Retrospective Study to Determine Diagnostic Utility of 6 Commonly Used Lung Cancer Biomarkers Among Han and Uygur Population in Xinjiang Uygur Autonomous Region of People's Republic of China

Determination of Tumor Marker Screening for Lung Cancer Using ROC Curves

Introduction

Lung cancer ranks first among malignant tumors worldwide and is a leading cause of cancer-related mortality in both men and women. Combining tumor marker testing is a strategy to screen individuals at high risk of pulmonary cancer and minimize pulmonary cancer mortality. Therefore, tumor marker screening is crucial. In this study, we analyzed combinations of tumor markers for lung cancer screening using receiver operating characteristic (ROC) curve analysis.

Methods

A retrospective descriptive study was conducted on patients diagnosed with lung cancer, as well as healthy and benign lung diseases, using data from the China Huludao Central Hospital database between January 2016 and July 2022. The t-test and ROC curve were utilized to assess the effectiveness of individual tumor marker and the combination of multiple tumor markers. Tumor markers are molecular products metabolized and secreted by tumor tissues, characterized by cells or body fluids. They serve as indicators of tumor stage and grading, monitor treatment response, and predict recurrence.

Results

In this study, 267 healthy participants, 385 patients with benign lesions, and 296 patients with lung cancer underwent tumor marker screening. The sensitivity of five tumor markers-CEA, CYFRA21-1, NSE, pro-GRP, and CA125-was found to be <55%. This study revealed that a single tumor marker had limited value in lung cancer screening. However, combining two or more markers yielded varying area under the curves (AUC), with no significant impact on screening accuracy. The combination of CEA + CA125 demonstrated the highest accuracy for lung cancer screening in healthy participants. At a cutoff of 0.447 for CEA + CA125, the combination showed a sensitivity of 0.676 and specificity of 0.846 for lung cancer screening. Conversely, for patients with benign lung lesions, the optimal combination was CEA + NSE, with a cutoff of 0.393, yielding a sensitivity of 0.645 and specificity of 0.766 for lung cancer screening.

Conclusion

The five tumor markers-CEA, CA125, CY211, NSE, GRP-show promising results in screening healthy individuals and patients with lung cancer. However, only CEA, NSE, and GRP effectively differentiate patients with benign lung lesions from those with lung cancer. A single tumor marker has limited utility in detecting and screening for lung cancer and should be combined with other tumor markers. CEA + CA125 emerges as a superior tumor marker for distinguishing healthy individuals from those with lung cancer, whereas the CEA + NSE combination is more effective in identifying tumor markers in patients with benign lung lesions and lung cancer.

Unsupervised machine learning method for indirect estimation of reference intervals for chronic kidney disease in the Puerto Rican population

Reference intervals (RIs) for clinical laboratory values are extremely important for diagnostics and treatment of patients. However, the determination of these ranges is costly and time-consuming. As a result, often different unverified RIs are used in practice for the same analyte and the same range is used for all patients despite evidence that the values are gender, age, and ethnicity dependent. Moreover, the abnormal flags are rudimentary, merely indicating if a value is within the RI. At the same time, clinical lab data generated in the everyday medical practice contains a wealth of information, that given the correct methodology, can help determine the RIs for each specific segment of the population, including populations that suffer from health disparities. In this work, we develop unsupervised machine learning methods, based on Gaussian mixtures, to determine RIs of analytes related to chronic kidney disease, using millions of routine lab results for the Puerto Rican population. We show that the measures are both gender and age dependent and we find evidence for normal age-related organ function deterioration and failure. We also show that the joint distribution of measures improves the diagnostic value of the lab results.

Current status and challenges in establishing reference intervals based on real-world data

Reference intervals (RIs) are the cornerstone for evaluation of test results in clinical practice and are invaluable in judging patient health and making clinical decisions. Establishing RIs based on clinical laboratory data is a branch of real-world data mining research. Compared to the traditional direct method, this indirect approach is highly practical, widely applicable, and low-cost. Improving the accuracy of RIs requires not only the collection of sufficient data and the use of correct statistical methods, but also proper stratification of heterogeneous subpopulations. This includes the establishment of age-specific RIs and taking into account other characteristics of reference individuals. Although there are many studies on establishing RIs by indirect methods, it is still very difficult for laboratories to select appropriate statistical methods due to the lack of formal guidelines. This review describes the application of real-world data and an approach for establishing indirect reference intervals (iRIs). We summarize the processes for establishing iRIs using real-world data and analyze the principle and applicable scope of the indirect method model in detail. Moreover, we compare different methods for constructing growth curves to establish age-specific RIs, in hopes of providing laboratories with a reference for establishing specific iRIs and giving new insight into clinical laboratory RI research. (201 words).

Elevated preoperative CEA is associated with subclinical nodal involvement and worse survival in stage I non-small cell lung cancer: a systematic review and meta-analysis

BACKGROUND

The standard for clinical staging of lung cancer is the use of CT and PET scans, however, these may underestimate the burden of the disease. The use of serum tumor markers might aid in the detection of subclinical advanced disease. The aim of this study is to review the predictive value of tumor markers in patients with clinical stage I NSCLC.

METHODS

A comprehensive search was performed using the Medline, EMBASE, Scopus data bases. Abstracts included based on the following inclusion criteria: 1) adult ≥18 years old, 2) clinical stage I NSCLC, 3) Tumor markers (CEA, SCC, CYFRA 21-1), 4) further imaging or procedure, 5) > 5 patients, 6) articles in English language. The primary outcome of interest was utility of tumour markers for predicting nodal involvement and oncologic outcomes in patients with clinical stage I NSCLC. Secondary outcomes included sub-type of lung cancer, procedure performed, and follow-up duration.

RESULTS

Two hundred seventy articles were screened, 86 studies received full-text assessment for eligibility. Of those, 12 studies were included. Total of 4666 patients were involved. All studies had used CEA, while less than 50% used CYFRA 21-1 or SCC. The most common tumor sub-type was adenocarcinoma, and the most frequently performed procedure was lobectomy. Meta-analysis revealed that higher CEA level is associated with higher rates of lymph node involvement and higher mortality.

CONCLUSION

There is significant correlation between the CEA level and both nodal involvement and survival. Higher serum CEA is associated with advanced stage, and poor prognosis. Measuring preoperative CEA in patient with early stage NSCLC might help to identify patients with more advanced disease which is not detected by CT scans, and potentially identify candidates for invasive mediastinal lymph node staging, helping to select the most effective therapy for patients with potentially subclinical nodal disease. Further prospective studies are needed to standardize the use of CEA as an adjunct for NSCLC staging.

Tumour biomarkers: association with heart failure outcomes

BACKGROUND

There is increasing recognition that heart failure (HF) and cancer are conditions with a number of shared characteristics.

OBJECTIVES

To explore the association between tumour biomarkers and HF outcomes.

METHODS

In 2,079 patients of BIOSTAT-CHF cohort, we measured six established tumour biomarkers: CA125, CA15-3, CA19-9, CEA, CYFRA 21-1 and AFP.

RESULTS

During a median follow-up of 21 months, 555 (27%) patients reached the primary end-point of all-cause mortality. CA125, CYFRA 21-1, CEA and CA19-9 levels were positively correlated with NT-proBNP quartiles (all P < 0.001, P for trend < 0.001) and were, respectively, associated with a hazard ratio of 1.17 (95% CI 1.12-1.23; P < 0.0001), 1.45 (95% CI 1.30-1.61; P < 0.0001), 1.19 (95% CI 1.09-1.30; P = 0.006) and 1.10 (95% CI 1.05-1.16; P < 0.001) for all-cause mortality after correction for BIOSTAT risk model (age, BUN, NT-proBNP, haemoglobin and beta blocker). All tumour biomarkers (except AFP) had significant associations with secondary end-points (composite of all-cause mortality and HF hospitalization, HF hospitalization, cardiovascular (CV) mortality and non-CV mortality). ROC curves showed the AUC of CYFRA 21-1 (0.64) had a noninferior AUC compared with NT-proBNP (0.68) for all-cause mortality (P = 0.08). A combination of CYFRA 21-1 and NT-proBNP (AUC = 0.71) improved the predictive value of the model for all-cause mortality (P = 0.0002 compared with NT-proBNP).

CONCLUSIONS

Several established tumour biomarkers showed independent associations with indices of severity of HF and independent prognostic value for HF outcomes. This demonstrates that pathophysiological pathways sensed by these tumour biomarkers are also dysregulated in HF.

Long non-coding RNA in lung cancer

Lung cancer is the leading cause of cancer-related death worldwide. Owing to the difficulty in early diagnosis and the lack of effective treatment strategies, the 5-year survival rates for lung cancer remain very low. With the development of whole genome and transcriptome sequencing technology, long non-coding RNA (lncRNA) has attracted increasing attention. LncRNAs regulate gene expression at the epigenetic, transcriptional and post-transcriptional levels and are widely involved in a variety of diseases, including tumorigenesis. In lung cancer studies, multiple differentially expressed lncRNAs have been identified; several lncRNAs were identified as oncogenic lncRNAs with tumor-driving effects, while other lncRNAs play a role in tumor inhibition and are called tumor-suppressive lncRNAs. These tumor-suppressive lncRNAs are involved in multiple physiological processes such as cell proliferation, apoptosis, and metastasis and thus participate in tumor progression. In this review, we discussed the oncogenic and tumor-suppressive lncRNAs in lung cancer, as well as their biological functions and regulatory mechanisms. Furthermore, we found the potential significance of lncRNAs in clinical diagnosis and treatment.

Circulating long non-coding RNA GAS5 and SOX2OT as potential biomarkers for diagnosis and prognosis of non-small cell lung cancer

Early diagnosis of non-small cell lung cancer (NSCLC) is essential for patient treatment and prognosis. Long noncoding RNA (lncRNA) have potential roles in tumor initiation and differentiation. The objective of this study was to investigate whether the circulating lncRNA, growth arrest-specific transcript 5 (GAS5) and SOX2 overlapping transcript (SOX2OT), could be used as noninvasive biomarkers for NSCLC diagnosis. Moreover, we aimed at evaluating the association between lncRNA and the clinicopathological features of NSCLC in order to predict the cancer prognosis. The results showed significant downregulation of GAS5 expression and upregulation of SOX2OT in NSCLC patients compared with controls (P < 0.001). Furthermore, the expression level of GAS5 was declined in stage IV of NSCLC, but SOX2OT expression was increased sharply in stages III and IV. The expression levels of lncRNAs were used to distinguish NSCLC patients from control with an area under curve of 0.81 (sensitivity 82.5% and specificity 80%) for GAS5 and 0.73 (sensitivity 76.3% and specificity 78.6%) for SOX2OT. The combination of GAS5 and SOX2OT showed differentiation NSCLC patients from controls with increased sensitivity (83.8) and specificity (81.4). In conclusion, the newly developed diagnostic panel involving of circulating GAS5 and SOX2OT could be perfect biomarker for diagnosis and prognosis of NSCLC.

Knockdown of Long Noncoding RNA uc.338 by siRNA Inhibits Cellular Migration and Invasion in Human Lung Cancer Cells

Lung cancer remains a critical health concern worldwide. Long noncoding RNAs with ultraconserved elements have recently been implicated in human tumorigenesis. The present study investigated the role of ultraconserved element 338 (uc.338) in the regulation of cell proliferation and metastasis in human lung cancer. Our data showed that the expression of uc.338 in lung cancer was remarkably increased in vivo and in vitro. Depletion of uc.338 with specific siRNA interference retarded the cell proliferative rate in lung cancer cell lines NCI-H929 and H1688. Furthermore, knockdown of uc.338 caused cell cycle arrest in the G₀/G₁ phase in both cell lines. Transwell assays showed that inhibition of uc.338 notably decreased migration and invasion in NCI-H929 and H1688 cells. Moreover, uc.338 depletion decreased the expression of cyclin B1, Cdc25C, Snail, vimentin, and N-cadherin while increasing the protein level of E-cadherin, shown with Western blot analysis. These results suggested the pro-oncogenic potential of uc.338 in lung cancer, which might provide novel clues for the diagnosis and treatment of lung cancer in the clinic.

Diagnostic Value of Combining Tumor and Inflammatory Markers in Lung Cancer

BACKGROUND

Despite major advances in lung cancer treatment, early detection remains the most promising way of improving outcomes. To detect lung cancer in earlier stages, many serum biomarkers have been tested. Unfortunately, no single biomarker can reliably detect lung cancer. We combined a set of 2 tumor markers and 4 inflammatory or metabolic markers and tried to validate the diagnostic performance in lung cancer.

METHODS

We collected serum samples from 355 lung cancer patients and 590 control subjects and divided them into training and validation datasets. After measuring serum levels of 6 biomarkers (human epididymis secretory protein 4 [HE4], carcinoembryonic antigen [CEA], regulated on activation, normal T cell expressed and secreted [RANTES], apolipoprotein A2 [ApoA2], transthyretin [TTR], and secretory vascular cell adhesion molecule-1 [sVCAM-1]), we tested various sets of biomarkers for their diagnostic performance in lung cancer.

RESULTS

In a training dataset, the area under the curve (AUC) values were 0.821 for HE4, 0.753 for CEA, 0.858 for RANTES, 0.867 for ApoA2, 0.830 for TTR, and 0.552 for sVCAM-1. A model using all 6 biomarkers and age yielded an AUC value of 0.986 and sensitivity of 93.2% (cutoff at specificity 94%). Applying this model to the validation dataset showed similar results. The AUC value of the model was 0.988, with sensitivity of 93.33% and specificity of 92.00% at the same cutoff point used in the validation dataset. Analyses by stages and histologic subtypes all yielded similar results.

CONCLUSIONS

Combining multiple tumor and systemic inflammatory markers proved to be a valid strategy in the diagnosis of lung cancer.