Molecular profiling of key driver genes improves staging accuracy in multifocal non–small cell lung cancer

Zheng, Richard; Shen, Qian; Mardekian, Stacey K.; Solomides, Charalambos; Wang, Zixuan; Evans, Nathaniel R.

doi:10.1016/j.jtcvs.2019.11.126

Cited by 24 publications

(20 citation statements)

References 23 publications

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“…With the development of next-generation sequencing (NGS) technologies, there were more and more risk factors being identified for LUSCC patients, including miRNAs, lncRNAs and mRNAs [17][18][19][20].…”

Section: Discussionmentioning

confidence: 99%

Construction and Validation of a Protein Prognostic Model for Lung Squamous Cell Carcinoma

Fang¹,

Liu²,

Weng³

et al. 2020

Int. J. Med. Sci.

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Lung squamous cell carcinoma (LUSCC), as the major type of lung cancer, has high morbidity and mortality rates. The prognostic markers for LUSCC are much fewer than lung adenocarcinoma. Besides, protein biomarkers have advantages of economy, accuracy and stability. The aim of this study was to construct a protein prognostic model for LUSCC. The protein expression data of LUSCC were downloaded from The Cancer Protein Atlas (TCPA) database. Clinical data of LUSCC patients were downloaded from The Cancer Genome Atlas (TCGA) database. A total of 237 proteins were identified from 325 cases of LUSCC patients based on the TCPA and TCGA database. According to Kaplan-Meier survival analysis, univariate and multivariate Cox analysis, a prognostic prediction model was established which was consisted of 6 proteins (CHK1_pS345, CHK2, IRS1, PAXILLIN, BRCA2 and BRAF_pS445). After calculating the risk values of each patient according to the coefficient of each protein in the risk model, the LUSCC patients were divided into high risk group and low risk group. The survival analysis demonstrated that there was significant difference between these two groups (p= 4.877e−05). The area under the curve (AUC) value of the receiver operating characteristic (ROC) curve was 0.699, which suggesting that the prognostic risk model could effectively predict the survival of LUSCC patients. Univariate and multivariate analysis indicated that this prognostic model could be used as independent prognosis factors for LUSCC patients. Proteins co-expression analysis showed that there were 21 proteins co-expressed with the proteins in the risk model. In conclusion, our study constructed a protein prognostic model, which could effectively predict the prognosis of LUSCC patients.

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Section: Discussionmentioning

confidence: 99%

Construction and Validation of a Protein Prognostic Model for Lung Squamous Cell Carcinoma

Fang¹,

Liu²,

Weng³

et al. 2020

Int. J. Med. Sci.

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“…Lung cancer has become the rst cause of death from malignant tumors in China, accounting for 22.7% of all malignant tumor deaths [23,24]. Synchronous radiotherapy and chemotherapy for non-small cell lung cancer that is not suitable for surgery has become the current standard treatment mode, and RILI is a common complication of lung cancer radiotherapy [25,26]. RILI not only poses a great threat to the patient's prognosis and quality of life, but also hinders the clinical application of effective radiation dose [27,28].…”

Section: Discussionmentioning

confidence: 99%

WITHDRAWN: Correlation between DVH parameters and lung function changes before and after radiotherapy and the occurrence of radiation-induced lung injury (RILI)

Sha

Dong

Wang

et al. 2020

Preprint

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Background: The purpose of the study was to explore the correlation between DVH parameters and lung function changes before and after radiotherapy and the occurrence of radiation-induced lung injury (RILI), and to evaluate its value in predicting the risk of RILI.Methods: 120 patients with advanced non-small cell lung cancer who had been diagnosed in Jiaozhou Central Hospital of Qingdao City in the past three years and received chest conformal (intensity modulated) radiation therapy were selected. Before radiotherapy, irradiation of 45-50 Gy, and 1 month after the end of radiotherapy, the patients were tested for lung function, and the ventilation and diffusion function of the patients were tested using the Japanese CHESTAC-8800 lung function tester. The evaluation of radiation lung injury was based on the RTOG acute radiation lung injury classification standard, and the observation end point was ≥2 grade RILI.Results: A total of 34 patients with ≥2 grade RILI among 120 patients in this study, including 23 cases of grade 2 and 11 cases of grade 3, the incidence rate was 28.33%. The difference between FVC, FEV1, FEV1 / FVC, DLCO, V5, V10, V15 before radiotherapy, 45-50 Gy, and 1 month after the end of radiotherapy were statistically significant (P <0.05). Univariate analysis showed that lung function, V5, V10, and V15 before radiotherapy were related factors for RILI (P <0.05). Multivariate logistic analysis showed that the risk of RILI was 1.855 times that of patients with higher FEV1 / FVC before radiation therapy (OR = 1.855, 95% CI = 1.199-1.946, P = 0.037), patients with V10 ≥50% were 3.673 times higher than patients with V10 <50% (OR = 3.673, 95% CI = 1.548-7.582, P = 0.039).Conclusions: V10≥50% and FEV1 / FVC are high-risk factors for RILI before radiotherapy, which has certain value in predicting the risk of RILI.

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“…With the development of high throughput next-generation sequencing (NGS), many studies have developed novel risk model to provide opportunities to identify distinguishing patterns for cancer diagnosis or predict prognosis [4][5][6][7]. However, the limitation of NGS had restricted its application, such as high cost and poor reproducibility.…”

Section: Discussionmentioning

confidence: 99%

A prognostic risk model constructed by proteins in predicting prognosis for ovarian cancer

Yan

Niu

2021

Preprint

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BackgroundsOvarian cancer is the most lethal malignant tumor in gynecological cancers worldwide. Approximately 70% of patients have a poor prognosis, who experienced progression or recurrence within 5 years. The aim of this study attempts is to screen out the potential prognosis-related proteins and establish a prognostic risk model for predicting the prognostic risk for patients with ovarian cancer.MethodData were obtained from the Cancer Proteome Atlas (TCPA) and the Cancer Genome Atlas (TCGA). The proteins significantly related to survival risk in ovarian cancer patients were screened out by Kaplan-Meier test and COX regression analysis. A prognostic risk model was constructed based on the optimal proteins selected by multivariate Cox analysis. The prognostic risk model was validated in different clinical characteristics. The sankyl diagram was used to visualize the relationship between the prognosis-related proteins and their co-expression proteins.ResultsA prognostic risk model consisting of seven proteins that significantly related to prognosis was established. Patients with high risk score were associated with poor survival and relative protein expression. In the multivariate cox regress analysis, only age and the risk score were the independence prognosis factors. The AUC for the risk score was 0.721 in ROC curve for patients under 70 years old. Pearson’s correlation analysis showed that 25 co-expression proteins correlated with the prognosis-related proteins.ConclusionOur study demonstrated that a novel prognostic risk model constructed by proteins could predict prognosis for patients with ovarian cancer.

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Molecular profiling of key driver genes improves staging accuracy in multifocal non–small cell lung cancer

Cited by 24 publications

References 23 publications

Construction and Validation of a Protein Prognostic Model for Lung Squamous Cell Carcinoma

Construction and Validation of a Protein Prognostic Model for Lung Squamous Cell Carcinoma

WITHDRAWN: Correlation between DVH parameters and lung function changes before and after radiotherapy and the occurrence of radiation-induced lung injury (RILI)

A prognostic risk model constructed by proteins in predicting prognosis for ovarian cancer

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