distinct molecular subtypes, C1 and C2. Patients with the C1 subtype exhibited significantly better prognoses than those with the C2 subtype. Low-risk group patients consistently showed superior prognoses compared to high-risk groups across the entire TCGA, GEO, and TCGA training cohorts. Furthermore, the LLPS-related gene model demonstrated prognostic value independent of other clinical traits. This study identifies LLPS-associated gene clusters and establishes an independent, accurate prognostic model for BLCA. The model holds potential for clinical application in BLCA prognosis assessment.

Validation of a 5-Year Prognostic Model for Parkinson's Disease.

A simple prognostic model was previously developed to predict the probability of recently-diagnosed patients reaching negative outcomes (postural instability, dementia or death) in a 5-year period. To validate this model in an independent cohort and establish utility at later time points. Validation was performed using data collected in an incident cohort at baseline, 2 and 4 years. Predicted negative outcome probabilities were compared to actual 5-year outcomes. The model, based on age, MDS-UPDRS axial score and 60-second animal fluency, predicted poor 5-year outcome when applied at baseline, (area under the curve (AUC) 0.80), 2 years (AUC 0.82) and 4 years (AUC 0.71). Power calculations showed that selecting a subgroup with prognostic score >0.5 reduced the sample size required for a disease-modifying trial. This 5-year prognostic model has good accuracy when employed up to 4 years from diagnosis and may help stratification for disease-modifying trials.

Construction and validation of a pancreatic cancer prognostic model based on genes related to the hypoxic tumor microenvironment.

Pancreatic cancer is one of the most lethal malignancies, characterized by poor prognosis and low survival rates. Traditional prognostic factors for pancreatic cancer offer inadequate predictive accuracy, often failing to capture the complexity of the disease. The hypoxic tumor microenvironment has been recognized as a significant factor influencing cancer progression and resistance to treatment. This study aims to develop a prognostic model based on key hypoxia-related molecules to enhance prediction accuracy for patient outcomes and to guide more effective treatment strategies in pancreatic cancer. To develop and validate a prognostic model for predicting outcomes in patients with pancreatic cancer using key hypoxia-related molecules. This pancreatic cancer prognostic model was developed based on the expression levels of the hypoxia-associated genes CAPN2, PLAU, and CCNA2. The results were validated in an independent dataset. This study also examined the correlations between the model risk score and various clinical features, components of the immune microenvironment, chemotherapeutic drug sensitivity, and metabolism-related pathways. Real-time quantitative PCR verification was conducted to confirm the differential expression of the target genes in hypoxic and normal pancreatic cancer cell lines. The prognostic model demonstrated significant predictive value, with the risk score showing a strong correlation with clinical features: It was significantly associated with tumor grade (G) (b P < 0.01), moderately associated with tumor stage (T) (a P < 0.05), and significantly correlated with residual tumor (R) status (b P < 0.01). There was also a significant negative correlation between the risk score and the half-maximal inhibitory concentration of some chemotherapeutic drugs. Furthermore, the risk score was linked to the enrichment of metabolism-related pathways in pancreatic cancer. The prognostic model based on hypoxia-related genes effectively predicts pancreatic cancer outcomes with improved accuracy over traditional factors and can guide treatment selection based on risk assessment.

Clarifying new molecular subtyping and precise treatment of melanoma based on disulfidptosis-related lncRNA signature

Disulfidptosis, the newest form of programmed cell death, is closely associated with the immune microenvironment of cancer cells. Long non-coding RNA (lncRNA) has also been found to play a crucial role in melanoma. However, the role of disulfidptosis-related lncRNA in melanoma remains unclear. Through bioinformatic analysis of the transcriptional, clinical, and pathological data from the TCGA-SKCM (The Cancer Genome Atlas—Skin cutaneous melanoma) database, we established a 2-Disulfidptosis-related lncRNA (DRL) prognostic model and a novel molecular subtype for melanoma. The survival and ROC curves of the 2-DRL prognostic model demonstrated its strong efficacy in predicting the prognosis of melanoma. The high-risk group of melanoma exhibited a significant decrease in ESTIMATEScore, ImmuneScore, and StromalScore, indicative of pronounced immune suppression and exhaustion. Subgroup C2 of melanoma displayed an immune-activated state, while subgroups C1 and C3 showed immune suppression and exhaustion, potentially leading to poorer prognosis. Subgroup C1 demonstrated better sensitivity to Zoledronate, UMI-77, Nilotinib, and Cytarabine. Subgroup C2 exhibited greater sensitivity to Ribociclib, XAV939, Topotecan, and Ruxolitinib. Subgroup C3 showed higher sensitivity to VX-11e, Ulixertinib, Trametinib, and Afatinib. This study revealed the immune microenvironment status and targeted drug sensitivity in melanoma patients with different risk scores and molecular subtypes, offering valuable guidance for clinical treatment and identifying significant DRL targets for future in-depth research.

Identification of Key Immune and Cell Cycle Modules and Prognostic Genes for Glioma Patients through Transcriptome Analysis.

Gliomas, the most prevalent type of primary brain tumor, stand out as one of the most aggressive and lethal types of human cancer. To uncover potential prognostic markers, we employed the weighted correlation network analysis (WGCNA) on the Chinese Glioma Genome Atlas (CGGA) 693 dataset to reveal four modules significantly associated with glioma clinical traits, primarily involved in immune function, cell cycle regulation, and ribosome biogenesis. Using the least absolute shrinkage and selection operator (LASSO) regression algorithm, we identified 11 key genes and developed a prognostic risk score model, which exhibits precise prognostic prediction in the CGGA 325 dataset. More importantly, we also validated the model in 12 glioma patients with overall survival (OS) ranging from 4 to 132 months using mRNA sequencing and immunohistochemical analysis. The analysis of immune infiltration revealed that patients with high-risk scores exhibit a heightened immune infiltration, particularly immune suppression cells, along with increased expression of immune checkpoints. Furthermore, we explored potentially effective drugs targeting 11 key genes for gliomas using the library of integrated network-based cellular signatures (LINCS) L1000 database, identifying that in vitro, both torin-1 and clofarabine exhibit promising anti-glioma activity and inhibitory effect on the cell cycle, a significant pathway enriched in the identified glioma modules. In conclusion, our study provides valuable insights into molecular mechanisms and identifying potential therapeutic targets for gliomas.

Exploring the prognostic value of T follicular helper cell levels in chronic lymphocytic leukemia

Chronic lymphocytic leukemia (CLL) presents with heterogeneous clinical outcomes, suggesting varied underlying pathogenic mechanisms. This study aims to elucidate the impact of T follicular helper (Tfh) cells on CLL progression and prognosis. Gene expression profile data for CLL were collected from GSE22762 and GSE39671 datasets. Patients were divided into high and low groups using Tfh levels using the optimal cutoff value based on overall survival (OS) and time-to-first treatment (TTFT). Differential expression analysis was performed between these groups, followed by co-expression network analysis and single-sample Gene Set Enrichment Analysis (ssGSEA). Marker genes of Tfh cells were used to construct prognostic models. Additionally, 40 CLL patients were recruited and categorized based on median Tfh levels. Marker gene expression was assessed using RT-qPCR and Western Blot, and immune cell levels were determined through flow cytometry. The high group showed better prognosis compared to the low group. Among the 1121 differentially expressed genes identified, five co-expression networks were constructed, with the turquoise module showing the highest correlation with Tfh cells. Genes within this module significantly participate in cytokine-cytokine receptor interaction, PI3K-Akt signaling pathway, and natural killer cell mediated cytotoxicity. Tfh cells were significantly negatively correlated with activated B cells and positively correlated with Tregs. The Random Survival Forest (RSF) model identified 10 marker genes, and further analysis using Lasso regression and nomogram selected CLEC4A, RAE1, CD84, and PRDX1 as prognostic markers. In the high group, levels of CLEC4A and RAE1 were higher than in the low group, whereas CD84 and PRDX1 were lower. Flow cytometry revealed that the level of activated B cells in the high Tfh group was significantly lower than in the low Tfh group, while the level of Tregs is significantly higher in the high Tfh group. This study seeks to contribute to a more detailed understanding of the pathogenesis of CLL, delving into the prognostic significance of Tfh.

Treatment Outcomes and Prognostic Factors of Chemotherapy Combined With Radiation Therapy for Patients With Early-Stage Extranodal Natural Killer/T-Cell Lymphoma

BackgroundThis study aimed to assess the treatment outcomes, toxicity, and potential prognostic factors in patients with early stage extranodal natural killer/T-cell lymphoma treated with radiotherapy combined with chemotherapy. MethodsOne hundred and eighteen patients with Stage I/II extranodal natural killer/T-cell lymphoma who were treated with radiotherapy combined with chemotherapy were retrospectively analyzed between July 2003 and January 2019. The median dose was 50 Gy (range, 45–61.2 Gy). The Kaplan–Meier method was used to calculate progression-free survival and overall survival. The patients were scored according to their prognostic indices. ResultsThe overall and complete response rates were 93.2% and 82.2%, respectively. At a median follow-up of 43 months, the 5-year overall survival and progression-free survival rates were 73.9% and 68.4%, respectively. Adverse events of grade 3 or higher was observed in 20 patients (16.9%). Patients with primary disease in the Waldeyer's ring had poorer survival (P=0.015). Compared with anthracycline-based regimens, non-anthracycline-based regimens significantly improved the 5-year overall survival (76.6% vs. 54.8%, P=0.027) and progression-free survival (72.4% vs. 53.1%, P=0.013). After treatment, the 5-year overall survival rate was 78.6% in complete response patients versus 44.9% in non-complete response patients (P=0.003). For patients with low-and intermediate-low-risk according to the nomogram-revised risk index model, the complete response rate was 100%. When primary lesion data were added to the nomogram-revised risk index as the basis for another prognostic index (modified nomogram-revised risk index), the low-risk (zero to two risk factors) and high-risk (three or more risk factors) categories were noted (84.2% vs 62.2%, P=0.036). ConclusionPatients with early stage extranodal natural killer/T-cell lymphoma had high response rates and favorable survival rates with radiotherapy and non-anthracycline-based chemotherapy regimens. Patients who achieved complete response had better survival than those who did not. The extranodal natural killer/T-cell lymphoma-specific prognostic models may require further optimization.

Exploring copper metabolism-induced cell death in gastric cancer: a single-cell RNA sequencing study and prognostic model development

BackgroundGastric cancer (GC) is the third leading cause of cancer-related deaths globally. Despite advancements in treatment, the overall 5-year survival rate remains below 30%, particularly in advanced stages. Copper metabolism, vital for various cellular processes, has been linked to cancer progression, but its role in GC, especially at the single-cell level, is not well understood.ObjectiveThis study aims to investigate copper metabolism in GC by integrating single-cell RNA sequencing (scRNA-seq) data and developing a prognostic model based on copper metabolism-related gene (CMRG) expression. The study explores how copper metabolism affects the tumor microenvironment and identifies potential therapeutic targets.MethodsscRNA-seq data from gastric cancer and normal tissues were analyzed using the Seurat package. Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP) were used for dimensionality reduction and clustering. Non-negative matrix factorization (NMF) was employed for T cell subpopulation analysis. A high-dimensional weighted gene co-expression network analysis (HdWGCNA) identified key molecular features. LASSO regression and Random Survival Forest (RSF) techniques were used to create and validate a prognostic model. Survival analysis, immune microenvironment assessment, and drug sensitivity analysis were conducted.ResultsSixteen cell clusters and nine distinct cell types were identified, with T cells showing significant roles in cell communication. The NMF analysis of CD8 +T cells revealed five copper metabolism-related subtypes. The prognostic model based on nine CMRGs indicated significant survival differences between high- and low-risk groups. High-risk patients showed shorter survival times, increased immune cell infiltration, and altered immune responses. Drug sensitivity analysis suggested higher efficacy of certain drugs in high-CMRG patients.

Systematic Modeling of Risk-Associated Copy Number Alterations in Cancer.

The determination of the cancer prognosis is paramount for patients and medical personnel so that they can devise treatment strategies. Transcriptional-based signatures and subtypes derived from cancer biopsy material have been used in clinical practice for several cancer types to aid in setting the patient prognosis and forming treatment strategies. Other genomic features in cancer biopsies, such as copy number alterations (CNAs), have been underused in clinical practice, and yet they represent a complementary source of molecular information that can add detail to the prognosis, which is supported by recent work in breast, ovarian, and lung cancers. Here, through a systematic strategy, we explored the prognostic power of CNAs in 37 cancer types. In this analysis, we defined two modes of informative features, deep and soft, depending on the number of alleles gained or lost. These informative modes were grouped by amplifications or deletions to form four single-data prognostic models. Finally, the single-data models were summed or combined to generate four additional multidata prognostic models. First, we show that the modes of features are cancer-type dependent, where deep alterations generate better models. Nevertheless, some cancers require soft alterations to generate a feasible model due to the lack of significant deep alterations. Then, we show that the models generated by summing coefficients from amplifications and deletions appear to be more practical for many but not all cancer types. We show that the CNA-derived risk group is independent of other clinical factors. Furthermore, overall, we show that CNA-derived models can define clinically relevant risk groups in 33 of the 37 (90%) cancer types analyzed. Our study highlights the use of CNAs as biomarkers that are potentially clinically relevant to survival in cancer patients.

Comment on, "Creactive protein levels, the prognostic nutritional index, and the lactate dehydrogenasetolymphocyte ratio are important prognostic factors in primary central nervous system lymphoma: a singlecenter study of 223 patients".

This study by Zuo et al. (2024) investigates the prognostic significance of C-reactive protein (CRP) levels, the prognostic nutritional index (PNI), and the lactate dehydrogenase-to-lymphocyte ratio (LLR) in primary central nervous system lymphoma (PCNSL) using data from 223 patients. The research demonstrates that these markers are critical in predicting patient outcomes, offering novel insights beyond traditional prognostic models like the MSKCC and IELSG scores. Despite its strengths, the study's retrospective design and lack of validation cohort limit its generalizability. Future research should focus on validating these findings in diverse, multicenter settings and integrating these markers with existing prognostic models to improve clinical decision-making. Longitudinal studies and advanced statistical methods are recommended to further explore the interactions between these factors and their impact on patient outcomes, potentially leading to the development of targeted therapies for PCNSL.

Development and validation of machine learning models for diagnosis and prognosis of lung adenocarcinoma, and immune infiltration analysis.

The aim of our study was to develop robust diagnostic and prognostic models for lung adenocarcinoma (LUAD) using machine learning (ML) techniques, focusing on early immune infiltration. Feature selection was performed on The Cancer Genome Atlas (TCGA) data using least absolute shrinkage and selection Operator (LASSO), random forest (RF), and support vector machine (SVM) algorithms. Six ML algorithms were employed to construct the diagnostic models, which were evaluated through receiver operating characteristic (ROC) curves, precision-recall curves (PRC), and classification error (CE), and validated on the GSE7670 dataset. Additionally, a lasso cox prognostic model was built on the TCGA-LUAD dataset and externally validated using independent Gene Expression Omnibus datasets (GSE30219, GSE31210, GSE50081, and GSE37745). Single-sample gene set enrichment analysis (ssGSEA) was performed to assess immune cell infiltration in stage I LUAD samples, revealing significant differences in immune cell types. These findings demonstrate a positive correlation between immune infiltration in stage I LUAD and Th2 cells, Tcm cells, and T helper cells, while a negative correlation was observed with Macrophages, Eosinophils, and Tem cells. These insights provide novel perspectives for clinical diagnosis and treatment of LUAD.

Exploring and validating the necroptotic gene regulation and related lncRNA mechanisms in colon adenocarcinoma based on multi-dimensional data.

Necroptosis is intimately associated with the initiation and progression of colon adenocarcinoma (COAD). However, studies on necroptosis-related genes (NRGs) and the regulating long non-coding RNAs (NRGlncRNAs) in the context of COAD are limited. We retrieved the cancer genome atlas (TCGA) to collect datasets of NRGs and NRGlncRNAs on COAD patients. The risk model constructed using Cox and least absolute shrinkage and selection operator (LASSO) regression was then employed to identify NRGs and NRGlncRNAs with prognostic significance. Subsequently, we validated the results using gene expression omnibus (GEO) datasets from different populations, conducted Mendelian randomization (MR) analysis to explore the potential causal relationships between prognostic NRGs and COAD, and conducted cell experiments to verify the expression of prognostic NRGlncRNAs in COAD. Furthermore, we explored potential pathways and regulatory mechanisms of these prognostic NRGlncRNAs and NRGs in COAD through enrichment analysis, immune cell correlation analysis, tumor microenvironment analysis, immune checkpoint analysis, tumor sample clustering, and so on. We identified eight NRGlncRNAs (AC245100.5, AP001619.1, LINC01614, AC010463.3, AL162595.1, ITGB1-DT, LINC01857, and LINC00513) used for constructing the prognostic model and nine prognostic NRGs (AXL, BACH2, CFLAR, CYLD, IPMK, MAP3K7, ATRX, BRAF, and OTULIN) with regulatory relationships with them, and their validation was performed using GEO and GWAS datasets, as well as cell experiments, which showed largely consistent results. These prognostic NRGlncRNAs and NRGs modulate various biological functions, including immune inflammatory response, oxidative stress, immune escape, telomere regulation, and cytokine response, influencing the development of COAD. Additionally, stratified analysis of the high-risk and low-risk groups based on the prognostic model revealed elevated expression of immune cells, increased expression of tumor microenvironment cells, and upregulation of immune checkpoint gene expression in the high-risk group. Finally, through cluster analysis, we identified tumor subtypes, and the results of cluster analysis were essentially consistent with the analysis between risk groups. The prognostic NGRlncRNAs and NRGs identified in our study serve as prognostic indicators and potential therapeutic targets for COAD, providing a theoretical basis for the clinical diagnosis and treatment of COAD and offering guidance for further research.