Humoral immune response to tumor-associated antigen Ubiquilin 1 (UBQLN1) and its tumor-promoting potential in lung cancer.

BACKGROUND: This study aims to investigate the expression of UBQLN1 in lung cancer (LC) tissue and the diagnostic capability of autoantibody to UBQLN1 (anti-UBQLN1) in the detection of LC and the discrimination of pulmonary nodules (PNs). METHODS: Sera from 798 participants were used to discover and validate the level of autoantibodies via HuProt microarray and Enzyme-linked immunosorbent assay (ELISA). Logistic regression analysis was applied to establish model. Receiver operating characteristic curve (ROC) analysis was performed to evaluate the diagnostic potential. Immunohistochemistry was performed to detect UBQLN1 expression in 88 LC tissues and 88 para-tumor tissues. qRT-PCR and western blotting were performed to detect the expression of UBQLN1 at the mRNA and protein levels, respectively. Trans-well assay and cell counting kit-8 (CCK-8) was used to investigate the function of UBQLN1. RESULTS: Anti-UBQLN1 was identified with the highest fold change by protein microarray. The level of anti-UBQLN1 in LC patients was obviously higher than that in NC or patients with benign lung disease of validation cohort 1 (P<0.05). The area under the curve (AUC) of anti-UBQLN1 was 0.610 (95%CI: 0.508-0.713) while reached at 0.822 (95%CI: 0.784-0.897) when combining anti-UBQLN1 with CEA, CYFRA21-1, CA125 and three CT indicators (vascular notch sign, lobulation sign and mediastinal lymph node enlargement) in the discrimination of PNs. UBQLN1 protein was overexpressed in lung adenocarcinoma (LUAD) tissues compared to para-tumor tissues. UBQLN1 knockdown remarkably inhibited the migration, invasion and proliferation of LUAD cell lines. CONCLUSIONS: Anti-UBQLN1 might be a potential biomarker for the diagnosis of LC and the discrimination of PNs.

Integrative Multianalytical Model Based on Novel Plasma Protein Biomarkers for Distinguishing Lung Adenocarcinoma and Benign Pulmonary Nodules.

Given the pressing clinical problem of making a decision in diagnosis for subjects with pulmonary nodules, we aimed to discover novel plasma protein biomarkers for lung adenocarcinoma (LUAD) and benign pulmonary nodules (BPNs) and then develop an integrative multianalytical model to guide the clinical management of LUAD and BPN patients. Through label-free quantitative plasma proteomic analysis (data are available via ProteomeXchange with identifier PXD046731), 12 differentially expressed proteins (DEPs) in LUAD and BPN were screened. The diagnostic abilities of DEPs were validated in two independent validation cohorts. The results showed that the levels of three candidate proteins (PRDX2, PON1, and APOC3) were lower in the plasma of LUAD than in BPN. The three candidate proteins were combined with three promising computed tomography indicators (spiculation, vascular notch sign, and lobulation) and three traditional markers (CEA, CA125, and CYFRA21-1) to construct an integrative multianalytical model, which was effective in distinguishing LUAD from BPN, with an AUC of 0.904, a sensitivity of 81.44%, and a specificity of 90.14%. Moreover, the model possessed impressive diagnostic performance between early LUADs and BPNs, with the AUC, sensitivity, specificity, and accuracy of 0.868, 65.63%, 90.14%, and 82.52%, respectively. This model may be a useful auxiliary diagnostic tool for LUAD and BPN by achieving a better balance of sensitivity and specificity.

Novel Biomarker miRNA-625-3p Promotes Cell Proliferation and Metastasis of Lung Adenocarcinoma by Targeting KLF9.

BACKGROUND: MicroRNAs (miRNAs) are emerging as potential blood-based biomarkers involved in various types of carcinogenesis, including lung adenocarcinoma (LUAD). MATERIALS AND METHODS: In the present study, microarray was used to screen 2,549 miRNAs in serum samples from seven patients with LUAD and seven from healthy controls. Quantitative real-time polymerase chain reaction was used to validate the expression of miRNA in serum samples from 30 patients with LUAD and 30 heathy individuals. The area under the receiver operating characteristic curve was determined to evaluate the diagnostic capability of miR-625-3p. Cell counting kit-8 assay and Transwell assays were used to explore cell proliferation, migration and invasion. Bioinformatics prediction was applied in the search for the target genes of miR-625-3p. Quantitative real-time polymerase chain reaction, western blot and dual luciferase assay were used to validate target genes of miR-625-3p. A xenograft tumor model was established to evaluate cell proliferation in vivo. RESULTS: miR-625-3p was the miRNA most highly expressed in serum samples from patients with LUAD according to microarray analysis, this finding was verified in sera from an independent cohort, as well as in tissues based on The Cancer Genome Atlas database. Serum miR-625-3p provided a high diagnostic accuracy for LUAD (area under the curve=0.790, 95% confidence interval=0.6640-0.9152). Functionally, miR-625-3p promoted LUAD cell proliferation, migration and invasion both in vivo and in vitro. Mechanistically, we found miR-625-3p promoted cell proliferation and metastasis of LUAD by directly targeting KLF transcription factor 9 (Kruppel-like factor 9, KLF9). CONCLUSION: Our study identified that miR-625-3p plays an oncogenic role in LUAD, targeting KLF9. miR-625-3p might be a potential novel diagnostic biomarker and target for LUAD therapy.

Construction of lncRNA TYMSOS/hsa-miR-101-3p/CEP55 and TYMSOS/hsa-miR-195-5p/CHEK1 Axis in Non-small Cell Lung Cancer.

As novel master regulators of initiation and progression, long non-coding RNAs (lncRNAs) can response to therapy in a wide variety of malignances. This study aimed to explore the regulatory mechanisms in non-small cell lung cancer (NSCLC) by constructing lncRNA-miRNA-mRNA networks. The whole transcriptome sequence was performed in five NSCLC tissues and their corresponding paired adjacent normal tissues. MicroRNAs (miRNAs) and mRNAs expression profiles were obtained from TCGA database. "EdgeR" R package was used for gene expression comparisons and the genes with | log2FC |> 2 and false discovery rate (FDR) adjusted P<0.05 were considered significant. Totally, 559 differentially expressed lncRNAs (DELs) (235 up-regulated, 324 down-regulated) were identified via whole transcriptome sequence analysis. Subsequently, 14 up-regulated lncRNAs were obtained in the intersection of our RNA-seq data and TCGA dataset. The dysregulations of the selected lncRNAs were evaluated through GEPIA. Three candidate lncRNAs (LINC01426, TYMSOS, VPS9D1-AS1) were finally selected for further study. Through bioinformatics prediction, with the three lnRNAs and their associated miRNAs (n=14) and mRNAs (n=218), a lncRNA-miRNA-mRNA network was constructed. Furthermore, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway associated with the targeted genes were proceeded. In addition, the protein-protein interaction (PPI) network construction was performed by STRING and lncRNA-miRNA-mRNA regulatory network was visualized via Cytoscape. The top ten significant hub-genes in the PPI network are identified based on their node degrees. Moreover, the lncRNA-miRNA and miRNA-mRNA interactions were predicted using the StarBase and survival analyses were performed in Kaplan-Meier plotter website. We concluded that TYMSOS/hsa-miR-101-3p/CEP55 and TYMSOS/hsa-miR-195-5p/CHEK1 ceRNA regulatory aixs may play vital roles in the occurrence and development of NSCLC, and may provide novel therapeutic targets in the future.

CT and CEA-based machine learning model for predicting malignant pulmonary nodules.

Computed tomography (CT), an efficient radiological technology, is used to detect lung cancer in the clinic. Carcinoembryonic antigen (CEA), a common tumor biomarker, is applied in the detection of various tumors. To highlight the advantages of two-dimensional techniques and assist clinicians in optimizing lung cancer diagnostic schemes, we established a favorable model combining CT and CEA. In the study, univariate analysis was performed to screen independent predictors in a training cohort of 271 patients with malignant pulmonary nodules (MPNs) and 92 with benign pulmonary nodules (BPNs). Six machine learning-based models involving five CT predictors (mediastinal lymph node enlargement, lobulation, vascular notch sign, spiculation, and nodule number) and lnCEA were constructed and validated in an independent cohort of 129 participants (92 MPNs and 37 BPNs) by SPSS Modeler. A nomogram and the Delong test were generated by R software. Finally, the model established by logistic regression had highest diagnostic efficiency (area under the curve [AUC] = 0.912). Moreover, the diagnostic ability of the logistic model in the validation cohort (AUC = 0.882, 80.4% sensitivity, 75.7% specificity) was higher than that of the Peking University model (AUC = 0.712, 68.5% sensitivity, 70.3% specificity) and the Mayo model (AUC = 0.745, 62.0% sensitivity, 75.7% specificity). Interestingly, for the participants with intermediate (10-30 mm) and CEA-negative nodule, the model reached an AUC of 0.835 (72.3% sensitivity, 83.3% specificity). The AUC for the early lung cancer was as high as 0.822 with 67.3% sensitivity and 78.9% specificity. As a conclusion, this promising model presents a new diagnostic strategy for the clinic to distinguish MPNs from BPNs.

Identification of tumor-associated antigens of lung cancer: SEREX combined with bioinformatics analysis.

The aim of this study is to identify novel tumor-associated antigens (TAAs) of lung cancer by using serological analysis of recombinant cDNA expression library (SEREX) and bioinformatics analysis as well as to explore their humoral immune response. SEREX and pathway enrichment analysis were used to immunoscreen TAAs of lung cancer and elaborate their function in biological pathways, respectively. Subsequently, the sera level of autoantibodies against the selected TAAs (TOP2A, TRIM37, HSP90AB1, EEF1G and TPP1) was detected by immunoserological analysis to explore the immune response of these antigens. The Gene Expression Profiling Interactive Analysis (GEPIA) and Human Protein Atlas (HPA) database were applied to explore the mRNA and protein expression level of TOP2A, TRIM37 and HSP90AB1 in tissues, respectively. Seventy positive clones were identified by SEREX which contain 63 different genes, and 35 genes of them have been reported. These 35 genes were mainly related to regulation of different transcription factor and performed enrichment in legionellosis, RNA transport, IL-17 signaling pathway via enrichment analysis. Additionally, the positive rate of autoantibodies against TOP2A, TRIM37 and HSP90AB1 in lung cancer patients were typically higher than normal control (NC; P < 0.05). Moreover, the combination of the autoantibodies against TOP2A, TRIM37 and HSP90AB1 possessed an excellent diagnostic performance with sensitivity of 84% and specificity of 60%. The mRNA expression level of TOP2A was obviously unregulated in squamous cell carcinoma (SCC) tissues and adenocarcinoma (ADC) tissues compared to normal tissues (P < 0.05). In addition, TRIM37 and HSP90AB1 also showed a significant difference between SCC and NC at the mRNA expression level (P < 0.05). This study combining comprehensive autoantibody and gene expression assays has added to the growing list of lung cancer antigens, which may aid the development of diagnostic and immunotherapeutic targets for lung cancer patients.

Serum MiR-4687-3p Has Potential for Diagnosis and Carcinogenesis in Non-small Cell Lung Cancer.

The lack of a useful biomarker partly contributes to the increased mortality of non-small cell lung cancer (NSCLC). MiRNAs have become increasingly appreciated in diagnosis of NSCLC. In the present study, we used microarray to screen 2,549 miRNAs in serum samples from the training cohort (NSCLC, n = 10; the healthy, n = 10) to discover differentially expressed miRNAs (DEMs). Quantitative reverse-transcription polymerase chain reaction (qRT-PCR) assay was applied to validate the expression level of selected overexpressed DEMs of NSCLC in a validation cohort (NSCLC, n = 30; the healthy, n = 30). Area under the receiver operating characteristic curve (AUC) was performed to evaluate diagnostic capability of the DEMs. The expression of the miRNAs in tissues was analyzed based on the TCGA database. Subsequently, the target genes of the miR-4687-3p were predicted by TargetScan. Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were tested by R software (ClusterProfiler package). NSCLC cells were transfected with inhibitor or mimic to down-regulate or up-regulate the miR-4687-3p level. The function of miR-4687-3p on proliferation, invasion, and migration of lung cancer cells were investigated through CCK-8 and Transwell assays, respectively. In the results, we identified serum miR-4687-3p that provided a high diagnostic accuracy of NSCLC (AUC = 0.679, 95%CI: 0.543-0.815) in the validation cohort. According to the TCGA database, we found that the miR-4687-3p level was significantly higher in NSCLC tissues than in normal lung tissues (p < 0.05). GO and KEGG pathway enrichment analysis showed that postsynaptic specialization and TGF-ß signaling pathway were significantly enriched. Down-regulation of miR-4687-3p could suppress the proliferation, invasion, and migration of the NSCLC cells, compared with inhibitor negative control (NC). Meanwhile, overexpression of miR-4687-3p could promote the proliferation, invasion, and migration of the NSCLC cells compared with mimic NC. As a conclusion, our study first discovered that serum miR-4687-3p might have clinical potential as a non-invasive diagnostic biomarker for NSCLC and play an important role in the development of NSCLC.

A panel of autoantibodies against tumor-associated antigens in the early immunodiagnosis of lung cancer.

OBJECTIVE: Lung cancer (LC) is one of the most common malignant tumors worldwide with low five-year survival rate due to lack of effective diagnosis. This study aims to find an optimal combination of autoantibodies for detecting of early-stage LC. METHODS: Nine relatively novel autoantibodies against tumor-associated (TAAs) (PSIP1, TOP2A, ACTR3, RPS6KA5, HMGB3, MMP12, GREM1, ZWINT and NUSAP1) were detected by using ELISA. Diagnostic models were developed by using the training set (n = 644) and further validated in another independent set (n = 248). We also evaluated the diagnostic accuracy of the model to detect benign lung diseases (BLD) from the early-stage lung cancer. RESULTS: The areas under the receiver operating characteristic curve (AUC) for the model with three TAAs panel (GREM1, HMGB3 and PSIP1) was 0.711(95% CI 0.674-0.746) in the training set and 0.858 (95% CI 0.808-0.899) in the validation set, which demonstrated a higher diagnostic capability. The AUC of this three TAAs model was 0.833 (95%CI 0.780-0.878) in discriminating LC from BLD. This model could identify early-stage LC patients from normal control (NC) individuals, with AUC of 0.687(95% CI 0.634-0.736) in training set and AUC of 0.920(95% CI 0.860-0.960) in validation set, and the overall AUC for early-stage LC was 0.779(95% CI 0.739-0.816) when the training set and validation set were combined. CONCLUSIONS: The model with three TAAs panel would detect LC with higher effectiveness, and might be potential screening method for the early LC.