Summary: Biological sequence diagrams are fundamental for visualizing various functional elements in protein or nucleotide sequences that enable a summarization and presentation of existing information as well as means of intuitive new discoveries. Here, we present a software package called illustrator of biological sequences (IBS) that can be used for representing the organization of either protein or nucleotide sequences in a convenient, efficient and precise manner. Multiple options are provided in IBS, and biological sequences can be manipulated, recolored or rescaled in a user-defined mode. Also, the final representational artwork can be directly exported into a publication-quality figure. Availability and implementation: The standalone package of IBS was implemented in JAVA, while the online service was implemented in HTML5 and JavaScript. Both the standalone package and online service are freely available at http://ibs.biocuckoo.org. Contact:renjian.sysu@gmail.com or xueyu@hust.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online.

Small ubiquitin-like modifiers (SUMOs) regulate a variety of cellular processes through two distinct mechanisms, including covalent sumoylation and non-covalent SUMO interaction. The complexity of SUMO regulations has greatly hampered the large-scale identification of SUMO substrates or interaction partners on a proteome-wide level. In this work, we developed a new tool called GPS-SUMO for the prediction of both sumoylation sites and SUMO-interaction motifs (SIMs) in proteins. To obtain an accurate performance, a new generation group-based prediction system (GPS) algorithm integrated with Particle Swarm Optimization approach was applied. By critical evaluation and comparison, GPS-SUMO was demonstrated to be substantially superior against other existing tools and methods. With the help of GPS-SUMO, it is now possible to further investigate the relationship between sumoylation and SUMO interaction processes. A web service of GPS-SUMO was implemented in PHP + JavaScript and freely available at http://sumosp.biocuckoo.org.

Circulating tumor DNA (ctDNA) has emerged as a useful diagnostic and prognostic biomarker in many cancers. Here, we conducted a study to investigate the potential use of ctDNA methylation markers for the diagnosis and prognostication of colorectal cancer (CRC) and used a prospective cohort to validate their effectiveness in screening patients at high risk of CRC. We first identified CRC-specific methylation signatures by comparing CRC tissues to normal blood leukocytes. Then, we applied a machine learning algorithm to develop a predictive diagnostic and a prognostic model using cell-free DNA (cfDNA) samples from a cohort of 801 patients with CRC and 1021 normal controls. The obtained diagnostic prediction model discriminated patients with CRC from normal controls with high accuracy (area under curve = 0.96). The prognostic prediction model also effectively predicted the prognosis and survival of patients with CRC (P < 0.001). In addition, we generated a ctDNA-based molecular classification of CRC using an unsupervised clustering method and obtained two subgroups of patients with CRC with significantly different overall survival (P = 0.011 in validation cohort). Last, we found that a single ctDNA methylation marker, cg10673833, could yield high sensitivity (89.7%) and specificity (86.8%) for detection of CRC and precancerous lesions in a high-risk population of 1493 participants in a prospective cohort study. Together, our findings showed the value of ctDNA methylation markers in the diagnosis, surveillance, and prognosis of CRC.

Background: Long noncoding RNAs (lncRNAs) play nonnegligible roles in the epigenetic regulation of cancer cells. This study aimed to identify a specific lncRNA that promotes the colorectal cancer (CRC) progression and could be a potential therapeutic target. Methods: We screened highly expressed lncRNAs in human CRC samples compared with their matched adjacent normal tissues. The proteins that interact with LINRIS (Long Intergenic Noncoding RNA for IGF2BP2 Stability) were confirmed by RNA pull-down and RNA immunoprecipitation (RIP) assays. The proliferation and metabolic alteration of CRC cells with LINRIS inhibited were tested in vitro and in vivo. Results: LINRIS was upregulated in CRC tissues from patients with poor overall survival (OS), and LINRIS inhibition led to the impaired CRC cell line growth. Moreover, knockdown of LINRIS resulted in a decreased level of insulin-like growth factor 2 mRNA-binding protein 2 (IGF2BP2), a newly found N⁶-methyladenosine (m⁶A) ‘reader’. LINRIS blocked K139 ubiquitination of IGF2BP2, maintaining its stability. This process prevented the degradation of IGF2BP2 through the autophagy-lysosome pathway (ALP). Therefore, knockdown of LINRIS attenuated the downstream effects of IGF2BP2, especially MYC-mediated glycolysis in CRC cells. In addition, the transcription of LINRIS could be inhibited by GATA3 in CRC cells. In vivo experiments showed that the inhibition of LINRIS suppressed the proliferation of tumors in orthotopic models and in patient-derived xenograft (PDX) models. Conclusion: LINRIS is an independent prognostic biomarker for CRC. The LINRIS-IGF2BP2-MYC axis promotes the progression of CRC and is a promising therapeutic target.

Immune-checkpoint inhibitor (ICI) therapy, including antibodies targeting programmed cell death protein 1(PD-1), programmed death-ligand 1 (PD-L1), or cytotoxic T-lymphocyte–associated protein 4 (CTLA4), has demonstrated impressive clinical efficacy in controlling advanced cancers. Recent studies have identified several positive predictive markers for ICI, including high levels of microsatellite instability (MSI-high), PD-L1 overexpression, and elevated tumor mutation burden (TMB).¹ The genes that encode DNA polymerase epsilon (POLE) and delta 1 (POLD1) are essential for proofreading and fidelity in DNA replication.² Their germline or somatic mutations can lead to DNA-repair deficiencies and carcinogenesis via a DNA hypermutated molecular phenotype.^3,4 An association between POLE or POLD1 mutations and clinical benefit to ICI has been observed in several case reports.^5,6 However, to our knowledge, a comprehensive analysis of POLE or POLD1 mutation frequency and their predictive value for ICI treatment outcome has not yet been reported. In this study, we conducted a combined analysis using a large data set and found that POLE/POLD1 mutations are promising potential predictive biomarkers for positive ICI outcomes.