Advances in integrating single-cell sequencing data to unravel the mechanism of ferroptosis in cancer.

Ferroptosis, a commonly observed type of programmed cell death caused by abnormal metabolic and biochemical mechanisms, is frequently triggered by cellular stress. The occurrence of ferroptosis is predominantly linked to pathophysiological conditions due to the substantial impact of various metabolic pathways, including fatty acid metabolism and iron regulation, on cellular reactions to lipid peroxidation and ferroptosis. This mode of cell death serves as a fundamental factor in the development of numerous diseases, thereby presenting a range of therapeutic targets. Single-cell sequencing technology provides insights into the cellular and molecular characteristics of individual cells, as opposed to bulk sequencing, which provides data in a more generalized manner. Single-cell sequencing has found extensive application in the field of cancer research. This paper reviews the progress made in ferroptosis-associated cancer research using single-cell sequencing, including ferroptosis-associated pathways, immune checkpoints, biomarkers, and the identification of cell clusters associated with ferroptosis in tumors. In general, the utilization of single-cell sequencing technology has the potential to contribute significantly to the investigation of the mechanistic regulatory pathways linked to ferroptosis. Moreover, it can shed light on the intricate connection between ferroptosis and cancer. This technology holds great promise in advancing tumor-wide diagnosis, targeted therapy, and prognosis prediction.

Pigment Diversity in Leaves of Caladium × hortulanum Birdsey and Transcriptomic and Metabolic Comparisons between Red and White Leaves.

Leaf color is a key ornamental characteristic of cultivated caladium (Caladium × hortulanum Birdsey), a plant with diverse leaf colors. However, the genetic improvement of leaf color in cultivated caladium is hindered by the limited understanding of leaf color diversity and regulation. In this study, the chlorophyll and anthocyanin content of 137 germplasm resources were measured to explore the diversity and mechanism of leaf color formation in cultivated caladium. Association analysis of EST-SSR markers and pigment traits was performed, as well as metabolomics and transcriptomics analysis of a red leaf variety and its white leaf mutant. We found significant differences in chlorophyll and anthocyanin content among different color groups of cultivated caladium, and identified three, eight, three, and seven EST-SSR loci significantly associated with chlorophyll-a, chlorophyll-b, total chlorophyll and total anthocyanins content, respectively. The results further revealed that the white leaf mutation was caused by the down-regulation of various anthocyanins (such as cyanidin-3-O-rutinoside, quercetin-3-O-glucoside, and others). This change in concentration is likely due to the down-regulation of key genes (four PAL, four CHS, six CHI, eight F3H, one F3'H, one FLS, one LAR, four DFR, one ANS and two UFGT) involved in anthocyanin biosynthesis. Concurrently, the up-regulation of certain genes (one FLS and one LAR) that divert the anthocyanin precursors to other pathways was noted. Additionally, a significant change in the expression of numerous transcription factors (12 NAC, 12 bZIP, 23 ERF, 23 bHLH, 19 MYB_related, etc.) was observed. These results revealed the genetic and metabolic basis of leaf color diversity and change in cultivated caladium, and provided valuable information for molecular marker-assisted selection and breeding of leaf color in this ornamental plant.

RPI-EDLCN: An Ensemble Deep Learning Framework Based on Capsule Network for ncRNA-Protein Interaction Prediction.

Noncoding RNAs (ncRNAs) play crucial roles in many cellular life activities by interacting with proteins. Identification of ncRNA-protein interactions (ncRPIs) is key to understanding the function of ncRNAs. Although a number of computational methods for predicting ncRPIs have been developed, the problem of predicting ncRPIs remains challenging. It has always been the focus of ncRPIs research to select suitable feature extraction methods and develop a deep learning architecture with better recognition performance. In this work, we proposed an ensemble deep learning framework, RPI-EDLCN, based on a capsule network (CapsuleNet) to predict ncRPIs. In terms of feature input, we extracted the sequence features, secondary structure sequence features, motif information, and physicochemical properties of ncRNA/protein. The sequence and secondary structure sequence features of ncRNA/protein are encoded by the conjoint k-mer method and then input into an ensemble deep learning model based on CapsuleNet by combining the motif information and physicochemical properties. In this model, the encoding features are processed by convolution neural network (CNN), deep neural network (DNN), and stacked autoencoder (SAE). Then the advanced features obtained from the processing are input into the CapsuleNet for further feature learning. Compared with other state-of-the-art methods under 5-fold cross-validation, the performance of RPI-EDLCN is the best, and the accuracy of RPI-EDLCN on RPI1807, RPI2241, and NPInter v2.0 data sets was 93.8%, 88.2%, and 91.9%, respectively. The results of the independent test indicated that RPI-EDLCN can effectively predict potential ncRPIs in different organisms. In addition, RPI-EDLCN successfully predicted hub ncRNAs and proteins in Mus musculus ncRNA-protein networks. Overall, our model can be used as an effective tool to predict ncRPIs and provides some useful guidance for future biological studies.

GATLGEMF: A graph attention model with line graph embedding multi-complex features for ncRNA-protein interactions prediction.

Non-coding RNA (ncRNA) plays an important role in many fundamental biological processes, and it may be closely associated with many complex human diseases. NcRNAs exert their functions by interacting with proteins. Therefore, identifying novel ncRNA-protein interactions (NPIs) is important for understanding the mechanism of ncRNAs role. The computational approach has the advantage of low cost and high efficiency. Machine learning and deep learning have achieved great success by making full use of sequence information and structure information. Graph neural network (GNN) is a deep learning algorithm for complex network link prediction, which can extract and discover features in graph topology data. In this study, we propose a new computational model called GATLGEMF. We used a line graph transformation strategy to obtain the most valuable feature information and input this feature information into the attention network to predict NPIs. The results on four benchmark datasets show that our method achieves superior performance. We further compare GATLGEMF with the state-of-the-art existing methods to evaluate the model performance. GATLGEMF shows the best performance with the area under curve (AUC) of 92.41% and 98.93% on RPI2241 and NPInter v2.0 datasets, respectively. In addition, a case study shows that GATLGEMF has the ability to predict new interactions based on known interactions. The source code is available at https://github.com/JianjunTan-Beijing/GATLGEMF.

Cross-Compatibility in Interspecific Hybridization of Different Curcuma Accessions.

Curcuma is extensively cultivated as a medicinal and ornamental plant in tropical and subtropical regions. Due to the bright bract color, distinctive inflorescence and long blooming period, it has become a new favorite in terms of the urban landscape, potted flowers and cut flowers. However, little research on breeding new cultivars using traditional plant breeding methods is available on the genus Curcuma. In the present study, pollen viability and stigma receptivity evaluation were performed, and the genetic relationship of 38 Curcuma accessions was evaluated, then 5 C. alismatifolia Gagnep. (Ca), 2 C. hybrid (Ch), 2 C. sparganiifolia Gagnep. cultivars and 4 Curcuma native species were selected as parents for subsequent interspecific cross-breeding. A total of 132 reciprocal crosses were carried out for interspecific hybridization, including 70 obverse and 62 inverse crosses. Obvious discrepancies among fruit-setting rates were manifested in different combinations and in reciprocal crosses. Results showed that the highest fruit-setting rate (87.5%) was observed in the Ca combinations. There were 87 combinations with a fruit-setting rate of 0%, which meant nearly 65.9% was incompatible. We concluded that C. alismatifolia 'Siam Shadow' (Ch34) was suitable as a male parent and C. petiolata Roxb. (Cpet) was suitable as a female parent to improve the fruit-setting rates. The maximum number of seeds per fruit (45.4) was obtained when C. alismatifolia 'Chiang Mai Pink' (Ca01) was used as a female parent followed by C. attenuata Wall. ex Baker (Catt) (42.8) and C. alismatifolia 'Splash' (Ca63) (39.6) as male parents. The highest germination rate was observed for the Ca group followed by Catt and C. sparganiifolia 'Maetang Sunrise' (Csms). The germination rates of Ca accessions ranged from 58.2% (C. alismatifolia 'Siam Scarlet' (Ca06) as a male parent) to 89.3% (C. alismatifolia 'Sitone' (Ca10) as a male parent) with an average value of 74.0%. Based on the results of hybrid identification, all the individuals from the four combinations exhibited paternal-specific bands, indicating that the true hybrid rates of crossings were 100%. Our results would facilitate the interspecific hybridization and introduction of genetic variation from wild species into the cultivars in Curcuma in the future, which could be helpful in realizing the sustainable application in urban green areas.

Metastasis-related long non-coding RNAs AL359220.1, SH3BP5-AS1 and ZF-AS1 are significant for prognostic assessment of lung adenocarcinoma.

BACKGROUND: Metastasis of lung adenocarcinoma (LUAD) severely worsens prognosis. Genetic alteration in the tumor microenvironment (TME) is closely associated with metastasis and other malignant biological properties of LUAD. In this study, we establish a metastasis-related risk model to accurately predict LUAD prognosis. METHODS: RNA-sequencing profiles and clinical data of LUAD patients including 503 tumor tissues and 54 adjacent normal tissues were collected in TCGA database. Additionally, the paired specimens from 156 LUAD patients were obtained in a single center. The metastatic relevance and clinical significance of metastasis-related long non-coding RNA (MRLNRs) was validated by series of in vitro experiments including western blotting, qPCR and transwell assays. RESULTS: Six MRLNRs were significantly correlated to prognoses of LUAD patients, of which AL359220.1, SH3BP5-AS1 and ZF-AS1 were further used to establish a metastasis-related risk scoring model (MRRS) due to the close associations with overall survival of LUAD patients. According to the MRRS, patients with higher scores in the high-risk group obtained poorer prognoses and survival outcomes. ZFAS1 expressed highly in tumor tissues and showed the inverse results compared to SH3BP5-AS1 and AL359220.1. In addition, the high expression of ZFAS1 was prominently correlated to the more advanced T-stage and distant metastasis. The reduction of ZFAS1 induced by siRNAs dramatically diminished the migration and invasion abilities of LUAD cells. CONCLUSIONS: In the present research, we elucidate the metastatic relevance and clinical significance of AL359220.1, SH3BP5-AS1 and ZF-AS1 in LUAD. Moreover, MRRS provide a promising assessing model for clinical decision making and prognosis of LUAD.

Identification of coagulation-associated subtypes of lung adenocarcinoma and establishment of prognostic models.

Lung adenocarcinoma (LUAD), the most common subtype of lung cancer, is a global health challenge with high recurrence and mortality rates. The coagulation cascade plays an essential role in tumor disease progression and leads to death in LUAD. We differentiated two coagulation-related subtypes in LUAD patients in this study based on coagulation pathways collected from the KEGG database. We then demonstrated significant differences between the two coagulation-associated subtypes regarding immune characteristics and prognostic stratification. For risk stratification and prognostic prediction, we developed a coagulation-related risk score prognostic model in the Cancer Genome Atlas (TCGA) cohort. The GEO cohort also validated the predictive value of the coagulation-related risk score in terms of prognosis and immunotherapy. Based on these results, we identified coagulation-related prognostic factors in LUAD, which may serve as a robust prognostic biomarker for therapeutic and immunotherapeutic efficacy. It may contribute to clinical decision-making in patients with LUAD.

Scutellarin inhibits glioma cell proliferation by up-regulating miR-15a expression.

OBJECTIVE: To investigate the effect of scutellarin on the proliferation of glioma cells through microRNA (miR)-15a. METHODS: Human glioma cell line T98G was cultured in vitro and divided into control group (without treatment), scutellarin group (with 10, 20, 40, 80, 160 µg/mL scutellarin, respectively), miR-15a negative control group (transfected with negative control-miR-15a + 80 µg/mL scutellarin) and miR-15a inhibitor group (transfected with miR-15a siRNA + 80 µg/mL scutellarin). The proliferation of T98G cells was detected by cell counting kit-8 (CCK-8), and the expression of miR-15a in T98G cells was detected by real-time fluorescence quantitative PCR (qRT-PCR). The apoptosis of T98G cells was assessed by flow cytometry, and the invasion of T98G cells was compared by Transwell method. The levels of proliferating cell nuclear antigen (PCNA), Bcl-2 related X protein (Bax) and matrix metalloproteinase 9 (MMP-9) in T98G cells were detected by Western blot (WB). RESULTS: Compared with that in the control group, the OD value of T98G cells in scutellarin group was significantly lower (P<0.05), with the increase of scutellarin concentration, the OD value of T98G cells decreased in turn, and 80 µg/mL was used as the optimal concentration of scutellarin to treat T98G cells for subsequent experiments. Compared with those in the control group, the miR-15a expression, apoptosis rate and Bax protein expression in T98G cells of scutellarin group were higher (P<0.05), and the OD value, number of invasive cells, PCNA and MMP-9 protein levels were lower (P<0.05). Compared with scutellarin group and miR-15a negative control group, the miR-15a expression, apoptosis rate and Bax protein expression in T98G cells of miR-15a inhibitor group were lower (P<0.05), and the OD value, number of invasive cells, PCNA and MMP-9 protein levels were higher (P<0.05). CONCLUSIONS: Scutellarin can inhibit the proliferation, invasion and induce the apoptosis of glioma cells, which may be mediated by up-regulating the expression of miR-15a.

Diversity, classification, and EST-SSR-based association analysis of caladium ornamental traits.

Caladium (Caladium × Hortulanum Birdsey) is a popular ornamental plant with a wide range of vibrant leaf color among Araceae. Even after years of breeding, creating new caladium leaf color variations is extremely difficult. Molecular marker-assisted selection is an effective approach for accelerating breeding, but few studies on the molecular markers associated with caladium traits have been performed. In the current study, 144 caladium accessions were used to examine 12 phenotypic characteristics. The coefficient of variation for four numerical characters ranged from 23.94% to 43.22%, and the Shannon-Wiener indexes for eight descriptive characters ranged from 0.13 to 1.52. Based on L*, a*, b*, C, h° values determined by a colorimeter and hierarchical cluster analysis, the leaf color can be divided into four groups: pale green, green, light pink, and red. Furthermore, 7708 new SSR loci were identified by transcriptome sequencing, and 26 SSR markers with high polymorphism and reproducibility were screened. Genetic structure, NJ clustering, and PCoA analysis revealed that 144 accessions could be divided into three clusters, with genetic structure being closely related to germplasm origin. An association analysis revealed that the SSR markers 2, 1, 1, 1, 1, and 1 were mainly associated with petiole color, main vein color, blade upperside glossiness, and C, b*, and L* of leaf color (p < 0.01). These findings will serve as a valuable reference for evaluating germplasm resources and caladium molecular marker-assisted breeding.

Recent advances in predicting lncRNA-disease associations based on computational methods.

Mutations in and dysregulation of long non-coding RNAs (lncRNAs) are closely associated with the development of various human complex diseases, but only a few lncRNAs have been experimentally confirmed to be associated with human diseases. Predicting new potential lncRNA-disease associations (LDAs) will help us to understand the pathogenesis of human diseases and to detect disease markers, as well as in disease diagnosis, prevention and treatment. Computational methods can effectively narrow down the screening scope of biological experiments, thereby reducing the duration and cost of such experiments. In this review, we outline recent advances in computational methods for predicting LDAs, focusing on LDA databases, lncRNA/disease similarity calculations, and advanced computational models. In addition, we analyze the limitations of various computational models and discuss future challenges and directions for development.

Recent advances in machine learning methods for predicting LncRNA and disease associations.

Long non-coding RNAs (lncRNAs) are involved in almost the entire cell life cycle through different mechanisms and play an important role in many key biological processes. Mutations and dysregulation of lncRNAs have been implicated in many complex human diseases. Therefore, identifying the relationship between lncRNAs and diseases not only contributes to biologists' understanding of disease mechanisms, but also provides new ideas and solutions for disease diagnosis, treatment, prognosis and prevention. Since the existing experimental methods for predicting lncRNA-disease associations (LDAs) are expensive and time consuming, machine learning methods for predicting lncRNA-disease associations have become increasingly popular among researchers. In this review, we summarize some of the human diseases studied by LDAs prediction models, association and similarity features of LDAs prediction, performance evaluation methods of models and some advanced machine learning prediction models of LDAs. Finally, we discuss the potential limitations of machine learning-based methods for LDAs prediction and provide some ideas for designing new prediction models.

Complete Chloroplast Genome Sequences of Four Species in the Caladium Genus: Comparative and Phylogenetic Analyses.

Caladiums are promising colorful foliage plants due to their dazzling colors of the leaves, veins, stripes, and patches, which are often cultivated in pots or gardens as decorations. Four wild species, including C. bicolor, C. humboldtii, C. praetermissum, and C. lindenii, were employed in this study, where their chloroplast (cp) genomes were sequenced, assembled, and annotated via high-throughput sequencing. The whole cp genome size ranged from 162,776 bp to 168,888 bp, and the GC contents ranged from 35.09% to 35.91%. Compared with the single large copy (LSC) and single small copy (SSC) regions, more conserved sequences were identified in the inverted repeat regions (IR). We further analyzed the different region borders of nine species of Araceae and found the expansion or contraction of IR/SSC regions might account for the cp genome size variation. Totally, 131 genes were annotated in the cp genomes, including 86 protein-coding genes (PCGs), 37 tRNAs, and eight rRNAs. The effective number of codons (ENC) values and neutrality plot analyses provided the foundation that the natural selection pressure could greatly affect the codon preference. The GC3 content was significantly lower than that of GC1 and GC2, and codons ending with A/U had higher usage preferences. Finally, we conducted phylogenetic relationship analysis based on the chloroplast genomes of twelve species of Araceae, in which C. bicolor and C. humboldtii were grouped together, and C. lindenii was furthest from the other three Caladium species occupying a separate branch. These results will provide a basis for the identification, development, and utilization of Caladium germplasm.

The genomic and bulked segregant analysis of Curcuma alismatifolia revealed its diverse bract pigmentation.

Compared with most flowers where the showy part comprises specialized leaves (petals) directly subtending the reproductive structures, most Zingiberaceae species produce showy "flowers" through modifications of leaves (bracts) subtending the true flowers throughout an inflorescence. Curcuma alismatifolia, belonging to the Zingiberaceae family, a plant species originating from Southeast Asia, has become increasingly popular in the flower market worldwide because of its varied and esthetically pleasing bracts produced in different cultivars. Here, we present the chromosome-scale genome assembly of C. alismatifolia "Chiang Mai Pink" and explore the underlying mechanisms of bract pigmentation. Comparative genomic analysis revealed C. alismatifolia contains a residual signal of whole-genome duplication. Duplicated genes, including pigment-related genes, exhibit functional and structural differentiation resulting in diverse bract colors among C. alismatifolia cultivars. In addition, we identified the key genes that produce different colored bracts in C. alismatifolia, such as F3'5'H, DFR, ANS and several transcription factors for anthocyanin synthesis, as well as chlH and CAO in the chlorophyll synthesis pathway by conducting transcriptomic analysis, bulked segregant analysis using both DNA and RNA data, and population genomic analysis. This work provides data for understanding the mechanism of bract pigmentation and will accelerate breeding in developing novel cultivars with richly colored bracts in C. alismatifolia and related species. It is also important to understand the variation in the evolution of the Zingiberaceae family. Supplementary Information: The online version contains supplementary material available at 10.1007/s42994-022-00081-6.

LPI-CSFFR: Combining serial fusion with feature reuse for predicting LncRNA-protein interactions.

Long non-coding RNAs (LncRNAs) play important roles in a series of life activities, and they function primarily with proteins. The wet experimental-based methods in lncRNA-protein interactions (lncRPIs) study are time-consuming and expensive. In this study, we propose for the first time a novel feature fusion method, the LPI-CSFFR, to train and predict LncRPIs based on a Convolutional Neural Network (CNN) with feature reuse and serial fusion in sequences, secondary structures, and physicochemical properties of proteins and lncRNAs. The experimental results indicate that LPI-CSFFR achieves excellent performance on the datasets RPI1460 and RPI1807 with an accuracy of 83.7 % and 98.1 %, respectively. We further compare LPI-CSFFR with the state-of-the-art existing methods on the same benchmark datasets to evaluate the performance. In addition, to test the generalization performance of the model, we independently test sample pairs of five model organisms, where Mus musculus are the highest prediction accuracy of 99.5 %, and we find multiple hotspot proteins after constructing an interaction network. Finally, we test the predictive power of the LPI-CSFFR for sample pairs with unknown interactions. The results indicate that LPI-CSFFR is promising for predicting potential LncRPIs. The relevant source code and the data used in this study are available at https://github.com/JianjunTan-Beijing/LPI-CSFFR.

Analysis of single-cell RNA-sequencing data identifies a hypoxic tumor subpopulation associated with poor prognosis in triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is an aggressive subtype of mammary carcinoma characterized by low expression levels of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). Along with the rapid development of the single-cell RNA-sequencing (scRNA-seq) technology, the heterogeneity within the tumor microenvironment (TME) could be studied at a higher resolution level, facilitating an exploration of the mechanisms leading to poor prognosis during tumor progression. In previous studies, hypoxia was considered as an intrinsic characteristic of TME in solid tumors, which would activate downstream signaling pathways associated with angiogenesis and metastasis. Moreover, hypoxia-related genes (HRGs) based risk score models demonstrated nice performance in predicting the prognosis of TNBC patients. However, it is essential to further investigate the heterogeneity within hypoxic TME, such as intercellular communications. In the present study, utilizing single-sample Gene Set Enrichment Analysis (ssGSEA) and cell-cell communication analysis on the scRNA-seq data retrieved from Gene Expression Omnibus (GEO) database with accession number GSM4476488, we identified four tumor subpopulations with diverse functions, particularly a hypoxia-related one. Furthermore, results of cell-cell communication analysis revealed the dominant role of the hypoxic tumor subpopulation in angiogenesis- and metastasis-related signaling pathways as a signal sender. Consequently, regard the TNBC cohorts acquired from The Cancer Genome Atlas (TCGA) and GEO as train set and test set respectively, we constructed a risk score model with reliable capacity for the prediction of overall survival (OS), where ARTN and L1CAM were identified as risk factors promoting angiogenesis and metastasis of tumors. The expression of ARTN and L1CAM were further analyzed through tumor immune estimation resource (TIMER) platform. In conclusion, these two marker genes of the hypoxic tumor subpopulation played vital roles in tumor development, indicating poor prognosis in TNBC patients.

The Identification and Analysis of MicroRNAs Combined Biomarkers for Hepatocellular Carcinoma Diagnosis.

BACKGROUND: Hepatocellular carcinoma (HCC) is a common malignant tumor with high morbidity and mortality globally. Compared with traditional diagnostic methods, microRNAs (miRNAs) are novel biomarkers with higher accuracy. OBJECTIVE: We aimed to identify combinatorial biomarkers of miRNAs to construct a classification model for the diagnosis of HCC. METHODS: The mature miRNA expression profile data of six cancers (liver, lung, gastric, breast, prostate, and colon) were retrieved from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database with accession number GSE36915, GSE29250, GSE99417, GSE41970, GSE64333 and GSE35982. The messenger RNA (mRNA) expression profile data of these six cancers were obtained from TCGA. Three R software packages, student's t-test, and a normalized foldchange method were utilized to identify HCC-specific differentially expressed miRNAs (DEMs). Using all combinations of obtained HCC-specific DEMs as input features, we constructed a classification model by support vector machine searching for the optimal combination. Furthermore, target genes prediction was conducted on the miRWalk 2.0 website to obtain differentially expressed mRNAs (DEmRNAs), and KEGG pathway enrichment was analyzed on the DAVID website. RESULTS: The optimal combination consisted of four miRNAs (hsa-miR-130a-3p, hsa-miR-450b-5p, hsa-miR-136-5p, and hsa-miR-24-1-5p), of which the last one has not been currently reported to be relevant to HCC. The target genes of hsa-miR-24-1-5p (CDC7, ACACA, CTNNA1, and NF2) were involved in the cell cycle, AMPK signaling pathway, Hippo signaling pathway, and insulin signaling pathway, which affect the proliferation, metastasis, and apoptosis of cancer cells. Moreover, the area under the receiver operating characteristic curves of the four miRNAs were all higher than 0.85. CONCLUSION: These results suggest that the miRNAs combined biomarkers were reliable for the diagnosis of HCC. Hsa-miR-24-1-5p was a novel biomarker for HCC diagnosis identified in this study.

EDLMFC: an ensemble deep learning framework with multi-scale features combination for ncRNA-protein interaction prediction.

BACKGROUND: Non-coding RNA (ncRNA) and protein interactions play essential roles in various physiological and pathological processes. The experimental methods used for predicting ncRNA-protein interactions are time-consuming and labor-intensive. Therefore, there is an increasing demand for computational methods to accurately and efficiently predict ncRNA-protein interactions. RESULTS: In this work, we presented an ensemble deep learning-based method, EDLMFC, to predict ncRNA-protein interactions using the combination of multi-scale features, including primary sequence features, secondary structure sequence features, and tertiary structure features. Conjoint k-mer was used to extract protein/ncRNA sequence features, integrating tertiary structure features, then fed into an ensemble deep learning model, which combined convolutional neural network (CNN) to learn dominating biological information with bi-directional long short-term memory network (BLSTM) to capture long-range dependencies among the features identified by the CNN. Compared with other state-of-the-art methods under five-fold cross-validation, EDLMFC shows the best performance with accuracy of 93.8%, 89.7%, and 86.1% on RPI1807, NPInter v2.0, and RPI488 datasets, respectively. The results of the independent test demonstrated that EDLMFC can effectively predict potential ncRNA-protein interactions from different organisms. Furtherly, EDLMFC is also shown to predict hub ncRNAs and proteins presented in ncRNA-protein networks of Mus musculus successfully. CONCLUSIONS: In general, our proposed method EDLMFC improved the accuracy of ncRNA-protein interaction predictions and anticipated providing some helpful guidance on ncRNA functions research. The source code of EDLMFC and the datasets used in this work are available at https://github.com/JingjingWang-87/EDLMFC .

Lung adenocarcinoma patients have higher risk of SARS-CoV-2 infection.

Both lung adenocarcinoma and coronavirus disease 2019 would cause pulmonary inflammation. Angiotensin-converting enzyme 2, the functional receptor of SARS-CoV-2, also plays a key role in lung adenocarcinoma. To study the risk of SARS-CoV-2 infection in lung adenocarcinoma patients, mRNA and microRNA profiles were obtained from The Cancer Genome Atlas and Gene Expression Omnibus followed by bioinformatics analysis. A network which regards angiotensin-converting enzyme 2 as the center was structured. In addition, via immunological analysis to explore the essential mechanism of SARS-CoV-2 susceptibility in lung adenocarcinoma. Compared with normal tissue, angiotensin-converting enzyme 2 was increased in lung adenocarcinoma patients. Furthermore, a total of 7 correlated differently expressed mRNAs (ACE2, CXCL9, MMP12, IL6, AZU1, FCN3, HYAL1 and IRAK3) and 5 correlated differently expressed microRNAs (miR-125b-5p, miR-9-5p, miR-130b-5p, miR-381-3p and miR-421) were screened. Interestingly, the most frequent toll-like receptor signaling pathway was enriched by mRNA (interlukin 6) and miRNA (miR-125b-5p) sets simultaneously. In conclusion, it was assumed that miR-125b-5p-ACE2-IL6 axis could alter the risk of SARS-CoV-2 infection in lung adenocarcinoma patients.

Quantitative Structure Activity/Pharmacokinetics Relationship Studies of HIV-1 Protease Inhibitors Using Three Modelling Methods.

BACKGROUND: HIV-1 protease inhibitor (PIs) is a good choice for AIDS patients. Nevertheless, for PIs, there are several bugs in clinical application, like drug resistance, the large dose, the high costs and so on, among which, the poor pharmacokinetics property is one of the important reasons that leads to the failure of its clinical application. OBJECTIVE: We aimed to build computational models for studying the relationship between PIs structure and its pharmacological activities. METHODS: We collected experimental values of koff/Ki and structures of 50 PIs through a careful literature and database search. Quantitative structure activity/pharmacokinetics relationship (QSAR/QSPR) models were constructed by support vector machine (SVM), partial-least squares regression (PLSR) and back-propagation neural network (BPNN). RESULTS: For QSAR models, SVM, PLSR and BPNN all generated reliable prediction models with the r2 of 0.688, 0.768 and 0.787, respectively, and r2pred of 0.748, 0.696 and 0.640, respectively. For QSPR models, the optimum models of SVM, PLSR and BPNN obtained the r2 of 0.952, 0.869 and 0.960, respectively, and the r2pred of 0.852, 0.628 and 0.814, respectively. CONCLUSION: Among these three modelling methods, SVM showed superior ability than PLSR and BPNN both in QSAR/QSPR modelling of PIs, thus, we suspected that SVM was more suitable for predicting activities of PIs. In addition, 3D-MoRSE descriptors may have a tight relationship with the Ki values of PIs, and the GETAWAY descriptors have significant influence on both koff and Ki in PLSR equations.

[Effects of LncRNA UNC5B-AS1 on adhesion, invasion and migration of lung cancer cells and its mechanism].

Objective: To investigate the effects of long-chain non-coding RNA (lncRNA) UNC5B-AS1 on the adhesion, invasion and migration of lung cancer cells by regulating the expression of miR-218-5p. Methods: Twenty specimens of lung cancer patients and corresponding paracancerous tissues were surgically removed and collected from the oncology department of Chongqing Three Gorges Central Hospital from June 2017 to June 2019. Real-time quantitative PCR (qRT-PCR) was used to detect the expressions of UNC5B-AS1 in human bronchial epithelial cells HBE and different lung cancer cells of A549, H1437, H1975, H1299 and H460. UNC5B-AS1 siRNA was transfected into lung cancer A549 cells. Adhesion assay, transwell invasion assay and scratch assay were used to detect the effect of UNC5B-AS1 on adhesion, invasion and migration of A549 cells. qRT-PCR and dual luciferase reporter gene were used for the detection and identification of UNC5B-AS1 targeting miR-218-5p. The expression of epithelial-mesenchymal transition (EMT)-related protein was detected by Western blot. Results: The expression of UNC5B-AS1 in lung cancer tissues and cells was significantly higher than that in adjacent tissues and bronchial epithelial cells (P＜0.05). The expression of UNC5B-AS1 in lung cancer A549 cells was the highest (P＜0.05). Down-regulation of UNC5B-AS1 expression inhibited adhesion, invasion and migration of A549 cells (P＜0.05). qRT-PCR and dual luciferase reporter assay experiments showed that UNC5B-AS1 targeted the regulation of miR-218-5p expression. Down-regulation of UNC5B-AS1 inhibited E-cadherin protein expression and promoted Vimentin and Twist protein expression. Conclusion: lncRNA UNC5B-AS1 promotes adhesion, invasion and migration of lung cancer cells through targeted regulation of miR-218-5p expression, and its mechanism may be related to the promotion of EMT.