Lasso-penalized Cox Regression Research Articles

Senescence-related Genes as Prognostic Markers for STEMI Patients: LASSO Regression-Based Bioinformatics and External Validation.

The prognostic value of differentially expressed senescence-related genes(DESRGs) in ST-segment elevation myocardial infarction(STEMI) patients is unclear. We used GEO2R to identify DESRGs from GSE60993 and performed functional enrichment analysis. We built an optimal prognostic model with LASSO penalized Cox regression via GSE49925. We evaluated the model with survival analysis, ROC curve, decision curve analysis, nomogram, and external validation with plasma samples. We created a prognostic signature with three dysregulated DESRGs (CDC25B, FKBP5, and ECHDC3) and two clinical variables (serum creatinine, Gensini score). The signature stratified patients into low- and high-risk groups and showed strong predictive performance within two years. The external validation confirmed the survival difference between the groups. We identified three DESRGs that were differentially expressed and prognostic in STEMI patients. The model incorporating three DESRGs showed promising prediction and utility for stratifying patients and estimating survival risk in STEMI patients.

Inter- and intra-tumoral heterogeneity on [68Ga]Ga-DOTA-TATE/[68Ga]Ga-DOTA-TOC PET/CT predicts response to [177Lu]Lu-DOTA-TATE PRRT in neuroendocrine tumor patients

BackgroundIndices of tumor heterogeneity on somatostatin receptor PET/CT scans may potentially serve as predictive biomarkers of treatment efficacy in neuroendocrine tumor (NET) patients undergoing [177Lu]Lu-DOTA-TATE PRRT.MethodsNET patients who underwent [177Lu]Lu-DOTA-TATE therapy at the University of Iowa from August 2018 to February 2021 were retrospectively evaluated. Radiomic features on the pre-PRRT somatostatin receptor PET/CT were evaluated using a custom MIM Software® LesionID workflow. Conventional PET/CT metrics of tumor burden, such as somatostatin receptor expression and tumor volume, were calculated in addition to the indices of tumor heterogeneity for each lesion (intra-lesional) and then summarized across all lesions throughout the body (inter-lesional). Endpoints included post-PRRT 24-month time to progression (TTP) and overall survival (OS). Cox regression models were used to assess the predictive ability of the imaging factors on post-PRRT 24-month TTP and OS. LASSO-penalized Cox regression was used to build a multivariable model for each outcome.ResultsEighty patients with a mean age of 65.1 years were included, with most (71.3%) completing 4 cycles of PRRT. Median TTP was 19.1 months, and OS at 60 months was 50%. A large degree of variability between patients was evidenced for imaging features related to somatostatin receptor expression. On multivariable analysis, total receptor expression and mean liver-corrected SUVmean were selected for 24-month TTP. The model was not able to significantly predict progression (C-statistic = 0.58, 95% CI 0.50–0.62). Total receptor expression and mean skewness were selected for OS. The resulting model was able to significantly predict death (C-statistic = 0.62, 95% CI 0.53–0.67), but the predictive ability was limited, as evidenced by the low C-statistic.ConclusionsOur exploratory analysis provides preliminary results showing that imaging indices of inter- and intra-tumor heterogeneity from pretreatment PET/CT images may potentially predict treatment efficacy in NET patients undergoing [177Lu]Lu-DOTA-TATE therapy. However, prospective evaluation in a larger cohort is needed to further assess whether a comprehensive characterization of tumor heterogeneity within a patient can help guide treatment decisions.

Single- and multi-site radiomics may improve overall survival prediction for patients with metastatic lung adenocarcinoma

PurposeThe purpose of this study was to assess whether single-site and multi-site radiomics could improve the prediction of overall survival (OS) of patients with metastatic lung adenocarcinoma compared to clinicopathological model. Materials and methodsAdults with metastatic lung adenocarcinoma, pretreatment whole-body contrast-enhanced computed tomography examinations, and performance status (WHO-PS) ≤ 2 were included in this retrospective single-center study, and randomly assigned to training and testing cohorts. Radiomics features (RFs) were extracted from all measurable lesions with volume ≥ 1 cm3. Radiomics prognostic scores based on the largest tumor (RPSlargest) and the average RF values across all tumors per patient (RPSaverage) were developed in the training cohort using 5-fold cross-validated LASSO-penalized Cox regression. Intra-patient inter-tumor heterogeneity (IPITH) metrics were calculated to quantify the radiophenotypic dissimilarities among all tumors within each patient. A clinicopathological model was built in the training cohort using stepwise Cox regression and enriched with combinations of RPSaverage, RPSlargest and IPITH. Models were compared with the concordance index in the independent testing cohort. ResultsA total of 300 patients (median age: 63.7 years; 40.7% women; median OS, 16.3 months) with 1359 lesions were included (200 and 100 patients in the training and testing cohorts, respectively). The clinicopathological model included WHO-PS = 2 (hazard ratio [HR] = 3.26; P < 0.0001), EGFR, ALK, ROS1 or RET mutations (HR = 0.57; P = 0.0347), IVB stage (HR = 1.65; P = 0.0211), and liver metastases (HR = 1.47; P = 0.0670). In the testing cohort, RPSaverage, RPSlargest and IPITH were associated with OS (HR = 85.50, P = 0.0038; HR = 18.83, P = 0.0082 and HR = 8.00, P = 0.0327, respectively). The highest concordance index was achieved with the combination of clinicopathological variables and RPSaverage, significantly better than that of the clinicopathological model (concordance index = 0.7150 vs. 0.695, respectively; P = 0.0049) ConclusionSingle-site and multi-site radiomics-based scores are associated with OS in patients with metastatic lung adenocarcinoma. RPSaverage improves the clinicopathological model.

Incremental Value of a Metabolic Risk Score for Heart Failure Mortality: A Population-Based Study.

Heart failure is heterogeneous syndrome with persistently high mortality. Nuclear magnetic resonance spectroscopy enables high-throughput metabolomics, suitable for precision phenotyping. We aimed to use targeted metabolomics to derive a metabolic risk score (MRS) that improved mortality risk stratification in heart failure. Nuclear magnetic resonance was used to measure 21 metabolites (lipoprotein subspecies, branched-chain amino acids, alanine, GlycA (glycoprotein acetylation), ketone bodies, glucose, and citrate) in plasma collected from a heart failure community cohort. The MRS was derived using least absolute shrinkage and selection operator penalized Cox regression and temporal validation. The association between the MRS and mortality and whether risk stratification was improved over the Meta-Analysis Global Group in Chronic Heart Failure clinical risk score and NT-proBNP (N-terminal pro-B-type natriuretic peptide) levels were assessed. The study included 1382 patients (median age, 78 years, 52% men, 43% reduced ejection fraction) with a 5-year survival rate of 48% (95% CI, 46%-51%). The MRS included 9 metabolites measured. In the validation data set, a 1 standard deviation increase in the MRS was associated with a large increased rate of death (hazard ratio, 2.2 [95% CI, 1.9-2.5]) that remained after adjustment for Meta-Analysis Global Group in Chronic Heart Failure score and NT-proBNP (hazard ratio, 1.6 [95% CI, 1.3-1.9]). These associations did not differ by ejection fraction. The integrated discrimination and net reclassification indices, and Uno's C statistic, indicated that the addition of the MRS improved discrimination over Meta-Analysis Global Group in Chronic Heart Failure and NT-proBNP. This MRS developed in a heart failure community cohort was associated with a large excess risk of death and improved risk stratification beyond an established risk score and clinical markers.

A novel ferroptosis-related gene signature for overall survival prediction in patients with gastric cancer.

The global diagnosis rate and mortality of gastric cancer (GC) are among the highest. Ferroptosis and iron-metabolism have a profound impact on tumor development and are closely linked to cancer treatment and patient's prognosis. In this study, we identified six PRDEGs (prognostic ferroptosis- and iron metabolism-related differentially expressed genes) using LASSO-penalized Cox regression analysis. The TCGA cohort was used to establish a prognostic risk model, which allowed us to categorize GC patients into the high- and the low-risk groups based on the median value of the risk scores. Our study demonstrated that patients in the low-risk group had a higher probability of survival compared to those in the high-risk group. Furthermore, the low-risk group exhibited a higher tumor mutation burden (TMB) and a longer 5-year survival period when compared to the high-risk group. In summary, the prognostic risk model, based on the six genes associated with ferroptosis and iron-metabolism, performs well in predicting the prognosis of GC patients.

Activated CD4 T cells/Tregs derived immune-metabolism signature provide precise prognosis assessment for gastric cancer and beneficial for treatment option

BackgroundThe prognostic significance of the ratio of activated CD4 T cells to Tregs infiltrating tumor tissues in gastric cancer (GC) remains unknown. Materials and methodsFor the quantification of infiltration of immune cells, the ssGSEA algorithm, which is a single sample gene set enrichment analysis, was utilized. Group A was defined as having activated CD4 T cells/Tregs >1, while group B was defined as having activated CD4 T cells/Tregs <1. To compare the overall survival (OS) of the two groups, the Kaplan-Meier survival analysis was employed. The R package 'limma' was used to identify the immune and metabolism related genes that were expressed differentially between the two groups, with a false discovery rate (FDR) less than 0.05. The risk score (RS) was constructed by combining univariate Cox regression analysis, LASSO penalized Cox regression analysis, and multivariate Cox regression analysis. The median RS was used to classify high-risk (HR) and low-risk (LR) groups. ResultsA predicted unfavorable outcome of GC was observed when the ratio of activated CD4 T cells to Tregs was less than 1. Our proposed RS was utilized for prognostic risk categorization in ten distinct independent cohorts (TCGA-STAD, n = 371; GSE84437, n = 433; GSE26253, n = 432; GSE13861, n = 65; GSE15459, n = 192; GSE26899, n = 93; GSE26901, n = 109; GSE28541, n = 40; GSE34942, n = 56; GSE62254, n = 300) and exhibited exceptional precision. In terms of tumor microenvironment (TME) and treatment strategies, compared to the LR group, the HR group was characterized by a higher infiltration levels of stromal cells, Tregs, macrophages, Tfh, mast cells, and NK cells, inclined to activated CD4 T cells/Tregs <1, and exhibited insensitivity to immunotherapy and multiple chemotherapy drugs. In relation to the potential molecular mechanism, the excessive activation of oncogenic pathways such as MAPK, hedgehog, WNT, calcium, and TGF-β signaling pathways may accelerate the malignant progression of GC by stimulating angiogenesis, promoting EMT, and altering ECM. Conversely, the overactivation of the P53 pathway is likely to inhibit tumor proliferation by regulating the cell cycle. ConclusionThe immune-metabolism signature associated with the ratio of activated CD4 T cells and Tregs could be used to assess prognosis, TME, and treatment strategies in GC patients.

Elucidating B4GALNT1 as potential biomarker in hepatocellular carcinoma using machine learning models and mutational dynamics explored through MD simulation

Liver hepatocellular carcinoma (LIHC) is considered one of the primary contributors to cancer-related mortality on a global scale. The identification of new biomarkers is of utmost importance due to the fact that patients with LIHC are frequently detected at advanced stages, leading to an increased mortality rate. The study utilized TCGA-LIHC gene expression datasets to identify biomarkers and to address the complexity of datasets. A combination of feature selection (FS) techniques was used, and the performance of this strategy was assessed using ten machine learning classifiers. The findings were integrated, revealing biomarkers identified through at least five FS techniques. Through our proposed approach, we identified 55 potential biomarkers for LIHC. The Gaussian Naive Bayes Classifier (AUC = 0.99) was found to be the most effective classifier, achieving 98.67% accuracy when utilizing the 55 identified biomarkers in the test dataset. Additionally, we conducted differential gene expression, survival analysis, and enrichment analysis for all the identified biomarkers. Subsequently, Lasso-penalized Cox regression further refined the identified biomarkers to thirteen. Out of thirteen genes, we singled out B4GALNT1 because of its statistical significance in differential expression analysis and increasing importance across various cancer types, including LIHC. We carried out comprehensive bioinformatics and molecular dynamics simulation studies along with other structural analysis of B4GALNT1 in LIHC. In LIHC, six mutations (P64Q, S131F, A311S, R340Q, D478H, and P507Q) have been predicted to be probably damaging by evaluating in-silico prediction algorithms. In comparison to the wild type, the B4GALNT1 variations, specifically P64Q and S131F, demonstrate increased stability. However, these mutations lead to decreased atomic fluctuations, indicating a rigid protein structure. Again, mutations like A311S and P507Q induce increased flexibility, highlighting their structural impact on B4GALNT1. The study demonstrated the combination of various feature selection methods effectively reveals new biomarkers, thereby directly impacting their biological significance. Furthermore, our findings indicate a link between increased B4GALNT1 expression in individuals with liver cancer and a poorer prognosis, highlighting its potential as a promising therapeutic target.

ASB6 as an Independent Prognostic Biomarker for Colorectal Cancer Progression Involves Lymphatic Invasion and Immune Infiltration.

Background: ASB6, an E3 ubiquitin ligase, mediates the proteasomal degradation of its substrate proteins via the ubiquitin-proteasome pathway. ASB6 has been reported to play significant roles in several biological processes, including tumor stemness and endoplasmic reticulum stress. However, the underlying role and mechanism of ASB6 in colorectal cancer, particularly its association with immune infiltration levels and its prognostic significance, remain to be fully elucidated. Methods: We identified key prognostic genes in CRC patients through LASSO-penalized Cox regression, Univariate and Multivariate Cox regression analyses. Subsequently, we comprehensively analyzed the prognostic value of hub genes and constructed a prognostic nomogram. Finally, we identified ASB6 interacting proteins through immunoprecipitation-mass spectrometry (IP-MS) and constructed protein-protein interaction (PPI) networks and performed pathway enrichment analysis to explore the potential mechanisms of ASB6. Meanwhile, we evaluated the functions of ASB6 in CRC cells through in vitro cell experiments. Results: We identified ASB6 as a hub gene in CRC. ASB6 was highly expressed in CRC, and patients with high ASB6 expression had worse Disease-Free Interval (DFI), Disease-Specific Survival (DSS), Overall Survival (OS), and Progression-Free Interval (PFI). Correlation analysis showed that ASB6 expression were positively correlated with lymph node invasion and distal metastasis. Overexpression of ASB6 enhanced the migration ability of CRC cells. Multivariate Cox regression analysis revealed that ASB6 was an independent prognostic factor for OS and DSS in CRC. The nomogram model constructed based on multivariate analysis results had good predictive effects, with C-indexes of 0.811 and 0.934 for OS and DSS, respectively. Furthermore, analysis of immune infiltration levels showed that ASB6 expression were positively correlated with M2-type macrophage infiltration levels in CRC, and patients with high levels of both ASB6 and M2-type macrophages had a worse prognosis. Furthermore, pathway enrichment analysis of ASB6 interacting proteins identified by IP-MS suggested that ASB6 may play a crucial role through the response to unfolded protein pathway and protein processing in the endoplasmic reticulum pathway. Conclusions: ASB6 is significantly upregulated in CRC tissues and is a risk factor for prognosis in CRC patients. ASB6 enhances the migration ability of CRC cells. Therefore, ASB6 may be an independent prognostic biomarker and potential therapeutic target for CRC patients.

Construction and validation of a novel lysosomal signature for hepatocellular carcinoma prognosis, diagnosis, and therapeutic decision-making

Lysosomes is a well-recognized oncogenic driver and chemoresistance across variable cancer types, and has been associated with tumor invasiveness, metastasis, and poor prognosis. However, the significance of lysosomes in hepatocellular carcinoma (HCC) is not well understood. Lysosomes-related genes (LRGs) were downloaded from Genome Enrichment Analysis (GSEA) databases. Lysosome-related risk score (LRRS), including eight LRGs, was constructed via expression difference analysis (DEGs), univariate and LASSO-penalized Cox regression algorithm based on the TCGA cohort, while the ICGC cohort was obtained for signature validation. Based on GSE149614 Single-cell RNA sequencing data, model gene expression and liver tumor niche were further analyzed. Moreover, the functional enrichments, tumor microenvironment (TME), and genomic variation landscape between LRRSlow/LRRShigh subgroup were systematically investigated. A total of 15 Lysosomes-related differentially expressed genes (DELRGs) in HCC were detected, and then 10 prognosis DELRGs were screened out. Finally, the 8 optimal DELRGs (CLN3, GBA, CTSA, BSG, APLN, SORT1, ANXA2, and LAPTM4B) were selected to construct the LRRS prognosis signature of HCC. LRRS was considered as an independent prognostic factor and was associated with advanced clinicopathological features. LRRS also proved to be a potential marker for HCC diagnosis, especially for early-stage HCC. Then, a nomogram integrating the LRRS and clinical parameters was set up displaying great prognostic predictive performance. Moreover, patients with high LRRS showed higher tumor stemness, higher heterogeneity, and higher genomic alteration status than those in the low LRRS group and enriched in metabolism-related pathways, suggesting its underlying role in the progression and development of liver cancer. Meanwhile, the LRRS can affect the proportion of immunosuppressive cell infiltration, making it a vital immunosuppressive factor in the tumor microenvironment. Additionally, HCC patients with low LRRS were more sensitive to immunotherapy, while patients in the high LRRS group responded better to chemotherapy. Upon single-cell RNA sequencing, CLN3, GBA, and LAPTM4B were found to be specially expressed in hepatocytes, where they promoted cell progression. Finally, RT-qPCR and external datasets confirmed the mRNA expression levels of model genes. This study provided a direct links between LRRS signature and clinical characteristics, tumor microenvironment, and clinical drug-response, highlighting the critical role of lysosome in the development and treatment resistance of liver cancer, providing valuable insights into the prognosis prediction and treatment response of HCC, thereby providing valuable insights into prognostic prediction, early diagnosis, and therapeutic response of HCC.

Establishment of a prognostic signature based on fatty acid metabolism genes in HCC associated with hepatitis B

BackgroundHepatitis B virus (HBV)-associated hepatocellular carcinoma (HCC) is one of the most common and deadly cancer and often accompanied by varying degrees of liver damage, leading to the dysfunction of fatty acid metabolism (FAM). This study aimed to investigate the relationship between FAM and HBV-associated HCC and identify FAM biomarkers for predicting the prognosis of HBV-associated HCC.MethodsGene Set Enrichment Analysis (GSEA) was used to analyze the difference of FAM pathway between paired tumor and adjacent normal tissue samples in 58 HBV-associated HCC patients from the Gene Expression Omnibus (GEO) database. Next, 117 HBV-associated HCC patients from The Cancer Genome Atlas (TCGA) database were analyzed to establish a prognostic signature based on 42 FAM genes. Then, the prognostic signature was validated in an external cohort consisting of 30 HBV-associated HCC patients. Finally, immune infiltration analysis was performed to evaluate the FAM-related immune cells in HBV-associated HCC.ResultsAs a result, FAM pathway was clearly downregulated in tumor tissue of HBV-associated HCC, and survival analysis demonstrated that 12 FAM genes were associated with the prognosis of HBV-associated HCC. Lasso-penalized Cox regression analysis identified and established a five-gene signature (ACADVL, ACAT1, ACSL3, ADH4 and ECI1), which showed effective discrimination and prediction for the prognosis of HBV-associated HCC both in the TCGA cohort and the validation cohort. Immune infiltration analysis showed that the high-risk group, identified by FAM signature, of HBV-associated HCC had a higher ratio of Tregs, which was associated with the prognosis.ConclusionsCollectively, these findings suggest that there is a strong connection between FAM and HBV-associated HCC, indicating a potential therapeutic strategy targeting FAM to block the accumulation of Tregs into the tumor microenvironment of HBV-associated HCC.

Analysis and experimental validation of fatty acid metabolism-related genes prostacyclin synthase (PTGIS) in endometrial cancer.

The deregulation of fatty acid metabolism plays a pivotal role in cancer. Our objective is to construct a prognostic model for patients with endometrial carcinoma (EC) based on genes related to fatty acid metabolism-related genes (FAMGs). RNA sequencing and clinical data for EC were obtained from The Cancer Genome Atlas (TCGA). Lasso-Penalized Cox regression was employed to derive the risk formula for the model, the score = esum(corresponding coefficient × each gene's expression). Gene set enrichment analysis (GSEA) was utilized to examine the enrichment of KEGG and GO pathways within this model. Correlation analysis of immune function was conducted using Single-sample GSEA (ssGSEA). The "ESTIMATE" package in R was utilized to evaluate the tumor microenvironment. The support vector machine recursive feature elimination (SVM-RFE) and randomforest maps were employed to identify key genes. The effects of PTGIS on the malignant biological behavior of EC were assessed through CCK-8 assay, transwell invasion assay, cell cycle analysis, apoptosis assay, and tumor xenografts in nude mice. A novel prognostic signature comprising 10 FAMGs (INMT, ACACB, ACOT4, ACOXL, CYP4F3, FAAH, GPX1, HPGDS, PON3, PTGIS) was developed. This risk score serves as an independent prognostic marker validated for EC. According to ssGSEA analysis, the low- and high-risk groups exhibited distinct immune enrichments. The key gene PTGIS was screened by SVM-RFE and randomforest method. Furthermore, we validated the expression of PTGIS through qRT-PCR. In vitro and in vivo experiments also confirmed the effect of PTGIS on the malignant biological behavior of EC.

Comprehensive analysis of cuproptosis and copper homeostasis genotyping and related immune land scape in lung adenocarcinoma

Cuproptosis is a manner of cell death which is related to the homeostasis of copper ions in the cellular environment and is expected to open a new direction of anti-tumor therapy. However, the studies on cuproptosis and copper homeostasis in lung adenocarcinoma (LUAD) are still limited. In this study, we identified new cuproptosis and copper homeostasis related genes (CHRGs) which were effective in stratifying genotyping clusters with survival differences based on transcriptomic data obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Weighted Gene Co-expression Network Analysis (WGCNA) further expands the screening boundary of CHRGs, and finally we established a 10-CHRGs-based prognostic signature using lasso-penalized cox regression method, which were validated in GSE30219. Comprehensive bioinformatics analysis revealed these genes are potential regulators of modulating immunotherapy efficacy, drug resistance, tumor microenvironment infiltration, and tumor mutation patterns. Lastly, the scRNA-seq datasets GSE183219 and GSE203360 offers the evidences that CHRGs signature are mainly distributed in cancer epithelial cells, real time quantitative polymerase chain reaction (RT-qPCR) also confirmed the differential expression of these genes between normal lung cell line and lung adenocarcinoma cell lines. Collectively, our findings revealed new cuproptosis and copper homeostasis related genotyping clusters and genes which may play important roles in predicting prognosis, influencing tumor microenvironment and drug efficacy in LUAD patients.

Identification of novel serum protein biomarkers in the context of 3P medicine for intravenous leiomyomatosis: a data-independent acquisition mass spectrometry-based proteomics study.

Intravenous leiomyomatosis (IVL) is a rare endocrine-associated tumor with unique characteristics of intravascular invasion. This study aimed to identify reliable biomarkers to supervise the development or recurrence of IVL in the context of predictive, preventive, and personalized medicine (PPPM/3PM). A total of 60 cases were recruited to detect differentially expressed proteins (DEPs) in serum samples from IVL patients. These cases included those with recurrent IVL, non-recurrent IVL, uterine myoma, and healthy individuals without uterine myoma, with 15 cases in each category. Then, weighted gene co-expression network analysis (WGCNA), lasso-penalized Cox regression analysis (Lasso), trend clustering, and a generalized linear regression model (GLM) were utilized to screen the hub proteins involved in IVL progression. First, 93 differentially expressed proteins (DEPs) were determined from 2582 recognizable proteins, with 54 proteins augmented in the IVL group, and the remaining proteins declined. These proteins were enriched in the modulation of the immune environment, mainly by activating the function of B cells. After the integrated analyses mentioned above, a model based on four proteins (A0A5C2FUE5, A0A5C2GPQ1, A0A5C2GNC7, and A0A5C2GBR3) was developed to efficiently determine the potential of IVL lesions to progress. Among these featured proteins, our results demonstrated that the risk factor A0A5C2FUE5 was associated with IVL progression (OR = 2.64). Conversely, A0A5C2GPQ1, A0A5C2GNC7, and A0A5C2GBR3 might act in a protective manner and prevent disease development (OR = 0.32, 0.60, 0.53, respectively), which was further supported by the multi-class receiver operator characteristic curve analysis. Four hub proteins were eventually identified based on the integrated bioinformatics analyses. This study potentiates the promising application of these novel biomarkers to predict the prognosis or progression of IVL by a 3PM approach. The online version contains supplementary material available at 10.1007/s13167-023-00338-0.

Machine learning analysis of lung squamous cell carcinoma gene expression datasets reveals novel prognostic signatures

BackgroundLung squamous cell carcinoma (LUSC) patients are often diagnosed at an advanced stage and have poor prognoses. Thus, identifying novel biomarkers for the LUSC is of utmost importance. MethodsMultiple datasets from the NCBI-GEO repository were obtained and merged to construct the complete dataset. We also constructed a subset from this complete dataset with only known cancer driver genes. Further, machine learning classifiers were employed to obtain the best features from both datasets. Simultaneously, we perform differential gene expression analysis. Furthermore, survival and enrichment analyses were performed. ResultsThe kNN classifier performed comparatively better on the complete and driver datasets' top 40 and 50 gene features, respectively. Out of these 90 gene features, 35 were found to be differentially regulated. Lasso-penalized Cox regression further reduced the number of genes to eight. The median risk score of these eight genes significantly stratified the patients, and low-risk patients have significantly better overall survival. We validated the robust performance of these eight genes on the TCGA dataset. Pathway enrichment analysis identified that these genes are associated with cell cycle, cell proliferation, and migration. ConclusionThis study demonstrates that an integrated approach involving machine learning and system biology may effectively identify novel biomarkers for LUSC.

A cuproptosis-related lncRNA signature for predicting prognosis and immunotherapy response of lung adenocarcinoma.

Copper-induced cell death (cuproptosis) is a new regulatory cell death mechanism. Long noncoding RNAs (lncRNAs) are related to tumor immunity and metastasis. However, the correlation of cuproptosis-related lncRNAs with the immunotherapy response and prognosis of lung adenocarcinoma (LUAD) patients is not clear. We obtained the clinical characteristics and transcriptome data from TCGA-LUAD dataset (containing 539 LUAD and 59 paracancerous tissues). By utilizing LASSO-penalized Cox regression analysis, we identified a prognostic signature composed of cuproptosis-related lncRNAs. This signature was then utilized to segregate patients into two different risk categories based on their respective risk scores. The identification of differentially expressed genes (DEGs) between high- and low-risk groups was carried out using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. We evaluated the immunotherapy response by analyzing tumor mutational burden (TMB), immunocyte infiltration and Tumor Immune Dysfunction and Exclusion (TIDE) web application. The "pRRophetic" R package was utilized to conduct further screening of potential therapeutic drugs for their sensitivity. We ultimately identified a prognostic risk signature that includes six cuproptosis-related lncRNAs (AP003778.1, AC011611.2, CRNDE, AL162632.3, LY86-AS1, and AC090948.1). Compared with clinical characteristics, the signature was significantly correlated with prognosis following the control of confounding variables (HR = 2.287, 95% CI = 1.648-3.174, p ˂ 0.001), and correctly predicted 1-, 2-, and 3-year overall survival (OS) rates (AUC value = 0.725, 0.715, and 0.662, respectively) in LUAD patients. In terms of prognosis, patients categorized as low risk exhibited more positive results in comparison to those in the high-risk group. The enrichment analysis showed that the two groups had different immune signaling pathways. Immunotherapy may offer a more appropriate treatment option for high-risk patients due to their higher TMB and lower TIDE scores. The higher risk score may demonstrate increased sensitivity to bexarotene, cisplatin, epothilone B, and vinorelbine. Based on cuproptosis-related lncRNAs, we constructed and validated a novel risk signature that may be used to predict immunotherapy efficacy and prognosis in LUAD patients.

Immunotherapy Efficacy-related Risk Classifier Differentiate Prognostic Characteristics of Gastric Cancer-A Large-scale Retrospective Study.

Prognostic signatures related to the efficacy of immunotherapy have not been determined in gastric cancer (GC). We identified the differentially expressed genes between the CR/PR and SD/PD groups with the R package "limma" (false discovery rate <0.05) in the IMvigor210 data set. The GSE13861 (n=65), GSE15459 (n=192), GSE26899 (n=93), GSE26901 (n=109), GSE28541 (n=40), GSE34942 (n=56), and GSE62254 (n=300) cohorts were merged into a training cohort (n=855). Univariate Cox regression analysis, LASSO penalized Cox regression analysis, and multivariate Cox regression analysis were jointly applied to construct the prognostic model. The Cancer Genome Atlas (TCGA)-STAD (n=371), GSE84437 (n=433), GSE26253 (n=432), and IMvigor210 (n=348) cohorts were utilized for external validation. The GC patients were divided into 16 subgroups according to clinical features for universal applicability validation. Repeated validation confirmed that the overall survival of the high-risk (HR) group was significantly reduced compared with that of the low-risk (LR) group. The HR group showed a higher infiltration abundance of regulatory T cells, macrophages, T follicular helper cells, and natural killer T cells, whereas the infiltration levels of activated CD4 T cells and monocytes were upregulated in the LR group. The calcium, TGF-β, MAPK, Hedgehog, and KRAS signaling pathways were overactivated in the HR group, while the hallmarks related to DNA damage repair and metabolism were enriched in the LR group. In addition, the LR group had high tumor mutation burden, FLG, and OBSCN mutations. A prognostic risk classifier for GC patients was identified and validated by carrying out a multicenter retrospective study.

Exploration of epithelial-mesenchymal transition-related lncRNA signature and drug sensitivity in breast cancer.

Breast cancer (BRCA) has become the most diagnosed cancer worldwide for female and seriously endanger female health. The epithelial-mesenchymal transition (EMT) process is associated with metastasis and drug resistance in BRCA patients. However, the prognostic value of EMT-related lncRNA in BRCA still needs to be revealed. The aim of this study is to construct an EMT-related lncRNA (ERL) signature with accuracy predictive ability for the prognosis of BRCA patients. RNA-seq expression data and Clinical characteristics obtained from the TCGA (The Cancer Genome Atlas) were used in the study. First, we identified the EMT-related lncRNA by the Pearson correlation analysis. An EMT-related lncRNAs prognostic risk signature was constructed using univariate Cox regression and Lasso-penalized Cox regression analyses. The model's performance was validated using Kaplan-Meier (KM) survival analysis, ROC curve and C-index. Finally, a nomogram was constructed for clinical practice in evaluating the patients with BRCA and validated by calibration curve and decision curve analysis (DCA). We also evaluated the drug sensitivity of signature lncRNA and the tumor immune cell infiltration in breast cancer. We constructed a 10-lncRNA risk score signature based on the lncRNAs associated with the EMT process. We could assign BRCA patients to the high- and low-risk group according to the median risk score. The prognostic risk signature showed excellent accuracy and demonstrated sufficient independence from other clinical characteristics. The immune cell infiltration analysis showed that the prognostic risk signature was related to the infiltration of the immune cell subtype. Drug sensitivity analysis proved ERLs signature could effectively predict the sensitivity of patients to common chemotherapy drugs in BRCA and provide guidance for chemotherapy drugs for high-risk and low-risk patients. Our ERL signature and nomogram have excellent prognostic value and could become reliable tools for clinical guidance.

1266-P: Validation of a Predictive Model of Diabetes in People with Overweight and Obesity

Obesity is the most common chronic disease in the U.S. A substantial subset of people with obesity are at high risk for type 2 diabetes mellitus (T2DM) and many of their risk factors for T2DM are modifiable. It is important to identify this at-risk subset to appropriately direct treatment and resources for earlier intervention and the prevention of T2DM. The original model was developed using EMR data of 338,653 adults &amp;lt; 65 years old with BMI ≥ 25 kg/m2 and without baseline T2DM between 2000 and 2019. Variables were selected from a literature-based pool of risk factors for T2DM using LASSO penalized Cox regression. A Cox model (adjusted for the competing risk of death) was developed using an 80% training dataset with selected variables and internally validated on a held-out 20% dataset of EMR data. This model was then externally validated on a dataset of 103,419 participants of the Nurses’ Health Study (NHS) and Health Professionals Follow-Up Study (HPFS). Over a mean of 7.0 years of follow-up, 30,100 (8.9%) patients in the EMR dataset developed T2DM (1.3% annual incidence). The predictive model included BMI (HR 1.47 for 5 kg/m2; 95% CI 1.46 - 1.49), age, history of hypertension, systolic blood pressure and family history of diabetes. The model achieved a Harrell’s C-statistic of 0.76 on the held-out EMR dataset and the calibration slope for 10-year estimates was 1.09. In the NHS-HPFS external validation dataset, 12,402 (12.0%) individuals developed T2DM over a mean 14.2 years of follow-up (0.85% annual incidence). On this dataset, the model achieved Harrell’s C-statistic of 0.72 and the calibration slope for 10-year estimates of 0.78. It is feasible to use predictive modeling to identify people with overweight and obesity at high risk of T2DM. This approach could be utilized to guide population management and clinical treatment decisions. Disclosure A.Turchin: Consultant; Proteomics International, Covance, Research Support; AstraZeneca, Edwards Lifesciences Corporation, Eli Lilly and Company, Novo Nordisk, Stock/Shareholder; Brio Systems. M.Shubina: None. F.J.Morrison: None. N.Ahmad: Employee; Eli Lilly and Company. L.M.Neff: Employee; Eli Lilly and Company, Research Support; Aegerion Pharmaceuticals, Stock/Shareholder; Eli Lilly and Company. H.Kan: Employee; Eli Lilly and Company, Stock/Shareholder; Eli Lilly and Company. Funding Eli Lilly and Company

A novel predictive model based on inflammatory response-related genes for predicting endometrial cancer prognosis and its experimental validation.

Inflammatory response is an important feature of most tumors. Local inflammation promotes tumor cell immune evasion and chemotherapeutic drug resistance. We aimed to build a prognostic model for endometrial cancer patients based on inflammatory response-related genes (IRGs). RNA sequencing and clinical data for uterine corpus endometrial cancer were obtained from TCGA datasets. LASSO-penalized Cox regression was used to obtain the risk formula of the model: the score = esum(corresponding coefficient × each gene's expression). The "ESTIMATE" and "pRRophetic" packages in R were used to evaluate the tumor microenvironment and the sensitivity of patients to chemotherapy drugs. Data sets from IMvigor210 were used to evaluate the efficacy of immunotherapy in cancer patients. For experimental verification, 37 endometrial cancer and 43 normal endometrial tissues samples were collected. The mRNA expression of the IRGs was measured using qRT-PCR. The effects of IRGs on the malignant biological behaviors of endometrial cancer were detected using CCK-8, colony formation, Transwell invasion, and apoptosis assays. We developed a novel prognostic signature comprising 13 IRGs, which is an independent prognostic marker for endometrial cancer. A nomogram was developed to predict patient survival accurately. Three key IRGs (LAMP3, MEP1A, and ROS1) were identified in this model. Furthermore, we verified the expression of the three key IRGs using qRT-PCR. Functional experiments also confirmed the influence of the three key IRGs on the malignant biological behavior of endometrial cancer. Thus, a characteristic model constructed using IRGs can predict the survival, chemotherapeutic drug sensitivity, and immunotherapy response in patients with endometrial cancer.

Immune-related lncRNAs pairs prognostic score model for prediction of survival in acute myeloid leukemia patients

Acute myeloid leukemia (AML) is one of the most common malignant and aggressive hematologic tumors, and risk stratification is indispensable to ensure proper treatment. But immune-related long noncoding RNAs (ir-lncRNAs) pairs prognostic risk models used to stratify AML have yet to be reported. In this study, we established a prognostic risk model based on eight ir-lncRNAs pairs using LASSO-penalized Cox regression analysis and successfully validated the model in an independent cohort. According to risk scores, patients were divided into a high-risk group and a low-risk group. High-risk patients presented more tumor mutation frequency and higher expression of human leukocyte antigen (HLA)-related genes and immune checkpoint molecules. Gene Set Enrichment Analysis (GSEA) indicated that the transforming growth factors β (TGFβ) pathway was activated in the high-risk group; meanwhile, we found that TGFβ1 mRNA levels were significantly elevated in AML patients and correlated with poor prognosis, which is closely related to drug resistance. Consistently, in vitro studies found that exogenous TGFβ1 can protect AML cells from chemotherapy-induced apoptosis. Collectively, we developed an ir-lncRNA prognostic model that helps predict the prognosis of AML patients and provides valuable information about their response to immune checkpoint inhibitors, and we found that increased TGFβ1 levels resulting in chemoresistance may be one of the leading causes of treatment failure in high-risk AML patients.