Linc01018 / hsa-mir-182-5p / ADH4 Axis Study on the Role and Mechanism in the Occurrence and Development of Liver Cancer

Background To construct the lncRNA-miRNA-mRNA axis based on the study of molecular oncology, to explore the role and mechanism of this axis in the occurrence and development of liver cancer, so as to provide a new channel for the treatment of liver cancer. Methods Using public online databases to establish lncRNA-miRNA-mRNA ceRNA regulation network, after which using QPCR and other experimental techniques to verify that this axis is established and the mechanism of participating in the development of liver cancer. Results It can be concluded from database mining that the expressions of hsa-miR-182-5p and ADH4 are negatively correlated in hepatocellular carcinoma, and LINC01018 is also negatively correlated hsa-miR-182-5p-ADH4, indicating that LINC01018, hsa-miR-182-5p and ADH4 are strongly correlated. Constitute the regulatory axis to participate in the occurrence and development tendency of tumors. LINC01018 regulates ADH4 to inhibit LIHC cell growth by inhibiting hsa-miR-182-5p, providing a feasible theoretical basis for the treatment of HCC.The regulatory axis may also regulate the occurrence and development tendency in liver cancer by adjusting the expression levels of key proteins and phosphorylation proteins in GO and KEGG signaling pathways. Conclusions In this study, it was found that LINC01018/hsa-miR-182-5p/ADH4 ceRNA regulatory axis exists in the human body, and this axis has the possibility of becoming an immune checkpoint inhibitor for liver cancer, which is regarded as a new entry point in the diagnosis and therapy of liver cancer.

An Improved Similarity-Based Clustering Algorithm for Multi-Database Mining

Clustering algorithms for multi-database mining (MDM) rely on computing (n2−n)/2 pairwise similarities between n multiple databases to generate and evaluate m∈[1,(n2−n)/2] candidate clusterings in order to select the ideal partitioning that optimizes a predefined goodness measure. However, when these pairwise similarities are distributed around the mean value, the clustering algorithm becomes indecisive when choosing what database pairs are considered eligible to be grouped together. Consequently, a trivial result is produced by putting all the n databases in one cluster or by returning n singleton clusters. To tackle the latter problem, we propose a learning algorithm to reduce the fuzziness of the similarity matrix by minimizing a weighted binary entropy loss function via gradient descent and back-propagation. As a result, the learned model will improve the certainty of the clustering algorithm by correctly identifying the optimal database clusters. Additionally, in contrast to gradient-based clustering algorithms, which are sensitive to the choice of the learning rate and require more iterations to converge, we propose a learning-rate-free algorithm to assess the candidate clusterings generated on the fly in fewer upper-bounded iterations. To achieve our goal, we use coordinate descent (CD) and back-propagation to search for the optimal clustering of the n multiple database in a way that minimizes a convex clustering quality measure L(θ) in less than (n2−n)/2 iterations. By using a max-heap data structure within our CD algorithm, we optimally choose the largest weight variable θp,q(i) at each iteration i such that taking the partial derivative of L(θ) with respect to θp,q(i) allows us to attain the next steepest descent minimizing L(θ) without using a learning rate. Through a series of experiments on multiple database samples, we show that our algorithm outperforms the existing clustering algorithms for MDM.

An Improved Similarity-based Clustering Algorithm for Multi-database Mining

Clustering algorithms for multi-database mining (MDM) rely on computing $(n^2-n)/2$ pairwise similarities between $n$ multiple databases to generate and evaluate $m\in[1, (n^2-n)/2]$ candidate clusterings in order to select the ideal partitioning which optimizes a predefined goodness measure. However, when these pairwise similarities are distributed around the mean value, the clustering algorithm becomes indecisive when choosing what database pairs are considered eligible to be grouped together. Consequently, a trivial result is produced by putting all the $n$ databases in one cluster or by returning $n$ singleton clusters. To tackle the latter problem, we propose a learning algorithm to reduce the fuzziness in the similarity matrix by minimizing a weighted binary entropy loss function via gradient descent and back-propagation. As a result, the learned model will improve the certainty of the clustering algorithm by correctly identifying the optimal database clusters. Additionally, in contrast to gradient-based clustering algorithms which are sensitive to the choice of the learning rate and require more iterations to converge, we propose a learning-rate-free algorithm to assess the candidate clusterings generated on the fly in a fewer upper-bounded iterations. Through a series of experiments on multiple database samples, we show that our algorithm outperforms the existing clustering algorithms for MDM.

Identification of Bioisosteric Scaffolds using Scaffold Keys

<p>Replacement of a central scaffold in a bioactive molecule by another scaffold with similar structural features (a procedure called sometimes "scaffold hopping") is a classical medicinal chemistry technique used to improve molecular properties and explore novel interesting areas of chemical space. The new scaffolds may be identified by database mining, match in physicochemical properties and often just by applying medicinal chemistry knowledge. In this study a novel method to find bioisosteric scaffolds is described when these are identified using similarity in simple substructure features called Scaffold Keys. Performance of the method is illustrated on several examples and a freely-available web tool https://bit.ly/scaffoldkeys allowing to find bioisosteric scaffold analogs is introduced.</p><div><br></div>

Identification of Bioisosteric Scaffolds using Scaffold Keys

<p>Replacement of a central scaffold in a bioactive molecule by another scaffold with similar structural features (a procedure called sometimes "scaffold hopping") is a classical medicinal chemistry technique used to improve molecular properties and explore novel interesting areas of chemical space. The new scaffolds may be identified by database mining, match in physicochemical properties and often just by applying medicinal chemistry knowledge. In this study a novel method to find bioisosteric scaffolds is described when these are identified using similarity in simple substructure features called Scaffold Keys. Performance of the method is illustrated on several examples and a freely-available web tool https://bit.ly/scaffoldkeys allowing to find bioisosteric scaffold analogs is introduced.</p><div><br></div>

Identification of Bioisosteric Scaffolds using Scaffold Keys

<p>Replacement of a central scaffold in a bioactive molecule by another scaffold with similar structural features (a procedure called sometimes "scaffold hopping") is a classical medicinal chemistry technique used to improve molecular properties and explore novel interesting areas of chemical space. The new scaffolds may be identified by database mining, match in physicochemical properties and often just by applying medicinal chemistry knowledge. In this study a novel method to find bioisosteric scaffolds is described when these are identified using similarity in simple substructure features called Scaffold Keys. Performance of the method is illustrated on several examples and a freely-available web tool https://bit.ly/scaffoldkeys allowing to find bioisosteric scaffold analogs is introduced.</p><div><br></div>