Diagnostic value of conventional tumor markers in young patients with pulmonary nodules

Focusing on the Abnormal Events of NPC1, NPC2, and NPC1L1 in Pan-Cancer and Further Constructing LUAD and KICH Prediction Models

Cancer's high incidence and death rate jeopardize human health and life, and it has become a global public health issue. Some members of NPCs have been studied in a few cancers, but comprehensive and prognostic analysis is lacking in most cancers. In this study, we used the Cancer Genome Atlas (TCGA) data genomics and transcriptome technology to examine the differential expression and prognosis of NPCs in 33 cancer samples, as well as to investigate NPCs mutations and their effect on patient prognosis and to evaluate the methylation level of NPCs in cancer. The linked mechanisms and medication resistance were subsequently investigated in order to investigate prospective tumor therapy approaches. The relationships between NPCs and immune infiltration, immune cells, immunological regulatory substances, and immune pathways were also investigated. Finally, the LUAD and KICH prognostic prediction models were built using univariate and multivariate COX regression analysis. Additionally, the mRNA and protein levels of NPCs were also identified.

Improvement of differential diagnosis of lung cancer by use of multiple protein tumor marker combinations

BACKGROUND

Differential diagnosis of non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) in hospitalized patients is crucial for appropriate treatment choice.

OBJECTIVE

To investigate the relevance of serum tumor markers (STMs) and their combinations for the differentiation of NSCLC and SCLC subtypes.

METHODS

Between 2000 and 2003, 10 established STMs were assessed retrospectively in 311 patients with NSCLC, 128 with SCLC prior systemic first-line therapy and 51 controls with benign lung diseases (BLD), by automatized electrochemiluminescence immunoassay technology. Receiver operating characteristic (ROC) curves and logistic regression analyses were used to evaluate the diagnostic efficacy of both individual and multiple STMs with corresponding sensitivities at 90% specificity. Standards for Reporting of Diagnostic Accuracy (STARD guidelines) were followed.

RESULTS

CYFRA 21-1 (cytokeratin-19 fragment), CEA (carcinoembryonic antigen) and NSE (neuron specific enolase) were significantly higher in all lung cancers vs BLD, reaching AUCs of 0.81 (95% CI 0.76-0.87), 0.78 (0.73-0.84), and 0.88 (0.84-0.93), respectively. By the three marker combination, the discrimination between benign and all malignant cases was improved resulting in an AUC of 0.93 (95% CI 0.90-0.96). In NSCLC vs. BLD, CYFRA 21-1, CEA and NSE were best discriminative STMs, with AUCs of 0.86 (95% CI 0.81-0.91), 0.80 (0.74-0.85), and 0.85 (0.79-0.91). The three marker combination also improved the AUC: 0.92; 95% CI 0.89-0.96). In SCLC vs. BLD, ProGRP (pro-gastrin-releasing peptide) and NSE were best discriminative STMs, with AUCs of 0.89 (95% CI 0.84-0.94) and 0.96 (0.93-0.98), respectively, and slightly improved AUC of 0.97 (95% CI 0.95-0.99) when in combination. Finally, discrimination between SCLC and NSCLC was possible by ProGRP (AUC 0.86; 95% CI 0.81-0.91), NSE (AUC 0.83; 0.78-0.88) and CYFRA 21-1 (AUC 0.69; 0.64-0.75) and by the combination of the 3 STMs (AUC 0.93; 0.91-0.96), with a sensitivity of 88% at 90% specificity.

CONCLUSIONS

The results confirm the power of STM combinations for the differential diagnosis of lung cancer from benign lesions and between histological lung cancer subtypes.

A combined diagnostic model based on circulating tumor cell in patients with solitary pulmonary nodules

BACKGROUND

Although many prediction models in diagnosis of solitary pulmonary nodules (SPNs) have been developed, few are widely used in clinical practice. It is therefore imperative to identify novel biomarkers and prediction models supporting early diagnosis of SPNs. This study combined folate receptor-positive circulating tumor cells (FR+ CTC) with serum tumor biomarkers, patient demographics and clinical characteristics to develop a prediction model.

METHODS

A total of 898 patients with a solitary pulmonary nodule who received FR+ CTC detection were randomly assigned to a training set and a validation set in a 2:1 ratio. Multivariate logistic regression was used to establish a diagnostic model to differentiate malignant and benign nodules. The receiver operating curve (ROC) and the area under the curve (AUC) were calculated to assess the diagnostic efficiency of the model.

RESULTS

The positive rate of FR+ CTC between patients with non-small cell lung cancer (NSCLC) and benign lung disease was significantly different in both the training and the validation dataset (p < 0.001). The FR+ CTC level was significantly higher in the NSCLC group compared with that of the benign group (p < 0.001). FR+ CTC (odds ratio, OR, 95% confidence interval, CI: 1.13, 1.07-1.19, p < 0.0001), age (OR, 95% CI: 1.06, 1.01-1.12, p = 0.03) and sex (OR, 95% CI: 1.07, 1.01-1.13, p = 0.01) were independent risk factors of NSCLC in patients with a solitary pulmonary nodule. The area under the curve (AUC) of FR+ CTC in diagnosing NSCLC was 0.650 (95% CI, 0.587-0.713) in the training set and 0.700 (95% CI, 0.603-0.796) in the validation set, respectively. The AUC of the combined model was 0.725 (95% CI, 0.659-0.791) in the training set and 0.828 (95% CI, 0.754-0.902) in the validation set, respectively.

CONCLUSIONS

We confirmed the value of FR+ CTC in diagnosing SPNs and developed a prediction model based on FR+ CTC, demographic characteristics, and serum biomarkers for differential diagnosis of solitary pulmonary nodules.

Preoperative diagnosis of solitary pulmonary nodules with a novel hematological index model based on circulating tumor cells

Objective

Preoperative noninvasive diagnosis of the benign or malignant solitary pulmonary nodule (SPN) is still important and difficult for clinical decisions and treatment. This study aimed to assist in the preoperative diagnosis of benign or malignant SPN using blood biomarkers.

Methods

A total of 286 patients were recruited for this study. The serum FR⁺CTC, TK1, TP, TPS, ALB, Pre-ALB, ProGRP, CYFRA21-1, NSE, CA50, CA199, and CA242 were detected and analyzed.

Results

In the univariate analysis, age, FR⁺CTC, TK1, CA50, CA19.9, CA242, ProGRP, NSE, CYFRA21-1, and TPS showed the statistical significance of a correlation with malignant SPNs (P <0.05). The highest performing biomarker is FR⁺CTC (odd ratio [OR], 4.47; 95% CI: 2.57-7.89; P <0.001). The multivariate analysis identified that age (OR, 2.69; 95% CI: 1.34-5.59, P = 0.006), FR⁺CTC (OR, 6.26; 95% CI: 3.09-13.37, P <0.001), TK1 (OR, 4.82; 95% CI: 2.4-10.27, P <0.001), and NSE (OR, 2.06; 95% CI: 1.07-4.06, P = 0.033) are independent predictors. A prediction model based on age, FR⁺CTC, TK1, CA50, CA242, ProGRP, NSE, and TPS was developed and presented as a nomogram, with a sensitivity of 71.1% and a specificity of 81.3%, and the AUC was 0.826 (95% CI: 0.768-0.884).

Conclusions

The novel prediction model based on FR⁺CTC showed much stronger performance than any single biomarker, and it can assist in predicting benign or malignant SPNs.

Efficacy of autoantibodies combined with tumor markers in the detection of lung cancer

BACKGROUND

The purpose of this study was to explore the detection value of seven autoantibodies (TAAbs): p53, PGP9.5, SOX2, GBU4-5, MAGE A1, CAGE, and GAGE7 and three tumor markers: CYFRA21-1, NSE, and SCCA in the diagnosis of lung cancer.

METHODS

ELISA was used to detect the levels of the TAAbs, and chemiluminescence immunoassay was used to test the levels of the tumor markers. The diagnostic efficacy of the TAAbs combined with the tumor markers for lung cancer was evaluated by receiver operating characteristic (ROC) curves.

RESULTS

The positive rate of the combined detection of seven TAAbs and three tumor markers in lung cancer (37.8%) was higher than that in other three groups. The positive rates of SOX2, GAGE7, MAGE A1, CAGE, CYFRA21-1, and SCCA had differences among the four groups. Compared with the benign lung disease group, only GAGE7, CYFRA21-1, and SCCA differed among the groups. The combined sensitivity of the TAAbs was 29.07% (AUC, 0.594), the combined sensitivity of all the markers was 37.76% (AUC, 0.660 [p < 0.05]), and Youden's index was 0.196. In the lung cancer group, CYFRA21-1 had a significant difference in age and sex, and SOX2, MAGE A1, CYFRA21-1, NSE, and SCCA were significantly different in pathological type and TNM. In contrast, p53 and GBU4-5 showed no significant differences in age, sex, pathological type, and TNM.

CONCLUSIONS

The combined detection of seven TAAbs and three tumor markers could be useful in early diagnosis of lung cancer.