cox regression analysis
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2022 ◽  
Vol 12 ◽  
Ying Song ◽  
Shufang Tian ◽  
Ping Zhang ◽  
Nan Zhang ◽  
Yan Shen ◽  

Acute myeloid leukemia (AML) is a clonal malignant proliferative blood disorder with a poor prognosis. Ferroptosis, a novel form of programmed cell death, holds great promise for oncology treatment, and has been demonstrated to interfere with the development of various diseases. A range of genes are involved in regulating ferroptosis and can serve as markers of it. Nevertheless, the prognostic significance of these genes in AML remains poorly understood. Transcriptomic and clinical data for AML patients were acquired from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). Univariate Cox analysis was performed to identify ferroptosis-related genes with prognostic value, and the least absolute shrinkage and selection operator (LASSO) algorithm and stepwise multivariate Cox regression analysis were utilized to optimize gene selection from the TCGA cohort (132 samples) for model construction. Tumor samples from the GEO database (136 samples and 104 samples) were used as validation groups to estimate the predictive performance of the risk model. Finally, an eight-gene prognostic signature (including CHAC1, CISD1, DPP4, GPX4, AIFM2, SQLE, PGD, and ACSF2) was identified for the prediction of survival probability and was used to stratify AML patients into high- and low-risk groups. Survival analysis illustrated significantly prolonged overall survival and lower mortality in the low-risk group. The area under the receiver operating characteristic curve demonstrated good results for the training set (1-year: 0.846, 2-years: 0.826, and 3-years: 0.837), which verified the accuracy of the model for predicting patient survival. Independent prognostic analysis indicated that the model could be used as a prognostic factor (p ≤ 0.001). Functional enrichment analyses revealed underlying mechanisms and notable differences in the immune status of the two risk groups. In brief, we conducted and validated a novel ferroptosis-related prognostic model for outcome prediction and risk stratification in AML, with great potential to guide individualized treatment strategies in the future.

2022 ◽  
Vol 23 (1) ◽  
Dandan Guo ◽  
Huifang Wang ◽  
Jun Liu ◽  
Hang Liu ◽  
Ming Zhang ◽  

Abstract Background We aimed to develop and validate a nomogram model for predicting CKD after orthotopic liver transplantation (OLT). Methods The retrospective data of 399 patients who underwent transplantation and were followed in our centre were collected. They were randomly assigned to the training set (n = 293) and validation set (n = 106). Multivariable Cox regression analysis was performed in the training set to identify predictors of CKD. According to the Cox regression analysis results, a nomogram model was developed and validated. The renal function of recipients was monitored, and the long-term survival prognosis was assessed. Results The incidence of CKD at 5 years after OLT was 25.6%. Cox regression analysis identified several predictors of post-OLT CKD, including recipient age at surgery (HR 1.036, 95% CI 1.006-1.068; p = 0.018), female sex (HR 2.867, 95% CI 1.709-4.810; p < 0.001), preoperative hypertension (HR 1.670, 95% CI 0.962-2.898; p = 0.068), preoperative eGFR (HR 0.996, 95% CI 0.991-1.001; p = 0.143), uric acid at 3 months (HR 1.002, 95% CI 1.001-1.004; p = 0.028), haemoglobin at 3 months (HR 0.970, 95% CI 0.956-0.983; p < 0.001), and average concentration of cyclosporine A at 3 months (HR 1.002, 95% CI 1.001-1.003; p < 0.001). According to these parameters, a nomogram model for predicting CKD after OLT was constructed and validated. The C-indices were 0.75 and 0.80 in the training and validation sets. The calibration curve of the nomogram showed that the CKD probabilities predicted by the nomogram agreed with the observed probabilities at 1, 3, and 5 years after OLT (p > 0.05). Renal function declined slowly year by year, and there were significant differences between patients divided by these predictors. Kaplan-Meier survival analysis showed that the survival prognosis of recipients decreased significantly with the progression of renal function. Conclusions With excellent predictive abilities, the nomogram may be a simple and reliable tool to identify patients at high risk for CKD and poor long-term prognosis after OLT.

2022 ◽  
Vol 2022 ◽  
pp. 1-10
Tengfei Zhang ◽  
Yaxuan Wang ◽  
Yiming Dong ◽  
Lei Liu ◽  
Yikai Han ◽  

Prostate cancer is still a significant global health burden in the coming decade. Novel biomarkers for detection and prognosis are needed to improve the survival of distant and advanced stage prostate cancer patients. The tumor microenvironment is an important driving factor for tumor biological functions. To investigate RNA prognostic biomarkers for prostate cancer in the tumor microenvironment, we obtained relevant data from The Cancer Genome Atlas (TCGA) database. We used the bioinformatics tools Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) algorithm and weighted coexpression network analysis (WGCNA) to construct tumor microenvironment stromal-immune score-based competitive endogenous RNA (ceRNA) networks. Then, the Cox regression model was performed to screen RNAs associated with prostate cancer survival. The differentially expressed gene profile in tumor stroma was significantly enriched in microenvironment functions, like immune response, cancer-related pathways, and cell adhesion-related pathways. Based on these differentially expressed genes, we constructed three ceRNA networks with 152 RNAs associated with the prostate cancer tumor microenvironment. Cox regression analysis screened 31 RNAs as the potential prognostic biomarkers for prostate cancer. The most interesting 8 prognostic biomarkers for prostate cancer included lncRNA LINC01082, miRNA hsa-miR-133a-3p, and genes TTLL12, PTGDS, GAS6, CYP27A1, PKP3, and ZG16B. In this systematic study for ceRNA networks in the tumor environment, we screened out potential biomarkers to predict prognosis for prostate cancer. Our findings might apply a valuable tool to improve prostate cancer clinical management and the new target for mechanism study and therapy.

Cancers ◽  
2022 ◽  
Vol 14 (2) ◽  
pp. 432
Joohyun Woo ◽  
Hyungju Kwon

Multifocality increases the risk of recurrence in patients with papillary thyroid carcinoma (PTC); however, it is unclear whether multifocality justifies more extensive or aggressive surgical treatment. Here, we evaluated the effect of the operative extent on the recurrence-free survival (RFS) of patients with multifocal PTC. Between 2010 and 2019, 718 patients with unilateral multifocal PTC were enrolled; 115 patients (16.0%) underwent ipsilateral thyroid lobectomy, and 606 patients (84.0%) underwent total thyroidectomy. With a mean follow up of 5.2 years, RFS was comparable between the total thyroidectomy and lobectomy groups (p = 0.647) after adjusting for potential confounders. Multivariable Cox regression analysis also demonstrated that the operative extent was not an independent predictor of recurrence (HR 1.686, 95% CI: 0.321–8.852). Subgroup analyses further indicated that both total thyroidectomy and thyroid lobectomy resulted in comparable RFS for multifocal PTC patients with other high-risk factors, including tumor size > 1 cm (p = 0.711), lymph node metastasis (p = 0.536), and intermediate ATA risk of recurrence (p = 0.682). In conclusion, thyroid lobectomy was not associated with the risk of recurrence in patients with multifocal PTCs. Multifocality in PTC may not always require aggressive surgery.

2022 ◽  
Vol 15 (1) ◽  
Yao Peng ◽  
Hui Wang ◽  
Qi Huang ◽  
Jingjing Wu ◽  
Mingjun Zhang

Abstract Background Long noncoding RNAs (lncRNAs) are important regulators of gene expression and can affect a variety of physiological processes. Recent studies have shown that immune-related lncRNAs play an important role in the tumour immune microenvironment and may have potential application value in the treatment and prognosis prediction of tumour patients. Epithelial ovarian cancer (EOC) is characterized by a high incidence and poor prognosis. However, there are few studies on immune-related lncRNAs in EOC. In this study, we focused on immune-related lncRNAs associated with survival in EOC. Methods We downloaded mRNA data for EOC patients from The Cancer Genome Atlas (TCGA) database and mRNA data for normal ovarian tissue from the Genotype-Tissue Expression (GTEx) database and identified differentially expressed genes through differential expression analysis. Immune-related lncRNAs were obtained through intersection and coexpression analysis of differential genes and immune-related genes from the Immunology Database and Analysis Portal (ImmPort). Samples in the TCGA EOC cohort were randomly divided into a training set, validation set and combination set. In the training set, Cox regression analysis and LASSO regression were performed to construct an immune-related lncRNA signature. Kaplan–Meier survival analysis, time-dependent ROC curve analysis, Cox regression analysis and principal component analysis were performed for verification in the training set, validation set and combination set. Further studies of pathways and immune cell infiltration were conducted through Gene Set Enrichment Analysis (GSEA) and the Timer data portal. Results An immune-related lncRNA signature was identified in EOC, which was composed of six immune-related lncRNAs (KRT7-AS, USP30-AS1, AC011445.1, AP005205.2, DNM3OS and AC027348.1). The signature was used to divide patients into high-risk and low-risk groups. The overall survival of the high-risk group was lower than that of the low-risk group and was verified to be robust in both the validation set and the combination set. The signature was confirmed to be an independent prognostic biomarker. Principal component analysis showed the different distribution patterns of high-risk and low-risk groups. This signature may be related to immune cell infiltration (mainly macrophages) and differential expression of immune checkpoint-related molecules (PD-1, PDL1, etc.). Conclusions We identified and established a prognostic signature of immune-related lncRNAs in EOC, which will be of great value in predicting the prognosis of clinical patients and may provide a new perspective for immunological research and individualized treatment in EOC.

2022 ◽  
Vol 11 ◽  
Qian He ◽  
Wei Zhao ◽  
Qinglan Ren

BackgroundStudies confirmed the predictive value of the prognostic nutrition index (PNI) in many malignant tumors. However, it did not reach a consensus in glioma. Therefore, this study investigated the prognostic value of preoperative PNI in operable high-grade glioma and established a nomogram.MethodsClinical data of high-grade glioma patients were retrospectively analyzed. The primary endpoint was overall survival (OS). Survival analysis was conducted by the Kaplan–Meier method, log-rank test, and Cox regression analysis. A nomogram was established. The prediction effect of the nomogram covering PNI was verified by area under the curve (AUC).ResultsA total of 91 operable high-grade glioma patients were included. Kaplan–Meier analysis showed that among grade IV gliomas (n = 55), patients with higher PNI (&gt;44) showed a trend of OS benefit (p = 0.138). In grade III glioma (n = 36), patients with higher PNI (&gt;47) had longer OS (p = 0.023). However, the intersecting Kaplan–Meier curve suggested that there may be some confounding factors. Cox regression analysis showed that higher PNI was an independent prognostic factor for grade IV glioma (HR = 0.388, p = 0.040). In grade III glioma, there was no statistically relationship between PNI levels and prognosis. When evaluating the prognostic ability of PNI alone by ROC, the AUC in grade III and IV gliomas was low, indicating that PNI alone had poor predictive power for OS. Interestingly, we found that the nomogram including preoperative PNI, age, extent of resection, number of gliomas, and MGMT methylation status could predict the prognosis of patients with grade IV glioma well.ConclusionThe PNI level before surgery was an independent prognostic factor for patients with grade IV glioma. The nomogram covering PNI in patients with grade IV glioma also proved the value of PNI. However, the value of PNI in grade III glioma needs to be further evaluated. More prospective studies are needed to verify this conclusion.

2022 ◽  
Nayan Lamba ◽  
Malia McAvoy ◽  
Vasileios K Kavouridis ◽  
Timothy R Smith ◽  
Mehdi Touat ◽  

Abstract Background The optimal chemotherapy regimen between temozolomide and procarbazine, lomustine, and vincristine (PCV) remains uncertain for W.H.O. grade 3 oligodendroglioma (Olig3) patients. We therefore investigated this question using national data. Methods Patients diagnosed with radiotherapy-treated 1p/19q-codeleted Olig3 between 2010-2018 were identified from the National Cancer Database. The OS associated with first-line single-agent temozolomide vs. multi-agent PCV was estimated by Kaplan-Meier techniques and evaluated by multivariable Cox regression. Results 1,596 radiotherapy-treated 1p/19q-codeleted Olig3 patients were identified: 88.6% (n=1,414) treated with temozolomide and 11.4% (n=182) with PCV (from 5.4% in 2010 to 12.0% in 2018) in the first-line setting. The median follow-up was 35.5 months (interquartile range [IQR] 20.7-60.6 months) with 63.3% of patients alive at time of analysis. There was a significant difference in unadjusted OS between temozolomide (5yr-OS 58.9%, 95%CI: 55.6-62.0) and PCV (5yr-OS 65.1%, 95%CI: 54.8-73.5; p=0.04). However, a significant OS difference between temozolomide and PCV was not observed in the Cox regression analysis adjusted by age and extent of resection (PCV vs. temozolomide HR 0.81, 95%CI: 0.59-1.11, p=0.18). PCV was more frequently used for younger Olig3s, but otherwise was not associated with patient’s insurance status or care setting. Conclusions In a national analysis of Olig3s, first-line PCV chemotherapy was associated with a slightly improved unadjusted short-term OS compared to temozolomide; but not following adjustment by patient age and extent of resection. There has been an increase in PCV utilization since 2010. These findings provide preliminary data while we await the definitive results from the CODEL trial.

2022 ◽  
Vol 12 ◽  
Shaodi Wen ◽  
Yuzhong Chen ◽  
Chupeng Hu ◽  
Xiaoyue Du ◽  
Jingwei Xia ◽  

BackgroundHepatocellular carcinoma (HCC) is the most common pathological type of primary liver cancer. The lack of prognosis indicators is one of the challenges in HCC. In this study, we investigated the combination of tertiary lymphoid structure (TLS) and several systemic inflammation parameters as a prognosis indicator for HCC.Materials and MethodsWe retrospectively recruited 126 postoperative patients with primary HCC. The paraffin section was collected for TLS density assessment. In addition, we collected the systemic inflammation parameters from peripheral blood samples. We evaluated the prognostic values of those parameters on overall survival (OS) using Kaplan-Meier curves, univariate and multivariate Cox regression. Last, we plotted a nomogram to predict the survival of HCC patients.ResultsWe first found TLS density was positively correlated with HCC patients’ survival (HR=0.16, 95% CI: 0.06 − 0.39, p &lt; 0.0001), but the power of TLS density for survival prediction was found to be limited (AUC=0.776, 95% CI:0.772 − 0.806). Thus, we further introduced several systemic inflammation parameters for survival analysis, we found neutrophil-to-lymphocyte ratio (NLR) was positively associated with OS in univariate Cox regression analysis. However, the combination of TLS density and NLR better predicts patient’s survival (AUC=0.800, 95% CI: 0.698-0.902, p &lt; 0.001) compared with using any single indicator alone. Last, we incorporated TLS density, NLR, and other parameters into the nomogram to provide a reproducible approach for survival prediction in HCC clinical practice.ConclusionThe combination of TLS density and NLR was shown to be a good predictor of HCC patient survival. It also provides a novel direction for the evaluation of immunotherapies in HCC.

2022 ◽  
Thongher Lia ◽  
Yanxiang Shao ◽  
Parbatraj Regmi ◽  
Xiang Li

Bladder cancer is one of the highly heterogeneous disorders accompanied by a poor prognosis. This study aimed to construct a model based on pyroptosis‑related lncRNA to evaluate the potential prognostic application in bladder cancer. The mRNA expression profiles of bladder cancer patients and corresponding clinical data were downloaded from the public database from The Cancer Genome Atlas (TCGA). Pyroptosis‑related lncRNAs were identified by utilizing a co-expression network of Pyroptosis‑related genes and lncRNAs. The lncRNA was further screened by univariate Cox regression analysis. Finally, 8 pyroptosis-related lncRNA markers were established using Lasso regression and multivariate Cox regression analysis. Patients were separated into high and low-risk groups based on the performance value of the median risk score. Patients in the high-risk group had significantly poorer overall survival (OS) than those in the low-risk group (p &lt; 0.001), and In multivariate Cox regression analysis, the risk score was an independent predictive factor of OS ( HR&gt;1, P&lt;0.01). The area under the curve (AUC) of the 3- and 5-year OS in the receiver operating characteristic (ROC) curve were 0.742 and 0.739 respectively. In conclusion, these 8 pyroptosis-related lncRNA and their markers may be potential molecular markers and therapeutic targets for bladder cancer patients.

2022 ◽  
Vol 2022 ◽  
pp. 1-13
Jianjun Li ◽  
Hongbo Zhu ◽  
Qiao Yang ◽  
Hua Xiao ◽  
Haibiao Wu ◽  

Background. Esophagus cancer (ESCA) is the sixth most frequent cancer in males, with 5-year overall survival of 15%–25%. RNA modifications function critically in cancer progression, and m6A regulators are associated with ESCA prognosis. This study further revealed correlations between m6A and ESCA development. Methods. Univariate Cox regression analysis and consensus clustering were applied to determine molecular subtypes. Functional pathways and gene ontology terms were enriched by gene set enrichment analysis. Protein-protein interaction (PPI) analysis on differentially expressed genes (DEGs) was conducted for hub gene screening. Public drug databases were employed to study the interactions between hub genes and small molecules. Results. Three molecular subtypes related to ESCA prognosis were determined. Based on multiple analyses among molecular subtypes, 146 DEGs were screened, and a PPT network of 15 hub genes was visualized. Finally, 8 potential small-molecule drugs (BMS-754807, gefitinib, neratinib, zuclopenthixol, puromycin, sulfasalazine, and imatinib) were identified for treating ESCA. Conclusions. This study applied a new approach to analyzing the relation between m6A and ESCA prognosis, providing a reference for exploring potential targets and drugs for ESCA treatment.

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