The role of seven autoantibodies in lung cancer diagnosis

Association between high-density lipoprotein cholesterol and 7-autoantibodies: a study on physical examination data from 2018 to 2023

Background

Limited research has explored the effect of high-density lipoprotein cholesterol (HDL-C) on lung cancer's seven autoantibodies (7-AABs). This study investigated the association between serum HDL-C and 7-AABs among 5,574 Chinese adults aged ≥ 18 years from January 2018 to December 2023.

Methods

This cross-sectional study utilized physical examination data from the Department of Health Management at Henan Provincial People's Hospital. The associations between HDL-C and autoantibodies, such as tumor protein 53(P53), SRY-box containing gene 2 (SOX2), and ATP-dependent RNA helicase 4-5 (GBU4-5), were modeled using a restricted cubic spline logistic regression model.

Results

After the adjustment for factors, such as age and body mass index, the binary logistic regression model showed distinct correlations between serum HDL-C levels and autoantibodies, including P53, SOX2, and 7-AABs. Restricted cubic spline logistic regression analysis indicated that the increased level of serum HDL-C was associated with a decreased risk of positive P53 (all participants: HDL-C: 1.227-1.366 mmol/L, P HDL-C=0.028), SOX2 (all participants: HDL-C ≥ 1.227 mmol/L, P HDL-C =0.021; all women: HDL-C ≥ 1.224 mmol/L, P HDL-C=0.037), GBU4-5 (all women: HDL-C ≥ 1.269 mmol/L, P HDL-C=0.039), and 7-AABs (all women: HDL-C ≥ 1.224 mmol/L, P HDL-C=0.015). In women, HDL-C levels between 1.163 and 1.224 mmol/L correlated with an increased risk of positive 7-AABs test results.

Conclusions

Elevated HDL-C levels exhibited an independent association with a reduced risk of positivity for 7-AABs of lung cancer, especially in the female physical examination population. These findings suggest that high HDL-C levels may play a role in hindering lung cancer development with gender differences. However, further confirmation is still needed in the future.

Epigenetic modifications in early stage lung cancer: pathogenesis, biomarkers, and early diagnosis

The integration of liquid biopsy with epigenetic markers offers significant potential for early lung cancer detection and personalized treatment. Epigenetic alterations, including DNA methylation, histone modifications, and noncoding RNA changes, often precede genetic mutations and are critical in cancer progression. In this study, we explore how liquid biopsy, combined with epigenetic markers, can provide early detection of lung cancer, potentially predicting onset up to 4 years before clinical diagnosis. We discuss the challenges of targeting epigenetic regulators, which could disrupt cellular balance if overexploited, and the need for maintaining key gene expressions in therapeutic applications. This review highlights the promise and challenges of using liquid biopsy and epigenetic markers for early-stage lung cancer diagnosis, with a focus on optimizing treatment strategies for personalized and precision medicine.

Serum autoantibody-based biomarkers for prognosis in early-stage lung cancer patients with surgical resection

BACKGROUND

Lung cancer is the cancer with the highest morbidity and mortality in the world. With the increasing diagnosis rate of patients with early-stage lung cancer, surgery treatment becomes an option for more patients. However, there is a lack of effective indicators to assess the risk of recurrence after lung cancer surgery.

METHODS

We collected levels of serum autoantibodies and evaluated their roles as biomarkers especially for postoperative recurrence of lung cancer. In vitro experiments including antibody-dependent cellular cytotoxicity (ADCC), antibody-dependent cellular phagocytosis (ADCP) and complement-dependent cytotoxicity (CDC) were performed to explore the functions of serum autoantibodies.

RESULTS

Our study demonstrated that serum autoantibody-positive patients with early-stage lung cancer had a longer postoperative progression period. The levels of serum autoantibodies in patients with lung cancer were higher than that in patients with benign lung diseases. But all the serum autoantibodies had no difference between patients with stage I and II. In addition, the results of in vitro experiments indicated that serum autoantibodies can mediate immune responses and enhance anti-tumour effects.

CONCLUSION

This study proposed effective biomarkers for prognosis in lung cancer patients after surgery which is critical to reduce the recurrence.

Biomarkers Suitable for Early Detection of Intrathoracic Cancers in Primary Care: A Systematic Review

Intrathoracic cancers, including lung cancer, mesothelioma, and thymoma, present diagnostic challenges in primary care. Biomarkers could resolve some challenges. We synthesized evidence on biomarker performance for intrathoracic cancer detection in low-prevalence settings. A search in Embase and MEDLINE included studies that recruited participants with suspected intrathoracic cancer and reported on at least one diagnostic measure for a validated, noninvasive biomarker. Studies were excluded if participants were recruited based on a preestablished diagnosis. A total of 52 studies were included, reporting on 108 individual biomarkers and panels. Carcinoembryonic antigen, CYFRA 21-1, and VEGF were evaluated for lung cancer and mesothelioma. For lung cancer, carcinoembryonic antigen and CYFRA 21-1 were the most studied, with AUCs of 0.48 to -0.90 and 0.48 to -0.83, respectively. Pro-gastrin-releasing peptide (Pro-GRP) and neuron-specific enolase (NSE) had the highest negative predictive values (NPV) (98.2% and 96.9%, respectively), whereas Early Cancer Detection Test - Lung (Early CDT) and miRNA signature classifier panels showed NPVs of 99.3% and 99.0%, respectively, in smokers. For mesothelioma, fibrillin-3 and mesothelin plus osteopontin had AUCs of 0.93 and 0.91, respectively. Thymoma panels (binding AcHR + StrAb and binding AcHR + modulating AcHR + StrAb) had 100% NPVs in patients with myasthenia gravis. The review highlights the performance of some biomarkers. However, few were evaluated in low-prevalence settings. Further evaluation is necessary before implementing these biomarkers for intrathoracic cancers in primary care.

Evaluation and integration of cell-free DNA signatures for detection of lung cancer

Cell-free DNA (cfDNA) analysis has shown potential in detecting early-stage lung cancer based on non-genetic features. To distinguish patients with lung cancer from healthy individuals, peripheral blood were collected from 926 lung cancer patients and 611 healthy individuals followed by cfDNA extraction. Low-pass whole genome sequencing and targeted methylation sequencing were conducted and various features of cfDNA were evaluated. With our customized algorithm using the most optimal features, the ensemble stacked model was constructed, called ESim-seq (Early Screening tech with Integrated Model). In the independent validation cohort, the ESim-seq model achieved an area under the curve (AUC) of 0.948 (95 % CI: 0.915-0.981), with a sensitivity of 79.3 % (95 % CI: 71.5-87.0 %) across all stages at a specificity of 96.0 % (95 % CI: 90.6-100.0 %). Specifically, the sensitivity of the ESim-seq model was 76.5 % (95 % CI: 67.3-85.8 %) in stage I patients, 100 % (95 % CI: 100.0-100.0 %) in stage II patients, 100 % (95 % CI: 100.0-100.0 %) in stage III patients and 87.5 % (95 % CI: 64.6%-100.0 %) in stage IV patients in the independent validation cohort. Besides, we constructed LCSC model (Lung Cancer Subtype multiple Classification), which was able to accurately distinguish patients with small cell lung cancer from those with non-small cell lung cancer, achieving an AUC of 0.961 (95 % CI: 0.949-0.957). The present study has established a framework for assessing cfDNA features and demonstrated the benefits of integrating multiple features for early detection of lung cancer.

Machine Learning for Early Discrimination Between Lung Cancer and Benign Nodules Using Routine Clinical and Laboratory Data

BACKGROUND

Lung cancer poses a global health threat necessitating early detection and precise staging for improved patient outcomes. This study focuses on developing and validating a machine learning-based risk model for early lung cancer screening and staging, using routine clinical data.

METHODS

Two medical center, observational, retrospective studies were conducted, involving 2312 lung cancer patients and 653 patients with benign nodules. Machine learning techniques, including differential analysis and feature selection, were employed to identify key factors for modeling. The study focused on variables such as nodule density, carcinoembryonic antigen (CEA), age, and lifestyle habits. The Logistic Regression model was utilized for early diagnoses, and the XGBoost model was utilized for staging based on selected features.

RESULTS

For early diagnoses, the Logistic Regression model achieved an area under the curve (AUC) of 0.716 (95% confidence interval [CI] 0.607-0.826), with 0.703 sensitivity and 0.654 specificity. The XGBoost model excelled in distinguishing late-stage from early-stage lung cancer, exhibiting an AUC of 0.913 (95% CI 0.862-0.963), with 0.909 sensitivity and 0.814 specificity. These findings highlight the model's potential for enhancing diagnostic accuracy and staging in lung cancer.

CONCLUSION

This study introduces a novel machine learning-based risk model for early lung cancer screening and staging, leveraging routine clinical information and laboratory data. The model shows promise in enhancing accuracy, mitigating overdiagnosis, and improving patient outcomes.

An analysis of the influencing factors of false negative autoantibodies in patients with non-small cell lung cancer

Objectives

To analyze the clinical significance of seven autoantibodies (P53, PGP9.5, SOX2, GAGE7, GBU4-5, MAGE, and CAGE) in patients with non-small cell lung cancer (NSCLC) and the factors that influence false-negative results.

Methods

Seven autoantibodies were measured in the serum of 502 patients with non-small cell lung cancer (NSCLC) using ELISA, and their correlations with age, sex, smoking history, pathological type, clinical stage, and PD-L1 gene expression were analyzed. The clinicopathological data of the false-negative and positive groups for the seven autoantibodies were compared to determine the influencing factors.

Results

P53 antibody expression level was correlated with lobulation sign, PGP9.5 antibody expression level with sex and vascular convergence; SOX2 antibody expression level with pathological type, clinical stage, and enlarged lymph nodes; and MAGE antibody expression level with the pathological type (P<0.05). False-negative autoantibodies are prone to occur in lung cancer patients with ground-glass nodules, no enlarged lymph nodes, no vascular convergence, and PD-L1 gene expression <1% (P <0.05).

Conclusion

Detection of seven autoantibodies was clinically significant in patients with NSCLC. However, poor sensitivity should be considered in clinical diagnoses to prevent missed diagnoses.

Application value of the automated machine learning model based on modified CT index combined with serological indices in the early prediction of lung cancer

Background and objective

Accurately predicting the extent of lung tumor infiltration is crucial for improving patient survival and cure rates. This study aims to evaluate the application value of an improved CT index combined with serum biomarkers, obtained through an artificial intelligence recognition system analyzing CT features of pulmonary nodules, in early prediction of lung cancer infiltration using machine learning models.

Patients and methods

A retrospective analysis was conducted on clinical data of 803 patients hospitalized for lung cancer treatment from January 2020 to December 2023 at two hospitals: Hospital 1 (Affiliated Changshu Hospital of Soochow University) and Hospital 2 (Nantong Eighth People's Hospital). Data from Hospital 1 were used for internal training, while data from Hospital 2 were used for external validation. Five algorithms, including traditional logistic regression (LR) and machine learning techniques (generalized linear models [GLM], random forest [RF], gradient boosting machine [GBM], deep neural network [DL], and naive Bayes [NB]), were employed to construct models predicting early lung cancer infiltration and were analyzed. The models were comprehensively evaluated through receiver operating characteristic curve (AUC) analysis based on LR, calibration curves, decision curve analysis (DCA), as well as global and individual interpretative analyses using variable feature importance and SHapley additive explanations (SHAP) plots.

Results

A total of 560 patients were used for model development in the training dataset, while a dataset comprising 243 patients was used for external validation. The GBM model exhibited the best performance among the five algorithms, with AUCs of 0.931 and 0.99 in the validation and test sets, respectively, and accuracies of 0.857 and 0.955 in the validation and test groups, respectively, outperforming other models. Additionally, the study found that nodule diameter and average CT value were the most significant features for predicting lung cancer infiltration using machine learning models.

Conclusion

The GBM model established in this study can effectively predict the risk of infiltration in early-stage lung cancer patients, thereby improving the accuracy of lung cancer screening and facilitating timely intervention for infiltrative lung cancer patients by clinicians, leading to early diagnosis and treatment of lung cancer, and ultimately reducing lung cancer-related mortality.

Improvement in the performance of an autoantibody panel in combination with heat shock protein 90a for the detection of early‑stage lung cancer

The early diagnosis of lung cancer is closely associated with the decline of mortality. A panel consisting of seven lung cancer-related autoantibodies (7-AABs) has been shown to be a reliable and specific indicator for the early detection of lung cancer, with a specificity of ~90% and a positive predictive value of ~85%. However, its low sensitivity and negative predictive value limit its wide application. To improve its diagnostic value, the diagnostic efficiencies of 7-AABs in combination with non-specific tumor markers were retrospectively investigated for the detection of early-stage lung cancer. A total of 217 patients with small lung nodules who presented with ground-glass opacity or solid nodules as well as 30 healthy controls were studied. The concentrations of 7-AABs and heat shock protein 90a (HSP90a) were assessed using ELISA. Automated flow fluorescence immune analysis was used for the assessment of CEA, CYFRA21-1, CA199 and CA125 levels. The results showed that 7-AABs + HSP90a possessed a remarkably improved diagnostic efficiency for patients with small pulmonary nodules or for patients with lung nodules of different types, which suggested that 7-AABs in combination with HSP90a could have a high clinical value for the improvement of the diagnostic efficiency of early-stage lung cancer.

Virtual Screening and Quantitative Structure-Activity Relationship of Moringa oleifera with Melanoma Antigen A (MAGE-A) Genes against the Therapeutics of Non-Small Cell Lung Cancers (NSCLCs)

In the last decade, there have been significant advancements in the treatment of non-small cell lung cancer, including remarkable gains in detection, diagnosis, and therapy. The emergence of molecular targeted therapies, immunotherapeutic inhibitors, and antiangiogenesis medicines has largely fueled improvements in combination therapy and systemic treatments, all of which have dramatically ameliorated patient outcomes. The Moringa oleifera bioactive compounds have been effective in the suppression of cancers, making them the therapeutic agents of choice for the current investigation to treat MAGE-A presented in NSCLC. The ligand entrants were screened for their pharmacological properties, and 2,2-diphenyl-1,3-benzodioxole was stipulated as the lead candidate. 2,2-Diphenyl-1,3-benzodioxole exhibited better pharmacological properties and superior binding with branched-chain amino acids, making it an ideal candidate to address MAGE-A. The study concluded that addressing MAGE-A to impede their activity and antigenicity can be exploited as immunotarget(s).

Recent Advances in DNA Vaccines against Lung Cancer: A Mini Review

Lung cancer is regarded as the major causes of patient death around the world. Although the novel tumor immunotherapy has made great progress in the past decades, such as utilizing immune checkpoint inhibitors or oncolytic viruses, the overall 5-year survival of patients with lung cancers is still low. Thus, development of effective vaccines to treat lung cancer is urgently required. In this regard, DNA vaccines are now considered as a promising immunotherapy strategy to activate the host immune system against lung cancer. DNA vaccines are able to induce both effective humoral and cellular immune responses, and they possess several potential advantages such as greater stability, higher safety, and being easier to manufacture compared to conventional vaccination. In the present review, we provide a global overview of the mechanism of cancer DNA vaccines and summarize the innovative neoantigens, delivery platforms, and adjuvants in lung cancer that have been investigated or approved. Importantly, we highlight the recent advance of clinical studies in the field of lung cancer DNA vaccine, focusing on their safety and efficacy, which might accelerate the personalized design of DNA vaccine against lung cancer.

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.

The role of autoantibody detection in the diagnosis and staging of lung cancer

Background

Previously, the clinical value of seven autoantibodies (p53, PGP9.5, SOX2, GAGE7, GBU4-5, MAGEA1, and CAGE) has been surveyed in our pilot observation and other published studies. Herein, we aimed to further investigate the role of these autoantibodies in the diagnosis and staging of LC.

Methods

We included a total of 135 individuals, who were divided into a Lung cancer (LC) group and a control group according to the final diagnosis. Seven autoantibody detection kits were used (ELISA method) for the expression measurement. The patients’ demographics information (e.g., age, gender, and smoking history) were also documented.

Results

Among the seven types of autoantibodies, only P53 and GBU4-5 were significantly increased in the LC group compared to the controls. Also, the P53 autoantibody was markedly different among the various subtype groups. Meanwhile, the GBU4-5 level was significantly higher in the small cell lung cancer (SCLC) patients compared to patients with adenocarcinoma (ADC). Autoantibodies against PGP9.5, SOX2, GBU4-5, and CAGE were found to be associated with stages. Their expressions were notably higher in the advanced stage (IV) versus early stages (I–II). Using logistic regression, the outcomes of LC prediction and stage prediction showed that the area under curve (AUCs) of the receiver operating characteristic (ROC) curves were 0.743 and 0.798, respectively.

Conclusions

In summary, our study confirmed the diagnostic value of tumor-associated autoantibodies, which may be useful as latent tumor markers to facilitate the detection of early LC. Single autoantibody testing is not yet sufficient in LC cancer screening, and the combined detection of autoantibodies can improve the sensitivity of detection compared with single antibody detection, especially for P53, PGP9.5, SOX2, GBU4-5, and CAGE autoantibodies.