scholarly journals Development and validation of a prognostic model for non-lung cancer death in elderly patients treated with stereotactic body radiotherapy for non-small cell lung cancer

2021 ◽  
Author(s):  
Hideki Hanazawa ◽  
Yukinori Matsuo ◽  
Atsuya Takeda ◽  
Yuichiro Tsurugai ◽  
Yusuke Iizuka ◽  
...  
Keyword(s):  
Lung Cancer ◽  
High Risk ◽  
Elderly Patients ◽  
Prognostic Model ◽  
Risk Groups ◽  
Small Cell ◽  
Tumor Diameter ◽  
Cancer Death ◽  
Small Cell Lung ◽  
Lung Cancer Death

Abstract This study sought to develop and validate a prognostic model for non-lung cancer death (NLCD) in elderly patients with non-small cell lung cancer (NSCLC) treated with stereotactic body radiotherapy (SBRT). Patients aged ≥65 diagnosed with NSCLC (Tis-4N0M0), tumor diameter ≤5 cm and SBRT between 1998 and 2015 were retrospectively registered from two independent institutions. One institution was used for model development (arm D, 353 patients) and the other for validation (arm V, 401 patients). To identify risk factors for NLCD, multiple regression analysis on age, sex, performance status (PS), body mass index (BMI), Charlson comorbidity index (CCI), tumor diameter, histology and T-stage was performed on arm D. A score calculated using the regression coefficient was assigned to each factor and three risk groups were defined based on total score. Scores of 1.0 (BMI ≤18.4), 1.5 (age ≥ 5), 1.5 (PS ≥2), 2.5 (CCI 1 or 2) and 3 (CCI ≥3) were assigned, and risk groups were designated as low (total ≤ 3), intermediate (3.5 or 4) and high (≥4.5). The cumulative incidences of NLCD at 5 years in the low, intermediate and high-risk groups were 6.8, 23 and 40% in arm D, and 23, 19 and 44% in arm V, respectively. The AUC index at 5 years was 0.705 (arm D) and 0.632 (arm V). The proposed scoring system showed usefulness in predicting a high risk of NLCD in elderly patients treated with SBRT for NSCLC.

scholarly journals Construction of a prognostic model for non-small-cell lung cancer based on ferroptosis-related genes

2021 ◽  
Author(s):  
Ke Han ◽  
Ju Kun Kun Wang ◽  
Kun Qian ◽  
Teng Zhao ◽  
Xing Sheng Liu ◽  
...  
Keyword(s):  
Lung Cancer ◽  
High Risk ◽  
Small Cell Lung Cancer ◽  
Risk Score ◽  
Prognostic Model ◽  
Risk Groups ◽  
Small Cell ◽  
Low Risk ◽  
Small Cell Lung ◽  
Score Model

We wished to construct a prognostic model based on ferroptosis-related genes and to simultaneously evaluate the performance of the prognostic model and analyze differences between high-risk and low-risk groups at all levels. The gene-expression profiles and relevant clinical data of patients with non-small-cell lung cancer (NSCLC) were downloaded from public databases. Differentially expressed genes (DEGs) were obtained by analyzing differences between cancer tissues and paracancerous tissues, and common genes between DEGs and ferroptosis-related genes were identified as candidate ferroptosis-related genes. Next, a risk-score model was constructed using univariate Cox analysis and least absolute shrinkage and selection operator (Lasso) analysis. According to the median risk score, samples were divided into high-risk and low-risk groups, and a series of bioinformatics analyses were conducted to verify the predictive ability of the model. Single-sample gene set enrichment analysis (ssGSEA) was used to investigate differences in immune status between high-risk and low-risk groups, and differences in gene mutations between the two groups were investigated. A risk-score model was constructed based on 21 ferroptosis-related genes. A Kaplan–Meier curve and receiver operating characteristic curve showed that the model had good prediction ability. Univariate and multivariate Cox analyses revealed that ferroptosis-related genes associated with the prognosis may be used as independent prognostic factors for the overall survival time of NSCLC patients. The pathways enriched with DEGs in low-risk and high-risk groups were analyzed, and the enriched pathways were correlated significantly with immunosuppressive status.

scholarly journals F-080SUBLOBAR RESECTIONS VERSUS LOBECTOMY FOR STAGE I NON-SMALL-CELL LUNG CANCER: AN APPROPRIATE CHOICE IN HIGH-RISK ELDERLY PATIENTS?

2015 ◽  
Vol 21 (suppl_1) ◽  
pp. S23-S23
Author(s):  
Alfonso Fiorelli ◽  
F.P. Caronia ◽  
N. Daddi ◽  
D. Loizzi ◽  
L. Ampollini ◽  
...  
Keyword(s):  
Lung Cancer ◽  
High Risk ◽  
Elderly Patients ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Stage I ◽  
Small Cell Lung

Construction of a Prognostic Signature of Autophagy-Related lncRNAs in Non-Small Cell Lung Cancer

2021 ◽  
Author(s):  
Xinyang Zhang ◽  
Yu Cao ◽  
Li Chen
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Value At Risk ◽  
Prognostic Model ◽  
Risk Groups ◽  
Small Cell ◽  
Low Risk ◽  
Prognostic Signature ◽  
Small Cell Lung

Abstract Background: Autophagy inhibits tumorigenesis by limiting inflammation, LncRNA regulates gene expression levels in the form of RNA at various levels, so both of them are closely related to the occurrence and development of tumors.Methods: 232 autophagy-related genes were used to construct a co-expression network to extract autophagy-related lncRNAs. A prognostic signature was constructed by multivariate regression analysis. Kyoto Encyclopedia of Genes and Genomes (KEGG) was applied to analyze pathway enrichment in cancer pathways. Immunoinfiltration analysis was used to analyze the relationship between the prognostic model and the tumor.Results: Nine autophagy-related lncRNAs were used to construct a prognostic model for non-small cell lung cancer. The median value of the value at risk was used to distinguish between the high and low risk groups, and the low-risk group had better survival. Because the KEGG pathway analysis showed that the prognostic model was enriched in some immune pathways, further exploration of immune infiltration was conducted and it was found that the prognostic model did play a unique role in the immune microenvironment. And the prognostic model was associated with clinical factors.Conclusion: The prognostic model of autophagy-related lncRNAs constructed by us can predict the prognosis of non-small cell lung cancer.

scholarly journals Tumor Necrosis Factor Family Member Profile Predicts Prognosis and Adjuvant Chemotherapy Benefit for Patients With Small-Cell Lung Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhihui Zhang ◽  
Peng Wu ◽  
Chaoqi Zhang ◽  
Yuejun Luo ◽  
Guochao Zhang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
High Risk ◽  
Tumor Necrosis Factor ◽  
Tumor Necrosis ◽  
Family Members ◽  
Risk Groups ◽  
Small Cell ◽  
Small Cell Lung ◽  
Family Based ◽  
Necrosis Factor

Tumor necrosis factor (TNF) family members participate in the body’s antitumor immunity response and influence tumor prognosis and treatment response. However, little is known about the roles of TNF family members in small cell lung cancer (SCLC). Therefore, we conducted the first comprehensive investigation of TNF family members in patients with SCLC, with the goal of using them to predict prognosis and chemotherapy benefit. Abnormal genetic alterations and expression of TNF family members were found to be widespread in SCLC patients. Using LASSO Cox regression analysis, we constructed a TNF family-based signature that separated SCLC patients in the training set (n=77) into high- and low-risk groups with distinct survival and chemotherapy benefit, and the signature was well-validated in the validation set (n=137) by RT-qPCR. Importantly, the signature exhibited superior predictive performance and was identified as a novel independent prognostic factor. Additionally, different immune phenotypes were found between the low-risk and high-risk groups, and high-risk patients had higher CMTM6 expression, suggesting that these patients could benefit from therapeutic methods targeting CMTM6. We constructed the first clinically applicable TNF family-based signature for predicting prognosis and chemotherapy benefit for patients with SCLC. The findings reported here provide a new method for predicting the prognosis of SCLC patients and optimizing clinical management.

scholarly journals A prognostic model for elderly patients with squamous non-small cell lung cancer: a population-based study

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Siying Chen ◽  
Chunxia Gao ◽  
Qian Du ◽  
Lina Tang ◽  
Haisheng You ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Elderly Patients ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Prognostic Model ◽  
Small Cell ◽  
The Novel ◽  
Seer Database ◽  
Small Cell Lung ◽  
Ajcc Stage

Abstract Background Squamous cell carcinoma (SCC) is a main pathological type of non-small cell lung cancer. It is common among elderly patients with poor prognosis. We aimed to establish an accurate nomogram to predict survival for elderly patients (≥ 60 years old) with SCC based on the Surveillance, Epidemiology, and End Results (SEER) database. Methods The gerontal patients diagnosed with SCC from 2010 to 2015 were collected from the Surveillance, Epidemiology, and End Results (SEER) database. The independent prognostic factors were identified using multivariate Cox proportional hazards regression analysis, which were utilized to conduct a nomogram for predicting survival. The novel nomogram was evaluated by Concordance index (C-index), calibration curves, net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA). Results 32,474 elderly SCC patients were included in the analysis, who were randomly assigned to training cohort (n = 22,732) and validation cohort (n = 9742). The following factors were contained in the final prognostic model: age, sex, race, marital status, tumor site, AJCC stage, surgery, radiation and chemotherapy. Compared to AJCC stage, the novel nomogram exhibited better performance: C-index (training group: 0.789 vs. 0.730, validation group: 0.791 vs. 0.733), the areas under the receiver operating characteristic curve of the training set (1-year AUC: 0.846 vs. 0.791, 3-year AUC: 0.860 vs. 0.801, 5-year AUC: 0.859 vs. 0.794) and the validation set (1-year AUC: 0.846 vs. 0.793, 3-year AUC: 0.863 vs. 0.806, 5-year AUC: 0.866 vs. 0.801), and the 1-, 3- and 5-year calibration plots. Additionally, the NRI and IDI and 1-, 3- and 5-year DCA curves all confirmed that the nomogram was a great prognosis tool. Conclusions We constructed a novel nomogram that could be practical and helpful for precise evaluation of elderly SCC patient prognosis, thus helping clinicians in determining the appropriate therapy strategies for individual SCC patients.

scholarly journals A Nomogram to Predict Long-Term Survival Outcomes of Patients Who Undergo Pneumonectomy for Non-small Cell Lung Cancer With Stage I-IIIB

2021 ◽  
Vol 8 ◽  
Author(s):  
Lei-Lei Wu ◽  
Wu-Tao Chen ◽  
Xuan Liu ◽  
Wen-Mei Jiang ◽  
Yang-Yu Huang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
High Risk ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Risk Groups ◽  
Small Cell ◽  
Low Risk ◽  
Small Cell Lung ◽  
Risk Patients ◽  
Nsclc Patients

Background: In this study, we aim to establish a nomogram to predict the prognosis of non-small cell lung cancer (NSCLC) patients with stage I-IIIB disease after pneumonectomy.Methods: Patients selected from the Surveillance, Epidemiology, and End Results (SEER, N = 2,373) database were divided into two cohorts, namely a training cohort (SEER-T, N = 1,196) and an internal validation cohort (SEER-V, N = 1,177). Two cohorts were dichotomized into low- and high-risk subgroups by the optimal risk prognostic score (PS). The model was validated by indices of concordance (C-index) and calibration plots. Kaplan-Meier analysis and the log-rank tests were used to compare survival curves between the groups. The primary observational endpoint was cancer-specific survival (CSS).Results: The nomogram comprised six factors as independent prognostic indictors; it significantly distinguished between low- and high-risk groups (all P < 0.05). The unadjusted 5-year CSS rates of high-risk and low-risk groups were 33 and 60% (SEER-T), 34 and 55% (SEER-V), respectively; the C-index of this nomogram in predicting CSS was higher than that in the 8th TNM staging system (SEER-T, 0.629 vs. 0.584, P < 0.001; SEER-V, 0.609 vs. 0.576, P < 0.001). In addition, the PS might be a significant negative indictor on CSS of patients with white patients [unadjusted hazard ration (HR) 1.008, P < 0.001], black patients (unadjusted HR 1.007, P < 0.001), and Asian or Pacific Islander (unadjusted HR 1.008, P = 0.008). In cases with squamous cell carcinoma (unadjusted HR 1.008, P < 0.001) or adenocarcinoma (unadjusted HR 1.008, P < 0.001), PS also might be a significant risk factor.Conclusions: For post-pneumonectomy NSCLC patients, the nomogram may predict their survival with acceptable accuracy and further distinguish high-risk patients from low-risk patients.

scholarly journals Young age increases risk for lymph node positivity but decreases risk for non-small cell lung cancer death

2018 ◽  
Vol Volume 10 ◽  
pp. 41-48 ◽  
Author(s):  
Wenjie Xia ◽  
Anpeng Wang ◽  
Meng Jin ◽  
Qixing Mao ◽  
Wenying Xia ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Lymph Node ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Young Age ◽  
Cancer Death ◽  
Small Cell Lung ◽  
Lung Cancer Death

scholarly journals Identification and Validation of an Immune and Ferroptosis-Combined Index for Non–Small Cell Lung Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Yang Teng ◽  
Bo Wang ◽  
Desi Shang ◽  
Ning Yang
Keyword(s):  
Lung Cancer ◽  
High Risk ◽  
Small Cell Lung Cancer ◽  
Risk Score ◽  
Cell Lung Cancer ◽  
Risk Group ◽  
Risk Groups ◽  
Small Cell ◽  
Low Risk ◽  
Small Cell Lung

Background: Non–small cell lung cancer (NSCLC) is among the major health problems around the world. Reliable biomarkers for NSCLC are still needed in clinical practice. We aimed to develop a novel ferroptosis- and immune-based index for NSCLC.Methods: The training and testing datasets were obtained from TCGA and GEO databases, respectively. Immune- and ferroptosis-related genes were identified and used to establish a prognostic model. Then, the prognostic and therapeutic potential of the established index was evaluated.Results: Intimate interaction of immune genes with ferroptosis genes was observed. A total of 32 prognosis-related signatures were selected to develop a predictive model for NSCLC using LASSO Cox regression. Patients were classified into the high- and low-risk group based on the risk score. Patients in the low-risk group have better OS in contrast with that in the high-risk group in independent verification datasets. Besides, patients with a high risk score have shorter OS in all subgroups (T, N, and M0 subgroups) and pathological stages (stage I, II, and III). The risk score was positively associated with Immune Score, Stromal Score, and Ferroptosis Score in TCGA and GEO cohorts. A differential immune cell infiltration between the high-risk and the low-risk groups was also observed. Finally, we explored the significance of our model in tumor-related pathways, and different enrichment levels in the therapeutic pathway were observed between the high- and low-risk groups.Conclusion: The present study developed an immune and ferroptosis-combined index for the prognosis of NSCLC.

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