Laparoscopic cholecystectomy: Rate and predictors for conversion

Laparoscopic cholecystectomy (LC) was attempted in 847 patients, 823 (97.2%) were completed laparoscopically and 24 (2.8%) had to be converted to open cholecystectomy (OC). Acute cholecystitis was the commonest reason for conversion (13 out of 24 patients). Patients who had acute cholecystitis are five times at risk for conversion to open than other patients with non-acute cholecystitis (p< 0.00I ). Age and sex were not statistically significant predictors for conversion. There were no mortalities and no major bile duct injuries in our series. These data confirms the safety of LC, identify factors which predicts conversion to OC and may be helpful in selecting patients for day care ambulatory LC

Gene expression study of breast cancer using Welch Satterthwaite t-test, Kaplan-Meier estimator plot and Huber loss robust regression model

Objective: Breast Cancer (BC) is one of the deadliest diseases in women, causing thousands of deaths annually despite the advent of high-throughput genomic platforms in the recent past. Microarray-based gene expression profiling with different statistical methods have been extensively used to understand the disease at the molecular level. We plan to apply Welch Satterthwaite t-test, Kaplan-Meier estimator plot and Huber Loss robust regression model on microarray data to improve the analysis and find biomarkers for future diagnosis, prognosis, and treatment. Methods: We retrieved microarray data (GSE10810 dataset) of 31 breast tumor samples and 27 normal breast samples from Gene Expression Omnibus (GEO, NCBI). Welch Satterthwaite t-test was applied to identify the most statistically significant genes, Huber loss robust regression model was applied to investigate the existing mathematical relations between tumor and control variables, and Kaplan-Meier Plotter was used to confirm their association with overall metastatic relapse-free survival of BC patients. Results: We identified 1837 differentially expressed genes, including 638 overexpressed (COL11A1, KIAA0101, S100P, GJB2, TOP2A, LINC01614, RRM2, INHBA, C15orf48 and CKS2) and 1199 under expressed (LEP, ADIPOQ, PLIN1, PCK1, PCOLCE2, ADH1B, LYVE1, FABP4, ABCA8, and CHRDL1) genes passing the threshold (fold change ± 2 and p value < 0.001). KM analysis revealed 12 out of 20 DEGs (log rank p value < 0.05) as potential prognostic and therapeutic biomarkers. Conclusion: Huber loss robust regression model was found to be one of the best performing algorithms for the mathematical relationship between the control and breast tumor samples with co-relation coefficient of 0.4398 and mean absolute error of 1.069 ± 0.020. In conclusion, with high mathematical confidence, we detected DEGs have high potential to be BC biomarkers using Welch t-test and Kaplan-Meier plot having minimum underlying assumptions

Comprehensive molecular biomarker identification in breast cancer brain metastases

Abstract Background Breast cancer brain metastases (BCBM) develop in about 20–30% of breast cancer (BC) patients. BCBM are associated with dismal prognosis not at least due to lack of valuable molecular therapeutic targets. The aim of the study was to identify new molecular biomarkers and targets in BCBM by using complementary state-of-the-art techniques. Methods We compared array expression profiles of three BCBM with 16 non-brain metastatic BC and 16 primary brain tumors (prBT) using a false discovery rate (FDR) p  2. Biofunctional analysis was conducted on the differentially expressed probe sets. High-density arrays were employed to detect copy number variations (CNVs) and whole exome sequencing (WES) with paired-end reads of 150 bp was utilized to detect gene mutations in the three BCBM. Results The top 370 probe sets that were differentially expressed between BCBM and both BC and prBT were in the majority comparably overexpressed in BCBM and included, e.g. the coding genes BCL3, BNIP3, BNIP3P1, BRIP1, CASP14, CDC25A, DMBT1, IDH2, E2F1, MYCN, RAD51, RAD54L, and VDR. A number of small nucleolar RNAs (snoRNAs) were comparably overexpressed in BCBM and included SNORA1, SNORA2A, SNORA9, SNORA10, SNORA22, SNORA24, SNORA30, SNORA37, SNORA38, SNORA52, SNORA71A, SNORA71B, SNORA71C, SNORD13P2, SNORD15A, SNORD34, SNORD35A, SNORD41, SNORD53, and SCARNA22. The top canonical pathway was entitled, role of BRCA1 in DNA damage response. Network analysis revealed key nodes as Akt, ERK1/2, NFkB, and Ras in a predicted activation stage. Downregulated genes in a data set that was shared between BCBM and prBT comprised, e.g. BC cell line invasion markers JUN, MMP3, TFF1, and HAS2. Important cancer genes affected by CNVs included TP53, BRCA1, BRCA2, ERBB2, IDH1, and IDH2. WES detected numerous mutations, some of which affecting BC associated genes as CDH1, HEPACAM, and LOXHD1. Conclusions Using complementary molecular genetic techniques, this study identified shared and unshared molecular events in three highly aberrant BCBM emphasizing the challenge to detect new molecular biomarkers and targets with translational implications. Among new findings with the capacity to gain clinical relevance is the detection of overexpressed snoRNAs known to regulate some critical cellular functions as ribosome biogenesis

MOESM2 of Comprehensive molecular biomarker identification in breast cancer brain metastases

Additional file 2. Exon expression levels for a number of cancer associated genes. A, BRCA1; B, BRCA2; C, ERBB2; D, TP53; E, ER1 (ESR1); F, PR (PGR); G, SNORD116-4; H, MKI67; I, VDR; and J, BCL3. Comparably low expression of a number of exon probes can be presumbably attributed to splicing events of transcripts; asterisks mark examples. BCL3 is upregulated on the gene expression level (Additional file 1). Notably, 5` located exons of ERBB2 are lower expressed than 3` located exons in prBT and expression levels of the two probes covering the SNORD116-4 transcript diverge in BCBM and prBT compared to BC. Exon probes are displayed from 5` (left) to 3` (right) of the transcripts. Blue and red colors in heat maps refer to lower and higher expression, respectively