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Metastasis is the most common cause of mortality for non-small cell lung cancer (NSCLC). PTCH1, a receptor of Hedgehog (Hh) pathway, is reported to suppress cell proliferation. Interestingly, our previous study showed PTCH1 silencing promoted cell proliferation but inhibited cell migration and invasion of NSCLC cells. However, the precise mechanisms of PTCH1 regulating NSCLC metastasis remain unclear. PTCH1 has multiple splicing variants, which all share the same 3’UTR sequence, meanwhile, emerging studies have shown competing endogenous RNAs (ceRNAs) play important roles in regulating cancer progression. Therefore, we hypothesized the functions of PTCH1-3’UTR in NSCLC in present study to reveal its role as a ceRNA. Here, we find overexpression of PTCH1-3’UTR promotes cell migration, invasion and adhesion, but does not affect cell proliferation in NSCLC cells. By combining weighted correlation network analysis (WGCNA) analysis and experimental validation, we reported PTCH1-3’UTR acted as a sponge to absorb miR-101-3p and promoted SLC39A6 expression. Moreover, we observed low expression of miR-101-3p and PTCH1 and high SLC39A6 levels were positively correlated with NSCLC progression. Therefore, our results help to understand the function of PTCH1 in NSCLC tumorigenesis and provide novel insights for the prevention of NSCLC metastasis.

Liquid biopsy offers great promise for noninvasive cancer diagnostics, while the lack of adequate target characterization and analysis hinders its wide application. Single-cell RNA sequencing (scRNA-seq) is a powerful technology for cell characterization. Integrating scRNA-seq into a CTC-focused liquid biopsy study can perhaps classify CTCs by their original lesions. However, the lack of CTC scRNA-seq data accumulation and prior knowledge hinders further development. Therefore, we design CTC-Tracer, a transfer learning-based algorithm, to correct the distributional shift between primary cancer cells and CTCs to transfer lesion labels from the primary cancer cell atlas to CTCs. The robustness and accuracy of CTC-Tracer are validated by 8 individual standard datasets. We apply CTC-Tracer on a complex dataset consisting of RNA-seq profiles of single CTCs, CTC clusters from a BRCA patient, and two xenografts, and demonstrate that CTC-Tracer has potential in knowledge transfer between different types of RNA-seq data of lesions and CTCs.

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