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.

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.

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.

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 .

Computational identification and analysis of early diagnostic biomarkers for kidney cancer.

Renal cell carcinoma is one of the most common urinary system tumors in adults, it is usually asymptomatic in its early stage and the patients are often diagnosed late. MicroRNA has a higher diagnostic accuracy than traditional markers and may become a new type of early diagnostic biomarker for kidney cancer. Three computational methods and several bioinformatic methods including PPI network, overall survival analysis and enrichment analysis were used to identify the significant differentially expressed miRNAs. Thirteen miRNAs that were significantly differentially expressed in RCC patients were identified, 10 of them have been proved to be associated with kidney cancer in other studies, miR-576, miR-616 and miR-133a-2 are three newly discovered biomarkers of RCC in this study. We found that the target genes of miR-576 (CUL3 and RAC1) are involved in the regulation of multiple cancer-related biological pathways, and the target gene of miR-616 (ASB13 and FBXW2) has been reported to be associated with the development of other cancers. Our findings may have guiding significance for the early diagnosis of renal cell carcinoma.

A novel peptide shows excellent anti-HIV-1 potency as a gp41 fusion inhibitor.

Fusion inhibitors of HIV prevent the virus from entering into the target cell via the interaction with gp41, which stops the process of spatial rearrangement of the viral envelope protein. A series of peptides have been designed and screened to obtain a highly potent novel sequence. Among them, CT105 possesses the most potent anti-viral ability at low nanomolar IC50 values against a panel of HIV-1 pseudoviruses from A, B, C and A1/D subtypes, whereas T20 shows much weaker potency. CT105 also shows excellent inhibitory activity at 260 pico molar IC50 against HIV-1 replication. As a fusion inhibitor, CT105 has a strong ability to interrupt gp41 core formation. The terminal half-life of CT105 possesses 1.72-fold longer than that of T20 as determined by developing an indirect competitive ELISA method. The results suggest that this artificial peptide CT105 could be a favorable architype for further optimization and modification.

Design, synthesis and activity evaluation of novel peptide fusion inhibitors targeting HIV-1 gp41.

Human immunodeficiency virus type 1 (HIV-1), the pathogen of acquired immunodeficiency syndrome (AIDS), causes about 2 million people to death every year. Fusion inhibitors targeted the envelope protein (gp41) represent a novel and alternative approach for anti-AIDS therapy, which terminates the HIV-1 life cycle at an early stage. Using CP621-652 as a template, a series of peptides were designed, synthesized and evaluated in vitro assays. An interesting phenomenon was found that the substitution of hydrophobic residues at solvent accessible sites could increase the anti-HIV activity when the C-terminal sequence was extended with an enough numbers of amino acids. After the active peptides was synthesized and evaluated, peptide 8 showed the best anti-HIV-1 IIIB whole cell activity (MAGI IC50=53.02 nM). Further study indicated that peptide 8 bound with the gp41 NHR helix, and then blocked the conformation of 6-helix, thus inhibited virus-cell membrane fusion. The results would be helpful for the design of peptide fusion inhibitors against HIV-1 infection.

Cation-pi interactions at non-redundant protein--RNA interfaces.

Cation-pi interactions have proved to be important in proteins and protein-ligand complexes. Here, cation-pi interactions are analyzed for 282 non-redundant protein-RNA interfaces. The statistical results show that this kind of interactions exists in 65% of the interfaces. The four RNA bases are ranked as Gua>Ade>Ura>Cyt according to their propensity to participate in cation-pi interactions. The corresponding ranking for the involved amino acid residues is: Arg>Lys>Asn>Gln. The same trends are obtained based on the empirical energy calculation. The Arg-Gua pairs have the greatest stability and are also most frequently observed. The number of cation-pi pairs involving unpaired bases is 2.5 times as many as those involving paired bases. Hence, cation-pi interactions show sequence and structural specificities. For the bicyclic bases, Gua and Ade, their 5-atom rings participate in cation-pi interactions somewhat more than the 6-atom rings, with percentages of 54 and 46%, respectively, which is due to the higher cation-pi participation proportion (63%) of 5-atom rings in the paired bases. These results give a general view of cation-pi interactions at protein-RNA interfaces and are helpful in understanding the specific recognition between protein and RNA.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.