Differential whole-genome doubling based signatures for improvement on clinical outcomes and drug response in patients with breast cancer.

Whole genome doublings (WGD), a hallmark of human cancer, is pervasive in breast cancer patients. However, the molecular mechanism of the complete impact of WGD on survival and treatment response in breast cancer remains unclear. To address this, we performed a comprehensive and systematic analysis of WGD, aiming to identify distinct genetic alterations linked to WGD and highlight its improvement on clinical outcomes and treatment response for breast cancer. A linear regression model along with weighted gene co-expression network analysis (WGCNA) was applied on The Cancer Genome Atlas (TCGA) dataset to identify critical genes related to WGD. Further Cox regression models with random selection were used to optimize the most useful prognostic markers in the TCGA dataset. The clinical implication of the risk model was further assessed through prognostic impact evaluation, tumor stratification, functional analysis, genomic feature difference analysis, drug response analysis, and multiple independent datasets for validation. Our findings revealed a high aneuploidy burden, chromosomal instability (CIN), copy number variation (CNV), and mutation burden in breast tumors exhibiting WGD events. Moreover, 247 key genes associated with WGD were identified from the distinct genomic patterns in the TCGA dataset. A risk model consisting of 22 genes was optimized from the key genes. High-risk breast cancer patients were more prone to WGD and exhibited greater genomic diversity compared to low-risk patients. Some oncogenic signaling pathways were enriched in the high-risk group, while primary immune deficiency pathways were enriched in the low-risk group. We also identified a risk gene, ANLN (anillin), which displayed a strong positive correlation with two crucial WGD genes, KIF18A and CCNE2. Tumors with high expression of ANLN were more prone to WGD events and displayed worse clinical survival outcomes. Furthermore, the expression levels of these risk genes were significantly associated with the sensitivities of BRCA cell lines to multiple drugs, providing valuable insights for targeted therapies. These findings will be helpful for further improvement on clinical outcomes and contribution to drug development in breast cancer.

Establishment of comorbidity target pools and prediction of drugs candidate for multiple sclerosis and autoimmune thyroid diseases based on GWAS and transcriptome data.

BACKGROUND: Clinical observation has revealed that multiple sclerosis (MS) and autoimmune thyroid disease (AITD) are strongly correlated. The aim of this study was to explore the shared molecular causes of MS and AITD, and to conduct drug rearrangement on this basis, search for comorbidity drugs and feasible drugs for mutual reference between the two diseases. METHODS: Based on genome-wide association study (GWAS) data and transcriptome data, susceptibility genes and differentially expressed genes related to MS and AITD were identified by bioinformatics analysis. Pathway enrichment, gene ontology (GO), protein-protein interaction analysis, and gene-pathway network analysis of the above genes were performed to identify a common target pool, including common genes, common hub genes, and common pathways, and to explore the specific pathogenesis of the two diseases, respectively. Drugs that target the common pathways/genes were identified through the Comparative Toxicogenomics Database (CTD), DrugBank database, and Drug-Gene Interaction (DGI) Database. Common hub genes were compared with the target genes of drugs approved for treating MS/AITD and drugs under investigation identified by DrugBank and ClinicalTrials, respectively. RESULTS: We identified a pool of shared targets containing genes and pathways, including 46 common genetic susceptibility pathways and 9 common differentially expressed pathways, including JAK-STAT signaling pathway, Th17 cell differentiation, Th1 and Th2 cell differentiation, PD-L1 expression and PD-1 checkpoint pathway in cancer, etc. In addition, a total of 29 hub genes, including TYK2, JAK1, STAT3, IL2RA, HLA-DRB1, and TLR3, were identified. Drugs approved for treating MS or AITD, such as methylprednisolone, cyclophosphamide, glatiramer, natalizumab, and methimazole, can target the shared genes and pathways, among which methylprednisolone and cyclophosphamide have been shown to be beneficial for the treatment of the two diseases, indicating that these drugs have the potential to become a priority in the treatment of comorbidities. Moreover, drugs targeting multiple common genes and pathways, including tacrolimus, deucravacitinib, and nivolumab, were identified as potential drugs for the treatment of MS, AITD, and their comorbidities. CONCLUSION: We observed that T-cell activation-related genes and pathways play a major role in the pathogenesis of both MS and AITD, which may be the molecular basis of the comorbidity. Moreover, we identified a variety of drugs which may be used as priority or potential treatments for comorbidities.

Intermediate complex morphophysiological dormancy in seeds of Aconitum barbatum (Ranunculaceae).

BACKGROUND: Seed dormancy and germination are key components of plant regeneration strategies. Aconitum barbatum is a plant commonly found in northeast China. Although it has potential for use in gardening and landscaping, its seed dormancy and regeneration strategy, which adapt to its natural habitat, are not well understood. Our aim was to identify conditions for breaking A. barbatum seed dormancy and determine its dormancy type. Embryo growth and germination were determined by collecting seeds over time in the field. Laboratory experiments that control light, temperature, and stratification period were conducted to assess dormancy breaking and germination, and GA3 was used to identify dormancy type. RESULTS: Seeds of A. barbatum have undeveloped embryos with physiological dormancy at maturity in autumn. The embryo-to-seed length ratio increases from 0.33 to 0.78 before the emergence of the radical. Under natural environmental conditions, embryo development begins in early winter. Laboratory experiments have shown that long-term incubation under 4 °C (cold stratification) promotes embryo development and seed dormancy break. With an extension of cold stratification, an increase in germination percentages was observed when seeds were transferred from 4 °C to warmer temperatures. Seeds exposed to light during incubation show a higher germination percentage than those kept in the dark. Seed germination can also be enhanced by a 100 mg/L GA3 concentration. CONCLUSIONS: Seeds of A. barbatum display intermediate complex morphophysiological dormancy at maturity. In addition to the underdeveloped embryo, there are also physiological barriers that prevent the embryo from germinating. Dormancy breaking of A. barbatum seeds can be achieved by natural winter cold stratification, allowing seeds to germinate and sprout seedlings at the beginning of the following growing season. Our findings provide valuable insights into the seed dormancy and regeneration strategy of A. barbatum, which could facilitate its effective utilization in gardening and landscaping.

Single-cell RNA sequencing identifies critical transcription factors of tumor cell invasion induced by hypoxia microenvironment in glioblastoma.

Rationale: Glioblastoma (GBM) is an aggressive malignant primary brain cancer with poor survival. Hypoxia is a hallmark of GBM, which promotes tumor cells spreading (invasion) into the healthy brain tissue. Methods: To better elucidate the influence of hypoxia on GBM invasion, we proposed a data-driven modeling framework for predicting cellular hypoxia (CHPF) by integrating single cell transcriptome profiling and hypoxia gene signatures. Results: We characterized the hypoxia status landscape of GBM cells and observed that hypoxic cells were only present in the tumor core. Then, by investigating the cell-cell communication between immune cells and tumor cells, we discovered significant interaction between macrophages and tumor cells in hypoxic microenvironment. Notably, we dissected the functional heterogeneity of tumor cells and identified a hypoxic subpopulation that had highly invasive potential. By constructing cell status specific gene regulatory networks, we further identified 14 critical regulators of tumor invasion induced by hypoxic microenvironment. Finally, we confirmed that knocking down two critical regulators CEBPD and FOSL1 could reduce the invasive ability of GBM under hypoxic conditions. Additionally, we revealed the therapeutic effect of Axitinib and Entinostat through the mice model. Conclusion: Our work revealed the critical regulators in hypoxic subpopulation with high invasive potential in GBM, which may have practical implications for clinical targeted-hypoxia cancer drug therapy.

Systemically Identifying Triple-Negative Breast Cancer Subtype-Specific Prognosis Signatures, Based on Single-Cell RNA-Seq Data.

Triple-negative breast cancer (TNBC) is a highly heterogeneous disease with different molecular subtypes. Although progress has been made, the identification of TNBC subtype-associated biomarkers is still hindered by traditional RNA-seq or array technologies, since bulk data detected by them usually have some non-disease tissue samples, or they are confined to measure the averaged properties of whole tissues. To overcome these constraints and discover TNBC subtype-specific prognosis signatures (TSPSigs), we proposed a single-cell RNA-seq-based bioinformatics approach for identifying TSPSigs. Notably, the TSPSigs we developed mostly were found to be disease-related and involved in cancer development through investigating their enrichment analysis results. In addition, the prognostic power of TSPSigs was successfully confirmed in four independent validation datasets. The multivariate analysis results showed that TSPSigs in two TNBC subtypes-BL1 and LAR, were two independent prognostic factors. Further, analysis results of the TNBC cell lines revealed that the TSPSigs expressions and drug sensitivities had significant associations. Based on the preceding data, we concluded that TSPSigs could be exploited as novel candidate prognostic markers for TNBC patients and applied to individualized treatment in the future.

Immune cells transcriptome-based drug repositioning for multiple sclerosis.

Objective: Finding target genes and target pathways of existing drugs for drug repositioning in multiple sclerosis (MS) based on transcriptomic changes in MS immune cells. Materials and Methods: Based on transcriptome data from Gene Expression Omnibus (GEO) database, differentially expressed genes (DEGs) in MS patients without treatment were identified by bioinformatics analysis according to the type of immune cells, as well as DEGs in MS patients before and after drug administration. Hub target genes of the drug for MS were analyzed by constructing the protein-protein interaction network, and candidate drugs targeting 2 or more hub target genes were obtained through the connectivity map (CMap) database and Drugbank database. Then, the enriched pathways of MS patients without treatment and the enriched pathways of MS patients before and after drug administration were intersected to obtain the target pathways of the drug for MS, and the candidate drugs targeting 2 or more target pathways were obtained through Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Results: We obtained 50 hub target genes for CD4+ T cells in Fingolimod for MS, 15 hub target genes for Plasmacytoid dendritic cells (pDCs) and 7 hub target genes for Peripheral blood mononuclear cells (PBMC) in interferon-ß (IFN-ß) for MS. 6 candidate drugs targeting two or more hub targets (Fostamatinib, Copper, Artenimol, Phenethyl isothiocyanate, Aspirin and Zinc) were obtained. In addition, we obtained 4 target pathways for CD19+ B cells and 15 target pathways for CD4+ T cells in Fingolimod for MS, 7 target pathways for pDCs and 6 target pathways for PBMC in IFN-ß for MS, most of which belong to the immune system and viral infectious disease pathways. We obtained 69 candidate drugs targeting two target pathways. Conclusion: We found that applying candidate drugs that target both the "PI3K-Akt signaling pathway" and "Chemokine signaling pathway" (e.g., Nemiralisib and Umbralisib) or applying tyrosine kinase inhibitors (e.g., Fostamatinib) may be potential therapies for the treatment of MS.

Identification of Prognostic Gene Signatures by Developing a scRNA-Seq-Based Integration Approach to Predict Recurrence and Chemotherapy Benefit in Stage II-III Colorectal Cancer.

Prospective identification of robust biomarkers related to prognosis and adjuvant chemotherapy has become a necessary and critical step to predict the benefits of adjuvant therapy for patients with stage II-III colorectal cancer (CRC) before clinical treatment. We proposed a single-cell-based prognostic biomarker recognition approach to identify and construct CRC up- and down-regulated prognostic signatures (CUPsig and CDPsig) by integrating scRNA-seq and bulk datasets. We found that most genes in CUPsig and CDPsig were known disease genes, and they had good prognostic abilities in CRC validation datasets. Multivariate analysis confirmed that they were two independent prognostic factors of disease-free survival (DFS). Significantly, CUPsig and CDPsig could effectively predict adjuvant chemotherapy benefits in drug-treated validation datasets. Additionally, they also performed well in patients with CMS4 subtype. Subsequent analysis of drug sensitivity showed that expressions of these two signatures were significantly associated with the sensitivities of CRC cell lines to multiple drugs. In summary, we proposed a novel prognostic biomarker identification approach, which could be used to identify novel prognostic markers for stage II-III CRC patients who will undergo adjuvant chemotherapy and facilitate their further personalized treatments.

Identification of potential key genes and immune infiltration in Multiple sclerosis.

BACKGROUND: Multiple sclerosis (MS) is an extremely serious autoimmune disease of the nervous system. Extensive evidence indicated that immune system activation plays a crucial role in the development of MS. However, the exact mechanism of MS is still not well understood. Our objective was to identify potential key genes of Multiple sclerosis (MS) via bioinformatic analysis and apply CIBERSORT algorithms to calculate the proportion of infiltrating immune cells. METHODS: The differentially expressed genes (DEGs) were analyzed from two public datasets, which included 99 MS, 45 controls and 133 MS, 79 controls. Then the common DEGs were obtained (p < 0.05). LASSO regression analysis was performed on common DEGs of GSE17048. The receiver operating characteristic (ROC) curves were created. The key genes were screened based on area under the receiver operating characteristic curve (AUC). CIBERSORT algorithms were used to explore the immune infiltration in MS. RESULTS: 516 common DEGs were screened from two public datasets. And then 54 signature genes were obtained by constructing LASSO model. MS4A6A, CACNA1I, C9orf46, EIF4EBP2, SERTAD2, TGFBR2 and RAB34 with the largest AUC values were selected as the key genes. Neutrophils, Monocytes, resting memory CD4+ T cells, CD8+ T cells and resting NK cells accounted for a large proportion of infiltrating immune cells in MS. CONCLUSION: MS4A6A, CACNA1I, C9orf46, EIF4EBP2, SERTAD2, TGFBR2 and RAB34 may be closely related pathogenesis of MS, and may represent new candidate biomarkers. In addition, immune cell infiltration may also play an important role in the progression of MS.

Identification of multiple sclerosis-related genes regulated by EBV-encoded microRNAs in B cells.

BACKGROUND: Multiple sclerosis (MS) is driven by the interaction between genetic susceptibility and environmental triggers, particularly to Epstein-Barr virus (EBV) infection. EBV-encoded microRNAs (miRNAs) are abundantly expressed in all stages of EBV infection and latency, which can target both viral and host cellular mRNAs, allowing EBV-infected B cells to evade the host immune response. However, it remains a big gap to understand the roles of EBV miRNAs and their target genes in MS pathogenesis. METHODS: We investigated the correlation between MS-related viruses infection and MS risk quantitatively by systematic analysis. All MS-related genes in B cells were obtained by integrating MS susceptibility genes and differentially expressed genes from B cells. In comparison with differentially expressed genes from B cells after EBV infection in vitro, we confirmed EBV-regulated, MS-related genes. Subsequently, we obtained target EBV miRNAs which can regulate these genes from several online databases. By constructing pathway-pathway, pathway-gene and protein-protein interaction networks, we further screened out MS-related genes and risk pathways regulated by EBV miRNAs. Finally, we identified target EBV miRNAs which may directly regulate MS-related genes through bioinformatic prediction. RESULTS: EBV infection showed the strongest correlation with MS risk. A total of 568 MS-related genes and 80 risk pathways in B cells were obtained. We then identified 112 MS-related genes and 18 associated risk pathways that EBV was involved in. In addition, 33 human target genes regulated by 33 EBV miRNAs overlapped with EBV-regulated, MS-related genes. Finally, 15 target EBV miRNAs and their regulated, 7 MS-related genes (MALT1, BCL10, IFNGR2, STAT3, CXCR4, PTK2B and FOXP1) have been confirmed as crucial pathogenic molecules, which could promote the initiation and development of MS through NF-kappa B (MALT1 and BCL10) and PD-L1/PD-1 (IFNGR2 and STAT3) pathways. Surprisingly, ebv-miR-BHRF1-2-5p directly targeting MALT1 was confirmed by our experiments, and FOXP1 was identified as a target gene of ebv-miR-BART11. CONCLUSIONS: This work identified the target EBV miRNAs and their regulated, MS-related genes as well as risk pathways, which may provide a novel insight into discovering diagnostic biomarkers and therapeutic targets for MS.

Identification of common susceptibility genes and drug target genes in multiple sclerosis, systemic lupus erythematosus, and rheumatoid arthritis and its value to guide clinical treatment.

BACKGROUND: Multiple sclerosis (MS) is an autoimmune-mediated demyelinating disease of the white matter in the central nervous system (CNS). In clinical practice, it was found that MS is associated with a variety of autoimmune diseases, such as systemic lupus erythematosus (SLE), rheumatoid arthritis (RA). The aim of this study was to identify common susceptibility genes and drug target genes in MS, SLE, and RA and to provide new insights into treatment. METHODS: The common susceptibility genes of MS, SLE, and RA were obtained by searching the GWAS database and using microarray data to validate. The Genome Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed, and the common KEGG pathways were selected. All the genes enriched in the common pathways were obtained and intersected with the susceptibility genes of MS, SLE, and RA to obtain the pathway genes of them respectively, and found the common pathogenesis-related genes of the three diseases. By reviewing the literature and the DrugBank database, the drugs and drug target genes that have been approved for the treatment of the three diseases were obtained. Finally, the DGIdb database was searched to predict potential drugs or molecular compounds that interact with susceptibility genes common to MS, SLE, and RA. RESULTS: In MS, SLE, and RA, there were 46 common susceptibility genes, of which 23 were significantly differentially expressed in the microarray expression profile. Then, 2117 genes were obtained in the 42 common pathways, among which 17 pathogenesis-related genes were common in MS, SLE, and RA. The Drugbank database was used to obtain 29 drug target genes for MS, 43 drug target genes for RA, and 20 drug target genes for SLE. DHODH is a common drug target gene for MS, SLE, and RA, and its corresponding drugs are Leflunomide and Teriflunomide. A total of 13 genes and 366 potential drugs or molecular compounds were predicted to have interaction relationships after searching the DGIdb database. CONCLUSION: The common susceptibility genes and drug target genes among MS, SLE, and RA provide a theoretical basis for the co-morbidity phenomenon of the three diseases in clinical practice and may guide the clinical treatment.

A Comparison for Dimensionality Reduction Methods of Single-Cell RNA-seq Data.

Single-cell RNA sequencing (scRNA-seq) is a high-throughput sequencing technology performed at the level of an individual cell, which can have a potential to understand cellular heterogeneity. However, scRNA-seq data are high-dimensional, noisy, and sparse data. Dimension reduction is an important step in downstream analysis of scRNA-seq. Therefore, several dimension reduction methods have been developed. We developed a strategy to evaluate the stability, accuracy, and computing cost of 10 dimensionality reduction methods using 30 simulation datasets and five real datasets. Additionally, we investigated the sensitivity of all the methods to hyperparameter tuning and gave users appropriate suggestions. We found that t-distributed stochastic neighbor embedding (t-SNE) yielded the best overall performance with the highest accuracy and computing cost. Meanwhile, uniform manifold approximation and projection (UMAP) exhibited the highest stability, as well as moderate accuracy and the second highest computing cost. UMAP well preserves the original cohesion and separation of cell populations. In addition, it is worth noting that users need to set the hyperparameters according to the specific situation before using the dimensionality reduction methods based on non-linear model and neural network.

Systematical identification of cell-specificity of CTCF-gene binding based on epigenetic modifications.

The CCCTC-binding factor (CTCF) mediates transcriptional regulation and implicates epigenetic modifications in cancers. However, the systematically unveiling inverse regulatory relationship between CTCF and epigenetic modifications still remains unclear, especially the mechanism by which histone modification mediates CTCF binding. Here, we developed a systematic approach to investigate how epigenetic changes affect CTCF binding. Through integration analysis of CTCF binding in 30 cell lines, we concluded that CTCF generally binds with higher intensity in normal cell lines than that in cancers, and higher intensity in genome regions closed to transcription start sites. To facilitate the better understanding of their associations, we constructed linear mixed-effect models to analyze the effects of the epigenetic modifications on CTCF binding in four cancer cell lines and six normal cell lines, and identified seven epigenetic modifications as potential epigenetic patterns that influence CTCF binding intensity in promoter regions and six epigenetic modifications in enhancer regions. Further analysis of the effects in different locations revealed that the epigenetic regulation of CTCF binding was location-specific and cancer cell line-specific. Moreover, H3K4me2 and H3K9ac showed the potential association with immune regulation of disease. Taken together, our method can contribute to improve the understanding of the epigenetic regulation of CTCF binding and provide potential therapeutic targets for treating tumors associated with CTCF.

Revealing the subtyping of non-small cell lung cancer based on genomic evolutionary patterns by multi-region sequencing.

Accurately classifying patients with non-small cell lung cancer (NSCLC) from the perspective of tumor evolution has not been systematically studied to date. Here, we reconstructed phylogenetic relationships of somatic mutations in 100 early NSCLC patients (327 lesions) through reanalyzing the TRACERx data. Based on the genomic evolutionary patterns presented on the phylogenetic trees, we grouped NSCLC patients into three evolutionary subtypes. The phylogenetic trees among three subtypes exhibited distinct branching structures, with one subtype representing branched evolution and another reflecting the early accumulation of genomic variation. However, in the evolutionary pattern of the third subtype, some mutations experienced selective sweeps and were gradually replaced by multiple newly formed subclonal populations. The subtype patients with poor prognosis had higher intra-tumor heterogeneity and subclonal diversity. We combined genomic heterogeneity with clinical phenotypes analysis and found that subclonal expansion results in the progression and deterioration of the tumor. The molecular mechanisms of subtype-specific Early Driver Feature (EDF) genes differed across the evolutionary subtypes, reflecting the characteristics of the subtype itself. In summary, our study provided new insights on the stratification of NSCLC patients based on genomic evolution that can be valuable for us to understand the development of pulmonary tumor profoundly.

Dissecting the Functional Mechanisms of Somatic Copy-Number Alterations Based on Dysregulated ceRNA Networks across Cancers.

Somatic copy-number alterations (SCNAs) drive tumor growth and evolution. However, the functional roles of SCNAs across the genome are still poorly understood. We provide an integrative strategy to characterize the functional roles of driver SCNAs in cancers based on dysregulated competing endogenous RNA (ceRNA) networks. We identified 44 driver SCNAs in lower-grade glioma (LGG). The dysregulated patterns losing all correlation relationships dominated dysregulated ceRNA networks. Homozygous deletion of six genes in 9p21.3 characterized an LGG subtype with poor prognosis and contributed to the dysfunction of cancer-associated pathways in a complementary way. The pan-cancer analysis showed that different cancer types harbored different driver SCNAs through dysregulating the crosstalk with common ceRNAs. The same SCNAs destroyed their ceRNA networks through different miRNA-mediated ceRNA regulations in different cancers. Additionally, some SCNAs performed different functional mechanisms in different cancers, which added another layer of complexity to cancer heterogeneity. Compared with previous methods, our strategy could directly dissect functional roles of SCNAs from the view of ceRNA networks, which not only complemented the functions of protein-coding genes but also provided a new avenue to characterize the functions of noncoding RNAs. Also, our strategy could be applied to more types of cancers to identify pathogenic mechanism driven by the SCNAs.

Context-Specific Coordinately Regulatory Network Prioritize Breast Cancer Genetic Risk Factors.

Breast cancer (BC) is one of the most common tumors, leading the causes of cancer death in women. However, the pathogenesis of BC still remains unclear, and the atlas of BC-associated risk factors is far from complete. In this study, we constructed a BC-specific coordinately regulatory network (CRN) to prioritize potential BC-associated protein-coding genes (PCGs) and non-coding RNAs (ncRNAs). We integrated 813 BC sample transcriptome data from The Cancer Genome Atlas (TCGA) and eight types of regulatory relationships to construct BC-specific CRN, including 387 transcription factors (TFs), 174 microRNAs (miRNAs), 407 long non-coding RNAs (lncRNAs), and 905 PCGs. After that, the random walk with restart (RWR) method was performed on the CRN by using the known BC-associated factors as seeds, and potential BC-associated risk factors were prioritized. The leave-one-out cross-validation (LOOCV) was utilized on the BC-specific CRN and achieved an area under the curve (AUC) of 0.92. The performances of common CRN, common protein-protein interaction (PPI) network, and BC-specific PPI network were also evaluated, demonstrating that the context-specific CRN prioritizes BC risk factors. Functional analysis for the top 100-ranked risk factors in the candidate list revealed that these factors were significantly enriched in cancer-related functions and had significant semantic similarity with BC-related gene ontology (GO) terms. Differential expression analysis and survival analysis proved that the prioritized risk factors significantly associated with BC progression and prognosis. In total, we provided a computational method to predict reliable BC-associated risk factors, which would help improve the understanding of the pathology of BC and benefit disease diagnosis and prognosis.

Prioritizing Gene Cascading Paths to Model Colorectal Cancer Through Engineered Organoids.

Engineered organoids by sequential introduction of key mutations could help modeling the dynamic cancer progression. However, it remains difficult to determine gene paths which were sufficient to capture cancer behaviors and to broadly explain cancer mechanisms. Here, as a case study of colorectal cancer (CRC), functional and dynamic characterizations of five types of engineered organoids with different mutation combinations of five driver genes (APC, SMAD4, KRAS, TP53, and PIK3CA) showed that sequential introductions of all five driver mutations could induce enhanced activation of more hallmark signatures, tending to cancer. Comparative analysis of engineered organoids and corresponding CRC tissues revealed sequential introduction of key mutations could continually shorten the biological distance from engineered organoids to CRC tissues. Nevertheless, there still existed substantial biological gaps between the engineered organoid even with five key mutations and CRC samples. Thus, we proposed an integrative strategy to prioritize gene cascading paths for shrinking biological gaps between engineered organoids and CRC tissues. Our results not only recapitulated the well-known adenoma-carcinoma sequence model (e.g., AKST-organoid with driver mutations in APC, KRAS, SMAD4, and TP53), but also provided potential paths for delineating alternative pathogenesis underlying CRC populations (e.g., A-organoid with APC mutation). Our strategy also can be applied to both organoids with more mutations and other cancers, which can improve and innovate mechanism across cancer patients for drug design and cancer therapy.

Identification and functional analysis of specific MS risk miRNAs and their target genes.

BACKGROUND: It has been widely acknowledged that abnormal expression of microRNAs (miRNAs) may lead to the occurrence and development of MS through regulating target genes. Currently, only few studies have comprehensively evaluated the function and relationship between MS-related miRNAs and their target genes. METHODS: Differentially expressed miRNAs in MS patients' serum and plasma were selected by reviewing numerous literatures manually. Then, thousands of target genes were screened by several online databases, of which 899 MS-related genes were further identified. Gene ontology, protein-protein interaction and KEGG pathway analysis were used to determine high-risk pathways and MS risk genes. Transcriptomic datasets from GEO was analyzed to evaluate these risk genes. RESULTS: 28 MS-related miRNAs were extracted. MiR-30e, miR-93, miR-155 were identified as the most crucial miRNAs through targeting hub genes: PIK3CA, PIK3R1, PIK3R2 and MAPK8. Seven immune pathways were screened out according to KEGG pathway analysis. Six transcriptomic datasets were used to evaluate results, and PIK3CA was differentially expressed in MS patients compared with healthy donors. CONCLUSIONS: According to our research, MS-related miRNAs and their target genes of MS were identified and comprehensively evaluated. This work may provide a new insight for discovering pathogenesis and possible biomarkers of MS in future studies.

Dissecting the m6A methylation affection on afatinib resistance in non-small cell lung cancer.

Non-small cell lung cancer (NSCLC) is one of the leading causes of cancer deaths. Afatinib is the first-line anti-cancer agent for treatment of NSCLC. However, unexpected resistance has been a major obstacle for its clinical efficacy. In this study, we dissected afatinib resistance from the perspective of N6-Methyladenosine (m6A) modification. First, we depicted the m6A modification profiles for the afatinib resistant and sensitive NSCLC cell lines (H1299 and A549). We found that the sum enrichment scores of the resistant cell line (H1299) was much higher than that of the sensitive cell line (A549). Next, we identified the functionally m6A-modified genes, which were the intersection of the differentially m6A methylated genes and the differentially expressed genes between H1299 and A549, as well as negative correlation between m6A modification levels and gene expression levels. In addition, functional enrichment analysis of the functionally m6A-modified genes indicated that m6A methylation might modify cell cycle to affect afatinib response. Furthermore, the functionally m6A-modified genes were over-represented in the putative drug resistance-associated genes and the FDA-approved drug targets, and had significantly higher average degree and clustering coefficient than other genes in protein-protein interaction (PPI) network. We also identified five network modules, which were all related to drug resistance functions. Finally, survival analysis demonstrated that m6A modification could affect prognosis of NSCLC patients. In conclusion, we conducted a first attempt to dissect m6A methylation affection on afatinib resistance in NSCLC, and brought inspiration for the study of epigenetic roles in drug resistance.

Identification of Dysregulated Competitive Endogenous RNA Networks Driven by Copy Number Variations in Malignant Gliomas.

Gliomas represent 80% of malignant brain tumors. Because of the high heterogeneity, the oncogenic mechanisms in gliomas are still unclear. In this study, we developed a new approach to identify dysregulated competitive endogenous RNA (ceRNA) interactions driven by copy number variation (CNV) in both lower-grade glioma (LGG) and glioblastoma multiforme (GBM). By analyzing genome and transcriptome data from The Cancer Genome Atlas (TCGA), we first found out the protein coding genes and long non-coding RNAs (lncRNAs) significantly affected by CNVs and further determined CNV-driven dysregulated ceRNA interactions by a customized pipeline. We obtained 13,776 CNV-driven dysregulated ceRNA pairs (including 3,954 mRNAs and 306 lncRNAs) in LGG and 262 pairs (including 221 mRNAs and 11 lncRNAs) in GBM, respectively. Our results showed that most of the ceRNA interactions were weakened by CNVs in both LGG and GBM, and many CNV-driven genes shared the same ceRNAs in the dysregulated ceRNA networks. Functional analysis indicated that the CNV-driven ceRNA network involved in some important mechanisms of tumorigenesis, such as cell cycle, p53 signaling pathway and TGF-beta signaling pathway. Further investigation of the ceRNA pairs in the communities from the dysregulated ceRNA network revealed more detailed biological functions related to the oncogenesis of malignant gliomas. Moreover, by exploring the association of CNV-driven ceRNAs with prognosis and histological subtype, we found that the copy number status of MTAP, KLHL9, and ELAVL2 related to the overall survival in LGG and showed high correlation with histological subtype. In conclusion, this study provided new insight into the molecular mechanisms and clinical biomarkers in gliomas.

Identification of a Combined RNA Prognostic Signature in Adenocarcinoma of the Lung.

BACKGROUND Adenocarcinoma of the lung is a type of non-small cell lung cancer (NSCLC). Clinical outcome is associated with tumor grade, stage, and subtype. This study aimed to identify RNA expression profiles, including long noncoding RNA (lncRNA), microRNA (miRNA), and mRNA, associated with clinical outcome in adenocarcinoma of the lung using bioinformatics data. MATERIAL AND METHODS The miRNA and mRNA expression profiles were downloaded from The Cancer Genome Atlas (TCGA) database, and lncRNA expression profiles were downloaded from The Atlas of Noncoding RNAs in Cancer (TANRIC) database. The independent dataset, the Gene Expression Omnibus (GEO) accession dataset, GSE81089, was used. RNA expression profiles were used to identify comprehensive prognostic RNA signatures based on patient survival time. RESULTS From 7,704 lncRNAs, 787 miRNAs, and 28,937 mRNAs of 449 patients, four joint RNA molecular signatures were identified, including RP11-909N17.2, RP11-14N7.2 (lncRNAs), MIR139 (miRNA), KLHDC8B (mRNA). The random forest (RF) classifier was used to test the prediction ability of patient survival risk and showed a good predictive accuracy of 71% and also showed a significant difference in overall survival (log-rank P=0.0002; HR, 3.54; 95% CI, 1.74-7.19). The combined RNA signature also showed good performance in the identification of patient survival in the validation and independent datasets. CONCLUSIONS This study identified four RNA sequences as a prognostic molecular signature in adenocarcinoma of the lung, which may also provide an increased understanding of the molecular mechanisms underlying the pathogenesis of this malignancy.