The Didymellaceae was established in 2009 to accommodate Ascochyta, Didymella and Phoma, as well as several related phoma-like genera. The family contains numerous plant pathogenic, saprobic and endophytic species associated with a wide range of hosts. Ascochyta and Phoma are morphologically difficult to distinguish, and species from both genera have in the past been linked to Didymella sexual morphs. The aim of the present study was to clarify the generic delimitation in Didymellaceae by combing multi-locus phylogenetic analyses based on ITS, LSU, rpb2 and tub2, and morphological observations. The resulting phylogenetic tree revealed 17 well-supported monophyletic clades in Didymellaceae, leading to the introduction of nine genera, three species, two nomina nova and 84 combinations. Furthermore, 11 epitypes and seven neotypes were designated to help stabilise the taxonomy and use of names. As a result of these data, Ascochyta, Didymella and Phoma were delineated as three distinct genera, and the generic circumscriptions of Ascochyta, Didymella, Epicoccum and Phoma emended. Furthermore, the genus Microsphaeropsis, which is morphologically distinct from the members of Didymellaceae, grouped basal to the Didymellaceae, for which a new family Microsphaeropsidaceae was introduced.

Our findings indicated that up-regulation of DDR1 induced by miR-199a-5p down-regulation may contribute to the development and progression of CRC, and this effect may be associated with increased invasiveness, at least in part, via activating the EMT-related signaling.

N-staging is a determining factor for prognostic assessment and decision-making for stage-based cancer therapeutic strategies. Visual inspection of whole-slides of intact lymph nodes is currently the main method used by pathologists to calculate the number of metastatic lymph nodes (MLNs). Moreover, even at the same N stage, the outcome of patients varies dramatically. Here, we propose a deep-learning framework for analyzing lymph node whole-slide images (WSIs) to identify lymph nodes and tumor regions, and then to uncover tumor-area-to-MLN-area ratio (T/MLN). After training, our model’s tumor detection performance was comparable to that of experienced pathologists and achieved similar performance on two independent gastric cancer validation cohorts. Further, we demonstrate that T/MLN is an interpretable independent prognostic factor. These findings indicate that deep-learning models could assist not only pathologists in detecting lymph nodes with metastases but also oncologists in exploring new prognostic factors, especially those that are difficult to calculate manually.

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It is universally acknowledged that long non-coding RNAs (lncRNAs) involved in tumorigenesis in human cancers. However, the function and mechanism of many lncRNAs in colorectal cancer (CRC) remain unclear. By analyzing the two sets of CRC-related gene microarrays data, downloaded from the Gene Expression Omnibus (GEO) database and the lncRNA expression in a set of RNA sequencing data, we found that lncRNA SLCO4A1-AS1 was significantly upregulated in CRC tissues. We then collected CRC tissue samples and verified that SLCO4A1-AS1 is highly expressed in CRC tissues. Furthermore, SLCO4A1-AS1 was also upregulated in the CRC cell line. In situ hybridization results showed that high expression of SLCO4A1-AS1 was associated with poor prognosis in patients with CRC. Next, we found that SLCO4A1-AS1 promoted CRC cell proliferation, migration, and invasion. Results of western blotting assays show that its mechanism may relate to the epidermal growth factor receptor (EGFR)/mitogen-activated protein kinase (MAPK) pathway. Therefore, SLCO4A1-AS1 may be a potential biomarker for CRC prognosis and a new target for colorectal cancer therapy.