Differential expression profiling of transcripts of IDH1, CEA, Cyfra21-1, and TPA in stage IIIa non-small cell lung cancer (NSCLC) of smokers and non-smokers cases with air quality index

Mishra, Archana; Singh, Neetu; Shyam, Hari; Jain, Mayank; Sahu, Dinesh Kumar; Shankar, Pratap; Alam, Nawazish; Kumar, Anil; Jaiswal, Riddhi; Kumar, Shailendra

doi:10.1016/j.gene.2020.145151

Cited by 8 publications

(6 citation statements)

References 17 publications

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“…CEA is a serum tumor marker that is often used to detect early lung cancer ( 25 , 26 ). In this study, the clinical indicators related to lung cancer were statistically analyzed, and the results showed that CEA was highly correlated with lymph node metastasis of malignant lung nodules (P<0.001), which was an independent prognostic factor for constructing the nomogram model to predict lymph node metastasis in lung cancer patients, consistent with previous studies ( 27 - 30 ). In this study, the clinical indicator CEA and radiomics labels constructed by LASSO algorithm (the LASSO algorithm has low requirements on processed data and is a widely used data screening method) were used as predictors to construct the nomogram.…”

Section: Discussionsupporting

confidence: 86%

Artificial intelligence neural network analysis and application of CT imaging features to predict lymph node metastasis in non-small cell lung cancer

Geng¹,

Geng²,

Wei³

et al. 2022

J Thorac Dis

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Background Computed tomography (CT) is important in the diagnosing of lung cancer. The combination of CT features and artificial intelligence algorithm have been used in the diagnosis of various lung diseases. However, limited studies focused on the relationship between the combination of CT features and artificial intelligence algorithm and lymph node metastasis in non-small cell lung cancer (NSCLC). This study developed an algorithm for lung cancer CT image segmentation based on an artificial neural network model and investigated the role of a nomogram model based on CT images for predicting lymph node metastasis in lung cancer. Methods Wiener filtering and fuzzy enhancement were first used to suppress image noise and improve image contrast. Next, texture features and fractal features were extracted. In the third step, the artificial neural network model was trained and tested according to the best parameters of the network. Results The area under the curve (AUC) of the constructed nomogram model on the training set and the test set were 0.859 (sensitivity, 0.810; specificity, 0.773) and 0.864 (sensitivity, 0.820; specificity, 0.753), respectively. The decision curve indicated that the model had good clinical application value. The lung cancer CT images contained 13 significant regional features of cancer. The best classification function obtained from training and testing data was Levenberg-Marquardt backpropagation. The sensitivity, specificity, and accuracy in the training stage could reach 98.4%, 100%, and 98.6%, respectively, and the corresponding indexes in the test stage reached 90.9%, 100%, and 95.1%, respectively. Conclusions The image segmentation algorithm based on the artificial neural network model could extract CT lung cancer lesions efficiently and quasi-determinately, which could be used as an effective tool for radiologists to diagnose lung cancer. The nomogram model based on CT image features and related clinical indicators was an effective method for noninvasive prediction of lymph node metastasis in lung cancer.

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Section: Discussionsupporting

confidence: 86%

Artificial intelligence neural network analysis and application of CT imaging features to predict lymph node metastasis in non-small cell lung cancer

Geng¹,

Geng²,

Wei³

et al. 2022

J Thorac Dis

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“…CEA is a class of acidic glycoproteins located in hollow organs such as the respiratory tract and digestive tract and consists of sugar chains and peptide chains. It exists in macrophages, monocytes, and multinucleated cells, has human embryonic epitopes, and is specific for tumor-associated antigens that are associated with tumor recurrence [ 22 ]. CA125 is a class of tumor carbohydrate antigens with high concentrations in malignant tissues and tumor cells and can be released into the blood when tumor tissues are destroyed, which is of great value in the diagnosis and prognosis of NSCLC [ 23 ].…”

Section: Discussionmentioning

confidence: 99%

The Effect of PD-1 Inhibitor Combined with Chemotherapy on the Level of Peripheral Blood T Lymphocytes among Patients with Non-Small-Cell Lung Cancer and Its Relationship with Prognosis

Zhao

Zhou

et al. 2022

Computational and Mathematical Methods in Medicine

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Objective. To explore the effect of combined treatment of PD-1 inhibitor and chemotherapy on the level of peripheral blood T lymphocytes in non-small-cell lung cancer (NSCLC) patients and its relationship with prognosis. Methods. Retrospective analysis was conducted on 150 NSCLC patients treated in Guangxi Medical University Affiliated Tumor Hospital from June 2018 to September 2020, including 77 patients treated with PD-1 inhibitor combined with chemotherapy as the observation group (OG) and 73 patients with chemotherapy alone as the control group (CG). Therapeutic efficacy, immune function indexes, serum tumor markers, incidence of adverse reactions during hospitalization, 1-year survival rate, and life quality after 6 months of treatment were observed and compared between two groups. Results. Compared to the CG, the therapeutic effect of OG was evidently better. Six months after treatment, levels of CD4+/CD8+, NK cells, and CD4 + in two groups were elevated markedly, and indexes of OG were notably and comparatively higher than those in the other group. After treatment, OG was observed with a marked decline regarding levels of CYFRA21-1, CEA, and CA125 compared to those in the CG; and there was no notable difference in terms of adverse reaction occurrence between two groups, but the 1-year survival rate and 6-month life quality in OG over ranked those in CG. Conclusion. For NSCLC patients, the PD-1 inhibitor given on the basis of chemotherapy can further improve the clinical efficacy and improve immune function and long-term survival rate of patients on the premise of ensuring the safety of treatment, which is worth promoting in clinical practice.

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“…Among them, NSE and ProGRP are ideal indicators for diagnosing SCLC ( Li et al, 2022 ), CEA, CYFRA21-1, and SCC are helpful for the auxiliary diagnosis of NSCLC ( Mishra et al, 2021 ), and CYFRA21-1 and SCC are considered to be specific for squamous cell carcinoma ( Fatica et al, 2022 ). Another six biomarkers, namely, carcinoma antigen 125 (CA125) ( Yang et al, 2018 ), epidermal growth factor receptor (EGFR) ( Scharpenseel et al, 2019 ), isocitrate dehydrogenase 1 (IDH1) ( Mishra et al, 2021 ), thyroid transcription factor-1 (TTF-1) ( Liu et al, 2018 ), synaptophysin (SYN) ( Wang et al, 2020 ), and neural cell adhesion molecule (CD56) ( Svajdler et al, 2019 ) were selected according to their frequency of being mentioned in the relevant literature. Thus, 11 lung cancer biomarkers were chosen through extensive literature research in total.…”

Section: Methodsmentioning

confidence: 99%

Miniaturized microfluidic-based nucleic acid analyzer to identify new biomarkers of biopsy lung cancer samples for subtyping

Xue

Bo²,

Lv³

et al. 2022

Front. Chem.

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Identifying new biomarkers is necessary and important to diagnose and treat malignant lung cancer. However, existing protein marker detection methods usually require complex operation steps, leading to a lag time for diagnosis. Herein, we developed a rapid, minimally invasive, and convenient nucleic acid biomarker recognition method, which enabled the combined specific detection of 11 lung cancer typing markers in a microliter reaction system after only one sampling. The primers for the combined specific detection of 11 lung cancer typing markers were designed and screened, and the microfluidic chip for parallel detection of the multiple markers was designed and developed. Furthermore, a miniaturized microfluidic-based analyzer was also constructed. By developing a microfluidic chip and a miniaturized nucleic acid analyzer, we enabled the detection of the mRNA expression levels of multiple biomarkers in rice-sized tissue samples. The miniaturized nucleic acid analyzer could detect ≥10 copies of nucleic acids. The cell volume of the typing reaction on the microfluidic chip was only 0.94 μL, less than 1/25 of that of the conventional 25-μL Eppendorf tube PCR method, which significantly reduced the testing cost and significantly simplified the analysis of multiple biomarkers in parallel. With a simple injection operation and reverse transcription loop-mediated isothermal amplification (RT-LAMP), real-time detection of 11 lung cancer nucleic acid biomarkers was performed within 45 min. Given these compelling features, 86 clinical samples were tested using the miniaturized nucleic acid analyzer and classified according to the cutoff values of the 11 biomarkers. Furthermore, multi-biomarker analysis was conducted by a machine learning model to classify different subtypes of lung cancer, with an average area under the curve (AUC) of 0.934. This method shows great potential for the identification of new nucleic acid biomarkers and the accurate diagnosis of lung cancer.

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Differential expression profiling of transcripts of IDH1, CEA, Cyfra21-1, and TPA in stage IIIa non-small cell lung cancer (NSCLC) of smokers and non-smokers cases with air quality index

Cited by 8 publications

References 17 publications

Artificial intelligence neural network analysis and application of CT imaging features to predict lymph node metastasis in non-small cell lung cancer

Artificial intelligence neural network analysis and application of CT imaging features to predict lymph node metastasis in non-small cell lung cancer

The Effect of PD-1 Inhibitor Combined with Chemotherapy on the Level of Peripheral Blood T Lymphocytes among Patients with Non-Small-Cell Lung Cancer and Its Relationship with Prognosis

Miniaturized microfluidic-based nucleic acid analyzer to identify new biomarkers of biopsy lung cancer samples for subtyping

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