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Abstract DUSP22, an atypical dual-specificity phosphatase enzyme, plays a significant role in regulating multiple kinase signaling pathways by dephosphorylation. Our study demonstrated that decreased DUSP22 expression is associated with shorter disease-free survival, advanced TNM (tumor, lymph nodes, and metastasis), cancer stage, and higher tumor grade in lung adenocarcinoma (LUAD) patients. Exogenous DUSP22 expression reduces the colony-forming capacity of lung cancer cells and inhibits xenograft tumor growth primarily by targeting EGFR and suppressing its activity through dephosphorylation. Knockdown of DUSP22 using shRNA enhances EGFR dependency in HCC827 lung cancer cells and increases sensitivity to gefitinib, an EGFR inhibitor. Consistently, genetic deletion of DUSP22 enhances EGFRdel (exon 19 deletion)-driven lung tumorigenesis and elevates EGFR activity. Pharmacological inhibition of DUSP22 activates EGFR, ERK1/2, and upregulates downstream PD-L1 expression. Additionally, lentiviral deletion of DUSP22 by shRNA enhances lung cancer cell migration through EGFR/c-Met and PD-L1-dependent pathways. Gefitinib, an EGFR inhibitor, mechanistically suppresses migration induced by DUSP22 deletion and inhibits c-Met activity. Furthermore, cabozantinib, a c-Met inhibitor, reduces migration and attenuates EGFR activation caused by DUSP22 deletion. Collectively, our findings support the hypothesis that loss of DUSP22 function in lung cancer cells confers a survival advantage by augmenting EGFR signaling, leading to increased activation of downstream c-Met, ERK1/2, and PD-L1 axis, ultimately contributing to the progression of advanced lung cancer.

Abstract Background Interstitial lung disease (ILD) is the primary cause of mortality in systemic sclerosis (SSc), an autoimmune disease characterized by tissue fibrosis. SSc-related ILD (SSc-ILD) occurs more frequently in females aged 30–55 years, whereas idiopathic pulmonary fibrosis (IPF) is more prevalent in males aged 60–75 years. SSc-ILD occurs earlier than IPF and progresses rapidly. FCN1, FABP4, and SPP1 macrophages are involved in the pathogenesis of lung fibrosis; SPP1 macrophages demonstrate upregulated expression in both SSc-ILD and IPF. To identify the differences between SSc-ILD and IPF using single-cell analysis, clarify their distinct pathogeneses, and propose directions for prevention and treatment. Methods We performed single-cell RNA sequencing on NCBI Gene Expression Omnibus (GEO) databases GSE159354 and GSE212109, and analyzed lung tissue samples across healthy controls, IPF, and SSc-ILD. The primary measures were the filtered genes integrated with batch correction and annotated cell types for distinguishing patients with SSc-ILD from healthy controls. We proposed an SSc-ILD pathogenesis using cell–cell interaction inferences, and predicted transcription factors regulating target genes using SCENIC. Drug target prediction of the TF gene was performed using Drug Bank Online. Results A subset of macrophages activates the MAPK signaling pathway under oxidative stress. Owing to the lack of inhibitory feedback from ANNEXIN and the autoimmune characteristics, this leads to an earlier onset of lung fibrosis compared to IPF. During initial lung injury, fibroblasts begin to activate the IL6 pathway under the influence of SPP1 alveolar macrophages, but IL6 appears unrelated to other inflammatory and immune cells. This may explain why tocilizumab (an anti-IL6-receptor antibody) only preserves lung function in patients with early SSc-ILD. Finally, we identified BCLAF1 and NFE2L2 as influencers of MAPK activation in macrophages. Metformin downregulates NFE2L2 and could serve as a repurposed drug candidate. Conclusions SPP1 alveolar macrophages play a role in the profibrotic activity of IPF and SSc-ILD. However, SSc-ILD is influenced by autoimmunity and oxidative stress, leading to the continuous activation of MAPK in macrophages. This may result in an earlier onset of lung fibrosis than in IPF. Such differences could serve as potential research directions for early prevention and treatment.

Abstarct Ectopic ATP synthase on the plasma membrane (eATP synthase) has been found in various cancer types and is a potential target for cancer therapy. However, whether it provides a functional role in tumor progression remains unclear. Here, quantitative proteomics reveals that cancer cells under starvation stress express higher eATP synthase and enhance the production of extracellular vesicles (EVs), which are vital regulators within the tumor microenvironment. Further results show that eATP synthase generates extracellular ATP to stimulate EV secretion by enhancing P2X7 receptor–triggered Ca2+ influx. Surprisingly, eATP synthase is also located on the surface of tumor-secreted EVs. The EVs-surface eATP synthase increases the uptake of tumor-secreted EVs in Jurkat T-cells via association with Fyn, a plasma membrane protein found in immune cells. The eATP synthase-coated EVs uptake subsequently represses the proliferation and cytokine secretion of Jurkat T-cells. This study clarifies the role of eATP synthase on EV secretion and its influence on immune cells.

Abstract Ectopic ATP synthase complex (eATP synthase), located on cancer cell surface, has been reported to possess catalytic activity that facilitates the generation of ATP in the extracellular environment to establish a suitable microenvironment and to be a potential target for cancer therapy. However, the mechanism of intracellular ATP synthase complex transport remains unclear. Using a combination of spatial proteomics, interaction proteomics, and transcriptomics analyses, we find ATP synthase complex is first assembled in the mitochondria and subsequently delivered to the cell surface along the microtubule via the interplay of dynamin-related protein 1 (DRP1) and kinesin family member 5B (KIF5B). We further demonstrate that the mitochondrial membrane fuses to the plasma membrane in turn to anchor ATP syntheses on the cell surface using super-resolution imaging and real-time fusion assay in live cells. Our results provide a blueprint of eATP synthase trafficking and contribute to the understanding of the dynamics of tumor progression.

Single-cell RNA sequencing (scRNA-seq) technology allows massively parallel characterization of thousands of cells at the transcriptome level. scRNA-seq is emerging as an important tool to investigate the cellular components and their interactions in the tumor microenvironment. scRNA-seq is also used to reveal the association between tumor microenvironmental patterns and clinical outcomes and to dissect cell-specific effects of drug treatment in complex tissues. Recent advances in scRNA-seq have driven the discovery of biomarkers in diseases and therapeutic targets. Although methods for prediction of drug response using gene expression of scRNA-seq data have been proposed, an integrated tool from scRNA-seq analysis to drug discovery is required. We present scDrug as a bioinformatics workflow that includes a one-step pipeline to generate cell clustering for scRNA-seq data and two methods to predict drug treatments. The scDrug pipeline consists of three main modules: scRNA-seq analysis for identification of tumor cell subpopulations, functional annotation of cellular subclusters, and prediction of drug responses. scDrug enables the exploration of scRNA-seq data readily and facilitates the drug repurposing process. scDrug is freely available on GitHub at https://github.com/ailabstw/scDrug.

Abstract The small nucleolar RNA host gene 1 (SNHG1) is a novel oncogenic long non-coding RNA (lncRNA) aberrantly expressed in different tumor types. We previously found highly expressed SNHG1 was associated with poor prognosis and MYCN status in neuroblastoma (NB). However, the molecular mechanisms of SNHG1 in NB are still unclear. Here, we disrupted endogenous SNHG1 in the MYCN-amplified NB cell line SK-N-BE(2)C using the CRISPR/Cas9 system and demonstrated the proliferation and colony formation ability of SNHG1-knowndown cells were suppressed. The transcriptome analysis and functional assays of SNHG1-knockdown cells revealed SNHG1 was involved in various biological processes including cell growth, migration, apoptosis, cell cycle, and reactive oxygen species (ROS). Interestingly, the expression of core regulatory circuitry (CRC) transcription factors in MYCN-amplified NB, including PHOX2B, HAND2, GATA3, ISL1, TBX1, and MYCN, were decreased in SNHG1-knockdown cells. The chromatin-immunoprecipitation sequencing (ChIP-seq) and transposase-accessible chromatin using sequencing (ATAC-seq) analyses showed that chromatin status of these CRC members was altered, which might stem from interactions between SNHG1 and HDAC1/2. These findings demonstrate that SNHG1 plays a crucial role in maintaining NB identity via chromatin regulation and reveal the function of the lncRNA SNHG1 in NB.

Abstract Background Recently, non-coding RNAs are of growing interest, and more scientists attach importance to research on their functions. Long non-coding RNAs (lncRNAs) are defined as non-protein coding transcripts longer than 200 nucleotides. We already knew that lncRNAs are related to cancers and will be dysregulated in them. But most of their functions are still left to further study. A mechanism of RNA regulation, known as competing endogenous RNAs (ceRNAs), has been proposed to explain the complex relationships among mRNAs and lncRNAs by competing for binding with shared microRNAs (miRNAs). Methods We proposed an analysis framework to construct the association networks among lncRNA, mRNA, and miRNAs based on their expression patterns and decipher their network modules. Results We collected a large-scale gene expression dataset of 1,061 samples from breast invasive carcinoma (BRCA) patients, each consisted of the expression profiles of 4,359 lncRNAs, 16,517 mRNAs, and 534 miRNAs, and applied the proposed analysis approach to interrogate them. We have uncovered the underlying ceRNA modules and the key modulatory lncRNAs for different subtypes of breast cancer. Conclusions We proposed a modulatory analysis to infer the ceRNA effects among mRNAs and lncRNAs and performed functional analysis to reveal the plausible mechanisms of lncRNA modulation in the four breast cancer subtypes. Our results might provide new directions for breast cancer therapeutics and the proposed method could be readily applied to other diseases.

Abstract Background Increasing amount of long non-coding RNAs (lncRNAs) have been found involving in many biological processes and played salient roles in cancers. However, up until recently, functions of most lncRNAs in lung cancer have not been fully discovered, particularly in the co-regulated lncRNAs. Thus, this study aims to investigate roles of lncRNA modules and uncover a module-based biomarker in lung adenocarcinoma (LUAD). Results We used gene expression profiles from The Cancer Genome Atlas (TCGA) to construct the lncRNA association networks, from which the highly-associated lncRNAs are connected as modules. It was found that the expression of some modules is significantly associated with patient’s survival, including module N1 (HR = 0.62, 95% CI = 0.46–0.84, p = 0.00189); N2 (HR = 0.68, CI = 0.50–0.93, p = 0.00159); N4 (HR = 0.70, CI = 0.52–0.95, p = 0.0205) and P3 (HR = 0.68, CI = 0.50–0.92, p = 0.0123). The lncRNA signature consisting of these four prognosis-related modules, a 4-modular lncRNA signature, is associated with favourable prognosis in TCGA-LUAD (HR = 0.51, CI = 0.37–0.69, p value = 2.00e−05). Afterwards, to assess the performance of the generic modular signature as a prognostic biomarker, we computed the time-dependent area under the receiver operating characteristics (AUC) of this 4-modular lncRNA signature, which showed AUC equals 68.44% on 336th day. In terms of biological functions, these modules are correlated with several cancer hallmarks and pathways, including Myc targets, E2F targets, cell cycle, inflammation/immunity-related pathways, androgen/oestrogen response, KRAS signalling, DNA repair and epithelial-mesenchymal transition (EMT). Conclusion Taken together, we identified four novel LUAD prognosis-related lncRNA modules, and assessed the performance of the 4-modular lncRNA signature being a prognostic biomarker. Functionally speaking, these modules involve in oncogenic hallmarks as well as pathways. The results unveiled the co-regulated lncRNAs in LUAD and may provide a framework for further lncRNA studies in lung cancer.

Abstract Background A new class of regulatory elements called super-enhancers, comprised of multiple neighboring enhancers, have recently been reported to be the key transcriptional drivers of cellular, developmental, and disease states. Results Here, we defined super-enhancer RNAs as highly expressed enhancer RNAs that are transcribed from a cluster of localized genomic regions. Using the cap analysis of gene expression sequencing data from FANTOM5, we systematically explored the enhancer and messenger RNA landscapes in hundreds of different cell types in response to various environments. Applying non-negative matrix factorization (NMF) to super-enhancer RNA profiles, we found that different cell types were well classified. In addition, through the NMF of individual time-course profiles from a single cell-type, super-enhancer RNAs were clustered into several states with progressive patterns. We further investigated the enriched biological functions of the proximal genes involved in each pattern, and found that they were associated with the corresponding developmental process. Conclusions The proposed super-enhancer RNAs can act as a good alternative, without the complicated measurement of histone modifications, for identifying important regulatory elements of cell type specification and identifying dynamic cell states.

Ko-Han Lee et al. develop MHCfovea, a machine-learning method for predicting peptide-binding by MHC molecules and inferring peptide motifs and MHC allele signatures. They demonstrate that MHCfovea is capable of detecting meaningful hyper-motifs and allele signatures, making it a useful resource for the community.

Despite the fact that an increased amount of survival-related lncRNAs have been found in cancer, few drugs that target lncRNAs are approved for treatment. Here, we developed a network-based algorithm, LncTx, to repurpose the medications that potentially act on survival-related lncRNAs in lung cancer. We used eight survival-related lncRNAs derived from our previous study to test the efficacy of this method. LncTx calculates the shortest path length (proximity) between the drug targets and the lncRNA-correlated proteins in the protein–protein interaction network (interactome). LncTx contains seven different proximity measures, which are calculated in the unweighted or weighted interactome. First, to test the performance of LncTx in predicting correct indication of drugs, we benchmarked the proximity measures based on the accuracy of differentiating anticancer drugs from non-anticancer drugs. The closest proximity weighted by clustering coefficient (closestCC) has the best performance (AUC around 0.8) compared to other proximity measures across all survival-related lncRNAs. The majority of the other six proximity measures have decent performance as well, with AUC greater than 0.7. Second, to evaluate whether LncTx can repurpose the drugs effectively acting on the lncRNAs, we clustered the drugs according to their proximities by hierarchical clustering. The drugs with smaller proximity (proximal drugs) were proved to be more effective than the drugs with larger proximity (distal drugs). In conclusion, LncTx enables us to accurately identify anticancer drugs and can potentially be an index to repurpose effective agents acting on survival-related lncRNAs in lung cancer.

Abstract Background ZNF322A is an oncogenic transcription factor that belongs to the Cys2His2-type zinc-finger protein family. Accumulating evidence suggests that ZNF322A may contribute to the tumorigenesis of lung cancer, however, the ZNF322A-mediated downstream signaling pathways remain unknown. Methods To uncover ZNF322A-mediated functional network, we applied phosphopeptide enrichment and isobaric labeling strategies with mass spectrometry-based proteomics using A549 lung cancer cells, and analyzed the differentially expressed proteins of phosphoproteomic and proteomic profiles to determine ZNF322A-modulated pathways. Results ZNF322A highlighted a previously unidentified insulin signaling, heat stress, and signal attenuation at the post-translational level. Consistently, protein-phosphoprotein-kinase interaction network analysis revealed phosphorylation of IRS1 and HSP27 were altered upon ZNF322A-silenced lung cancer cells. Thus, we further investigated the molecular regulation of ZNF322A, and found the inhibitory transcriptional regulation of ZNF322A on PIM3, which was able to phosphorylate IRS1 at serine1101 in order to manipulate glucose uptake via the PI3K/AKT/mTOR signaling pathway. Moreover, ZNF322A also affects the unfolded protein response by phosphorylation of HSP27S82 and eIF2aS51, and triggers autophagosome formation in lung cancer cells. Conclusions These findings not only give new information about the molecular regulation of the cellular proteins through ZNF322A at the post-translational level, but also provides a resource for the study of lung cancer therapy.

Summary: Single-cell RNA sequencing (scRNA-seq) approach can broadly and specifically evaluate the individual cells with minimum detection bias. To explore the individual compositional and transcriptional alteration of intestinal leukocytes in the Dual Specificity Phosphatase six knockout (D6KO) mice, we performed a scRNA-seq followed by the cell type annotation based on ImmGen database. Composition assessments found that D6KO-derived intestinal leukocytes tend to stay inactivate or immature status. The enrichment analysis showed that D6KO-derived intestinal leukocytes are less sensitive to microbes. The mod PhEA phenotypic analysis showed that the D6KO leukocytes may link to not only immune-associated but also diverse previously non-immune-related diseases. Integrating our dataset with the published dataset GSE124880 generated a comprehensive dataset for exploring intestinal immunity. Down-regulation of Ccl17 gene was found in the D6KO-derived dendritic cells. Our results demonstrated the advantage of applying scRNA-seq for dissecting the individual alteration of intestinal leukocytes, particularly in the D6KO mice at a naive state.

Abstract Background Super-enhancer RNAs (seRNAs) are a kind of noncoding RNA transcribed from super-enhancer regions. The regulation mechanism and functional role of seRNAs are still unclear. Although super-enhancers play a critical role in the core transcriptional regulatory circuity of embryonic stem cell (ESC) differentiation, whether seRNAs have similar properties should be further investigated. Results We analyzed cap analysis gene expression sequencing (CAGE-seq) datasets collected during the differentiation of embryonic stem cells (ESCs) to cardiomyocytes to identify the seRNAs. A non-negative matrix factorization algorithm was applied to decompose the seRNA profiles and reveal two hidden stages during the ESC differentiation. We further identified 95 and 78 seRNAs associated with early- and late-stage ESC differentiation, respectively. We found that the binding sites of master regulators of ESC differentiation, including NANOG, FOXA2, and MYC, were significantly observed in the loci of the stage-specific seRNAs. Based on the investigation of genes coexpressed with seRNA, these stage-specific seRNAs might be involved in cardiac-related functions such as myofibril assembly and heart development and act in trans to regulate the co-expressed genes. Conclusions In this study, we used a computational approach to demonstrate the possible role of seRNAs during ESC differentiation.

Summary: Cancer is a complex disease that relies on both oncogenic mutations and non-mutated genes for survival, and therefore coined as oncogene and non-oncogene addictions. The need for more effective combination therapies to overcome drug resistance in oncology has been increasingly recognized, but the identification of potentially synergistic drugs at scale remains challenging. Here we propose a gene-expression-based approach, which uses the recurrent perturbation-transcript regulatory relationships inferred from a large compendium of chemical and genetic perturbation experiments across multiple cell lines, to engender a testable hypothesis for combination therapies. These transcript-level recurrences were distinct from known compound-protein target counterparts, were reproducible in external datasets, and correlated with small-molecule sensitivity. We applied these recurrent relationships to predict synergistic drug pairs for cancer and experimentally confirmed two unexpected drug combinations in vitro. Our results corroborate a gene-expression-based strategy for combinatorial drug screening as a way to target non-mutated genes in complex diseases. : Bioinformatics; Cancer Systems Biology; Pharmacoinformatics Subject Areas: Bioinformatics, Cancer Systems Biology, Pharmacoinformatics

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a causative agent for the outbreak of coronavirus disease 2019 (COVID-19). This global pandemic is now calling for efforts to develop more effective COVID-19 therapies. Here we use a host-directed approach, which focuses on cellular responses to diverse small-molecule treatments, to identify potentially effective drugs for COVID-19. This framework looks at the ability of compounds to elicit a similar transcriptional response to IFN-β, a type I interferon that fails to be induced at notable levels in response to SARS-CoV-2 infection. By correlating the perturbation profiles of ~3,000 small molecules with a high-quality signature of IFN-β-responsive genes in primary normal human bronchial epithelial cells, our analysis revealed four candidate COVID-19 compounds, namely homoharringtonine, narciclasine, anisomycin, and emetine. We experimentally confirmed that the predicted compounds significantly inhibited SARS-CoV-2 replication in Vero E6 cells at nanomolar, relatively non-toxic concentrations, with half-maximal inhibitory concentrations of 165.7 nM, 16.5 nM, and 31.4 nM for homoharringtonine, narciclasine, and anisomycin, respectively. Together, our results corroborate a host-centric strategy to inform protective antiviral therapies for COVID-19.

Abstract Extracellular particles (EPs) including extracellular vesicles (EVs) and exomeres play significant roles in diseases and therapeutic applications. However, their spatiotemporal dynamics in vivo have remained largely unresolved in detail due to the lack of a suitable method. Therefore, a bioluminescence resonance energy transfer (BRET)‐based reporter, PalmGRET, is created to enable pan‐EP labeling ranging from exomeres (200 nm) EVs. PalmGRET emits robust, sustained signals and allows the visualization, tracking, and quantification of the EPs from whole animal to nanoscopic resolutions under different imaging modalities, including bioluminescence, BRET, and fluorescence. Using PalmGRET, it is shown that EPs released by lung metastatic hepatocellular carcinoma (HCC) exhibit lung tropism with varying distributions to other major organs in immunocompetent mice. It is further demonstrated that gene knockdown of lung‐tropic membrane proteins, solute carrier organic anion transporter family member 2A1, alanine aminopeptidase/Cd13, and chloride intracellular channel 1 decreases HCC‐EP distribution to the lungs and yields distinct biodistribution profiles. It is anticipated that EP‐specific imaging, quantitative assays, and detailed in vivo characterization are a starting point for more accurate and comprehensive in vivo models of EP biology and therapeutic design.

Post-COVID-19 pulmonary fibrosis (PCPF) is a long-term complication that appears in some COVID-19 survivors. However, there are currently limited options for treating PCPF patients. To address this problem, we investigated COVID-19 patients’ transcriptome at single-cell resolution and combined biological network analyses to repurpose the drugs treating PCPF. We revealed a novel gene signature of PCPF. The signature is functionally associated with the viral infection and lung fibrosis. Further, the signature has good performance in diagnosing and assessing pulmonary fibrosis. Next, we applied a network-based drug repurposing method to explore novel treatments for PCPF. By quantifying the proximity between the drug targets and the signature in the interactome, we identified several potential candidates and provided a drug list ranked by their proximity. Taken together, we revealed a novel gene expression signature as a theragnostic biomarker for PCPF by integrating different computational approaches. Moreover, we showed that network-based proximity could be used as a framework to repurpose drugs for PCPF.

Summary: Biological systems often respond to a specific environmental or genetic perturbation without pervasive gene expression changes. Such robustness to perturbations, however, is not reflected on the current computational strategies that utilize gene expression similarity metrics for drug discovery and repositioning. Here we propose a new expression-intensity-based similarity metric that consistently achieved better performance than other state-of-the-art similarity metrics with respect to the gold-standard clustering of drugs with known mechanisms of action. The new metric directly emphasizes the genes exhibiting the greatest changes in expression in response to a perturbation. Using the new framework to systematically compare 3,332 chemical and 3,934 genetic perturbations across 10 cell types representing diverse cellular signatures, we identified thousands of recurrent and cell type-specific connections. We also experimentally validated two drugs identified by the analysis as potential topoisomerase inhibitors. The new framework is a valuable resource for hypothesis generation, functional testing, and drug repositioning. : Pharmaceutical Science; Genetics; Bioinformatics Subject Areas: Pharmaceutical Science, Genetics, Bioinformatics

Since 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been rapidly spreading worldwide, causing hundreds of millions of infections. Despite the development of vaccines, insufficient protection remains a concern. Therefore, the screening of drugs for the treatment of coronavirus disease 2019 (COVID-19) is reasonable and necessary. This study utilized bioinformatics for the selection of compounds approved by the U.S. Food and Drug Administration with therapeutic potential in this setting. In addition, the inhibitory effect of these compounds on the enzyme activity of transmembrane protease serine 2 (TMPRSS2), papain-like protease (PLpro), and 3C-like protease (3CLpro) was evaluated. Furthermore, the capability of compounds to attach to the spike-receptor-binding domain (RBD) was considered an important factor in the present assessment. Finally, the antiviral potency of compounds was validated using a plaque reduction assay. Our funnel strategy revealed that tamoxifen possesses an anti-SARS-CoV-2 property owing to its inhibitory performance in multiple assays. The proposed time-saving and feasible strategy may accelerate drug screening for COVID-19 and other diseases.

DNA replication is initiated with the recognition of the starting point of multiple replication forks by the origin recognition complex and activation of the minichromosome maintenance complex 10 (MCM10). Subsequently, DNA helicase, consisting of the MCM protein subunits MCM2-7, unwinds double-stranded DNA and DNA synthesis begins. In previous studies, replication factors have been used as clinical targets in cancer therapy. The results showed that MCM2 could be a proliferation marker for numerous types of malignant cancer. We analyzed samples obtained from patients with neuroblastoma, revealing that higher levels of MCM2 and MCM10 mRNA were associated with poor survival rate. Furthermore, we combined the results of the perturbation-induced reversal effects on the expression levels of MCM2 and MCM10 and the sensitivity correlation between perturbations and MCM2 and MCM10 from the Cancer Therapeutics Response Portal database. Small molecule BI-2536, a polo-like kinase 1 (PLK-1) inhibitor, is a candidate for the inhibition of MCM2 and MCM10 expression. To test this hypothesis, we treated neuroblastoma cells with BI-2536. The results showed that the drug decreased cell viability and reduced the expression levels of MCM2 and MCM10. Functional analysis further revealed enrichments of gene sets involved in mitochondria, cell cycle, and DNA replication for BI-2536-perturbed transcriptome. We used cellular assays to demonstrate that BI-2536 promoted mitochondria fusion, G2/M arrest, and apoptosis. In summary, our findings provide a new strategy for neuroblastoma therapy with BI-2536.

Abstract Carbonic anhydrase IX (CA9) expression level has been considered as a poor prognostic factor in hepatocellular carcinoma (HCC) patients. However, the judging criteria of CA9 level is hard to define for potential clinical applications. Unlike CA9 expression level, CA9 polymorphism is poorly documented in HCC. Here, we found that people carry A allele at CA9 rs1048638, a 3′UTR SNP, has higher risk of HCC. rs1048638-CA correlates with advanced stages, larger tumor sizes, more vascular invasion, and shorter survival of HCC patients. A allele at CA9 rs1048638 impairs miR-34a, a tumor suppressor miRNA in HCC, binding to CA9 3′UTR and desensitizes CA9 mRNA to miR-34a-dependent RNA degradation. CA9 expression levels were also correlated with miR-34a levels and rs1048638 genotypes in HCC patients. rs1048638 influences HCC risk and progression through effects on miR-34a-targeted CA9 expression in HCC. In conclusion, genetic variations of the CA9 3′UTR play important roles in regulating CA9 expression and cancer progression, which is a novel determinant and target for HCC metastasis and prognosis.

Abstract Lung cancer is the most common cause of cancer-related death worldwide, less than 7% of patients survive 10 years following diagnosis across all stages of lung cancer. Late stage of diagnosis and lack of effective and personalized medicine reflect the need for a better understanding of the mechanisms that underlie lung cancer progression. Quantitative proteomics provides the relative different protein abundance in normal and cancer patients which offers the information for molecular interactions, signaling pathways, and biomarker identification. Here we introduce both theoretical and practical applications in the use of quantitative proteomics approaches, with principles of current technologies and methodologies including gel-based, label free, stable isotope labeling as well as targeted proteomics. Predictive markers of drug resistance, candidate biomarkers for diagnosis, and prognostic markers in lung cancer have also been discovered and analyzed by quantitative proteomic analysis. Moreover, construction of protein networks enables to provide an opportunity to interpret disease pathway and improve our understanding in cancer therapeutic strategies, allowing the discovery of molecular markers and new therapeutic targets for lung cancer.