Analysis of risk factors for stage I lung adenocarcinoma using low‑dose high‑resolution computed tomography

Fang, Rui; Yang, Yong; Han, Haicheng; Fu, Xiaoqing; Dong, Liwen; Xie, Baisheng; Lü, Wei; Ma, Chenyang; Cui, Feng; Hu, Jian; Wang, Jun

doi:10.3892/ol.2018.8921

Cited by 4 publications

(3 citation statements)

References 40 publications

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“…previously conducted a case-control study demonstrating that stage I lung adenocarcinoma patients were signi cantly more likely to exhibit this nding 24 . GGO ndings have been reported to be associated with cancer rates as high as 63%, with many surgeons believing that GGO nodules should be resected, particularly if they grow in size.…”

Section: Discussionmentioning

confidence: 90%

Development and Analysis of a Predictive Nomogram Assessing Cancer Risk in a Chinese Cohort of Patients Presenting with Pulmonary Nodules

Liao

Zheng

et al. 2021

Preprint

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Background: Lung cancer is a major global threat to public health for which a novel prognostic nomogram is urgently needed.Patients and methods: Here, we designed a novel prognostic nomogram using a training dataset consisting of 178 pulmonary nodules for design and 124nodules for external validation. The R ‘caret’ package was used to separate patients for design into two groups, including a training cohort (n=126) for model construction and an internal validation cohort (n=52). Optimal feature selection for this model was achieved using the least absolute shrinkage and selection operator regression (LASSO) model. C-index values, calibration plots, and decision curve analyses were used to gauge the discrimination, calibration, and clinical utility, respectively, of this predictive model. Validation was then performed with the validation cohort.Results: A predictive nomogram was successfully constructed incorporating hypertension status, plasma fibrinogen levels, serum uric acid (SUA) levels, triglyceride (TG) and high-density lipoprotein (HDL) levels, density, spicule sign, ground-glass opacity (GGO), and pulmonary nodule size. This model exhibited good discriminative ability, with a C-index value of 0.795 (95% CI: 0.720–0.870), and was well-calibrated. When we used the validation cohort to evaluate the model, the C-indexes were 0.886 (95% CI: 0.800–0.972) and 0.817 (95% CI: 0.747–0.897) for internal validation and external validation, respectively. Decision curve analyses indicated the clinical value of this predictive nomogram when used at a lung cancer possibility threshold of 9%.Conclusion: The nomogram constructed in this study, which incorporates hypertension status, plasma fibrinogen levels, SUA, TG, HDL, density, spicule sign, GGO status, and pulmonary nodule size was able to reliably predict lung cancer risk in this Chinese cohort of patients presenting with pulmonary nodules.

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Section: Discussionmentioning

confidence: 90%

Development and Analysis of a Predictive Nomogram Assessing Cancer Risk in a Chinese Cohort of Patients Presenting with Pulmonary Nodules

Liao

Zheng

et al. 2021

Preprint

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“…Identification of differentially expressed secretory proteins from blood or other body fluids has become an effective method for biomarker research. The combination of several protein biomarkers, such as CEA, CA125, CA153, and Cyfra21–1, can significantly improve the diagnosis accuracy of lung cancer [ 15 ]. However, the research on screening and identification of clinical biomarkers for early diagnosis of LUAD is still lacking.…”

Section: Introductionmentioning

confidence: 99%

Secreted proteins MDK, WFDC2, and CXCL14 as candidate biomarkers for early diagnosis of lung adenocarcinoma

Hao

et al. 2023

BMC Cancer

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Background Early diagnosis of lung adenocarcinoma (LUAD), one of the most common types of lung cancer, is very important to improve the prognosis of patients. The current methods can’t meet the requirements of early diagnosis. There is a pressing need to identify novel diagnostic biomarkers. Secretory proteins are the richest source for biomarker research. This study aimed to identify candidate secretory protein biomarkers for early diagnosis of LUAD by integrated bioinformatics analysis and clinical validation. Methods Differentially expressed genes (DEGs) of GSE31210, gene expression data of early stage of LUAD, were analyzed by GEO2R. Upregulated DEGs predicted to encode secreted proteins were obtained by taking the intersection of the DEGs list with the list of genes encoding secreted proteins predicted by the majority decision-based method (MDSEC). The expressions of the identified secreted proteins in the lung tissues of early-stage LUAD patients were further compared with the healthy control group in mRNA and protein levels by using the UALCAN database (TCGA and CPTAC). The selected proteins expressed in plasma were further validated by using Luminex technology. The diagnostic value of the screened proteins was evaluated by receiver operating characteristic (ROC) analysis. Cell counting kit-8 assay was carried out to investigate the proliferative effects of these screened proteins. Results A total of 2183 DEGs, including 1240 downregulated genes and 943 upregulated genes, were identified in the GSE31210. Of the upregulated genes, 199 genes were predicted to encode secreted proteins. After analysis using the UALCAN database, 16 molecules were selected for further clinical validation. Plasma concentrations of three proteins, Midkine (MDK), WAP four-disulfide core domain 2 (WFDC2), and C-X-C motif chemokine ligand 14 (CXCL14), were significantly higher in LUAD patients than in healthy donors. The area under the curve values was 0.944, 0.881, and 0.809 for MDK, WFDC2, and CXCL14, 0.962 when combined them. Overexpression of the three proteins enhanced the proliferation activity of A549 cells. Conclusions MDK, WFDC2, and CXCL14 were identified as candidate diagnostic biomarkers for early-stage LUAD and might also play vital roles in tumorigenesis.

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“…Cancer is one of the major diseases that endanger human health and lifespan. [ 1 ] For most malignancies, chemotherapy has been considered as one of the major strategies following surgery. Despite traditional chemotherapeutic drugs, such as cisplatin (CDDP), 5‐fluorouracil, doxorubicin (DOX) and methotrexate (MTX) have exhibit great efficacy against cancer cells, [ 2 ] their systemic application is greatly restricted due to their multidrug resistance and severely toxic side effects and the multidrug resistance developed after application.…”

Section: Introductionmentioning

confidence: 99%

Multi‐functional Nanodrug Based on a Three‐dimensional Framework for Targeted Photo‐chemo Synergetic Cancer Therapy

Xing

Chen

et al. 2021

Adv Healthcare Materials

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Targeted synergistic therapy has broad prospects in tumor treatments. Here, a multi-functional nanodrug GDYO-CDDP/DOX@DSPE-PEG-MTX (GCDM) based on three traditional anticancer drugs (doxorubicin (DOX), cisplatin (CDDP) and methotrexate (MTX)) modified graphdiyne oxide (GDYO) is described, for diagnosis and targeted cancer photo-chemo synergetic therapy. In this system, for the first time, these three traditional anti-cancer drugs have played new roles and can reduce multidrug resistance through synergistic anti-tumor effects. Cisplatin can be hybridized with GDYO to form a multifunctional and well-dispersed three-dimensional framework, which can not only be used as nano-drug carriers to achieve high drug loading rates (40.3%), but also exhibit excellent photothermal conversion efficiency (47%) and good photodynamic effects under NIR irradiation. Doxorubicin (DOX) is loaded onto GDYO-CDDP through -stacking, which is used as an anticancer drug and as a fluorescent probe for nanodrug detection. Methotrexate (MTX) can be applied in tumor targeting and play a role in synergistic chemotherapy with DOX and CDDP. The synthesized multi-functional nanodrug GCDM has good biocompatibility, active targeting, long-term retention, sustained drug release, excellent fluorescence imaging capabilities, and remarkable photo-chemo synergistic therapeutic effects.

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Analysis of risk factors for stage I lung adenocarcinoma using low‑dose high‑resolution computed tomography

Cited by 4 publications

References 40 publications

Development and Analysis of a Predictive Nomogram Assessing Cancer Risk in a Chinese Cohort of Patients Presenting with Pulmonary Nodules

Development and Analysis of a Predictive Nomogram Assessing Cancer Risk in a Chinese Cohort of Patients Presenting with Pulmonary Nodules

Secreted proteins MDK, WFDC2, and CXCL14 as candidate biomarkers for early diagnosis of lung adenocarcinoma

Multi‐functional Nanodrug Based on a Three‐dimensional Framework for Targeted Photo‐chemo Synergetic Cancer Therapy

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