Stem signatures associated antibodies yield early diagnosis and precise prognosis predication of patients with non-small cell lung cancer

Autoantibodies in cancer: a systematic review of their clinical role in the most prevalent cancers

Although blood autoantibodies were initially associated with autoimmune diseases, multiple evidence have been accumulated showing their presence in many types of cancer. This has opened their use in clinics, since cancer autoantibodies might be useful for early detection, prognosis, and monitoring of cancer patients. In this review, we discuss the different techniques available for their discovery and validation. Additionally, we discuss here in detail those autoantibody panels verified in at least two different reports that should be more likely to be specific of each of the four most incident cancers. We also report the recent developed kits for breast and lung cancer detection mostly based on autoantibodies and the identification of novel therapeutic targets because of the screening of the cancer humoral immune response. Finally, we discuss unsolved issues that still need to be addressed for the implementation of cancer autoantibodies in clinical routine for cancer diagnosis, prognosis, and/or monitoring.

Extracellular Vesicles Derived from Glioma Stem Cells Affect Glycometabolic Reprogramming of Glioma Cells Through the miR-10b-5p/PTEN/PI3K/Akt Pathway

OBJECTIVE

Glioma is one of the most prevalently diagnosed types of primary malignant brain tumors. Glioma stem cells (GSCs) are crucial in glioma recurrence. This study aims to elucidate the mechanism by which extracellular vehicles (EVs) derived from GSCs modulate glycometabolic reprogramming in glioma.

METHODS

Xenograft mouse models and cell models of glioma were established and treated with GSC-EVs. Additionally, levels and activities of PFK1, LDHA, and FASN were assessed to evaluate the effect of GSC-EVs on glycometabolic reprogramming in glioma. Glioma cell proliferation, invasion, and migration were evaluated using MTT, EdU, Colony formation, and Transwell assays. miR-10b-5p expression was determined, with its target gene PTEN and downstream pathway PI3K/Akt evaluated. The involvement of miR-10b-5p and the PI3K/Akt pathway in the effect of GSC-EVs on glycometabolic reprogramming was tested through joint experiments.

RESULTS

GSC-EVs facilitated glycometabolic reprogramming in glioma mice, along with enhancing glucose uptake, lactate level, and adenosine monophosphate-to-adenosine triphosphate ratio. Moreover, GSC-EV treatment potentiated glioma cell proliferation, invasion, and migration, reinforced cell resistance to temozolomide, and raised levels and activities of PFK1, LDHA, and FASN. miR-10b-5p was highly-expressed in GSC-EV-treated glioma cells while being carried into glioma cells by GSC-EVs. miR-10b-5p targeted PTEN and activated the PI3K/Akt pathway, hence stimulating glycometabolic reprogramming.

CONCLUSION

GSC-EVs target PTEN and activate the PI3K/Akt pathway through carrying miR-10b-5p, subsequently accelerating glycometabolic reprogramming in glioma, which might provide new insights into glioma treatment.

Identification of biomarkers for the early detection of non-small cell lung cancer: a systematic review and meta-analysis

Lung cancer (LC) causes few symptoms in the earliest stages, leading to one of the highest mortality rates among cancers. Low-dose computerised tomography (LDCT) is used to screen high-risk individuals, reducing the mortality rate by 20%. However, LDCT results in a high number of false positives and is associated with unnecessary follow-up and cost. Biomarkers with high sensitivities and specificities could assist in the early detection of LC, especially in patients with high-risk features. Carcinoembryonic antigen (CEA), cytokeratin 19 fragments and cancer antigen 125 have been found to be highly expressed during the later stages of LC but have low sensitivity in the earliest stages. We determined the best biomarkers for the early diagnosis of LC, using a systematic review of eight databases. We identified 98 articles that focussed on the identification and assessment of diagnostic biomarkers and achieved a pooled area under curve of 0.85 (95% CI 0.82-0.088), indicating that the diagnostic performance of these biomarkers when combined was excellent. Of the studies, 30 focussed on single/antigen panels, 22 on autoantibodies, 31 on miRNA and RNA panels, and 15 suggested the use of circulating DNA combined with CEA or neuron-specific enolase (NSE) for early LC detection. Verification of blood biomarkers with high sensitivities (Ciz1, exoGCC2, ITGA2B), high specificities (CYFR21-1, antiHE4, OPNV) or both (HSP90α, CEA) along with miR-15b and miR-27b/miR-21 from sputum may improve early LC detection. Further assessment is needed using appropriate sample sizes, control groups that include patients with non-malignant conditions, and standardised cut-off levels for each biomarker.

Diagnostic performance of anti-MAGEA family protein autoantibodies in esophageal squamous cell carcinoma

MAGEA family proteins are immunogenic and can produce corresponding autoantibodies, and we aim to evaluate the diagnostic value of anti-MAGEA family protein autoantibodies in esophageal squamous cell carcinoma (ESCC). Protein chip was used to detect the expression level of anti-MAGEA autoantibodies (IgG and IgM) in 20 mixed serum samples. Enzyme linked immunosorbent assay was adopted to determine the expression level of autoantibodies in 1019 serum samples (423 ESCC, 423 healthy control (HC), 173 benign esophageal disease (BED)), and stepwise logistic regression analysis was used for developing a diagnostic model. Eight anti-MAGEA autoantibodies were screened out based on the protein chip. The levels of 7 autoantibodies (MAGEA1-IgG, MAGEA3-IgG, MAGEA3-IgM, MAGEA4-IgG, MAGEA6-IgG, MAGEA10-IgG, MAGEA12-IgG) in ESCC were significantly higher than that in HC, and the levels of anti-MAGEA1 IgG, anti-MAGEA3-IgG, anti-MAGEA4-IgG, anti-MAGEA10-IgG and anti-MAGEA12-IgG autoantibodies in ESCC group were significantly higher than those in BED group. The area under curve (AUC), sensitivity and specificity of the logistic regression model (MAGEA1-IgG, MAGEA4-IgG, MAGEA6-IgG, MAGEA12-IgG) in the training set and the validation set were 0.725 and 0.698, 55.2% and 51.8%, 80.4% and 84.5%, respectively, in distinguishing ESCC and HC. The model also could distinguish between ESCC and BED, with the AUC of 0.743, sensitivity of 55.4% and specificity of 89.0%. The positive rate of the model combined with cytokeratin 19 fragment to diagnose ESCC reached 78.0%. The study identified anti-MAGEA autoantibodies with potential diagnostic value for ESCC, which may provide new promising for the detection of the disease.

Identification of novel tumor-associated antigens and evaluation of a panel of autoantibodies in detecting oral cancer

BACKGROUND

We aimed to identify tumor-associated antigen (TAA) biomarkers through bioinformatic analysis and experimental verification, and to evaluate a panel of autoantibodies against tumor-associated antigens (TAAbs) for the detection of oral cancer (OC).

METHODS

GEO and TCGA databases were used to screen significantly up-regulated genes related to OC, and protein-protein interaction (PPI) analysis and Cystoscope software were used to identify key genes. Enzyme-linked immunosorbent assay (ELISA) was used to detect the expression levels of autoantibodies in 173 OC patients and 173 normal controls, and binary logistic regression analysis was used to build a diagnostic model.

RESULTS

Using bioinformatics, we identified 10 key genes (AURKA, AURKB, CXCL8, CXCL10, COL1A1, FN1, FOXM1, MMP9, SPP1 and UBE2C) that were highly expressed in OC. Three autoantibodies (anti-AURKA, anti-CXCL10, anti-FOXM1) were proven to have diagnostic value for OC in the verification set and the validation set. The combined assessment of these three autoantibodies improved the diagnostic value for OC, with an area under the curve (AUC), sensitivity and specificity of 0.741(95%CI:0.690-0.793),58.4% and 80.4%, respectively. In addition, the combination of these three autoantibodies also had high diagnostic value for oral squamous cell carcinoma (OSCC), with an AUC, sensitivity and specificity of 0.731(95%CI:0.674,0.786), 53.8% and 82.1%, respectively.

CONCLUSIONS

Our study revealed that AURKA, CXCL10 and FOXM1 may be potential biomarkers and the panel of three autoantibodies (anti-AURKA, anti-CXCL10 and anti-FOXM1) had good diagnostic value for OC.

Enhancement of TKI sensitivity in lung adenocarcinoma through m6A-dependent translational repression of Wnt signaling by circ-FBXW7

BACKGROUND

Tyrosine kinase inhibitors (TKIs) that specifically target mutational points in the EGFR gene have significantly reduced suffering and provided greater relief to patients with lung adenocarcinoma (LUAD). The third-generation EGFR-TKI, Osimertinib, has been successfully employed in clinical treatments to overcome resistance to both original and acquired T790M and L858R mutational points. Nevertheless, the issue of treatment failure response has emerged as an insurmountable problem.

METHODS

By employing a combination of multiple and integrated approaches, we successfully identified a distinct population within the tumor group that plays a significant role in carcinogenesis, resistance, and recurrence. Our research suggests that addressing TKI resistance may involve targeting the renewal and repopulation of stem-like cells. To investigate the underlying mechanisms, we conducted RNA Microarray and m6A Epi-Transcriptomic Microarray analyses, followed by assessment of transcription factors. Additionally, we specifically designed a tag to detect the polypeptide circRNA-AA, and its expression was confirmed through m6A regulations.

RESULTS

We initially identified unique molecular signatures present in cancer stem cells that contributed to poor therapeutic responses. Activation of the alternative Wnt pathway was found to sustain the renewal and resistant status of these cells. Through bioinformatics analysis and array studies, we observed a significant decrease in the expression of circFBXW7 in Osimertinib-resistant cell lines. Notably, the abnormal expression pattern of circFBXW7 determined the cellular response to Osimertinib. Functional investigations revealed that circFBXW7 inhibits the renewal of cancer stem cells and resensitizes both resistant LUAD cells and stem cells to Osimertinib. In terms of the underlying mechanism, we discovered that circFBXW7 can be translated into short polypeptides known as circFBXW7-185AA. These polypeptides interact with β-catenin in an m6A-dependent manner. This interaction leads to reduced stability of β-catenin by inducing subsequent ubiquitination, thereby suppressing the activation of canonical Wnt signaling. Additionally, we predicted that the m6A reader, YTHDF3, shares common binding sites with hsa-Let-7d-5p. Enforced expression of Let-7d post-transcriptionally decreases the levels of YTHDF3. The repression of Let-7d by Wnt signaling releases the stimulation of m6A modification by YTHDF3, promoting the translation of circFBXW7-185AA. This creates a positive feedback loop contributing to the cascade of cancer initiation and promotion.

CONCLUSIONS

Our bench study, in vivo experiments, and clinical validation have unequivocally shown that circFBXW7 effectively inhibits the abilities of LUAD stem cells and reverses resistance to TKIs by modulating Wnt pathway functions through the action of circFBXW7-185AA on β-catenin ubiquitination and inhibition. The regulatory role of circRNA in Osimertinib treatment has been rarely reported, and our findings reveal that this process operates under the influence of m6A modification. These results highlight the tremendous potential of this approach in enhancing therapeutic strategies and overcoming resistance to multiple TKI treatments.

Improving the efficiency of identifying malignant pulmonary nodules before surgery via a combination of artificial intelligence CT image recognition and serum autoantibodies

OBJECTIVE

To construct a new pulmonary nodule diagnostic model with high diagnostic efficiency, non-invasive and simple to measure.

METHODS

This study included 424 patients with radioactive pulmonary nodules who underwent preoperative 7-autoantibody (7-AAB) panel testing, CT-based AI diagnosis, and pathological diagnosis by surgical resection. The patients were randomly divided into a training set (n = 212) and a validation set (n = 212). The nomogram was developed through forward stepwise logistic regression based on the predictive factors identified by univariate and multivariate analyses in the training set and was verified internally in the verification set.

RESULTS

A diagnostic nomogram was constructed based on the statistically significant variables of age as well as CT-based AI diagnostic, 7-AAB panel, and CEA test results. In the validation set, the sensitivity, specificity, positive predictive value, and AUC were 82.29%, 90.48%, 97.24%, and 0.899 (95%[CI], 0.851-0.936), respectively. The nomogram showed significantly higher sensitivity than the 7-AAB panel test result (82.29% vs. 35.88%, p < 0.001) and CEA (82.29% vs. 18.82%, p < 0.001); it also had a significantly higher specificity than AI diagnosis (90.48% vs. 69.04%, p = 0.022). For lesions with a diameter of ≤ 2 cm, the specificity of the Nomogram was higher than that of the AI diagnostic system (90.00% vs. 67.50%, p = 0.022).

CONCLUSIONS

Based on the combination of a 7-AAB panel, an AI diagnostic system, and other clinical features, our Nomogram demonstrated good diagnostic performance in distinguishing lung nodules, especially those with ≤ 2 cm diameters.

KEY POINTS

• A novel diagnostic model of lung nodules was constructed by combining high-specific tumor markers with a high-sensitivity artificial intelligence diagnostic system. • The diagnostic model has good diagnostic performance in distinguishing malignant and benign pulmonary nodules, especially for nodules smaller than 2 cm. • The diagnostic model can assist the clinical decision-making of pulmonary nodules, with the advantages of high diagnostic efficiency, noninvasive, and simple measurement.

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.

Blood protein biomarkers in lung cancer

Lung cancer has consistently ranked first as the cause of cancer-associated mortality. The 5-year survival rate has risen slowly, and the main obstacle to improving the prognosis of patients has been that lung cancer is usually diagnosed at an advanced or incurable stage. Thus, early detection and timely intervention are the most effective ways to reduce lung cancer mortality. Tumor-specific molecules and cellular elements are abundant in circulation, providing real-time information in a noninvasive and cost-effective manner during lung cancer development. These circulating biomarkers are emerging as promising tools for early detection of lung cancer and can be used to supplement computed tomography screening, as well as for prognosis prediction and treatment response monitoring. Serum and plasma are the main sources of circulating biomarkers, and protein biomarkers have been most extensively studied. In this review, we summarize the research progress on three most common types of blood protein biomarkers (tumor-associated antigens, autoantibodies, and exosomal proteins) in lung cancer. This review will potentially guide researchers toward a more comprehensive understanding of candidate lung cancer protein biomarkers in the blood to facilitate their translation to the clinic.

Stem signatures associating SOX2 antibody helps to define diagnosis and prognosis prediction with esophageal cancer

BACKGROUND

esophageal cancer is one of the deadliest diseases worldwide. Due to the ineffectual screening methods referring to early diagnosis, most people have lost their chance of radical resection when diagnosed with esophageal cancer. This aim of this study was designed to evaluate the latent values of the stem signatures-associated autoantibodies (AABS) in predicting the early diagnosis, and particularly seeking the precise predictive outcomes with sensitive SOX2. We also studied the potential immunotherapeutic targets and prospective long-term prognosis predicators of esophageal cancer.

METHODS

The serum concentrations of selective antibodies were quantitated by enzyme-linked immunosorbent assay (ELISA), and a total of 203 local cases were enrolled. The TCGA databases were used to analyse distinct expression patterns and prognostic values of related genes. The TIMER database was used to explore the signatures of immune cell infiltration in related genes. The TISIDB database was used to analyse the association between related genes and immune regulators.

RESULTS

The stem signatures-associated with antibodies of TP53, PGP9.5, SOX2, and CAGE were highly expressed in esophageal cancer and were negatively correlated with the test group, the diagnostic sensitivity of P53, SOX2, PGP9.5 and CAGE reached to 54.3%, 56.5%, 80.4% and 47.8%, respectively, and the specificity reached 77.7%, 93.6%, 76.4% and 86.6%. Especially in stage I esophageal cancer, the diagnostic sensitivity of SOX2 reached 82.4% with a specificity of 85.4%, which demonstrated good value in early diagnosis.

CONCLUSIONS

The stem signatures-associated antibodies could be used as an effective indicator in early esophageal cancer diagnosis and could help to precisely predicate survival and prognosis.Key MessagesThe stem signatures-associated immune-antibodies could be used as effective indicators in early diagnosis of esophageal cancer and help to precisely predicate the survival and prognosis.The potential immunotherapeutic targets referring to esophageal cancer are screened and analysed, and the high sensitivity of SOX2 in detecting early esophageal cancer will yield early and effective treatments.

[Clinical Value of Autoantibody Prognostic Markers in Tumor Immune Checkpoint Inhibitor Therapy]

Serum autoantibody markers have the advantages of easy specimen acquisition, simple detection technology and dynamic real-time monitoring. With the wide application of immune checkpoint inhibitors in the treatment of malignant tumors, autoantibody markers in predicting tumor immune checkpoint inhibitors efficacy and forecasting irAEs (immune related adverse events) show good prediction of potential. This review mainly focused on the progress of autoantibody markers in the prediction of therapeutic effect and the monitoring of irAE in tumor immunotherapy.
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Long non-coding RNAs and exosomal lncRNAs: Potential functions in lung cancer progression, drug resistance and tumor microenvironment remodeling

Among the different kinds of tumors threatening human life, lung cancer is one that is commonly observed in both males and females. The aggressive behavior of lung cancer and interactions occurring in tumor microenvironment enhances the malignancy of this tumor. The lung tumor cells have demonstrated capacity in developing chemo- and radio-resistance. LncRNAs are a category of non-coding RNAs that do not encode proteins, but their aberrant expression is responsible for tumor development, especially lung cancer. In the present review, we focus on both lncRNAs and exosomal lncRNAs in lung cancer, and their ability in regulating proliferation and metastasis. Cell cycle progression and molecular mechanisms related to lung cancer metastasis such as EMT and MMPs are regulated by lncRNAs. LncRNAs interact with miRNAs, STAT, Wnt, EZH2, PTEN and PI3K/Akt signaling pathways to affect progression of lung cancer cells. LncRNAs demonstrate both tumor-suppressor and tumor-promoting functions in lung cancer. They can be considered as biomarkers in lung cancer and especially exosomal lncRNAs present in body fluids are potential tools for minimally invasive diagnosis. Furthermore, we discuss regulation of lncRNAs by anti-cancer drugs and genetic tools as well as the role of these factors in therapy response of lung cancer cells.

Evaluating a Panel of Autoantibodies Against Tumor-Associated Antigens in Human Osteosarcoma

Background: The aim of this study was to identify a panel of candidate autoantibodies against tumor-associated antigens in the detection of osteosarcoma (OS) so as to provide a theoretical basis for constructing a non-invasive serological diagnosis method in early immunodiagnosis of OS. Methods: The serological proteome analysis (SERPA) approach was used to select candidate anti-TAA autoantibodies. Then, indirect enzyme-linked immunosorbent assay (ELISA) was used to verify the expression levels of eight candidate autoantibodies in the serum of 51 OS cases, 28 osteochondroma (OC), and 51 normal human sera (NHS). The rank-sum test was used to compare the content of eight autoantibodies in the sera of three groups. The diagnostic value of each indicator for OS was analyzed by an ROC curve. Differential autoantibodies between OS and NHS were screened. Then, a binary logistic regression model was used to establish a prediction logistical regression model. Results: Through ELISA, the expression levels of seven autoantibodies (ENO1, GAPDH, HSP27, HSP60, PDLIM1, STMN1, and TPI1) in OS patients were identified higher than those in healthy patients (p < 0.05). By establishing a binary logistic regression predictive model, the optimal panel including three anti-TAAs (ENO1, GAPDH, and TPI1) autoantibodies was screened out. The sensitivity, specificity, Youden index, accuracy, and AUC of diagnosis of OS were 70.59%, 86.27%, 0.5686, 78.43%, and 0.798, respectively. Conclusion: The results proved that through establishing a predictive model, an optimal panel of autoantibodies could help detect OS from OC or NHS at an early stage, which could be used as a promising and powerful tool in clinical practice.

Stimulation of Let-7 Maturation by Metformin Improved the Response to Tyrosine Kinase Inhibitor Therapy in an m6A Dependent Manner

The molecular mechanism of the tyrosine kinase inhibitor (TKI) resistant lung adenocarcinoma is currently unclear, and the role of methylated adenosine at the N6 position in the resistance of cancer stem cells (CSCs) therapy is unknown. This study identified a novel and effective strategy to enhance TKIs therapy response. We first confirmed the sensitization of Metformin enforcing on Osimertinib treatment and revealed the mature miRNAs signatures of the Osimertinib resistant H1975 and HCC827 cells. Let-7b expression was stimulated when adding Metformin and then increasing the therapy sensitivity by decreasing the stem cell groups expanding. Methyltransferase-like 3 (METTL3) increased the pri-Let-7b, decreased both the pre-Let-7b and mature Let-7b, attenuating the Let-7b controlling of stem cell renewal. The addition of Metformin increased the bindings of DNA methyltransferase-3a/b (DNMT3a/b) to the METTL3 promoter. With the help of the readers of NKAP and HNRNPA2B1, the cluster mediated m6A formation on pri-Let-7b processing increased the mature Let-7b, the key player in suppressing Notch signaling and re-captivating Osimertinib treatment. We revealed that the maturation processing signaling stimulated the methylation regulation of the miRNAs, and may determine the stemness control of the therapy resistance. Our findings may open up future drug development, targeting this pathway for lung cancer patients.

A Panel of Tumor-associated Autoantibodies for the Detection of Early-stage Breast Cancer

We previously found a panel of autoantibodies against multiple tumor-associated antigens (BMI-1, HSP70, MMP-7, NY-ESO-1, p53 and PRDX6) that might facilitate early detection of esophagogastric junction adenocarcinoma and esophageal squamous cell carcinoma. Here we aimed at assessing the diagnostic performance of these autoantibodies in breast cancer patients. Enzyme-linked immunosorbent assay was applied to detect sera autoantibodies in 123 breast cancer patients and 123 age-matched normal controls. We adopted logistic regression analysis to identify optimized autoantibody biomarkers for diagnosis and receiver-operating characteristics to analyze diagnostic efficiency. Five of six autoantibodies, BMI-1, HSP70, NY-ESO-1, p53 and PRDX6 demonstrated significantly elevated serum levels in breast cancer compared to normal controls. An optimized panel composed of autoantibodies to BMI-1, HSP70, NY-ESO-1 and p53 showed an area under the curve (AUC) of 0.819 (95% CI 0.766-0.873), 63.4% sensitivity and 90.2% specificity for diagnosing breast cancer. Moreover, this autoantibody panel could differentiate patients with early stage breast cancer from normal controls, with AUC of 0.805 (95% CI 0.743-0.886), 59.6% sensitivity and 90.2% specificity. Our findings indicated that the panel of autoantibodies to BMI-1, HSP70, NY-ESO-1 and p53 as serum biomarkers have the potential to help detect early stage breast cancer.

Lung cancer pathogenesis and poor response to therapy were dependent on driver oncogenic mutations

AIMS

Lung cancer was the most fatal malignancy, dominated the cancer related mortality list for years, and we tried to distinguish the lung adenocarcinoma patients at higher risk from those bearing lower therapy resistance and recurrence risk.

MATERIALS

Patients information from clinical Sequencing Cohorts and from the Regional Medical Center of the Middle-West China were included. The whole-exome sequencing was analyzed for risk evaluation.

KEY FINDINGS

We found that Smoking stimulated the oncogenic genes mutations, and the most frequently mutated genes of EGFR, KRAS, and TP53 (E/K/P) were identified. Different N stage affected the survival prognosis of patients bearing E/K/P mutations, but the T stage and AJCC stage did not. Radiation failed to prolong survival of phase II/III patients who didn't receive surgery. In those received surgery, radiation also failed to prolong survival of phase II/III patients. Radiation did not improve the prognosis in patients bearing E/K/P mutation burdens, whose differences were identified in gender or smoking-history classified groups.

SIGNIFICANCE

Smoking status and history contributed to oncogenic mutation burdens associated therapy resistance, and the aggressive treatment, especially to radiation, may lead to worse therapy response to current and past smoking behavior.

Further discussion on the association between desmoglein 2 and tumor size of non-small cell lung cancer

We have read the article by Cai et al. and find there is a discrepancy between their data and conclusion. Their statement, "Specifically, DSG2 expression was associated with tumor size", is not supported by their own clinicopathological data and analysis. After reviewing some similar articles, we also found no available evidence showed a statistically significant association between them. Therefore, we would like to suggest Cai et al. to rectify the results they published.