LASSO Cox Regression Model Research Articles

Decoding the Ferroptosis-Related Gene Signatures and Immune Infiltration Patterns in Ovarian Cancer: Bioinformatic Prediction Integrated with Experimental Validation.

Ovarian cancer is a type of gynecological cancer with extremely high fatality rate. Ferroptosis, an iron-dependent regulated cell death, inhibits the immune infiltration of tumor cells. Therefore, it is worthwhile to explore the effects of ferroptosis-related gene signatures and immune infiltration patterns on the clinical prognosis of ovarian cancer. In this study, we used the mRNA expression matrix and related medical information of those who suffer from ovarian cancer in the TCGA database. After that, we established a ferroptosis-related gene signature based on LASSO Cox regression model, and employed several specific enrichment analyses to explore the bioinformatics functions of differentially expressed genes (DEGs). Additionally, we analyzed the link between ferroptosis and immune cells by single-sample gene set enrichment analysis (ssGSEA) to create a heatmap of gene-immune cell correlation. We then examined the expression of immune checkpoints and verified the gene expression in ovarian cancer tissues by qPCR assays. Finally, we induced ferroptosis in ovarian cancer cells using drugs and analyzed their migration, invasion and gene expression. According to LASSO Cox regression analysis, 9 prognostic DEGs were in association with overall survival (OS), which was utilized to construct a 9-gene signature for patients. Patients were divided into two groups, in which high-risk group's OS was markedly shorter than that of low-risk group (Log-rank p<0.001). KEGG enrichment analysis showed that these DEGs were linked to human cytomegalovirus (HCMV) infection. The ssGSEA analysis revealed significant differences in immune cell type and expression between ALOX12 and GLRX5 groups (p<0.05). Heatmap showed high correlation of prognostic genes with various immune cells. qPCR assay confirmed the 9 gene expression signature in ovarian cancer tissues. The ovarian cancer cell invasion and migration were significantly inhibited after induction of ferroptosis. We decoded the ferroptosis-related gene signatures and immune infiltration patterns that can be used to predict the prognosis of ovarian cancer patients.

The methylation signature of hepatocellular carcinoma trajectory based on pseudotime and chronological time for predicting precancerous patients.

Early screening of hepatocellular carcinoma (HCC) is strongly recommended for hepatitis B virus (HBV)-infected patients. We aimed to develop and validate a predictive nomogram based on HCC occurrence trajectory for screening precancerous patients with HCC. Peripheral blood mononuclear cells (PBMC) samples from 22 patients with HCC with their precancerous stage (n = 55) and 18 healthy controls were measured using HumanMethylation EPIC BeadChip assay. HCC trajectory was assessed by pseudotime based on TimeAx algorithm and chronological time. The 43 candidate CpG sites were selected from the methylation signature and measured using multiplex bisulfite sequencing in a retrospective cohort of HBV-infected patients (n = 604). A 5-CpG-classifier was built using the LASSO Cox regression model, based on the association between the methylation level of every CpG and the duration from enrollment to HCC occurrence of individual patient. We validated the risk stratification and predictive accuracy of this classifier in both the primary cohort (n = 300) and independent validation cohort (n = 304). Pseudotime and chronological time of HCC trajectory analysis revealed that the PD-1/PD-L1 pathway underwent changes in the precancerous stage. Based on the trajectory of methylation signature, we built a 5-CpG-classifier which remained powerful and independent predictive efficiency after stratified analysis by clinicopathological risk factors in both primary cohort and independent validation cohort. A predicting nomogram including the 5-CpG-classifier was constructed after multivariate analysis. One-year cumulative hazard of HCC in low- and high-risk groups of HBV-infected patients was 3.0% (0.1%-5.8%) and 17.90% (11.00%-24.3%) (P < .0001) in primary cohort, 4.5% (1.20%-7.80%) and 27.3 (18.90-34.90) (P < .0001) in the independent validation cohort. One-year before HCC was a critical period of transitional time when parts of the methylation profile underwent shifting toward HCC like. The nomogram could identify precancerous stage patients with HCC who should be screened for early diagnosis and intervention.

Prognostic and predictive value of pathohistological features in gastric cancer and identification of SLITRK4 as a potential biomarker for gastric cancer

The aim of this study was to develop a quantitative feature-based model from histopathologic images to assess the prognosis of patients with gastric cancer. Whole slide image (WSI) images of H&E-stained histologic specimens of gastric cancer patients from The Cancer Genome Atlas were included and randomly assigned to training and test groups in a 7:3 ratio. A systematic preprocessing approach was employed as well as a non-overlapping segmentation method that combined patch-level prediction with a multi-instance learning approach to integrate features across the slide images. Subjects were categorized into high- or low-risk groups based on the median risk score derived from the model, and the significance of this stratification was assessed using a log-rank test. In addition, combining transcriptomic data from patients and data from other large cohort studies, we further searched for genes associated with pathological features and their prognostic value. A total of 165 gastric cancer patients were included for model training, and a total of 26 features were integrated through multi-instance learning, with each process generating 11 probabilistic features and 2 predictive labeling features. We applied a 10-fold Lasso-Cox regression model to achieve dimensionality reduction of these features. The predictive accuracy of the model was verified using Kaplan-Meyer (KM) curves for stratification with a consistency index of 0.741 for the training set and 0.585 for the test set. Deep learning-based resultant supervised pathohistological features have the potential for superior prognostic stratification of gastric cancer patients, transforming image pixels into an effective and labor-saving tool to optimize the clinical management of gastric cancer patients. Also, SLITRK4 was identified as a prognostic marker for gastric cancer.

Impact of senescence cell signature in patients with non-small cell carcinoma and melanoma receiving PD-L1/PD-1 inhibitors

Tumor cell senescence plays a crucial role in tumor immunity. We investigated whether the senescent cell signature (SCS) could predict prognosis in non-small cell carcinoma (NSCLC) and melanoma datasets treated with PD-L1/PD-1 inhibitors. Patients with high SCS expression exhibited elevated levels of interferon-gamma and T cell-inflamed signatures in three lung adenocarcinomas (LUAD), two squamous cell carcinoma (LUSC) and three melanoma datasets. The high SCS group was associated with PD-L1-related pathways such as IL6/JAK/STAT3 and TNF-alpha signaling via NF-kB in LUAD, LUSC, and melanoma datasets. A positive correlation was observed between several immune checkpoint markers and the SCS, indicating an immunosuppressive state in LUAD, LUSC and melanoma datasets. In patients treated with PD-1/PD-L1 inhibitors, a higher SCS was associated with a better prognosis, and a positive correlation between SCS and PD-L1 was observed in six independent NSCLC and three independent melanoma datasets. We used the LASSO Cox regression model to build a risk model focusing on the SCS genes that particularly predict prognosis. We confirmed that the model accurately predicts prognosis. However, the senescent immunohistochemical markers p16 and p21 could predict the response to PD-1/PD-L1 inhibitors in patients with LUSC and melanoma but not in patients with LUAD. SCS could serve as a valuable biomarker to complement PD-L1 expression in patients receiving PD-L1/PD-1 inhibitors.

Construction and Validation of a Prognostic Model Based on Pyroptosis-related Genes in Bladder Cancer.

Bladder cancer (BCa) is a highly prevalent disease with a poor prognosis. There is no better forecasting method for it yet. Current studies demonstrate that pyroptosis is involved in the development and progression of various cancers. This study employed bioinformatics techniques to analyze the data of BCa patients obtained from the TCGA and GEO databases in order to construct a prognostic risk model. The TCGA dataset was used for the training set, and the multiple external datasets (including GSE13507, GSE31684, GSE48075, IMvigor210, and GSE32894) were applied as the validation sets. Prognostic-associated pyroptosis genes screened by univariate Cox regression analysis were utilized to construct the lasso Cox regression model. GO and KEGG analysis results identified the selected genes that are primarily involved in the inflammation and cell death processes. The related patients were grouped into low- and high-risk groups. Kaplan-Meier survival analysis was performed to compare survival differences between the risk groups. The accuracy of this risk prediction model was assessed by ROC. We also applied the Human Protein Atlas (HPA) to detect the protein expression of these genes. Subsequently, qRT-PCR was performed to verify the expression of these model genes. There are 29 pyroptosis-related genes with significant expression differences between BCa and corresponding adjacent tissues, and 11 genes (SH2D2A, CHMP4C, MRFAP1L1, GBP2, EHBP1, RAD9A, ANXA1, TMEM109, HEYL, APOL2, ORMDL1) were picked by univariate and LASSO Cox regression analysis. Immunological cell infiltration and ssGSEA results further indicated that the low and high-risk groups were substantially correlated with the immune status of BCa patients. According to TCGA and multiple external datasets, Kaplan-Meier survival curves showed the overall survival rate of the high-risk group to be decreased. ROC curves showed the model established to be accurate and reliable. Moreover, the HPA database also demonstrated the verification of the modeled genes' expression in BCa and normal bladder tissue using the HPA database. qRT-PCR results also suggested the up-regulated EHBP1 and down-regulated RAD9A mRNA expression levels to be confirmed in 15 pairs of BCa and corresponding adjacent tissues. This study presents the development and validation of a novel gene signature associated with pyroptosis, which holds the potential for predicting patient outcomes in BCa and providing insights into the immune microenvironment of BCa.

TARGET based m6A methylation-related genes predict prognosis relapsed B-cell acute lymphoblastic leukemia

PurposeThe current study aims to investigate the significance of N6-methyladenosine (m6A) methylationrelated genes in the clinical prognosis of childhood relapsed B-cell acute lymphoblastic leukemia (B-ALLL) patient.MethodsTranscriptome data and corresponding clinical data on m6A methylation-related genes (including 20 genes) were obtained from the Therapeutically Applicable Research To Generate Effective Treatments (TARGET) database.ResultsThe bone marrow (BM) samples of 134 newly diagnosed (naive) and 116 relapsed B-ALL from TARGET were enrolled in the current study. Three genes (FTO, HNRNPC, RBM15B) showed significant up-regulation in relapsed B-ALL compared with that in naive B-ALL.The three genes had a significantly worse survival (P < 0.05). The LASSO Cox regression model was used to select the most predictive genes as prognostic indicators, and YTHDC1 and FTO were identified as prognostic factors for relapsed B-ALL. Finally, the results of multivariate regression analysis showed that the risk score of m6A methylation-related genes was an independent prognostic factor in relapsed B-ALL (P < 0.05).ConclusionWe found that the expression levels of m6A methylation-related genes were different in naive and relapsed patients with B-ALL and correlated with survival and prognosis.This implies that m6A methylation-related genes may be promising prognostic indicators or therapeutic targets for relapsed B-ALL.

Longitudinal Changes of CT-radiomic and Systemic Inflammatory Features Predict Survival in Advanced Non-Small Cell Lung Cancer Patients Treated With Immune Checkpoint Inhibitors.

This study aims to determine whether longitudinal changes in CT radiomic features (RFs) and systemic inflammatory indices outperform single-time-point assessment in predicting survival in advanced non-small cell lung cancer (NSCLC) treated with immune checkpoint inhibitors (ICIs). We retrospectively acquired pretreatment (T0) and first disease assessment (T1) RFs and systemic inflammatory indices from a single-center cohort of stage IV NSCLC patients and computed their delta (Δ) variation as [(T1-T0)/T0]. RFs from the primary tumor were selected for building baseline-radiomic (RAD) and Δ-RAD scores using the linear combination of standardized predictors detected by LASSO Cox regression models. Cox models were generated using clinical features alone or combined with baseline and Δ blood parameters and integrated with baseline-RAD and Δ-RAD. All models were 3-fold cross-validated. A prognostic index (PI) of each model was tested to stratify overall survival (OS) through Kaplan-Meier analysis. We included 90 ICI-treated NSCLC patients (median age 70y [IQR=42 to 85], 63 males). Δ-RAD outperformed baseline-RAD for predicting OS [c-index: 0.632 (95%CI: 0.628 to 0.636) vs. 0.605 (95%CI: 0.601 to 0.608) in the test splits]. Integrating longitudinal changes of systemic inflammatory indices and Δ-RAD with clinical data led to the best model performance [Integrated-Δ model, c-index: 0.750 (95% CI: 0.749 to 0.751) in training and 0.718 (95% CI: 0.715 to 0.721) in testing splits]. PI enabled significant OS stratification within all the models (P-value <0.01), reaching the greatest discriminative ability in Δ models (high-risk group HR up to 7.37, 95% CI: 3.9 to 13.94, P<0.01). Δ-RAD improved OS prediction compared with single-time-point radiomic in advanced ICI-treated NSCLC. Integrating Δ-RAD with a longitudinal assessment of clinical and laboratory data further improved the prognostic performance.

A cuproptosis-related prognostic signature for guiding clinical diagnosis and treatment in uveal melanoma patients

BackgroundCuproptosis, one of the most recently discovered forms of cell death, is induced by the disruption of copper binding to the mitochondrial respiratory acylation components. However, the mechanism underlying cuproptosis in uveal melanoma (UM) has not yet been adequately studied. MethodsRNA and clinical data were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed cuproptosis-related genes were identified by R software. A prognostic signature was constructed by applying LASSO regression and Cox regression models. The associations between the signature and the immune microenvironment, overall survival, and drug sensitivity were studied. In addition, qPCR and Western blotting were performed on UM cells and RPE cell lines to verify the expression levels of the genes encoding dihydrolipoamide dehydrogenase (DLD) and dihydrolipoamide S-succinyltransferase (DLST) in UM cases. ResultsUsing a cuproptosis-related prognostic signature, UM samples were classified into high- and low-risk groups. A significant difference in overall survival between the two risk groups was evident. Receiver operating characteristic curves demonstrated that the signature is a reliable predictor of prognosis. Immune cell infiltration, drug sensitivity, and immune checkpoint expression were analysed. Significant immune difference between the two high-risk groups was found, and the high expression of immune checkpoints in high-risk groups suggests significant immunotherapy potential. In addition, drug sensitivity analysis experiments suggest that erlotinib may be a potential treatment for high-risk patients. The results of in vitro experiments confirmed that DLD and DLST had higher expression levels in UM cell lines. ConclusionsThe prognostic signature developed in this study is a reliable biomarker for predicting the prognosis of UM and may serve as a tool for personalised treatment of patients with UM.

Development and validation of a nomogram to predict mortality of patients with DIC in ICU.

Disseminated intravascular coagulation (DIC) is a devastating condition, which always cause poor outcome of critically ill patients in intensive care unit. Studies concerning short-term mortality prediction in DIC patients is scarce. This study aimed to identify risk factors contributing to DIC mortality and construct a predictive nomogram. A total of 676 overt DIC patients were included. A Cox proportional hazards regression model was developed based on covariates identified using least absolute shrinkage and selection operator (LASSO) regression. The prediction performance was independently evaluated in the MIMIC-III and MIMIC-IV Clinical Database, as well as the 908th Hospital Database (908thH). Model performance was independently assessed using MIMIC-III, MIMIC-IV, and the 908th Hospital Clinical Database. The Cox model incorporated variables identified by Lasso regression including heart failure, sepsis, height, SBP, lactate levels, HCT, PLT, INR, AST, and norepinephrine use. The model effectively stratified patients into different mortality risk groups, with a C-index of >0.65 across the MIMIC-III, MIMIC-IV, and 908th Hospital databases. The calibration curves of the model at 7 and 28 days demonstrated that the prediction performance was good. And then, a nomogram was developed to facilitate result visualization. Decision curve analysis indicated superior net benefits of the nomogram. This study provides a predictive nomogram for short-term overt DIC mortality risk based on a Lasso-Cox regression model, offering individualized and reliable mortality risk predictions.

Anoikis resistance regulates immune infiltration and drug sensitivity in clear-cell renal cell carcinoma: insights from multi omics, single cell analysis and in vitro experiment.

Anoikis is a form of programmed cell death essential for preventing cancer metastasis. In some solid cancer, anoikis resistance can facilitate tumor progression. However, this phenomenon is underexplored in clear-cell renal cell carcinoma (ccRCC). Using SVM machine learning, we identified core anoikis-related genes (ARGs) from ccRCC patient transcriptomic data. A LASSO Cox regression model stratified patients into risk groups, informing a prognostic model. GSVA and ssGSEA assessed immune infiltration, and single-cell analysis examined ARG expression across immune cells. Quantitative PCR and immunohistochemistry validated ARG expression differences between immune therapy responders and non-responders in ccRCC. ARGs such as CCND1, CDKN3, PLK1, and BID were key in predicting ccRCC outcomes, linking higher risk with increased Treg infiltration and reduced M1 macrophage presence, indicating an immunosuppressive environment facilitated by anoikis resistance. Single-cell insights showed ARG enrichment in Tregs and dendritic cells, affecting immune checkpoints. Immunohistochemical analysis reveals that ARGs protein expression is markedly elevated in ccRCC tissues responsive to immunotherapy. This study establishes a novel anoikis resistance gene signature that predicts survival and immunotherapy response in ccRCC, suggesting that manipulating the immune environment through these ARGs could improve therapeutic strategies and prognostication in ccRCC.

Landscape analysis of alternative splicing in kidney renal clear cell carcinoma and their clinical significance.

A growing number of studies reveal that alternative splicing (AS) is associated with tumorigenesis, progression, and metastasis. Systematic analysis of alternative splicing signatures in renal cancer is lacking. In our study, we investigated the AS landscape of kidney renal clear cell carcinoma (KIRC) and identified AS predictive model to improve the prognostic prediction of KIRC. We obtained clinical data and gene expression profiles of KIRC patients from the TCGA database to evaluate AS events. The calculation results for seven types of AS events indicated that 46276 AS events from 10577 genes were identified. Next, we applied Cox regression analysis to identify 5864 prognostic-associated AS events. We used the Metascape database to verify the potential pathways of prognostic-associated AS. Moreover, we constructed KIRC prediction systems with prognostic-associated AS events by the LASSO Cox regression model. AUCs demonstrated that these prediction systems had excellent prognostic accuracy simultaneously. We identified 34 prognostic associated splicing factors (SFs) and constructed homologous regulatory networks. Furthermore, in vitro experiments were performed to validate the favorable effect of SFs FMR1 in KIRC. In conclusion, we overviewed AS events in KIRC and identified AS-based prognostic models to assist the survival prediction of KIRC patients. Our study may provide a novel predictive signature to improve the prognostic prediction of KIRC, which might facilitate KIRC patient counseling and individualized management.

Identification and classification of glioma subtypes based on RNA-binding proteins

BackgroundGlioma is a common and aggressive primary malignant cancer known for its high morbidity, mortality, and recurrence rates. Despite this, treatment options for glioma are currently restricted. The dysregulation of RBPs has been linked to the advancement of several types of cancer, but their precise role in glioma evolution is still not fully understood. This study sought to investigate how RBPs may impact the development and prognosis of glioma, with potential implications for prognosis and therapy. MethodsRNA-seq profiles of glioma and corresponding clinical data from the CGGA database were initially collected for analysis. Unsupervised clustering was utilized to identify crucial tumor subtypes in glioma development. Subsequent time-series analysis and MS model were employed to track the progression of these identified subtypes. RBPs playing a significant role in glioma progression were then pinpointed using WGCNA and Lasso Cox regression models. Functional analysis of these key RBP-related genes was conducted through GSEA. Additionally, the CIBERSORT algorithm was utilized to estimate immune infiltrating cells, while the STRING database was consulted to uncover potential mechanisms of the identified biomarkers. ResultsSix tumor subgroups were identified and found to be highly homogeneous within each subgroup. The progression stages of these tumor subgroups were determined using time-series analysis and a MS model. Through WGCNA, Lasso Cox, and multivariate Cox regression analysis, it was confirmed that BCLAF1 is correlated with survival in glioma patients and is closely linked to glioma progression. Functional annotation suggests that BCLAF1 may impact glioma progression by influencing RNA splicing, which in turn affects the cell cycle, Wnt signaling pathway, and other cancer development pathways. ConclusionsThe study initially identified six subtypes of glioma progression and assessed their malignancy ranking. Furthermore, it was determined that BCLAF1 could serve as an RBP-related prognostic marker, offering significant implications for the clinical diagnosis and personalized treatment of glioma.

An androgen receptor-based signature to predict prognosis and identification of ORC1 as a therapeutical target for prostate adenocarcinoma.

Aberrant activation of androgen receptor (AR) signaling plays a crucial role in the progression of prostate adenocarcinoma (PRAD) and contributes significantly to the development of enzalutamide resistance. In this study, we aimed to identify a novel AR-driven signature that can predict prognosis and endows potentially reveal novel therapeutic targets for PRAD. The Seurat package was used to preprocess the single-cell RNA sequencing (scRNA-seq). Differentially expressed genes were visualized using limma and pheamap packages. LASSO and multi-variate Cox regression models were established using glmnet package. The package "Consensus Cluster Plus" was utilized to perform the consensus clustering analysis. The biological roles of origin recognition complex subunit 1 (ORC1) in PRAD were determined by gain- and loss-of-function studies in vitro and in vivo. We characterized the scRNA-seq data from GSE99795 and identified 10 AR-associated genes (ARGs). The ARGs model was trained and validated in internal and external cohorts. The ARGs were identified as an independent hazard factor in PRAD and correlated with clinical risk characteristics. In addition, the ARGs were found to be correlated with somatic tumor mutation burden (TMB) levels. Two groups that have distinct prognostic and molecular features were identified through consensus clustering analysis. ORC1 was identified as a critical target among these ARGs, and it ORC1 promoted proliferation and stem-like properties of PRAD cells. Chromatin immunoprecipitation (ChIP)-qPCR assay confirmed that AR could directly bind the promoter of ORC1. Activated AR/ORC1 axis contributed to enzalutamide resistance, and targeting ORC1 rendered PRAD cells more susceptible to enzalutamide. This study defines an AR-driven signature that AR activates ORC1 expressions to promote PRAD progression and enzalutamide resistance, which may provide novel targets for PRAD treatment.

Abstract 3457: Distinct signatures of tumor-associated macrophage subpopulations predict survival in renal cell carcinoma

Abstract Background: Comprising 90% of renal cancer cases in adults, renal cell carcinoma (RCC) is marked by significant heterogeneity in its tumor microenvironment. This study aims to test the hypothesis that tumor-associated macrophages (TAMs) play a role in modulating RCC progression and patient response to treatment. We explored the prognostic implications of TAM signatures on RCC survival, leveraging immunomics to decipher TAM-related alterations in the tumor microenvironment. Methods: Utilizing independent single-cell RNA-seq data from human RCC samples, we crafted 8 distinct RCC TAM signatures reflective of TAM presence. A LASSO Cox regression model was developed to prognosticate survival, tested against the TCGA dataset and independently validated across multiple RCC cohorts. Model performance was corroborated through Kaplan-Meier survival plots, receiver operating characteristic (ROC) curves, principal component analysis, and t-SNE analyses. Results: We identified two TAM signatures that correlate positively with patient survival, indicative of the abundance of specific subsets of TAMs in RCC. These signatures showed a strong association of specific TAM markers and macrophage infiltration. Survival analysis demonstrated that specific TAM signature gene expressions are significant prognostic markers. Forest plots underscored their prognostic relevance. A 32-gene risk score model was established, successfully stratifying patients into distinct risk categories, with low-risk patients exhibiting markedly improved overall survival. Conclusions: The study underscores the positive association between abundance of specific TAM subsets of and RCC patient survival. The precision of our LASSO Cox regression model, informed by novel abundance markers of specific TAM subsets, advances our understanding of TAM roles in TIME and RCC progression. These TAM signatures could potentially provide additional insights and targets to enhance the efficacy of RCC treatments. Keywords: Tumor associated macrophage, renal cell carcinoma, prognosis, tumor microenvironment, immunomics. Citation Format: Youngsoo Han, Aidan Shen, Zhaohui Wang, Aliesha Garrett, Chongming Jiang. Distinct signatures of tumor-associated macrophage subpopulations predict survival in renal cell carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3457.

Abstract 3510: A radiogenomic approach for triple-negative breast cancer risk stratification

Abstract Background: Triple-negative breast cancer (TNBC) is an aggressive disease that accounts for 15-20% of all breast cancers. Expressions of ER, PR and HER2 receptors are lacking in this disease, and thus targeted therapies are not effective. TNBC has a shorter relapse-free survival, higher metastasis rate and decreased overall survival compared with other breast cancers. However, when undergoing standard treatment, some patients respond well, while others have poor outcome, suggesting TNBC heterogeneity. Early stratification of patients with long versus short survival could identify the subgroup of patients who would not benefit from exposure to toxicity of chemotherapy treatment. Here, we developed a non-invasive radiogenomic approach for TNBC risk stratification. Methods: A transcriptomic-based prognostic gene signature was previously developed using the TCGA-BRCA cohort (n=860). Briefly, LASSO Cox regression model analysis with the ‘glmnet’ R package was used to identify the transcriptomic signature gene-set consisting of 50 genes. We tested this signature to prognosticate overall survival in a Stanford cohort (n=63) and a previously published SCANB cohort (n=604). The patients were stratified into high- and low-risk groups based on the median risk-score. Next, we developed a machine learning model that identified a radiomic feature set to predict the prognostic transcriptomic risk-groups. Radiomic features were extracted from pre-treatment breast MRI. Radiomics features were extracted using PyRadiomics. The model utilized Decision Tree Classifier and LeaveOneOut method was used for cross-validation. Results: The transcriptomic signature low-risk group was significantly associated with improved overall survival in the two TNBC cohorts, with hazard ratios of 0.11 [95% CI: 0.01-0.88] for the Stanford cohort and 0.71 [95% CI: 0.52-0.97] for the SCANB cohort (log-rank p-values p=0.012 and p=0.032, respectively). Including this transcriptomic signature in a multivariate analysis, which adjusted for clinical features (patient age, grade, stage and Ki67%), the transcriptomic prognostic signature remained a significant prognostic factor (p&amp;lt;0.05). The radiomic feature set (consisting of 20 features) predicted the high- and low-risk transcriptomic groups with a mean accuracy of 72.2% and a mean AUROC of 71%. The precision, F1 and recall scores were 67%, 74% and 82%, respectively. In an independent dataset consisting of 116 Stanford TNBC patients, we used this model to predict risk groups based on the MRI radiomics features, and evaluated the prognostic effects of predicted risk groups. The overall survival of the predicted high-risk group was significantly poorer than the predicted low-risk group (p=0.013). Conclusions: We present a prognostic model that can non-invasively stratify TNBC patients for low versus high mortality risk using radiomic features derived from pre-treatment patient MRI data. Citation Format: Humaira Noor, Yuanning Zheng, Adam Mantz, Ryle Zhou, Andrew Kozlov, Wendy B. DeMartini, Shu-tian Chen, Satoko Okamoto, Debra Ikeda, Sarah Mattonen, Sandy Napel, Melinda L. Telli, George Sledge, Allison Kurian, Mina Satoyoshi, Olivier Gevaert, Haruka Itakura. A radiogenomic approach for triple-negative breast cancer risk stratification [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3510.

Predictive nomogram for lymph node metastasis and survival in gastric cancer using contrast-enhanced computed tomography-based radiomics: a retrospective study.

Lymph node involvement significantly impacts the survival of gastric cancer patients and is a crucial factor in determining the appropriate treatment. This study aimed to evaluate the potential of enhanced computed tomography (CT)-based radiomics in predicting lymph node metastasis (LNM) and survival in patients with gastric cancer before surgery. Retrospective analysis of clinical data from 192 patients diagnosed with gastric carcinoma was conducted. The patients were randomly divided into a training cohort (n=128) and a validation cohort (n=64). Radiomic features of CT images were extracted using the Pyradiomics software platform, and distinctive features were further selected using a Lasso Cox regression model. Features significantly associated with LNM were identified through univariate and multivariate analyses and combined with radiomic scores to create a nomogram model for predicting lymph node involvement before surgery. The predictive performance of radiomics features, CT-reported lymph node status, and the nomogram model for LNM were compared in the training and validation cohorts by plotting receiver operating characteristic (ROC) curves. High-risk and low-risk groups were identified in both cohorts based on the cut-off value of 0.582 within the radiomics evaluation scheme, and survival rates were compared. Seven radiomic features were identified and selected, and patients were stratified into high-risk and low-risk groups using a 0.582 cut-off radiomics score. Univariate and multivariate analyses revealed that radiomics features, diabetes mellitus, Nutrition Risk Screening (NRS) 2002 score, and CT-reported lymph node status were significant predictors of LNM in patients with gastric cancer. A predictive nomogram model was developed by combining these predictors with the radiomics score, which accurately predicted LNM in gastric cancer patients before surgery and outperformed other models in terms of accuracy and sensitivity. The AUC values for the training and validation cohorts were 0.82 and 0.722, respectively. The high-risk and low-risk groups in both the training and validation cohorts showed significant differences in survival rates. The radiomics nomogram, based on contrast-enhanced computed tomography (CECT ), is a promising non-invasive tool for preoperatively predicting LNM in gastric cancer patients and postoperative survival.

Enhancing m6A modification of lncRNA through METTL3 and RBM15 to promote malignant progression in bladder cancer

Bladder cancer is one of the most prominent malignancies affecting the urinary tract, characterized by a poor prognosis. Our previous research has underscored the pivotal role of m6A methylation in the progression of bladder cancer. Nevertheless, the precise relationship between N6-methyladenosine (m6A) regulation of long non-coding RNA (lncRNA) and bladder cancer remains elusive. This study harnessed sequencing data and clinical records from 408 bladder cancer patients in the TCGA database. Employing R software, we conducted bioinformatics analysis to establish an m6A-lncRNA co-expression network. Analyzing the differences between high and low-risk groups, particularly at the immunological level, and subsequently investigating the primary regulatory factors of these lncRNA, validating the findings through experiments, and exploring their specific cellular functions. We identified 50 m6A-related lncRNA with prognostic significance through univariate Cox regression analysis. In parallel, we employed a LASSO-Cox regression model to pinpoint 11 lncRNA and calculate risk scores for bladder cancer patients. Based on the median risk score, patients were categorized into low-risk and high-risk groups. The high-risk cohort exhibited notably lower survival rates than their low-risk counterparts. Further analysis pointed to RBM15 and METTL3 as potential master regulators of these m6A-lncRNA. Experimental findings also shed light on the upregulated expression of METTlL3 and RBM15 in bladder cancer, where they contributed to the malignant progression of tumors. The experimental findings demonstrated a significant upregulation of METTL3 and RBM15 in bladder cancer specimens, implicating their contributory role in the oncogenic progression. Knockdown of METTL3 and RBM15 resulted in a marked attenuation of tumor cell proliferation, invasion, and migration, which was concomitant with a downregulation in the cellular m6A methylation status. Moreover, these results revealed that RBM15 and METTL3 function in a synergistic capacity, positing their involvement in cancer promotion via the upregulation of m6A modifications in long non-coding RNAs. Additionally, this study successfully developed an N-methyl-N-nitrosourea (MNU)-induced rat model of in situ bladder carcinoma, confirming the elevated expression of RBM15 and METTL3, which paralleled the overexpression of m6A-related- lncRNAs observed in bladder cancer cell lines. This congruence underscores the potential utility of these molecular markers in in vivo models that mirror human malignancies. This study not only offers novel molecular targets,but also enriches the research on m6A modification in bladder cancer, thereby facilitating its clinical translation.

The prognosis, chemotherapy and immunotherapy efficacy of the SUMOylation pathway signature and the role of UBA2 in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is one of the most common malignant tumors worldwide. Small Ubiquitin-like Modifier (SUMO)-ylation plays a crucial role in tumorigenesis. However, the SUMOylation pathway landscape and its clinical implications in LUAD remain unclear. Here, we analyzed genes involved in the SUMOylation pathway in LUAD and constructed a SUMOylation pathway signature (SUMOPS) using the LASSO-Cox regression model, validated in independent cohorts. Our analysis revealed significant dysregulation of SUMOylation-related genes in LUAD, comprising of favorable or unfavorable prognostic factors. The SUMOPS model was associated with established molecular and histological subtypes of LUAD, highlighting its clinical relevance. The SUMOPS stratified LUAD patients into SUMOPS-high and SUMOPS-low subtypes with distinct survival outcomes and adjuvant chemotherapy responses. The SUMOPS-low subtype showed favorable responses to adjuvant chemotherapy. The correlations between SUMOPS scores and immune cell infiltration suggested that patients with the SUMOPS-high subtype exhibited favorable immune profiles for immune checkpoint inhibitor (ICI) treatment. Additionally, we identified UBA2 as a key SUMOylation-related gene with an increased expression and a poor prognosis in LUAD. Cell function experiment confirmed the role of UBA2 in promoting LUAD cell proliferation, invasion, and migration. These findings provide valuable insights into the SUMOylation pathway and its prognostic implications in LUAD, paving the way for personalized treatment strategies and the development of novel therapeutic targets.

Who benefit from adjuvant chemotherapy in stage I lung adenocarcinoma? A multi-dimensional model for candidate selection

BackgroundDespite promising overall survival of stage I lung adenocarcinoma (LUAD) patients, 10-25 % of them still went through recurrence after surgery. [1] While it is still disputable whether adjuvant chemotherapy is necessary for stage I patients. [2] IASLC grading system for non-mucinous LUAD shows that minor high-grade patterns are significant indicator of poor prognosis. [3] Other risk factors, such as, pleura invasion, lympho-vascular invasion, STAS, etc. are also related to poor prognosis. [4-6] There still lack evidence whether IASLC grade itself or together with other risk factors can guide the use of adjuvant therapy in stage I patients. In this article, we tried to establish a multi-variable recurrence prediction model for stage I LUAD patients that is able to identify candidates of adjuvant chemotherapy. MethodsWe retrospectively collected patients who underwent lung surgery from 2018.8.1 to 2018.12.31 at our institution and diagnosed with lung adenocarcinoma pT1-2aN0M0 (stage I). Clinical data, manifestation on CT scan, pathologic features, driver gene mutations and follow-up information were collected. Cox proportional hazards regression analyses were performed utilizing the non-adjuvant cohort to predict disease free survival (DFS) and a nomogram was constructed and applied to the total cohort. Kaplan-Meier method was used to compare DFS between groups. Statistical analysis was conducted by R version 3.6.3. FindingsA total of 913 stage I LUAD patients were included in this study. Median follow-up time is 48.1 months.4-year and 5-year DFS are 92.9 % and 89.6 % for the total cohort. 65 patient experienced recurrence or death. 4-year DFS are 97.0 %,94.6 % and 76.2 %, and 5-year DFS are 95.5 %, 90.0 % and 74.1 % in IASLC Grade1, 2 and 3, respectively(p < 0.0001). High-risk patients defined by single risk factors, such as, IASLC grade 3, pleura invasion, STAS, less LN resected could not benefit from adjuvant therapy. A LASSO-COX regression model was built and patients are divided into high-risk and low-risk groups. In the high-risk group, patients underwent adjuvant chemotherapy have longer DFS than those who did not (p = 0.024), while in the low-risk group, patients underwent adjuvant chemotherapy have inferior DFS than those who did not (p < 0.001). InterpretationIASLC grading is a significant indicator of DFS, however it could not guide adjuvant therapy in our stage I LUAD cohort. Growth patterns and T indicators together with other risk factors could identify high-risk patients that are potential candidate of adjuvant therapy, including some stage IA LUAD patients.

Development and validation of prediction model for technique failure in peritoneal dialysis patients: An observational study.

This study aimed to establish a prediction model in peritoneal dialysis patients to estimate the risk of technique failure and guide clinical practice. Clinical and laboratory data of 424 adult peritoneal dialysis patients were retrospectively collected. The risk prediction models were built using univariate Cox regression, best subsets approach and LASSO Cox regression. Final nomogram was constructed based on the best model selected by the area under the curve. After comparing three models, the nomogram was built using the LASSO Cox regression model. This model included variables consisting of hypertension and peritonitis, serum creatinine, low-density lipoprotein, fibrinogen and thrombin time, and low red blood cell count, serum albumin, triglyceride and prothrombin activity. The predictive model constructed performed well using receiver operating characteristic curve and area under the curve value, C-index and calibration curve. This study developed and verified a new prediction instrument for the risk of technique failure among peritoneal dialysis patients.