MicroRNA-203 predicts human survival after resection of colorectal liver metastasis.

BackgroundResection of colorectal liver metastasis (CRLM) can be curative. Predicting which patients may benefit from resection, however, remains challenging. Some microRNAs (miRNAs) become deregulated in cancers and contribute to cancer progression. We hypothesized that miRNA expression can serve as a prognostic marker of survival after CRLM resection.ResultsMiR-203 was significantly overexpressed in tumors of short-term survivors compared to long-term survivors. R1/R2 margin status and high clinical risk score (CRS) were also significantly associated with short-term survival (both p = 0.001). After adjusting for these variables, higher miR-203 expression remained an independent predictor of shorter survival (p = 0.010). In the serum cohort, high CRS and KRAS mutation were significantly associated with short-term survival (p = 0.005 and p = 0.026, respectively). After adjusting for CRS and KRAS status, short-term survivors were found to have significantly higher miR-203 levels (p = 0.016 and p = 0.033, respectively).Materials and methodsWe employed next-generation sequencing of small-RNAs to profile miRNAs in solid tumors obtained from 38 patients who underwent hepatectomy for CRLM. To validate, quantitative reverse-transcription polymerase chain reaction (qRT-PCR) was performed on 91 tumor samples and 46 preoperative serum samples.ConclusionsAfter CRLM resection, short-term survivors exhibited significantly higher miR-203 levels relative to long-term survivors. MiR-203 may serve as a prognostic biomarker and its prognostic capacity warrants further investigation

Does the Mechanism of Lymph Node Invasion Affect Survival in Patients with Pancreatic Ductal Adenocarcinoma?

Background: Lymph node metastases are prognostically significant in pancreatic ductal adenocarcinoma. Little is known about the significance of direct lymph node invasion. Aim: The aim of this study is to find out whether direct lymph node invasion has the same prognostic significance as regional nodal metastases. Methods: Retrospective review of patients resected between 1/1/1993 and 7/31/2008. “Direct” was defined as tumor extension into adjacent nodes, and “regional” was defined as metastases to peripancreatic nodes. Results: Overall, 517 patients underwent pancreatic resection for adenocarcinoma, of whom 89 had one positive node (direct 26, regional 63), and 79 had two positive nodes (direct 6, regional 68, both 5). Overall, survival of node-negative patients was improved compared to patients with positive nodes (N0 30.8 months vs. N1 16.4 months; p < 0.001). There was no survival difference for patients with direct vs. regional lymph node invasion (p = 0.67). Patients with one positive node had a better overall survival compared to patients with ≥2 positive nodes (22.3 and 15 months, respectively; p < 0.001). The lymph node ratio (+LN/total LN) was prognostically significant after Cox regression (p < 0.001). Conclusions: Isolated direct invasion occurs in 20% of patients with one to two positive nodes. Node involvement by metastasis or by direct invasion are equally significant predictors of reduced survival. Both the number of positive nodes and the lymph node ratio are significant prognostic factors

Unraveling the functional dark matter through global metagenomics

30 pages, 4 figures, 1 table, supplementary information https://doi.org/10.1038/s41586-023-06583-7.-- Data availability: All of the analysed datasets along with their corresponding sequences are available from the IMG system (http://img.jgi.doe.gov/). A list of the datasets used in this study is provided in Supplementary Data 8. All data from the protein clusters, including sequences, multiple alignments, HMM profiles, 3D structure models, and taxonomic and ecosystem annotation, are available through NMPFamsDB, publicly accessible at www.nmpfamsdb.org. The 3D models are also available at ModelArchive under accession code ma-nmpfamsdb.-- Code availability: Sequence analysis was performed using Tantan (https://gitlab.com/mcfrith/tantan), BLAST (https://blast.ncbi.nlm.nih.gov/Blast.cgi), LAST (https://gitlab.com/mcfrith/last), HMMER (http://hmmer.org/) and HH-suite3 (https://github.com/soedinglab/hh-suite). Clustering was performed using HipMCL (https://bitbucket.org/azadcse/hipmcl/src/master/). Additional taxonomic annotation was performed using Whokaryote (https://github.com/LottePronk/whokaryote), EukRep (https://github.com/patrickwest/EukRep), DeepVirFinder (https://github.com/jessieren/DeepVirFinder) and MMseqs2 (https://github.com/soedinglab/MMseqs2). 3D modelling was performed using AlphaFold2 (https://github.com/deepmind/alphafold) and TrRosetta2 (https://github.com/RosettaCommons/trRosetta2). Structural alignments were performed using TMalign (https://zhanggroup.org/TM-align/) and MMalign (https://zhanggroup.org/MM-align/). All custom scripts used for the generation and analysis of the data are available at Zenodo (https://doi.org/10.5281/zenodo.8097349)Metagenomes encode an enormous diversity of proteins, reflecting a multiplicity of functions and activities1,2. Exploration of this vast sequence space has been limited to a comparative analysis against reference microbial genomes and protein families derived from those genomes. Here, to examine the scale of yet untapped functional diversity beyond what is currently possible through the lens of reference genomes, we develop a computational approach to generate reference-free protein families from the sequence space in metagenomes. We analyse 26,931 metagenomes and identify 1.17 billion protein sequences longer than 35 amino acids with no similarity to any sequences from 102,491 reference genomes or the Pfam database3. Using massively parallel graph-based clustering, we group these proteins into 106,198 novel sequence clusters with more than 100 members, doubling the number of protein families obtained from the reference genomes clustered using the same approach. We annotate these families on the basis of their taxonomic, habitat, geographical and gene neighbourhood distributions and, where sufficient sequence diversity is available, predict protein three-dimensional models, revealing novel structures. Overall, our results uncover an enormously diverse functional space, highlighting the importance of further exploring the microbial functional dark matterWith the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S)Peer reviewe