MAD2 Combined with Mitotic Spindle Apparatus (MSA) and Anticentromere Antibody (ACA) for Diagnosis of Small Cell Lung Cancer (SCLC)

The mitotic spindle-related seven-gene predicts the prognosis and immune microenvironment of lung adenocarcinoma

PURPOSE

Abnormalities in the mitotic spindle have been linked to a variety of cancers. Data on their role in the onset, progression, and treatment of lung adenocarcinoma (LUAD) need to be explored.

METHODS

The data were retrieved from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and Molecular Signatures Database (MSigDB), for the training cohort, external validation cohort, and the hallmark mitotic spindle gene set, respectively. Mitotic spindle genes linked to LUAD prognosis were identified and intersected with differentially expressed up-regulated genes in the training cohort. Nomogram prediction models were built based on least absolute shrinkage and selection operator (LASSO) regression, univariate cox, and multivariate cox analyses. The seven-gene immunological score was examined, as well as the correlation of immune checkpoints. The DLGAP5 and KIF15 expression in BEAS-2B, A549, H1299, H1975, and PC-9 cell lines was validated with western blot (WB).

RESULTS

A total of 965 differentially expressed up-regulated genes in the training cohort intersected with 51 mitotic spindle genes associated with LUAD prognosis. Finally, the seven-gene risk score was determined and integrated with clinical characteristics to construct the nomogram model. Immune cell correlation analysis revealed a negative correlation between seven-gene expression with B cell, endothelial cell (excluding LMNB1), and T cell CD8 + (p < 0.05). However, the seven-gene expression was positively correlated with multiple immune checkpoints (p < 0.05). The expression of DLGAP5 and KIF15 were significantly higher in A549, H1299, H1975, and PC-9 cell lines than that in BEAS-2B cell line.

CONCLUSION

High expression of the seven genes is positively correlated with poor prognosis of LUAD, and these genes are promising as prospective immunotherapy targets.

SPDL1 Overexpression Is Associated With the 18F-FDG PET/CT Metabolic Parameters, Prognosis, and Progression of Esophageal Cancer

Esophageal cancer (ESCA) is one of the common malignant tumors. The roles and signaling mechanisms of spindle apparatus coiled-coil protein 1 (SPDL1) in ESCA progression have not been reported previously. Therefore, the expression levels and potential clinical roles of SPDL1 were investigated using data from multiple databases and tissue samples of 53 ESCA patients who underwent 18F-FDG positron emission tomography (PET)/computed tomography (CT) before therapy. The signaling mechanisms of SPDL1 involved in ESCA progression were investigated via bioinformatics analysis. The effects of SPDL1 on the growth and migration of ESCA cells were investigated using CCK-8, Edu, and transwell assays. SPDL1 was upregulated in ESCA tissues. Increased SPDL1 expression was associated with age, grade, drinking history, cancer stage, lymph node metastasis, TP53 mutation, and poor prognosis in patients with ESCA. SPDL1 overexpression was significantly correlated with SUVmax, SUVmean, and TLG of PET/CT. SPDL1 silencing inhibited cell proliferation, migration, and invasion. SPDL1 was significantly enriched in cell cycle, spliceosome, DNA replication, and other processes. The hub genes of a constructed protein–protein interaction network included CDK1, BUB1, CCNB1, BUB1B, CCNA2, CDC20, MAD2L1, AURKB, NDC80, and PLK1, which were related to SPDL1 expression. The findings of this study suggest that SPDL1 may serve as a biomarker of ESCA prognosis.

TRAP1 Shows Clinical Significance in the Early Diagnosis of Small Cell Lung Cancer

Purpose

To explore the clinical significance of tumor necrosis factor receptor-associated protein (TRAP1), mitotic arrest deficient 2 (mad2) and anti-nuclear mitotic spindle apparatus antibody (MSA) in the diagnosis of small cell lung cancer (SCLC).

Patients and Methods

Serum concentrations of TRAP1 and MSA were determined by enzyme-linked immunosorbent assay (ELISA), including SCLC group (Num.=86), non-small cell lung cancer (NSCLC) group (Num.=105), pulmonary nodules (PN) group (Num.=94), and 60 healthy subjects as control group (Num.=60). Whereas fluorescence quantitative PCR (qt-PCR) method was used to detect the expression of mad2.

Results

The expression of TRAP1 was low in SCLC and NSCLC compared with the other two groups, and was the lowest in SCLC, which was negatively correlated with the occurrence of the disease (P<0.05); the sensitivity and specificity of TRAP1 for SCLC were 75.29%, 93.33%, and the area under SCLC curve was 0.903; compared with the other three groups, the level of MSA was the highest in the SCLC, and the results were significantly different (P<0.05), while the area under the SCLC curve was 0.856, and the sensitivity and specificity were 62.78% and 95.24%, respectively. Mad2 is overexpressed in SCLC, but not in PN. The area under the SCLC curve is 0.835, and the sensitivity and specificity are 56.98% and 92.38%; TRAP1 levels are negatively correlated with SCLC tumor stage, the level of TRAP1 was significantly lower in stage III–IV than in stage I–II (P<0.05); combined analysis of TRAP1 and MAD2 and MSA showed that the sensitivity and specificity for SCLS were 95.35% and 99.05%, respectively.

Conclusion

TRAP1 is of great value in the early diagnosis of SCLC as well as differential diagnosis with NSCLC. TRAP1 combined with MAD2 and MSA improved the sensitivity and specificity and provided a new idea for the clinical diagnosis of SCLC.

Machine Learning Models to Predict Primary Sites of Metastatic Cervical Carcinoma From Unknown Primary

Metastatic cervical carcinoma from unknown primary (MCCUP) accounts for 1–4% of all head and neck tumors, and identifying the primary site in MCCUP is challenging. The most common histopathological type of MCCUP is squamous cell carcinoma (SCC), and it remains difficult to identify the primary site pathologically. Therefore, it seems necessary and urgent to develop novel and effective methods to determine the primary site in MCCUP. In the present study, the RNA sequencing data of four types of SCC and Pan-Cancer from the cancer genome atlas (TCGA) were obtained. And after data pre-processing, their differentially expressed genes (DEGs) were identified, respectively. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis indicated that these significantly changed genes of four types of SCC share lots of similar molecular functions and histological features. Then three machine learning models, [Random Forest (RF), support vector machine (SVM), and neural network (NN)] which consisted of ten genes to distinguish these four types of SCC were developed. Among the three models with prediction tests, the RF model worked best in the external validation set, with an overall predictive accuracy of 88.2%, sensitivity of 88.71%, and specificity of 95.42%. The NN model is the second in efficacy, with an overall accuracy of 82.02%, sensitivity of 81.23%, and specificity of 93.04%. The SVM model is the last, with an overall accuracy of 76.69%, sensitivity of 74.81%, and specificity of 90.84%. The present analysis of similarities and differences among the four types of SCC, and novel models developments for distinguishing four types of SCC with informatics methods shed lights on precision MCCUP diagnosis in the future.

Identification of potential target genes and crucial pathways in small cell lung cancer based on bioinformatic strategy and human samples

Small cell lung cancer (SCLC) is a carcinoma of the lungs with strong invasion, poor prognosis and resistant to multiple chemotherapeutic drugs. It has posed severe challenges for the effective treatment of lung cancer. Therefore, searching for genes related to the development and prognosis of SCLC and uncovering their underlying molecular mechanisms are urgent problems to be resolved. This study is aimed at exploring the potential pathogenic and prognostic crucial genes and key pathways of SCLC via bioinformatic analysis of public datasets. Firstly, 117 SCLC samples and 51 normal lung samples were collected and analyzed from three gene expression datasets. Then, 102 up-regulated and 106 down-regulated differentially expressed genes (DEGs) were observed. And then, functional annotation and pathway enrichment analyzes of DEGs was performed utilizing the FunRich. The protein-protein interaction (PPI) network of the DEGs was constructed through the STRING website, visualized by Cytoscape. Finally, the expression levels of eight hub genes were confirmed in Oncomine database and human samples from SCLC patients. It showed that CDC20, BUB1, TOP2A, RRM2, CCNA2, UBE2C, MAD2L1, and BUB1B were upregulated in SCLC tissues compared to paired adjacent non-cancerous tissues. These suggested that eight hub genes might be viewed as new biomarkers for prognosis of SCLC or to guide individualized medication for the therapy of SCLC.

Multiomics global landscape of stemness-related gene clusters in adipose-derived mesenchymal stem cells

Background

Adipose-derived mesenchymal stem cells (AD-MSCs) are a type of stem cell that is abundant and widely used. The molecular characteristics of AD-MSCs from different passages from donors of different ages have not been well elucidated.

Methods

Six kinds of AD-MSCs ((E1, E2, E3, Y1, Y2, and Y3) with E denoting cells derived from an elderly patient, Y denoting cells derived from a young patient, and 1, 2, and 3 representing passages 3, 6, and 10) were obtained from human abdominal adipose tissue. We obtained the protein expression profile, the mRNA expression profile, the lncRNA expression profile, and the methylation profile of each kind of AD-MSC by sequencing. After calculating the stemness indices, genes related to stemness were extracted. The multiomics correlation analysis was performed in the stemness-related genes. In addition, short time-series expression miner (STEM) analysis was performed for all cell passages and donor ages. To further explore the biological functions of the stemness-related genes, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Finally, the lncRNA-KEGG network and transcription factor (TF)-KEGG network were constructed based on the RNAInter database and TRRUST v2 database.

Results

The stemness of the Y1, E1, and Y2 cells was higher than that of the E2, Y3, and E3 cells. The stemness was the highest for Y1 cells and the lowest for E3 cells. STEM analysis showed that five stemness-related gene clusters were associated with the cell passages, and only one gene cluster was associated with age. The enrichment analysis results showed that the biological processes (BPs) and KEGG pathways were mainly involved in the proliferation, differentiation, and migration of cells. The global regulatory landscape of AD-MSCs was constructed: 25 TFs and 16 lncRNAs regulated 21 KEGG pathways through 27 mRNAs. Furthermore, we obtained a core stemness-related gene set consisting of ITGAV, MAD2L1, and PCNA. These genes were expressed at higher levels in Y1 cells than in E3 cells.

Conclusion

The multiomics global landscape of stemness-related gene clusters was determined for AD-MSCs, which may be helpful for selecting AD-MSCs with increased stemness.