Background: MicroRNAs (miRNAs) play a key role in regulating various biological processes such as participating in the post-transcriptional pathway and affecting the stability and/or the translation of mRNA. Current methods have extracted feature information at different levels, among which the characteristic stem-loop structure makes the greatest contribution to the prediction of putative miRNA precursor (pre-miRNA). We find that none of these features alone is capable of identifying new pre-miRNA accurately. Results: In the present work, a pre-miRNA stem-loop secondary structure is translated to a network, which provides a novel perspective for its structural analysis. Network parameters are used to construct prediction model, achieving an area under the receiver operating curves (AUC) value of 0.956. Moreover, by repeating the same method on two independent datasets, accuracies of 0.976 and 0.913 are achieved, respectively. Conclusions: Network parameters effectively characterize pre-miRNA secondary structure, which improves our prediction model in both prediction ability and computation efficiency. Additionally, as a complement to feature extraction methods in previous studies, these multifaceted features can reflect natural properties of miRNAs and be used for comprehensive and systematic analysis on miRNA.

Neuroblastoma breakpoint family, member 1 (NBPF1), appears to be a double-edged sword with regard to its role in carcinogenesis. On the one hand, the tumor-suppressing functions of NBPF1 have been definitively observed in neuroblastoma, prostate cancer, cutaneous squamous cell carcinoma, and cervical cancer. On the other hand, there is evidence that NBPF1 regulates the colony formation, invasion, and maintenance of liver cancer cells and hence functions as an oncogene. The roles of NBPF1 are strictly dependent on the biological context and type of organization. However, a systematic pan-cancer analysis has thus far not been undertaken, and the significance of NBPF1 in the occurrence and progression of many malignancies is uncertain. In this paper, bioinformatics techniques were employed to analyze NBPF1 expression across different cancers and investigate the relationship between NBPF1 and clinical features, prognosis, genetic alteration, and tumor immune microenvironment, respectively. Our results show that NBPF1 is variably expressed in distinct tumor tissues and is also closely linked to clinical outcomes. In particular, compared to other tumor types, there was a strong negative correlation between NBPF1 expression and various components of the tumor microenvironment in adrenocortical carcinoma (ACC). We thus developed an NBPF1-derived immune risk model based on NBPF1-related immune genes; ACC patients with a high-risk score tended to have a poorer prognosis, accompanied by immune hyporesponsiveness. NBPF1 can be used as a prognostic biomarker for multiple cancers. Moreover, anti-NBPF1 immunotherapy may be suitable for treating ACC patients.

Motivation: Protein and peptide drugs, after decades of development have grown into a major drug class of the marketplace. Target identification and validation is crucial for their discovery, and bioinformatics estimation of candidate targets based on characteristics of successful target proteins will help improve efficiency and success rate of target selection. However, owing to the development history of the pharmaceutical industry, previous systematic exploration of target space mainly focused on traditional small-molecule drugs, whereas that for protein and peptide drugs is blank. Here we systematically explored target spaces in the human genome specially for protein and peptide drugs. Results: We found that compared with other proteins, targets of both successful protein and peptide drugs have their own characteristics in many aspects and are also significantly different from those of traditional small-molecule drugs. Further based on these features, we developed effective genome-wide target estimation models respectively for protein and peptide drugs.