Vol 19, No 2 (2024)

Long non-coding RNAs (LncRNAs) are significant contributors to bacterial infections and host defense responses, presenting a novel class of gene regulators beyond conventional protein-coding genes. This narrative review aimed to explore the involvement of LncRNAs as a potential biomarker in the diagnosis and treatment of bacterial infections, with a specific focus on Brucella infections. A comprehensive literature review was conducted to identify relevant studies examining the roles of LncRNAs in immune responses during bacterial infections, with a specific emphasis on Brucella infections. Pub- Med, Scopus and other major scientific databases were searched using relevant keywords. LncRNAs crucially regulate immune responses to bacterial infections, influencing transcription factors, proinflammatory cytokines, and immune cell behavior, with both positive and negative effects. The NF-κB pathway is a key regulator for many LncRNAs in bacterial infections. During Brucella infections, essential LncRNAs activate the innate immune response, increasing proinflammatory cytokine production and immune cell differentiation. LncRNAs are associated with human brucellosis, holding promise for screening, diagnostics, or therapeutics. Further research is needed to fully understand LncRNAs' precise functions in Brucella infection and pathogenesis. Specific LncRNAs, like IFNG-AS1 and NLRP3, are upregulated during brucellosis, while others, such as Gm28309, are downregulated, influencing immunosuppression and bacterial survival. Investigating the prognostic and therapeutic potential of Brucellarelated LncRNAs warrants ongoing investigation, including their roles in other immune cells like macrophages, dendritic cells, and neutrophils responsible for bacterial clearance. Unraveling the intricate relationship between LncRNAs and brucellosis may reveal novel regulatory mechanisms and LncRNAs' roles in infection regulation, expediting diagnostics and enhancing therapeutic strategies against Brucella infections.

Background:The oligometastatic disease has been proposed as an intermediate state between primary tumor and systemically metastatic disease, which has great potential curable with locoregional therapies. However, since no biomarker for the identification of patients with true oligometastatic disease is clinically available, the diagnosis of oligometastatic disease remains controversial.

Objective:We aim to identify potential biomarkers of colorectal cancer patients with true oligometastatic states, who will benefit most from local therapy.

Methods:This study retrospectively analyzed the transcriptome profiles and clinical parameters of 307 metastatic colorectal cancer patients. A novel network propagation method and network-based strategy were combined to identify oligometastatic biomarkers to predict the prognoses of metastatic colorectal cancer patients.

Results:We defined two metastatic risk groups according to twelve oligometastatic biomarkers, which exhibit distinct prognoses, clinicopathological features, immunological characteristics, and biological mechanisms. The metastatic risk assessment model exhibited a more powerful capacity for survival prediction compared to traditional clinicopathological features. The low-MRS group was most consistent with an oligometastatic state, while the high-MRS might be a potential polymetastatic state, which leads to the divergence of their prognostic outcomes and response to treatments. We also identified 22 significant immune check genes between the high-MRS and low- MRS groups. The difference in molecular mechanism between the two metastatic risk groups was associated with focal adhesion, nucleocytoplasmic transport, Hippo, PI3K-Akt, TGF-β, and EMCreceptor interaction signaling pathways.

Conclusion:Our study provided a molecular definition of the oligometastatic state in colorectal cancer, which contributes to precise treatment decision-making for advanced patients.

Background and Objective:Gene promoters play a crucial role in regulating gene transcription by serving as DNA regulatory elements near transcription start sites. Despite numerous approaches, including alignment signal and content-based methods for promoter prediction, accurately identifying promoters remains challenging due to the lack of explicit features in their sequences. Consequently, many machine learning and deep learning models for promoter identification have been presented, but the performance of these tools is not precise. Most recent investigations have concentrated on identifying sigma or plant promoters. While the accurate identification of Saccharomyces cerevisiae promoters remains an underexplored area. In this study, we introduced "iPromyeast", a method for identifying yeast promoters. Using genome sequences from the eukaryotic yeast Saccharomyces cerevisiae, we investigate vector encoding and promoter classification. Additionally, we developed a more difficult negative set by employing promoter sequences rather than nonpromoter regions of the genome. The newly developed negative reconstruction approach improves classification and minimizes the amount of false positive predictions.

Methods:To overcome the problems associated with promoter prediction, we investigate alternate vector encoding and feature extraction methodologies. Following that, these strategies are coupled with several machine learning algorithms and a 1-D convolutional neural network model. Our results show that the pseudo-dinucleotide composition is preferable for feature encoding and that the machine- learning stacking approach is excellent for accurate promoter categorization. Furthermore, we provide a negative reconstruction method that uses promoter sequences rather than non-promoter regions, resulting in higher classification performance and fewer false positive predictions.

Results:Based on the results of 5-fold cross-validation, the proposed predictor, iProm-Yeast, has a good potential for detecting Saccharomyces cerevisiae promoters. The accuracy (Acc) was 86.27%, the sensitivity (Sn) was 82.29%, the specificity (Sp) was 89.47%, the Matthews correlation coefficient (MCC) was 0.72, and the area under the receiver operating characteristic curve (AUROC) was 0.98. We also performed a cross-species analysis to determine the generalizability of iProm-Yeast across other species.

Conclusion:iProm-Yeast is a robust method for accurately identifying Saccharomyces cerevisiae promoters. With advanced vector encoding techniques and a negative reconstruction approach, it achieves improved classification accuracy and reduces false positive predictions. In addition, it offers researchers a reliable and precise webserver to study gene regulation in diverse organisms.