Expression of Pivotal Long Non-coding RNAs Implicated in Gastric Cancer: A Bioinformatic and Clinical Study.

Gastric cancer (GC) is a prominent public health issue and ranks as the third most prevalent cause of cancer-related mortality on a global scale. The role of long non-coding RNAs (lncRNAs) in cancer is not yet fully understood, particularly in relation to GC development. The objective of this study was to examine the expression levels of lncRNAs in GC tissues using a bioinformatics-based ranking approach. A bioinformatics methodology was employed to prioritize lncRNAs that are hypothesized to play a role in GC tumorigenesis. Moreover, a selection was made for experimental validation of the highest-ranked lncRNAs, which include HCG18, OIP5-AS1, FGD5-AS1, and NORAD. Additionally, quantitative real-time polymerase chain reaction (qRT-PCR) was employed to confirm the results obtained from bioinformatics analysis in a total of 35 GC samples and their corresponding adjacent non-tumoral samples. Receiver operating characteristic (ROC) curves and the corresponding area under the ROC curve (AUC) were utilized to evaluate the diagnostic efficacy of the lncRNAs. The bioinformatics analysis revealed that the lncRNA HCG18 is the highest-ranked lncRNA associated with GC. Furthermore, the expression levels of HCG18, OIP5-AS1, FGD5-AS1, and NORAD were found to be significantly elevated in GC samples when compared to adjacent non-tumoral samples. The calculated values for the AUC of HCG18, OIP5-AS1, FGD5-AS1, and NORAD were 0.80, 0.74, 0.73, and 0.71, respectively. The results of the study indicate that the lncRNAs HCG18, OIP5-AS1, FGD5-AS1, and NORAD may play a role in the development of GC. Additionally, the present study revealed that utilizing bioinformatic techniques can prove to be a highly effective strategy in identifying potential lncRNAs pertinent to the progression of GC.

Epidemiology of hip fractures in northwestern Italy: a multicentric regional study on incidence of hip fractures and their outcome at 3-year follow-up.

Piedmont is a region in northwestern Italy counting 4.2 million inhabitants. The purpose of our study was to update data on incidence and outcomes of hip fractures (HF) in our region to present days. The data of all patients affected by HF in 2003 in Piedmont (total: 5,386 patients) were analyzed, determining the incidence of HF, mean age, sex, fracture pattern and treatment adopted. Additionally, 564 patients underwent a questionnaire on comorbidities, complications, functional outcome and survivorship. Overall incidence of HF was 126.13/100,000 inhabitants-year. Mean hospitalization was 13.67 days. Mean time to surgery was 2.67 days. Survivorship was 94% at 3-month, 71.32% at 1-year and 60.21% at 3-year follow-up. These up-to-date data on HF in our region are in accordance with the international literature and could prove useful for Orthopaedic and Trauma surgeons for giving information to patients and their relatives.

Sensitivity and specificity of five abundance estimators for high-density oligonucleotide microarrays.

MOTIVATION: A number of algorithms have been proposed for the processing of feature-level data from high-density oligonucleotide microarrays to give estimates of transcript abundance. Performance in the common task of detecting differential expression between samples can be quantified by the statistical concepts of sensitivity and specificity, and represented by the use of receiver operating characteristic curves. These have been previously presented for small numbers of genes known to be differentially present in spiked-in samples. We present here a study of performance over a large number (thousands) of transcripts for which there is strong evidence of differential expression, with corresponding false positive rates controlled by comparisons between replicates. RESULTS: The straight-line regression analysis of a mixture series with replicates by five estimation algorithms produces a consensus set of 4462 transcripts with differential expression of agreed direction and high significance (p < 0.01) according to all algorithms. The more difficult task of two-sample tests between adjacent mixture levels produces performance curves of fraction true positive detected against significance level. Performance varies significantly between algorithms: at the p < 0.01 level, the detection rate varies between 41 and 66%. A control using comparisons between replicates at the same levels indicates that the tests produce empirical false positive rates closely matching the nominal p-values.