Protocadherin Gamma C3 (PCDHGC3) Is Strongly Expressed in Glioblastoma and Its High Expression Is Associated with Longer Progression-Free Survival of Patients.

Protocadherins (PCDHs) belong to the cadherin superfamily and represent the largest subgroup of calcium-dependent adhesion molecules. In the genome, most PCDHs are arranged in three clusters, α, ß, and γ on chromosome 5q31. PCDHs are highly expressed in the central nervous system (CNS). Several PCDHs have tumor suppressor functions, but their individual role in primary brain tumors has not yet been elucidated. Here, we examined the mRNA expression of PCDHGC3, a member of the PCDHγ cluster, in non-cancerous brain tissue and in gliomas of different World Health Organization (WHO) grades and correlated it with the clinical data of the patients. We generated a PCDHGC3 knockout U343 cell line and examined its growth rate and migration in a wound healing assay. We showed that PCDHGC3 mRNA and protein were significantly overexpressed in glioma tissue compared to a non-cancerous brain specimen. This could be confirmed in glioma cell lines. High PCDHGC3 mRNA expression correlated with longer progression-free survival (PFS) in glioma patients. PCDHGC3 knockout in U343 resulted in a slower growth rate but a significantly faster migration rate in the wound healing assay and decreased the expression of several genes involved in WNT signaling. PCDHGC3 expression should therefore be further investigated as a PFS-marker in gliomas. However, more studies are needed to elucidate the molecular mechanisms underlying the PCDHGC3 effects.

Screening human lung cancer with predictive models of serum magnetic resonance spectroscopy metabolomics.

The current high mortality of human lung cancer stems largely from the lack of feasible, early disease detection tools. An effective test with serum metabolomics predictive models able to suggest patients harboring disease could expedite triage patient to specialized imaging assessment. Here, using a training-validation-testing-cohort design, we establish our high-resolution magic angle spinning (HRMAS) magnetic resonance spectroscopy (MRS)-based metabolomics predictive models to indicate lung cancer presence and patient survival using serum samples collected prior to their disease diagnoses. Studied serum samples were collected from 79 patients before (within 5.0 y) and at lung cancer diagnosis. Disease predictive models were established by comparing serum metabolomic patterns between our training cohorts: patients with lung cancer at time of diagnosis, and matched healthy controls. These predictive models were then applied to evaluate serum samples of our validation and testing cohorts, all collected from patients before their lung cancer diagnosis. Our study found that the predictive model yielded values for prior-to-detection serum samples to be intermediate between values for patients at time of diagnosis and for healthy controls; these intermediate values significantly differed from both groups, with an F1 score = 0.628 for cancer prediction. Furthermore, values from metabolomics predictive model measured from prior-to-diagnosis sera could significantly predict 5-y survival for patients with localized disease.