A nomogram-based immune-serum scoring system predicts overall survival in patients with lung adenocarcinoma

Huang, Qun; Qu, Tongyuan; Qi, Lisha; Liu, Changxu; Guo, Yuhong; Guo, Qian; Li, Guangning; Wang, Yalei; Zhang, Wenshuai; Zhao, Wei; Ren, Danyang; Sun, Leina; Wang, Shengguang; Meng, Bin; Sun, Bei; Zhang, Bin; Ma, Wenjuan; Cao, Weijun

doi:10.20892/j.issn.2095-3941.2020.0648

Cited by 10 publications

(6 citation statements)

References 32 publications

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“…6 P–R). Interestingly, in contrast to our findings, these three scoring modalities have been shown to be predictive of prognosis in lung cancer patients [ 22 – 24 ]. These differences could be potentially caused by a low number of cases in each subgroup in our current study as well as our more specific classification of the different pathological types, which may reflect the state of the different tumor microenvironments.…”

Section: Discussioncontrasting

confidence: 99%

Four differentially expressed genes can predict prognosis and microenvironment immune infiltration in lung cancer: a study based on data from the GEO

et al. 2022

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Background Lung cancer is among the major diseases threatening human health. Although the immune response plays an important role in tumor development, its exact mechanisms are unclear. Materials and methods Here, we used CIBERSORT and ESTIMATE algorithms to determine the proportion of tumor-infiltrating immune cells (TICs) as well as the number of immune and mesenchymal components from the data of 474 lung cancer patients from the Gene Expression Omnibus database. And we used data from The Cancer Genome Atlas database (TCGA) for validation. Results We observed that immune, stromal, and assessment scores were only somewhat related to survival with no statistically significant differences. Further investigations revealed these scores to be associated with different pathology types. GO and KEGG analyses of differentially expressed genes revealed that they were strongly associated with immunity in lung cancer. In order to determine whether the signaling pathways identified by GO and KEGG signaling pathway enrichment analyses were up- or down-regulated, we performed a gene set enrichment analysis using the entire matrix of differentially expressed genes. We found that signaling pathways involved in hallmark allograft rejection, hallmark apical junction, hallmark interferon gamma response, the hallmark P53 pathway, and the hallmark TNF-α signaling via NF-ĸB were up-regulated in the high-ESTIMATE-score group. CIBERSORT analysis for the proportion of TICs revealed that different immune cells were positively correlated with the ESTIMATE score. Cox regression analysis of the differentially expressed genes revealed that CPA3, C15orf48, FCGR1B, and GNG4 were associated with patient prognosis. A prognostic model was constructed wherein patients with high-risk scores had a worse prognosis (p < 0.001 using the log-rank test). The Area Under Curve (AUC)value for the risk model in predicting the survival was 0.666. The validation set C index was 0.631 (95% CI: 0.580–0.652). The AUC for the risk formula in the validation set was 0.560 that confirmed predictivity of the signature. Conclusion We found that immune-related gene expression models could predict patient prognosis. Moreover, high- and low-ESTIMATE-score groups had different types of immune cell infiltration.

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Section: Discussioncontrasting

confidence: 99%

Four differentially expressed genes can predict prognosis and microenvironment immune infiltration in lung cancer: a study based on data from the GEO

et al. 2022

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“…High-throughput genomic studies provided cutting-edge sights into the molecular mechanisms and identi ed new potential targets of breast cancer [33][34][35][36]. Nevertheless, using a single biomarker for predicting often relatively lower predictive effects, and a risk model for predicting the OS and DRFS of patients [37][38][39][40][41][42][43] and a nomogram were popular which showed superior predictive effects as a better alternative [18, [44][45][46]. Meanwhile E2F related genes had been selected to build a model for predict the DRFS of breast cancer patients [47].…”

Section: Discussionmentioning

confidence: 99%

The Target E2F Family Gene PTTG1 for Prediction of Distant Relapse-Free Survival in Breast Cancer Patients Receiving Taxane and Anthracycline-Based NACT

Zheng

et al. 2021

Preprint

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Background: The breast cancer is the most commonly diagnosed cancer in recent years. The use of neoadjuvant chemotherapy (NACT) makes a significant contribution to chemotherapy in breast cancer. We aimed to develop the novel model as a predictor of distant relapse-free survival (DRFS) in breast cancer patients receiving taxane and anthracycline-based NACT.Methods: We collected the mRNA expression datasets of patients from GSE25055 and GSE25065 in Gene Expression Omnibus (GEO). Univariate and Multivariate Cox Regression Analyses were conducted to achieve the prognostic genes that associated with DRFS. Moreover, the E2F target genes were obtained from GSEA. We obtained the intersection genes between the prognostic genes and E2F target genes, then validated in GSE32603 dataset. And we established a nomogram model based on PTTG1 expression level and several clinical characteristics.Results: There were 95 genes confirmed to be associated with DRFS both in GSE25055 and GSE25065. And PTTG1 was validated as a gene related to DRFS and associated with E2F target hallmark using GSEA analysis and chosen as our target gene. The AUC of PTTG1 model was 0.682 indicating a relatively poor prediction performance. Then a novel nomogram was conducted, the receiver operating characteristic (AUC=0.849), C-index (0.805) and calibration plots were applied to assess the effect of this model.Conclusion: Our study found that the E2F target genes, such as the PTTG1 may serve as a potential biomarker in breast cancer, and provided superior estimation of DRFS, which can guide the clinical practice in NACT of breast cancer.

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“…Number at risk: n (%) Grade 9837 (79) 12427 ( 100) 5195 ( 42) 7296 (59) 2517 ( 20) 3679 (30) 805 ( 6) 1566 (13) 203 (2) GradeII 7375 ( 58) 12681 ( 100) 3163 ( 25) 4760 (38) 1419 ( 11) 2125 (17) 441 ( 3) 870 (7) 109 (1) GradeIII 177 ( 61) 288 ( 100) 77 ( 27) 114 (40) 34 ( 12) 52 (18) 13 ( 5) 23 ( 8 38) 9586 (53) 3345 ( 19) 4838 (27) 1090 ( 6) 2086 (12) 272 (2) Upper lobe 1194 (75) 1589 ( 100) 623 ( 39) 874 (55) 295 ( 19) 427 (27) 76 ( 5) 172 (11) 27 (2) Middle lobe 6915 (74) 9336 ( 100) 3552 ( 38) 4982 (53) 1699 ( 18) 2502 ( 27) 560 ( 6) 1070 (11) 137 (1) Lower lobe 184 ( 60) 309 ( 100) 94 ( 30) 133 (43) 46 (15) 69 …”

Section: Gradeiunclassified

“…Te majority of previous nomograms in LUAD have focused on factors such as lesion size, lymph node metastasis, histological types, treatment factors, pathological stage, lymph node invasion, and age [17][18][19]. Nonetheless, there is a lack of comprehensive and accurate nomograms for predicting the survival of LUAD patients.…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of a Prognostic Nomogram for Lung Adenocarcinoma: A Population-Based Study

Xie

Chen

Deng

et al. 2022

Journal of Healthcare Engineering

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Purpose. To establish an effective and accurate prognostic nomogram for lung adenocarcinoma (LUAD). Patients and Methods. 62,355 LUAD patients from 1975 to 2016 enrolled in the Surveillance, Epidemiology, and End Results (SEER) database were randomly and equally divided into the training cohort (n = 31,179) and the validation cohort (n = 31,176). Univariate and multivariate Cox regression analyses screened the predictive effects of each variable on survival. The concordance index (C-index), calibration curves, receiver operating characteristic (ROC) curve, and area under the ROC curve (AUC) were used to examine and validate the predictive accuracy of the nomogram. Kaplan–Meier curves were used to estimate overall survival (OS). Results. 10 prognostic factors associated with OS were identified, including age, sex, race, marital status, American Joint Committee on Cancer (AJCC) TNM stage, tumor size, grade, and primary site. A nomogram was established based on these results. C-indexes of the nomogram model reached 0.777 (95% confidence interval (CI), 0.773 to 0.781) and 0.779 (95% CI, 0.775 to 0.783) in the training and validation cohorts, respectively. The calibration curves were well-fitted for both cohorts. The AUC for the 3- and 5-year OS presented great prognostic accuracy in the training cohort (AUC = 0.832 and 0.827, respectively) and validation cohort (AUC = 0.835 and 0.828, respectively). The Kaplan–Meier curves presented significant differences in OS among the groups. Conclusion. The nomogram allows accurate and comprehensive prognostic prediction for patients with LUAD.

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A nomogram-based immune-serum scoring system predicts overall survival in patients with lung adenocarcinoma

Cited by 10 publications

References 32 publications

Four differentially expressed genes can predict prognosis and microenvironment immune infiltration in lung cancer: a study based on data from the GEO

Four differentially expressed genes can predict prognosis and microenvironment immune infiltration in lung cancer: a study based on data from the GEO

The Target E2F Family Gene PTTG1 for Prediction of Distant Relapse-Free Survival in Breast Cancer Patients Receiving Taxane and Anthracycline-Based NACT

Development and Validation of a Prognostic Nomogram for Lung Adenocarcinoma: A Population-Based Study

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