The aim of this study was to update a previous scoring system for patients with skeletal metastases, that was proposed by Katagiri et al. in 2005, by introducing a new factor (laboratory data) and analyzing a new patient cohort. Between January 2005 and January 2008, we treated 808 patients with symptomatic skeletal metastases. They were prospectively registered regardless of their treatments, and the last follow-up evaluation was performed in 2012. There were 441 male and 367 female patients with a median age of 64 years. Of these patients, 749 were treated nonsurgically while the remaining 59 underwent surgery for skeletal metastasis. A multivariate analysis was conducted using the Cox proportional hazards model. We identified six significant prognostic factors for survival, namely, the primary lesion, visceral or cerebral metastases, abnormal laboratory data, poor performance status, previous chemotherapy, and multiple skeletal metastases. The first three factors had a larger impact than the remaining three. The prognostic score was calculated by adding together all the scores for individual factors. With a prognostic score of ≥7, the survival rate was 27% at 6 months, and only 6% at 1 year. In contrast, patients with a prognostic score of ≤3 had a survival rate of 91% at 1 year, and 78% at 2 years. Comparing the revised system with the previous one, there was a significantly lower number of wrongly predicted patients using the revised system. This revised scoring system was able to predict the survival rates of patients with skeletal metastases more accurately than the previous system and may be useful for selecting an optimal treatment.

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BackgroundSkeletal metastasis is a common metastatic event for several carcinomas, and the treatment for skeletal metastasis of unknown primary (SMUP) are a critical issue in cancer therapy. Making a diagnosis of the primary site is the most crucial step in the treatment of SMUP; however, the procedures are sometimes difficult and time-consuming, and the primary site often remains unknown. Therefore, to establish optimal diagnostic strategies and elucidate the overall survival rates of SMUP, we conducted this retrospective study.MethodsWe retrospectively analyzed the clinical data for 286 SMUP cases from a total of 2,641 patients with skeletal metastases who were treated between 2002 and 2014 at our initiations.ResultsThe primary sites were identified in 254/286 patients (88.8%), while 32 (11.2%) primary sites were not detected by our diagnostic strategies. Lung cancer was identified in 72 (25.2%) cases, and was the most frequently observed primary lesion. The median survival time of the SMUP patients was 20.0 months, while the median survival times of solitary bone metastasis cases and multi-bone metastasis cases were 39.0 months and 16.0 months, respectively. The median survival times of prostate cancer cases was over 120 months, that of patients with primary lung cancers was 9.0 months and the median survival time of cases who were finally diagnosed with an unknown primary was 11.0 months.ConclusionsWe believe that our study would contribute to establishing an optimal strategy for diagnosing the primary site in SMUP patients, and our data provide definite indications for the survival times for different SMUP situations.

The subclassification of glioblastoma (GBM) into clinically relevant subtypes using microRNA (miRNA)- and messenger RNA (mRNA)-based integrated analysis has been attempted. Because miRNAs regulate multiple gene-signaling pathways, understanding miRNA-mRNA interactions is a prerequisite for understanding glioma biology. However, such associations have not been thoroughly examined using high-throughput integrated analysis. To identify significant miRNA-mRNA correlations, we selected and quantified signature miRNAs and mRNAs in 82 gliomas (grade II: 14, III: 16, IV: 52) using real-time reverse-transcriptase polymerase chain reaction. Quantitative expression data were integrated into a single analysis platform that evaluated the expression relationship between miRNAs and mRNAs. The 21 miRNAs include miR-15b, -21, -34a, -105, -124a, -128a, -135b, -184, -196a-b, -200a-c, -203, -302a-d, -363, -367, and -504. In addition, we examined 23 genes, including proneural markers (DLL3, BCAN, and OLIG2), mesenchymal markers (YKL-40, CD44, and Vimentin), cancer stem cell-related markers, and receptor tyrosine kinase genes. Primary GBM was characterized exclusively by upregulation of mesenchymal markers, whereas secondary GBM was characterized by significant downregulation of mesenchymal markers, miR-21, and -34a, and by upregulation of proneural markers and miR-504. Statistical analysis showed that expression of miR-128a, -504, -124a, and -184 each negatively correlated with the expression of mesenchymal markers in GBM. Our functional analysis of miR-128a and -504 as inhibitors demonstrated that suppression of miR-128a and -504 increased the expression of mesenchymal markers in glioblastoma cell lines. Mesenchymal signaling in GBM may be negatively regulated by miR-128a and -504.

A careful preoperative planning is necessary for the optimal treatment by distinguishing whether it is a resectable or non-resectable tumor based on the clinical and radiological findings, because they have characteristic findings each other.