GFRA1 promotes cisplatin-induced chemoresistance in osteosarcoma by inducing autophagy

<p>Recent progress in chemotherapy has significantly increased its efficacy, yet the development of chemoresistance remains a major drawback. In this study, we show that GFRA1/GFRα1 (GDNF family receptor α 1), contributes to cisplatin-induced chemoresistance by regulating autophagy in osteosarcoma. We demonstrate that cisplatin treatment induced GFRA1 expression in human osteosarcoma cells. Induction of GFRA1 expression reduced cisplatin-induced apoptotic cell death and it significantly increased osteosarcoma cell survival via autophagy. GFRA1 regulates AMPK-dependent autophagy by promoting SRC phosphorylation independent of proto-oncogene <i>RET</i> kinase. Cisplatin-resistant osteosarcoma cells showed NFKB1/NFκB-mediated GFRA1 expression. GFRA1 expression promoted tumor formation and growth in mouse xenograft models and inhibition of autophagy in a GFRA1-expressing xenograft mouse model during cisplatin treatment effectively reduced tumor growth and increased survival. In cisplatin-treated patients, treatment period and metastatic status were associated with GFRA1-mediated autophagy. These findings suggest that GFRA1-mediated autophagy is a promising novel target for overcoming cisplatin resistance in osteosarcoma.</p

Integrative analysis for the discovery of lung cancer serological markers and validation by MRM-MS

<div><p>Non-small-cell lung cancer (NSCLC) constitutes approximately 80% of all diagnosed lung cancers, and diagnostic markers detectable in the plasma/serum of NSCLC patients are greatly needed. In this study, we established a pipeline for the discovery of markers using 9 transcriptome datasets from publicly available databases and profiling of six lung cancer cell secretomes. Thirty-one out of 312 proteins that overlapped between two-fold differentially expressed genes and identified cell secretome proteins were detected in the pooled plasma of lung cancer patients. To quantify the candidates in the serum of NSCLC patients, multiple-reaction-monitoring mass spectrometry (MRM-MS) was performed for five candidate biomarkers. Finally, two potential biomarkers (BCHE and GPx3; AUC = 0.713 and 0.673, respectively) and one two-marker panel generated by logistic regression (BCHE/GPx3; AUC = 0.773) were identified. A validation test was performed by ELISA to evaluate the reproducibility of GPx3 and BCHE expression in an independent set of samples (BCHE and GPx3; AUC = 0.630 and 0.759, respectively, BCHE/GPx3 panel; AUC = 0.788). Collectively, these results demonstrate the feasibility of using our pipeline for marker discovery and our MRM-MS platform for verifying potential biomarkers of human diseases.</p></div

Analyses of 9 transcriptome datasets from the GEO.

<p><b>(A)</b> A heat map of 2,696 differentially expressed probes between tumor (n = 669) and non-tumor tissues (n = 218) collected from 9 GEO data sets (p-values < 1 x 10⁻⁶ and over two-fold changes; red: up-regulation; green: down-regulation) <b>(B)</b> Classification of DEGs based on their molecular function as suggested by DAVID. <b>(C)</b> Subcellular locations of DEGs (grey: up-regulated genes in NSCLC; black: down-regulated genes in NSCLC).</p

Analyses of proteomes from pooled plasma by mass spectrometry.

<p><b>(A)</b> SDS-PAGE (protein 10 μg) of plasma pooled from 10 healthy control patients and 10 lung cancer patients, divided into 25 fractions. <b>(B)</b> Schematic diagram of the high-pH RPLC fractionation (protein 10 μg) setup. The eluates were combined by column (1–12 columns, 12 fractions). The surrogate peptides were monitored by measuring the UV absorbance of the eluates at 215 nm. <b>(C)</b> Venn diagram of the number of proteins identified by GeLC-MS/MS and high-pH RPLC fractionation. <b>(D)</b> Venn diagram of the number of analyzed molecules among DEGs, secretomes, and plasma proteome.</p

Systematic evaluation of serum MRM assays.

<p><b>(A)</b> Total ion chromatogram (TIC) of endogenous (red) peptides and their respective SIS peptides (blue). <b>(B)</b> Circular heatmap of the relative expression of four proteins in the two groups. The 46 clinical samples are shown in the circular heat map, clockwise from the top: 23 controls and 23 NSCLCs (16 adenocarcinomas and 7 squamous cell carcinomas). Indexing was followed by the sequence of LC-MRM runs. <b>(C)</b> Serum levels of BCHE in the control and NSCLC groups <b>(D)</b> Serum levels of GPx3 in the control and NSCLC groups. <b>(E)</b> ROC curves of BCHE, GPx3, and the combination of the two proteins. NSCLC: non-small cell lung cancer</p

Analyses of conditioned media harvested from NSCLC cell lines.

<p><b>(A)</b> Proteins (10 μg) in the conditioned media (CM) and cell extracts (CE) were analyzed by Western blot analysis using an anti-α-tubulin antibody. <b>(B)</b> The number of identified proteins in the cell secretome (FDR a 1%). Secretion pathways were predicted by SignalP, SecretomeP, and TMHMM. Bovine contaminants were distinguished using the human-FBS database. <b>(C)</b> Venn diagram of DEGs in tissues and identified proteins from cell secretomes. <b>(D)</b> Predicted secretion pathways of identified secretome proteins in all cell lines.</p

Summary of 9 transcriptome datasets obtained from the GEO.

<p>Summary of 9 transcriptome datasets obtained from the GEO.</p

Quantitation of the two proteins in an independent set of the control (n = 50) and NSCLC (n = 50) samples using ELISA.

<p>Serum levels of <b>(A)</b> BCHE and <b>(B)</b> GPx3 in the control and NSCLC groups. <b>(C)</b> ROC curves of GPx3, BCHE, and a combination of the two proteins. NSCLC: non-small cell lung cancer</p