Gene mutation profiling and clinical significances in patients with renal cell carcinoma.

OBJECTIVES: The pathological mechanisms of patients with Renal Cell Carcinoma (RCC) remain defined. This study aimed to evaluate relationships between the landscape of gene mutations and their clinical significance in RCC patients. METHODS: Tissue and peripheral blood samples of 42 patients with RCC were collected and performed for the Next Generation Sequencing (NGS) with Geneseeq PrimeTM 425-gene panel probes. Their landscapes of gene mutation were analyzed. We also carried out an evaluation of Tumor-Node-Metastasis (TNM) staging, RENAL nephelometry score, surgery, and targeted drug treatment of patients. Then we compared the correlations of landscape in gene mutations and the prognosis. RESULTS: The most common gene alternations, including BAP1, PBRM1, SETD2, CSF1R, NPM1, EGFR, POLE, RB1, and VHL genes, were identified in tissue and blood samples of 75% of patients. EGFR, POLE, and RB1 gene mutations frequently occurred in relapsed and metastatic patients. BAP1, CCND2, KRAS, PTPN11, ERBB2/3, JAK2, and POLE were presented in the patients with > 9 RENAL nephelometry score. Univariable analysis indicated that SETD2, BAP1, and PBRM1 genes were key factors for Disease-Free Survival (DFS). Multivariable analysis confirmed that mutated SETD1, NPM1, and CSF1R were critical factors for the Progression Free Survival (PFS) of RCC patients with target therapy. CONCLUSIONS: Wild-type PBRM1 and mutated BAP1 in patients with RCC were strongly associated with the outcomes of the patient. The PFS of the patients with SETD2, NPM1, and CSF1R mutations were significantly shorter than those patients without variants.

EDLM: Ensemble Deep Learning Model to Detect Mutation for the Early Detection of Cholangiocarcinoma.

The most common cause of mortality and disability globally right now is cholangiocarcinoma, one of the worst forms of cancer that may affect people. When cholangiocarcinoma develops, the DNA of the bile duct cells is altered. Cholangiocarcinoma claims the lives of about 7000 individuals annually. Women pass away less often than men. Asians have the greatest fatality rate. Following Whites (20%) and Asians (22%), African Americans (45%) saw the greatest increase in cholangiocarcinoma mortality between 2021 and 2022. For instance, 60-70% of cholangiocarcinoma patients have local infiltration or distant metastases, which makes them unable to receive a curative surgical procedure. Across the board, the median survival time is less than a year. Many researchers work hard to detect cholangiocarcinoma, but this is after the appearance of symptoms, which is late detection. If cholangiocarcinoma progression is detected at an earlier stage, then it will help doctors and patients in treatment. Therefore, an ensemble deep learning model (EDLM), which consists of three deep learning algorithms-long short-term model (LSTM), gated recurrent units (GRUs), and bi-directional LSTM (BLSTM)-is developed for the early identification of cholangiocarcinoma. Several tests are presented, such as a 10-fold cross-validation test (10-FCVT), an independent set test (IST), and a self-consistency test (SCT). Several statistical techniques are used to evaluate the proposed model, such as accuracy (Acc), sensitivity (Sn), specificity (Sp), and Matthew's correlation coefficient (MCC). There are 672 mutations in 45 distinct cholangiocarcinoma genes among the 516 human samples included in the proposed study. The IST has the highest Acc at 98%, outperforming all other validation approaches.

Gene mutation profiling and clinical significances in patients with renal cell carcinoma

Abstract Objectives The pathological mechanisms of patients with Renal Cell Carcinoma (RCC) remain defined. This study aimed to evaluate relationships between the landscape of gene mutations and their clinical significance in RCC patients. Methods Tissue and peripheral blood samples of 42 patients with RCC were collected and performed for the Next Generation Sequencing (NGS) with Geneseeq PrimeTM 425-gene panel probes. Their landscapes of gene mutation were analyzed. We also carried out an evaluation of Tumor-Node-Metastasis (TNM) staging, RENAL nephelometry score, surgery, and targeted drug treatment of patients. Then we compared the correlations of landscape in gene mutations and the prognosis. Results The most common gene alternations, including BAP1, PBRM1, SETD2, CSF1R, NPM1, EGFR, POLE, RB1, and VHL genes, were identified in tissue and blood samples of 75% of patients. EGFR, POLE, and RB1 gene mutations frequently occurred in relapsed and metastatic patients. BAP1, CCND2, KRAS, PTPN11, ERBB2/3, JAK2, and POLE were presented in the patients with > 9 RENAL nephelometry score. Univariable analysis indicated that SETD2, BAP1, and PBRM1 genes were key factors for Disease-Free Survival (DFS). Multivariable analysis confirmed that mutated SETD1, NPM1, and CSF1R were critical factors for the Progression Free Survival (PFS) of RCC patients with target therapy. Conclusions Wild-type PBRM1 and mutated BAP1 in patients with RCC were strongly associated with the outcomes of the patient. The PFS of the patients with SETD2, NPM1, and CSF1R mutations were significantly shorter than those patients without variants.

MZPAQ: a FASTQ data compression tool.

BACKGROUND: Due to the technological progress in Next Generation Sequencing (NGS), the amount of genomic data that is produced daily has seen a tremendous increase. This increase has shifted the bottleneck of genomic projects from sequencing to computation and specifically storing, managing and analyzing the large amount of NGS data. Compression tools can reduce the physical storage used to save large amount of genomic data as well as the bandwidth used to transfer this data. Recently, DNA sequence compression has gained much attention among researchers. RESULTS: In this paper, we study different techniques and algorithms used to compress genomic data. Most of these techniques take advantage of some properties that are unique to DNA sequences in order to improve the compression rate, and usually perform better than general-purpose compressors. By exploring the performance of available algorithms, we produce a powerful compression tool for NGS data called MZPAQ. Results show that MZPAQ outperforms state-of-the-art tools on all benchmark datasets obtained from a recent survey in terms of compression ratio. MZPAQ offers the best compression ratios regardless of the sequencing platform or the size of the data. CONCLUSIONS: Currently, MZPAQ's strength is its higher compression ratio as well as its compatibility with all major sequencing platforms. MZPAQ is more suitable when the size of compressed data is crucial, such as long-term storage and data transfer. More efforts will be made in the future to target other aspects such as compression speed and memory utilization.

Microbial ecology dynamics of a partial nitritation bioreactor with Polar Arctic Circle activated sludge operating at low temperature.

A lab-scale partial nitritation SBR was operated at 11 °C for 300 days used for the treatment of high-ammonium wastewater, which was inoculated with activated sludge from Rovaniemi WWTP (located in Polar Arctic Circle) in order to evaluate the influence the temperature on the performance, stability and dynamics of its microbial community. The partial nitritation achieved steady-state long-term operation and granulation process was not affected despite the low temperature and high ammonia concentration. The steady conditions were reached after 60 days of operation where the granular biomass was fully-formed and the 50%-50% of ammonium-nitrite effluent was successful achieved. Inoculation with cold adapted inoculum showed to yield bigger, denser granules with faster start-up without necessity of low temperature adaptation period. Next-generation sequences techniques showed that Trichosporonaceae and Xanthomonadaceae were the dominant OTUs in the mature granules. Our study could be useful in the implementation of full-scale partial nitritation reactors in cold regions such as Nordic countries for treating wastewater with high concentration of ammonium.

Application of next generation sequencing toward sensitive detection of enteric viruses isolated from celery samples as an example of produce.

Next generation sequencing (NGS) holds promise as a single application for both detection and sequence identification of foodborne viruses; however, technical challenges remain due to anticipated low quantities of virus in contaminated food. In this study, with a focus on data analysis using several bioinformatics tools, we applied NGS toward amplification-independent detection and identification of norovirus at low copy (<103 copies) or within multiple strains from produce. Celery samples were inoculated with human norovirus (stool suspension) either as a single norovirus strain, a mixture of strains (GII.4 and GII.6), or a mixture of different species (hepatitis A virus and norovirus). Viral RNA isolation and recovery was confirmed by RT-qPCR, and optimized for library generation and sequencing without amplification using the Illumina MiSeq platform. Extracts containing either a single virus or a two-virus mixture were analyzed using two different analytic approaches to achieve virus detection and identification. First an overall assessment of viral genome coverage for samples varying in copy numbers (1.1×103 to 1.7×107) and genomic content (single or multiple strains in various ratios) was completed by reference-guided mapping. Not unexpectedly, this targeted approach to identification was successful in correctly mapping reads, thus identifying each virus contained in the inoculums even at low copy (estimated at 12 copies). For the second (metagenomic) approach, samples were treated as "unknowns" for data analyses using (i) a sequence-based alignment with a local database, (ii) an "in-house" k-mer tool, (iii) a commercially available metagenomics bioinformatic analysis platform cosmosID, and (iv) an open-source program Kraken. Of the four metagenomics tools applied in this study, only the local database alignment and in-house k-mer tool were successful in detecting norovirus (as well as HAV) at low copy (down to <103 copies) and within a mixture of virus strains or species. The results of this investigation provide support for continued investigation into the development and integration of these analytical tools for identification and detection of foodborne viruses.

Towards next-generation sequencing analytics for foodborne RNA viruses: Examining the effect of RNA input quantity and viral RNA purity.

Detection and identification of viruses in food samples are technically challenging due largely to the low viral copy number in contaminated food items, and the lack of effective culture enrichment methods that are amenable to regulatory applications for many of the common foodborne viruses. Using an Illumina MiSeq platform and two hepatitis A virus (HAV) cell-culture adapted strains as a representative enteric virus species, this study examined the limits of single-stranded RNA (ssRNA) viral detection following next-generation sequencing without pre-amplification of the viral genome. Complete viral genome sequences were obtained from HAV samples of varying purities and with an input as low as 2ng total RNA containing 1.4×10(5) copies of viral RNA. In addition, single nucleotide variations were reproducibly detected over the range of concentrations examined, and their identity confirmed by alternate sequencing technology. In summary, next-generation sequencing technology has the potential for sensitive detection/identification of a viral genome at a low copy number. This study provides a benchmark for metagenomic sequencing application as is required for virus detection in complex food matrices using a culture-independent diagnostic approach.