Lasso Regression Analysis Research Articles

Exploring and identifying the imaging biomarkers for predicting anti-VEGF treatment response in polypoidal choroidal vasculopathy: aprospective multicenter study.

Polypoidal choroidal vasculopathy (PCV) is a hemorrhagic fundus disease that can lead to permanent vision loss. Predicting the treatment response to anti-VEGF monotherapy in PCV is consistently challenging. We aimed to conduct a prospective multicenter study to explore and identify the imaging biomarkers for predicting the anti-VEGF treatment response in PCV patients, establish predictive model, and undergo multicenter validation. This prospective multicenter study utilized clinical characteristics and images of treatment naïve PCV patients from 15 ophthalmic centers nationwide to screen biomarkers, develop model, and validate its performance. Patients from Peking Union Medical College Hospital were randomly divided into a training set and an internal validation set. A nomogram was established by univariate, LASSO regression, and multivariate regression analysis. Patients from the other 14 centers served as an external test set. Area under the curve (AUC), sensitivity, specificity, and accuracy were calculated. Decision curve analysis (DCA) and clinical impact curve (CIC) were utilized to evaluate the practical utility in clinical decision-making. The eye distribution for the training set, internal validation set, and external test set were 66, 31, and 71, respectively. The 'Good responder' exhibited a thinner subfoveal choroidal thickness (SFCT) (230.67 ± 61.96 vs. 314.42 ± 88.00 μm, p < 0.001), lower choroidal vascularity index (CVI) (0.31 ± 0.08 vs. 0.36 ± 0.05, p = 0.006), fewer choroidal vascular hyperpermeability (CVH) (31.0 vs. 62.2%, p = 0.012), and more intraretinal fluid (IRF) (58.6 vs. 29.7%, p = 0.018). SFCT (OR 0.990; 95% CI 0.981-0.999; p = 0.033) and CVI (OR 0.844; 95% CI 0.732-0.971; p = 0.018) were ultimately included as the optimal predictive biomarkers and presented in the form of a nomogram. The model demonstrated AUC of 0.837 (95% CI 0.738-0.936), 0.891 (95% CI 0.765-1.000), and 0.901 (95% CI 0.824-0.978) for predicting 'Good responder' in the training set, internal validation set, and external test set, respectively, with excellent sensitivity, specificity, and practical utility. Thinner SFCT and lower CVI can serve as imaging biomarkers for predicting good treatment response to anti-VEGF monotherapy in PCV patients. The nomogram based on these biomarkers exhibited satisfactory performances.

Characteristics of regional lymph node metastasis in breast cancer and construction of a nomogram model based on ultrasonographic analysis: a retrospective study

ObjectiveThe ultrasonographic characteristics of lymph node metastasis in breast cancer patients were retrospectively analyzed, and a predictive nomogram model was constructed to provide an imaging basis for better clinical evaluation.MethodsB-mode ultrasound was used to retrospectively analyze the imaging characteristics of regional lymph nodes and tumors. Pathological examination confirmed the presence of lymph node metastasis in breast cancer patients. Univariable and multivariable logistic regression analyses were performed to analyze the risk factors for lymph node metastasis. LASSO regression analysis was performed to screen noninvasive indicators, and a nomogram prediction model was constructed for breast cancer patients with lymph node metastasis.ResultsA total of 187 breast cancer patients were enrolled, including 74 patients with lymph node metastasis in the positive group and 113 patients without lymph node metastasis in the negative group. Multivariate analysis revealed that pathological type (OR = 4.58, 95% CI: 1.44–14.6, p = 0.01), tumor diameter (OR = 1.37, 95% CI: 1.07–1.74, p = 0.012), spiculated margins (OR = 7.92, 95% CI: 3.03–20.67, p < 0.001), mixed echo of the breast tumor (OR = 37.09, 95% CI: 3.49–394.1, p = 0.003), and unclear lymphatic hilum structure (OR = 16.07, 95% CI: 2.41–107.02, p = 0.004) were independent risk factors for lymph node metastasis. A nomogram model was constructed for predicting breast cancer with lymph node metastasis, incorporating three significantly correlated indicators identified through LASSO regression analysis, namely, tumor spiculated margins, cortical thickness of lymph nodes, and unclear lymphatic hilum structure. The receiver operating characteristic (ROC) curve revealed that the area under the curve (AUC) was 0.717 (95% CI, 0.614–0.820) for the training set and 0.817 (95% CI, 0.738–0.890) for the validation set. The Hosmer–Lemeshow test results for the training set and the validation set were p = 0.9148 and p = 0.1648, respectively. The prediction nomogram has good diagnostic performance.ConclusionsB-mode ultrasound is helpful in the preoperative assessment of breast cancer patients with lymph node metastasis. The predictive nomogram model, which is based on logistic regression and LASSO regression analysis, is clinically safe, reliable, and highly practical.

Identification of key immune-related genes and potential therapeutic drugs in diabetic nephropathy based on machine learning algorithms

BackgroundDiabetic nephropathy (DN) is a major contributor to chronic kidney disease. This study aims to identify immune biomarkers and potential therapeutic drugs in DN.MethodsWe analyzed two DN microarray datasets (GSE96804 and GSE30528) for differentially expressed genes (DEGs) using the Limma package, overlapping them with immune-related genes from ImmPort and InnateDB. LASSO regression, SVM-RFE, and random forest analysis identified four hub genes (EGF, PLTP, RGS2, PTGDS) as proficient predictors of DN. The model achieved an AUC of 0.995 and was validated on GSE142025. Single-cell RNA data (GSE183276) revealed increased hub gene expression in epithelial cells. CIBERSORT analysis showed differences in immune cell proportions between DN patients and controls, with the hub genes correlating positively with neutrophil infiltration. Molecular docking identified potential drugs: cysteamine, eltrombopag, and DMSO. And qPCR and western blot assays were used to confirm the expressions of the four hub genes.ResultsAnalysis found 95 and 88 distinctively expressed immune genes in the two DN datasets, with 14 consistently differentially expressed immune-related genes. After machine learning algorithms, EGF, PLTP, RGS2, PTGDS were identified as the immune-related hub genes associated with DN. In addition, the mRNA and protein levels of them were obviously elevated in HK-2 cells treated with glucose for 24 h, as well as their mRNA expressions in kidney tissues of mice with DN.ConclusionThis study identified 4 hub immune-related genes (EGF, PLTP, RGS2, PTGDS), as well as their expression profiles and the correlation with immune cell infiltration in DN.

A disulfidptosis-associated long noncoding RNA signature to predict low-grade glioma classification, prognosis, tumor microenvironment, and therapy regimens: Observational study.

This study aimed to investigate the function of disulfidptosis-associated long noncoding RNAs (DAlncRNAs) in low-grade gliomas (LGG) through bioinformatics analysis and construct a signature to predict the classification, prognosis, tumor microenvironment, and selection of immunotherapy and chemotherapy in LGG. Genomic, clinical, and mutational information of 526 patients with LGG was retrieved from The Cancer Genome Atlas repository. A nonnegative matrix factorization algorithm was applied to classify patients with LGG. Univariate, LASSO regression, and multivariate Cox regression analyses were performed to determine prognostic DAlncRNAs. Following the median risk score, we defined the sample as a high-risk (HR) or low-risk group. Finally, survival, receiver operating characteristic curve, risk curve, principal component, independent prognosis, risk difference, functional enrichment, tumor microenvironment, immune cell infiltration, mutation, and drug sensitivity analyses were performed. Patients were classified into C1 and C2 subtypes associated with disulfidptosis. Eight prognostic DAlncRNAs (AC003035.2, AC010157.2, AC010273.3, AC011444.3, AC092667.1, AL450270.1, AL645608.2, and LINC01571) were identified, and a prognostic signature of LGG was developed. The DAlncRNA-based signature was found to be an independent prognostic factor in patients with LGG, thereby constructing a nomogram. In addition, in the HR group, immune function was more active and the tumor mutation burden was higher. The patients were mainly composed of subtype C2, and their prognosis was worse. Immunotherapy and chemotherapy were predicted in the HR and low-risk groups, respectively. Our study, based on DAlncRNAs, highlights 2 disulfidptosis-associated LGG subtypes with different prognostic and immune characteristics and creates a novel disulfidptosis-associated prognostic signature, which may inform the classification, prognosis, molecular pathogenesis, and therapeutic strategies for patients with LGG.

Identification of key anthropogenic and land use factors and ecological risk assessment of dissolved polycyclic aromatic hydrocarbons (PAHs) and organochlorine pesticides (OCPs) in an urbanized estuary in China

This study investigated dissolved PAHs and OCPs in Quanzhou Bay estuaries, assessed their ecological risk, and examined anthropogenic impacts on contaminant distribution. Results showed that dissolved ∑24PAH concentrations ranged from 117 to 709 ng/L (mean: 358 ng/L), with dominance of 2-ring PAHs (Naphthalene, 1-Methylnaphthalene, and 2-Methylnaphthalene). Dissolved DDT levels ranged from 0.06 to 0.49 ng/L (mean: 0.28 ng/L), while HCBz concentrations varied from 0.02 to 0.44 ng/L (mean: 0.20 ng/L). PAHs were higher in the north due to urbanization and transport, while OCPs showed higher levels in the south due to historical agricultural use. Rural areas, water bodies, and wetlands significantly influenced the behavior of PAHs according to Spearman correlation and lasso regression analyses. Quanzhou Bay was categorized as a low to medium risk area based on dispersion simulation and ecological risk assessment, highlighting implications for future sustainable development and policy planning. CapsuleThe coupled relationship between human activities and the distribution of dissolved PAHs and OCPs in urbanized estuaries was explored using statistical methods and GIS technology, providing valuable insights into environmental processes and pollutant control policies.

Predictive Value of the Nomogram Model Based on Multimodal Ultrasound Features for Benign and Malignant Thyroid Nodules of C-TIRADS Category 4.

To explore the predictive value of the nomogram model based on multimodal ultrasound features for benign and malignant thyroid nodules of C-TIRADS category 4. A retrospective analysis was conducted on the general conditions and ultrasound features of patients who underwent thyroid ultrasound examination and fine needle aspiration biopsy (FNA) or thyroidectomy at the Affiliated Hospital of Zunyi Medical University from April 2020 to April 2023. Predictive signs for benign and malignant nodules of thyroid C-TIRADS category 4 were screened through LASSO regression and multivariate logistic regression analysis to construct a nomogram prediction model. The predictive efficiency and accuracy of the model were assessed through ROC curves and calibration curves. Seven independent risk factors in the predictive model for benign and malignant thyroid nodules of C-TIRADS category 4 were growth pattern, morphology, microcalcifications, SR, arterial phase enhancement intensity, initial perfusion time, and PE [%]. Based on these features, the area under the curve (AUC) of the constructed prediction model was 0.971 (p ＜ .001, 95% CI: 0.952-0.989), with a prediction accuracy of 93.1%. Internal validation showed that the nomogram calibration curve was consistent with reality, and the decision curve analysis indicated that the model has high clinical application value. The nomogram prediction model constructed based on the multimodal ultrasound features of thyroid nodules of C-TIRADS category 4 has high clinical application value.

Identification of UNC5B as a novel aggressive biomarker for osteosarcoma based on basement membrane genes

BackgroundThe prognosis of patients with metastatic osteosarcoma is poor, and the variation of basement membrane genes (BMGs) is associated with cancer metastasis. However, the role of BMGs in osteosarcoma has been poorly studied. MethodsBMGs were collected and differentially expressed BMGs (DE-BMGs) were found through difference analysis. DE-BMGs were further screened by univariate Cox regression and Lasso regression analyses, and six key BMGs were identified and defined as basement membrane genes signatures (BMGS). Then, BMGS was used to construct the osteosarcoma BMGS risk score system, and the osteosarcoma patients were divided into high- and low-risk groups based on the median risk score. Single-sample gene set enrichment analysis (ssGSEA) and ESTIMATE scores were used to investigate the differences in immune infiltration between the two scoring groups. Additionally, we investigated whether UNC5B affects various features in tumors by bioinformatic analysis and whether UNC5B was involved in multiple biological functions of osteosarcoma cells by wound healing assay, transwell assay, and western blot. ResultsThe osteosarcoma BMGS risk score reliably predicts the risk of metastasis, patient prognosis, and immunity. UNC5B expression was elevated in osteosarcoma, and correlated with various characteristics such as immune infiltration, prognosis, and drug sensitivity. In vitro assays showed that UNC5B knockdown reduced osteosarcoma cells’ capacity for migration and invasion, and EMT process. ConclusionA novel BMGS risk score system that can effectively predict the prognosis of osteosarcoma was developed and validated. The UNC5B gene in this system is one of the key aggressive biomarkers of osteosarcoma.

Telomerase related molecular subtype and risk model reveal immune activity and evaluate prognosis and immunotherapy response in prostate cancer

BackgroundProstate cancer ranks among the six most lethal malignancies worldwide. Telomerase, a reverse transcriptase enzyme, plays a pivotal role in extending cellular telomeres and is intimately associated with cell proliferation and division. However, the interconnection between prostate cancer and telomerase-related genes (TEASEs) remains unclear.MethodsSomatic mutations and copy number alterations of TEASEs were comprehensively analyzed. Subsequently, the transcripts of prostate cancer patients in TCGA and GEO databases were integrated to delineate new molecular subtypes. Followed by constructing a risk model containing nine characteristic genes through Lasso regression and Cox prognostic analysis among different subtypes. Various aspects including prognosis, tumor microenvironment (TME), landscape of immunity, tumor mutational burden (TMB), stem cell correlation, and median inhibitory concentration amongst different risk groups were compared. Finally, the expression, prognosis, and malignant biological behavior of ZW10 interactor (ZWINT) in vitro was explored.ResultsTEASEs exhibited a notably high mutation frequency. Three distinct molecular subtypes and two gene subclusters based on TEASEs were delineated, displaying significant associations with prognosis, immune function regulation, and clinical characteristics. Low-risk patients demonstrated superior prognosis and better response to immunotherapy. Conversely, high-risk patients exhibited higher TMB and stronger stem cell correlations. It was also found that the patients’ sensitivity to chemotherapy agents was impacted by the risk score. Finally, ZWINT’s potential as a novel diagnostic and prognostic biomarker for prostate cancer was validated.ConclusionsTEASEs play a pivotal role in modulating immune regulation and immunotherapeutic responses, thereby significantly impacting the diagnosis, prognosis, and treatment strategies for affected patients.

Construction of a prognostic model for colon cancer by combining endoplasmic reticulum stress responsive genes

Endoplasmic reticulum stress may affect the occurrence and development of cancer. However, its effect on the prognosis of colon cancer (CC) patients is not clear yet. Herein, based on TCGA database, we screened 15 endoplasmic reticulum stress responsive genes (ERSRGs) associated with the prognosis of CC patients by Cox regression. By LASSO and multivariate Cox regression analyses, a prognostic risk assessment model involving 12 genes (DNAJB2, EIF4A1, YPEL4, COQ10A, IRX3, ASPHD1, NTRK2, TRIM39, XBP1, GRIN2B, LRRC59, and RORC) was built. The survival curves indicated that patients in the low-risk group had good prognosis. ROC curves demonstrated a good performance of this 12-gene prognostic model, and the Riskscore could be considered as an independent prognostic factor. Patients in low-risk group benefit more from immune checkpoint inhibitor and immune checkpoint blockade (ICB) treatment. Besides, the enrichment analysis suggested a remarkable difference in Ca2+ signaling in both groups. Finally, based on the cMAP database, we identified several potential drugs that could target high-risk groups, such as Dasatinib, GNF-2, Saracatinib, and WZ-1-84. To sum up, our research constructed an ERSRGs-characteristic prognostic model. The model is a promising biomarker for prediction of clinical outcomes and immune therapy response of CC patients. SignificanceBased on the transcriptomic data of colon cancer in the TCGA database, this study screens 12 endoplasmic reticulum stress-related genes (ERSRGs), including DNAJB2, EIF4A1, YPEL4, COQ10A, IRX3, ASPHD1, NTRK2, TRIM39, XBP1, asphD1, NTRK2. GRIN2B, LRRC59, and RORC, and a prognostic model was constructed. This model can be used as a predictor of prognosis and immunotherapy response in colon cancer patients. At the same time, model-based prediction of drugs can also be a potential option for colon cancer treatment in the future.

Predictive Factors for Tissue Inflammation in Patients with Benign Prostatic Hyperplasia: A Clinical Prediction Study

Introduction: Benign prostatic hyperplasia (BPH) is a common condition in older men, marked by the noncancerous enlargement of the prostate gland. Inflammation of the prostate plays a significant role in the progression of BPH and the symptoms it causes. The objective of this study was to create a predictive model for prostatic inflammation in men with BPH based on important clinical factors. Methods: A retrospective cohort study was conducted with 137 patients diagnosed with BPH. Data collected included various factors such as age, prostate volume (PV), preoperative international prostate symptom score, preoperative maximum urine flow rate (Qmax), preoperative post-void residue, weight of the excised tissue, body mass index, fasting blood glucose (FBG), cholesterol levels, prostate-specific antigen, blood calcium, blood phosphorus, blood uric acid, triglycerides, hypertension status, and presence of prostate calcifications. Multivariate logistic regression and LASSO regression analyses were performed to identify significant predictors and develop a nomogram. The model’s performance was evaluated using receiver operating characteristic curves, calibration plots, and decision curve analysis (DCA). Results: Among the patients, 9.49% showed no signs of prostatic inflammation, while 22.63% had mild, 47.45% had moderate, and 20.44% had severe inflammation. Factors such as PV, FBG, and prostate calcification were identified as important predictors of prostatic inflammation. The predictive model developed exhibited strong discrimination and calibration, as evidenced by a high area under the curve value, indicating reliable predictive accuracy. DCA further validated the clinical usefulness of the nomogram. Conclusion: The developed nomogram, incorporating PV, FBG, and prostate calcification, effectively predicts prostatic inflammation in men with BPH. This tool can aid in early intervention and targeted treatment, potentially improving patient outcomes. Further validation in diverse populations is recommended to enhance its generalizability and clinical applicability.

A prognostic model constructed by ferroptosis-associated genes (FAGs) in papillary renal cell carcinoma (PRCC) and its association with tumor mutation burden (TMB) and immune infiltration.

This study aimed to identify the prognostic-related differentially expressed ferroptosis-associated genes (DEFAGs) in papillary renal cell carcinoma (PRCC). Data encompassing simple nucleotide variation, transcriptome profiles, and relevant clinical information of PRCC patients were sourced from The Cancer Genome Atlas (TCGA) database. The expression matrix of ferroptosis-associated genes (FAGs) was analyzed using the "limma" package in R to identify differentially expressed DEFAGs. Lasso regression analysis, along with univariate and multivariate Cox proportional hazards regressions, was employed to identify independent prognostic-related DEFAGs and formulate a nomogram. Additionally, we examined potential independent survival-related clinical risk factors and compared immune cell infiltration and tumor mutation burden (TMB) differences between high- and low-risk patient groups. A cohort of 321 patients were analyzed, revealing twelve FAGs significantly influencing the overall survival (OS) of PRCC patients. Among them, two mRNAs (GCLC, HSBP1) emerged as independent prognostic-related DEFAGs. Smoking status, tumor stage, and risk score were identified as independent clinical risk factors for PRCC. Furthermore, notable disparities in immune cell infiltration and function were observed between high- and low-risk groups. GCLC and HSBP1 were associated with various immune cells and functions, TMB, and immune evasion. This finding revealed two independent prognostic-related DEFAGs in PRCC and established a robust prognostic model, offering potential therapeutic targets and promising insights for the management of this disease.

Homotypic cell-in-cell structure as a novel prognostic predictor in non-small cell lung cancer and frequently localized at the invasive front

Homotypic cell-in-cell structures (hoCICs) are associated with tumor proliferation, invasion, and metastasis and is considered a promising prognostic marker in various cancers. However, the role of hoCICs in non-small cell lung cancer (NSCLC) remains unclear. Tumor tissue sections were obtained from 411 NSCLC patients. We analyzed the relationship between clinicopathological variables and the number of hoCICs. LASSO and multivariate Cox regression analysis were employed to identify prognostic factors for NSCLC. The impact of hoCICs on overall survival (OS) and disease-free survival (DFS) was assessed using the Kaplan–Meier curves and log-rank test. Prognostic models for OS and DFS were developed and validated using the C-index, time-dependent area under the curve (AUC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), calibration curves and decision curve analysis (DCA). Among the cohort, 56% of patients had hoCICs while 44% did not. Notably, hoCICs were primarily found at the tumor invasion front. Male gender, smoking, squamous cell carcinoma, low differentiation, tumor size ≥ 3 cm, advanced TNM stage, lymph node metastasis, pleural invasion, vascular invasion, necrosis, P53 mutation, and high expression of Ki-67 were identified as relative risk factors for hoCICs. Furthermore, hoCICs was found to be a significant prognostic factor for both OS and DFS, with higher frequencies of hoCICs correlating with poorer outcomes. We constructed nomograms for predicting 1-, 3-, and 5-year OS and DFS based on hoCICs, and the calibration curves showed good agreement between the predicted and actual outcomes. The results of the C-index, time-dependent AUC, NRI, IDI, and DCA analyses demonstrated that incorporating hoCICs into the prognostic model significantly enhanced its predictive power and clinical applicability. HoCICs indicated independent perdictive value for OS and DFS in patients with NSCLC. Furthermore, the frequent localization of hoCICs at the tumor invasion front suggested a strong association between hoCICs and tumor invasion as well as metastasis.

Exploration of the molecular characteristics and potential clinical significance of shared immune-related genes between preterm preeclampsia and term preeclampsia

BackgroundPreeclampsia is a severe obstetric disorder that significantly affects the maternal and neonatal peri-partum safety and long-term quality of life. However, there is limited research exploring the common mechanisms and potential clinical significance between early-onset preeclampsia and full-term preeclampsia from an immunological perspective.MethodsIn this study, data analysis was conducted. Initially, immune-related co-expressed genes involving both subtypes of preeclampsia were identified through Weighted Gene Co-expression Network Analysis (WGCNA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were further employed to investigate the shared pathways regulated by immune-related genes. Binary logistic regression identified co-expressed genes with diagnostic value for preeclampsia, and a diagnostic model was constructed. Gene Set Enrichment Analysis (GSEA) predicted the potential biological functions of the selected genes. Lasso and Cox regression analyses identified genes closely associated with gestational duration, and a risk score model was established. A 4-gene feature, immune-related gene model for predicting the risk of preterm birth in preeclamptic pregnant women, was developed and validated through qPCR experiments. Immune cell infiltration analysis determined differences in immune cell infiltration between the two subtypes of preeclampsia.ResultsThis study identified 4 immune-related co-expressed genes (CXCR6, PIK3CB, IL1RAP, and OSMR). Additionally, diagnostic and preterm birth risk prediction models for preeclampsia were constructed based on these genes. GSEA analysis suggested the involvement of these genes in the regulation of galactose metabolism, notch signaling pathway, and RIG-I like receptor signaling pathway. Immune pathway analysis indicated that the activation of T cell co-inhibition could be a potential intervention target for immunotherapy in early-onset preeclampsia.ConclusionOur study provides promising insights into immunotherapy and mechanistic research for preeclampsia, discovering novel diagnostic and intervention biomarkers, and offering personalized diagnostic tools for preeclampsia.

Prognostic value of four immune-related genes in lower-grade gliomas: a biomarker discovery study.

The tumor microenvironment and IRGs are highly correlated with tumor occurrence, progression, and prognosis. However, their roles in grade II and III gliomas, termed LGGs in this study, remain to be fully elucidated. Our research aims to develop immune-related features for risk stratification and prognosis prediction in LGG. Using the ssGSEA method, we assessed the immune characteristics of the LGG population. We conducted differential analysis using LGG samples from the TCGA database and normal samples from GTEx, identifying 412 differentially expressed immune-related genes (DEIRGs). Subsequently, we utilized univariate Cox, LASSO, and multivariate Cox regression analyses to establish both a gene predictive model and a nomogram predictive model. Here, we found that the ESTIMATE score, immune score and stromal score of high-immunity, high-grade and isocitrate dehydrogenase (IDH) wild-type glioma were higher than those of the corresponding group, and the tumor purity was lower. Higher ESTIMATE scores, stromal scores and immune scores indicated a poor prognosis in patients with LGG. Our four-gene prognostic model demonstrated superior accuracy compared to other molecular features. Validation using the CGGA as a testing set and the combined TCGA and CGGA cohort confirmed its robust prognostic value. Additionally, a nomogram integrating the prognostic model and clinical variables showed enhanced predictive capability. Our study highlights the prognostic significance of the identified four DEIRGs (KLRC3, MR1, PDIA2, and RFXAP) in LGG patients. The predictive model and nomogram developed herein offer valuable tools for personalized treatment strategies in LGG. Future research should focus on further validating these findings and exploring the functional roles of these DEIRGs within the LGG tumor microenvironment.

Identification of PANoptosis-relevant subgroups and predicting signature to evaluate the prognosis and immune landscape of patients with biliary tract cancer.

This study conducted molecular subtyping of biliary tract cancer patients based on 19 PANoptosis-related gene signatures. Through consensus clustering, patients were categorized into two subtypes, A and B. By integrating multi-omics data and clinical information from different cohorts, we elucidated the association between different subtypes of biliary tract cancer and patient prognosis, which correlated with the immune infiltration characteristics of patients. LASSO regression analysis was performed on the 19 gene signatures, and we constructed and validated a 9-gene risk score prognostic model that accurately predicts the overall survival rate of different biliary tract cancer patients. Additionally, we developed a predictive nomogram demonstrating the clinical utility and robustness of our model. Further analysis of the risk score-based immune landscape highlighted potential associations with immune cell infiltration, chemotherapy, and immune therapy response. Our study provides valuable insights into personalized treatment strategies for biliary tract cancer, which are crucial for improving patient prognosis and guiding treatment decisions in clinical practice.

MUL1 identified as mitochondria-linked biomarker promoting cisplatin resistance in OC cells

Ovarian cancer (OC) ranks among the prevalent tumors affecting the female reproductive system. The aim of this study was to evaluate mitochondria-associated platinum resistance genes using organoid models. Univariate Cox regression, LASSO and multivariate Cox regression analyses were performed on The Cancer Genome Atlas (TCGA) database to construct 2-gene prognostic signature (MUL1 and SSBP1), and GSE26712 dataset was used for external validation. In addition, the relationship between MUL1 and platinum resistance was examined by organoid culture, lentiviral transduction, CCK8 assay, and Western blot. The results showed that patients in the high-risk group exhibited significantly worse OS (P = 0.002, P = 0.017). Drug sensitivity analysis revealed that platinum resistance increased with the upregulation of MUL1 expression (Cor = 0.5154, P = 0.02). Our experimental findings demonstrated that knockout of the MUL1 gene significantly increased apoptosis and enhanced the sensitivity of the OC cell line A2780 to cisplatin. Through this study, we have provided strong evidence for further research on prognostic risk factors and individualized treatment in OC patients, and provided new insights into addressing platinum resistance in OC.

Bioinformatics analysis of the tumor microenvironment in melanoma - Constructing a prognostic model based on CD8+ T cell-related genes: An observational study.

This research endeavor seeks to explore the microenvironment of melanoma tumors and construct a prognostic model by focusing on genes specific to CD8+ T cells. The single-cell sequencing data of melanoma underwent processing with the Seurat package, subsequent to which cell communication network analysis was conducted using the iTALK package and transcription factor analysis was performed using the SCENIC package. Univariate COX and LASSO regression analyses were utilized to pinpoint genes linked to the prognosis of melanoma patients, culminating in the creation of a prognostic model through multivariate COX analysis. The model was validated using the GSE65904 and GSE35640 datasets. Multi-omics analysis was conducted utilizing the maftools, limma, edgeR, ChAMP, and clusterProfiler packages. The examination of single-cell sequencing data revealed the presence of 8 cell types, with the transcription factors RFXAP, CLOCK, MGA, RBBP, and ZNF836 exhibiting notably high expression levels in CD8+ T cells as determined by the SCENIC package. Utilizing these transcription factors and their associated target genes, a prognostic model was developed through COX and LASSO analyses, incorporating the genes GPR171, FAM174A, and BPI. This study validated the model with independent datasets and conducted additional analysis involving multi-omics and immune infiltration to identify a more favorable prognosis for patients in the low-risk group. The findings provide valuable insights into the tumor microenvironment of melanoma and establish a reliable prognostic model. The integration of multi-omics and immune infiltration analyses enhances our understanding of the pathogenesis of melanoma. The identification of specific genes holds promise as potential biomarkers for individuals with melanoma, serving as important indicators for predicting patient outcomes and determining their response to immunotherapy.

Natural killer cell-associated prognosis model characterizes immune landscape and treatment efficacy of diffuse large B cell lymphoma

PurposeNK cells are essential for the detection, identification and prediction of cancer. However, so far, there is no prognostic risk model based on NK cell-related genes to predict the prognosis and treatment outcome of DLBCL patients. This study aimed to explore a risk assessment model that could accurately predict the prognosis and treatment efficacy of DLBCL. MethodsBioinformatics analysis of the expression profiles of DLBCL samples in the GEO database was performed. Cox regression and LASSO regression analysis were used to determine NK cell-related genes associated with patient’s prognosis. Based on these genes, a risk assessment model was constructed to predict the prognosis of patients and the effectiveness of treatment. Finally, qRT-PCR was used to verify the expression of gene tags in clinical samples. ResultsWe identified seven prognosis-related NK cell-related genes (MAP2K1, PRKCB, TNFRSF10B, IL18, LAMP1, RASGRP1, and SP110), and DLBCL patients were divided into low- and high-risk groups based on these genes. Survival analysis showed that the prognosis of patients with low-risk group was better. Pathway enrichment analysis showed that the differentially expressed genes between the two risk groups were related to immune response pathways. Compared with the high-risk group, the low-risk group had higher infiltration of immune cells in tumor tissues. Besides, compared with high-risk group, low-risk patients by immunotherapy or other commonly used anti-tumor drugs might have better efficacy after treatment. In addition, qRT-PCR showed that the expression of risk genes including TNFRSF10B, IL18 and LAMP1 were significantly increased in most DLBCL samples compared to control samples, while the expression of protective genes including MAP2K1, PRKCB, RASGRP1 and SP110 were significantly decreased. ConclusionThe NK cell-related gene signatures were proved to be a reliable indicator of the success of immunotherapy in patients with DLBCL, thus providing a unique evaluation method.

A Comprehensive Analysis Exploring the Impact of an Immunogenic Cell Death-Related Panel for Ovarian Cancer.

Ovarian cancer (OV) is a malignant tumor that ranks first among gynecological cancers, thus posing a significant threat to women's health. Immunogenic cell death (ICD) can regulate cell death by activating the adaptive immune system. Here, we aimed to comprehensively characterize the features of ICD-associated genes in ovarian cancer, and to investigate their prognostic value and role in the response to immunotherapy. After analyzing datasets from The Cancer Genome Atlas, we utilized weighted gene coexpressionnetwork analysis to screen for hub genes strongly correlated with ICD genes in OV, which was subsequently validated with OV samples from the Gene Expression Omnibus (GEO) database. A prognostic risk model was then constructed after combining univariate, multivariate Cox regression and LASSO regression analysis to recognize nine ICD-associated molecules. Next, we stratified all OV patients into two subgroups according to the median value. The multivariate Cox regression analysis showed that the risk model could predict OV patient survival with good accuracy. The same results were also found in the validation set from GEO. We then compared the degree of immune cell infiltration in the tumor microenvironment between the two subgroups of OV patients, and revealed that the high-risk subtype had a higher degree of immune infiltration than the low-risk subtype. Additionally, in contrast to patients in the high-risk subgroup, those in the low-risk subgroup were more susceptible to chemotherapy. In conclusion, our research offers an independent and validated model concerning ICD-related molecules to estimate the prognosis, degree of immune infiltration, and chemotherapy susceptibility inpatients with OV.

Unveiling an anoikis-related risk model and the role of RAD9A in colon cancer

ObjectiveColorectal cancer (CRC), specifically colon adenocarcinoma, is the third most prevalent and the second most lethal form of cancer. Anoikis is found to be specialized form of programmed cell death (PCD), which plays a pivotal role in tumor progression. This study aimed to investigate the role of the anoikis related genes (ARGs) in colon cancer. MethodsConsensus unsupervised clustering, differential expression analysis, tumor mutational burden analysis, and analysis of immune cell infiltration were utilized in the study. For the analysis of RNA sequences and clinical data of COAD patients, data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) were obtained. A prognostic scoring system for overall survival (OS) prediction was developed using Cox regression and LASSO regression analysis. Furthermore, loss-of-function assay was utilized to explore the role of RAD9A played in the progression of colon cancer. ResultsThe prognostic value of a risk score composed of NTRK2, EPHA2, RAD9A, CDC25C, and SNAI1 genes was significant. Furthermore, these findings suggested potential mechanisms that may influence prognosis, supporting the development of individualized treatment plans and management of patient outcomes. Further experiments confirmed that RAD9A could promote proliferation and metastasis of colon cancer cells. These effects may be achieved by affecting the phosphorylation of AKT. ConclusionDifferences in survival time and the tumor immune microenvironment (TIME) were observed between two gene clusters associated with ARGs. In addition, a prognostic risk model was established and confirmed as an independent risk factor. Furthermore, our data indicated that RAD9A promoted tumorigenicityby activating AKT in colon cancer.