DUSP22 inhibits lung tumorigenesis by suppression of EGFR/c-Met signaling.

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.

Macrophages as determinants and regulators of systemic sclerosis-related interstitial lung disease.

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.

Multiomics Reveals Induction of Neuroblastoma SK-N-BE(2)C Cell Death by Mitochondrial Division Inhibitor 1 through Multiple Effects.

Mitochondrial division inhibitor 1 (Mdivi-1) is a well-known synthetic compound aimed at inhibiting dynamin-related protein 1 (Drp1) to suppress mitochondrial fission, making it a valuable tool for studying mitochondrial dynamics. However, its specific effects beyond Drp1 inhibition remain to be confirmed. In this study, we employed integrative proteomics and phosphoproteomics to delve into the molecular responses induced by Mdivi-1 in SK-N-BE(2)C cells. A total of 3070 proteins and 1945 phosphorylation sites were identified, with 880 of them represented as phosphoproteins. Among these, 266 proteins and 97 phosphorylation sites were found to be sensitive to the Mdivi-1 treatment. Functional enrichment analysis unveiled their involvement in serine biosynthesis and extrinsic apoptotic signaling pathways. Through targeted metabolomics, we observed that Mdivi-1 enhanced intracellular serine biosynthesis while reducing the production of C24:1-ceramide. Within these regulated phosphoproteins, dynamic dephosphorylation of proteasome subunit alpha type 3 serine 250 (PSMA3-S250) occurred after Mdivi-1 treatment. Further site-directed mutagenesis experiments revealed that the dephosphorylation-deficient mutant PSMA3-S250A exhibited a decreased cell survival. This research confirms that Mdivi-1's inhibition of mitochondrial division leads to various side effects, ultimately influencing cell survival, rather than solely targeting Drp1 inhibition.

Translating GWAS Findings to Inform Drug Repositioning Strategies for COVID-19 Treatment.

We developed a computational framework that integrates Genome-Wide Association Studies (GWAS) and post-GWAS analyses, designed to facilitate drug repurposing for COVID-19 treatment. The comprehensive approach combines transcriptomic-wide associations, polygenic priority scoring, 3D genomics, viral-host protein-protein interactions, and small-molecule docking. Through GWAS, we identified nine druggable host genes associated with COVID-19 severity and SARS-CoV-2 infection, all of which show differential expression in COVID-19 patients. These genes include IFNAR1, IFNAR2, TYK2, IL10RB, CXCR6, CCR9, and OAS1. We performed an extensive molecular docking analysis of these targets using 553 small molecules derived from five therapeutically enriched categories, namely antibacterials, antivirals, antineoplastics, immunosuppressants, and anti-inflammatories. This analysis, which comprised over 20,000 individual docking analyses, enabled the identification of several promising drug candidates. All results are available via the DockCoV2 database (https://dockcov2.org/drugs/). The computational framework ultimately identified nine potential drug candidates: Peginterferon alfa-2b, Interferon alfa-2b, Interferon beta-1b, Ruxolitinib, Dactinomycin, Rolitetracycline, Irinotecan, Vinblastine, and Oritavancin. While its current focus is on COVID-19, our proposed computational framework can be applied more broadly to assist in drug repurposing efforts for a variety of diseases. Overall, this study underscores the potential of human genetic studies and the utility of a computational framework for drug repurposing in the context of COVID-19 treatment, providing a valuable resource for researchers in this field.

Homoharringtonine as a PHGDH inhibitor: Unraveling metabolic dependencies and developing a potent therapeutic strategy for high-risk neuroblastoma.

Neuroblastoma, a childhood cancer affecting the sympathetic nervous system, continues to challenge the development of potent treatments due to the limited availability of druggable targets for this aggressive illness. Recent investigations have uncovered that phosphoglycerate dehydrogenase (PHGDH), an essential enzyme for de novo serine synthesis, serves as a non-oncogene dependency in high-risk neuroblastoma. In this study, we show that homoharringtonine (HHT) acts as a PHGDH inhibitor, inducing intricate alterations in cellular metabolism, and thus providing an efficient treatment for neuroblastoma. We have experimentally verified the reliance of neuroblastoma on PHGDH and employed molecular docking, thermodynamic evaluations, and X-ray crystallography techniques to determine the bond interactions between HHT and PHGDH. Administering HHT to treat neuroblastoma resulted in effective cell elimination in vitro and tumor reduction in vivo. Metabolite and functional assessments additionally disclosed that HHT treatment suppressed de novo serine synthesis, initiating intricate metabolic reconfiguration and oxidative stress in neuroblastoma. Collectively, these discoveries highlight the potential of targeting PHGDH using HHT as a potent approach for managing high-risk neuroblastoma.

Ectopic ATP synthase stimulates the secretion of extracellular vesicles in cancer cells.

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.

Spatial and temporal dynamics of ATP synthase from mitochondria toward the cell surface.

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.

Context-dependent gene regulatory network reveals regulation dynamics and cell trajectories using unspliced transcripts.

Gene regulatory networks govern complex gene expression programs in various biological phenomena, including embryonic development, cell fate decisions and oncogenesis. Single-cell techniques are increasingly being used to study gene expression, providing higher resolution than traditional approaches. However, inferring a comprehensive gene regulatory network across different cell types remains a challenge. Here, we propose to construct context-dependent gene regulatory networks (CDGRNs) from single-cell RNA sequencing data utilizing both spliced and unspliced transcript expression levels. A gene regulatory network is decomposed into subnetworks corresponding to different transcriptomic contexts. Each subnetwork comprises the consensus active regulation pairs of transcription factors and their target genes shared by a group of cells, inferred by a Gaussian mixture model. We find that the union of gene regulation pairs in all contexts is sufficient to reconstruct differentiation trajectories. Functions specific to the cell cycle, cell differentiation or tissue-specific functions are enriched throughout the developmental process in each context. Surprisingly, we also observe that the network entropy of CDGRNs decreases along differentiation trajectories, indicating directionality in differentiation. Overall, CDGRN allows us to establish the connection between gene regulation at the molecular level and cell differentiation at the macroscopic level.

scDrug: From single-cell RNA-seq to drug response prediction.

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.

BIC: a database for the transcriptional landscape of bacteria in cancer.

Microbial communities are massively resident in the human body, yet dysbiosis has been reported to correlate with many diseases, including various cancers. Most studies focus on the gut microbiome, while the bacteria that participate in tumor microenvironments on site remain unclear. Previous studies have acquired the bacteria expression profiles from RNA-seq, whole genome sequencing, and whole exon sequencing in The Cancer Genome Atlas (TCGA). However, small-RNA sequencing data were rarely used. Using TCGA miRNA sequencing data, we evaluated bacterial abundance in 32 types of cancer. To uncover the bacteria involved in cancer, we applied an analytical process to align unmapped human reads to bacterial references and developed the BIC database for the transcriptional landscape of bacteria in cancer. BIC provides cancer-associated bacterial information, including the relative abundance of bacteria, bacterial diversity, associations with clinical relevance, the co-expression network of bacteria and human genes, and their associated biological functions. These results can complement previously published databases. Users can easily download the result plots and tables, or download the bacterial abundance matrix for further analyses. In summary, BIC can provide information on cancer microenvironments related to microbial communities. BIC is available at: http://bic.jhlab.tw/.

LncRNA SNHG1 regulates neuroblastoma cell fate via interactions with HDAC1/2.

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.

Stratification of lncRNA modulation networks in breast cancer.

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.

Quantitative phosphoproteomics reveals ectopic ATP synthase on mesenchymal stem cells to promote tumor progression via ERK/c-Fos pathway activation.

The tumor microenvironment (TME), which comprises cellular and noncellular components, is involved in the complex process of cancer development. Emerging evidence suggests that mesenchymal stem cells (MSCs), one of the vital regulators of the TME, foster tumor progression through paracrine secretion. However, the comprehensive phosphosignaling pathways that are mediated by MSC-secreting factors have not yet been fully established. In this study, we attempt to dissect the MSC-triggered mechanism in lung cancer using quantitative phosphoproteomics. A total of 1958 phosphorylation sites are identified in lung cancer cells stimulated with MSC-conditioned medium. Integrative analysis of the identified phosphoproteins and predicted kinases demonstrates that MSC-conditioned medium functionally promotes the proliferation and migration of lung cancer via the ERK/phospho-c-Fos-S374 pathway. Recent studies have reported that extracellular ATP accumulates in the TME and stimulates the P2X7R on the cancer cell membrane via purinergic signaling. We observe that ectopic ATP synthase is located on the surface of MSCs and excreted extracellular ATP into the lung cancer microenvironment to trigger the ERK/phospho-c-Fos-S374 pathway, which is consistent with these previous findings. Our results suggest that ectopic ATP synthase on the surface of MSCs releases extracellular ATP into the TME, which promotes cancer progression via activation of the ERK/phospho-c-Fos-S374 pathway.

Targeting protein interaction networks in mitochondrial dynamics for cancer therapy.

Mitochondria are crucial organelles that provide energy via oxidative phosphorylation in eukaryotic cells and also have critical roles in growth, division, and the cell cycle, as well as the rapid adaptation required to meet the metabolic needs of the cell. Mitochondrial processes are highly dynamic; fusion and fission can vary with cell type, cellular context, and stress levels. Accumulating evidence demonstrates that an imbalance in mitochondrial dynamics leads to death in numerous types of human cancer cells. Therefore, modulating mitochondrial dynamics could be a therapeutic target. In this review, we provide an overview of the protein interaction networks involved in mitochondrial dynamics as effective and feasible drug targets and discuss the related potential therapeutic strategies for cancer.

Modular signature of long non-coding RNA association networks as a prognostic biomarker in lung cancer.

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.

Identification of a gut microbiota member that ameliorates DSS-induced colitis in intestinal barrier enhanced Dusp6-deficient mice.

Strengthening the gut epithelial barrier is a potential strategy for management of gut microbiota-associated illnesses. Here, we demonstrate that dual-specificity phosphatase 6 (Dusp6) knockout enhances baseline colon barrier integrity and ameliorates dextran sulfate sodium (DSS)-induced colonic injury. DUSP6 mutation in Caco-2 cells enhances the epithelial feature and increases mitochondrial oxygen consumption, accompanied by altered glucose metabolism and decreased glycolysis. We find that Dusp6-knockout mice are more resistant to DSS-induced dysbiosis, and the cohousing and fecal microbiota transplantation experiments show that the gut/fecal microbiota derived from Dusp6-knockout mice also confers protection against colitis. Further culturomics and mono-colonialization experiments show that one gut microbiota member in the genus Duncaniella confers host protection from DSS-induced injury. We identify Dusp6 deficiency as beneficial for shaping the gut microbiota eubiosis necessary to protect against gut barrier-related diseases.

Connecting MHC-I-binding motifs with HLA alleles via deep learning.

The selection of peptides presented by MHC molecules is crucial for antigen discovery. Previously, several predictors have shown impressive performance on binding affinity. However, the decisive MHC residues and their relation to the selection of binding peptides are still unrevealed. Here, we connected HLA alleles with binding motifs via our deep learning-based framework, MHCfovea. MHCfovea expanded the knowledge of MHC-I-binding motifs from 150 to 13,008 alleles. After clustering N-terminal and C-terminal sub-motifs on both observed and unobserved alleles, MHCfovea calculated the hyper-motifs and the corresponding allele signatures on the important positions to disclose the relation between binding motifs and MHC-I sequences. MHCfovea delivered 32 pairs of hyper-motifs and allele signatures (HLA-A: 13, HLA-B: 12, and HLA-C: 7). The paired hyper-motifs and allele signatures disclosed the critical polymorphic residues that determine the binding preference, which are believed to be valuable for antigen discovery and vaccine design when allele specificity is concerned.

LncTx: A network-based method to repurpose drugs acting on the survival-related lncRNAs in lung cancer.

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.

Proteomic Analysis Reveals That Metformin Suppresses PSMD2, STIP1, and CAP1 for Preventing Gastric Cancer AGS Cell Proliferation and Migration.

Metformin is one of the most widely used anti-diabetic drugs in type-II diabetes treatment. The mechanism of decreasing blood glucose is believed to suppress hepatic gluconeogenesis by increasing muscular glucose uptake and insulin sensitivity. Recent studies suggest that metformin may reduce cancer risk; however, its anticancer mechanism in gastric cancers remains unclear. Here, we aim to evaluate the anticancer effects of metformin on human gastric adenocarcinoma (AGS) cells. Our results showed that metformin inhibited AGS cell proliferation in a dose-dependent manner. Using small-scale quantitative proteomics, we identified 177 differentially expressed proteins upon metformin treatment; among these, nine proteins such as 26S proteasome non-ATPase regulatory subunit 2 (PSMD2), stress-induced phosphoprotein 1 (STIP1), and adenylyl cyclase-associated protein 1 (CAP1) were significantly altered. We found that metformin induced cell cycle arrest at the G0/G1 phase, suppressed cell migration, and affected cytoskeleton distribution. Additionally, patients with highly expressed PSMD2, STIP1, and CAP1 have a poor clinical outcome. Our study provides a novel view of developing therapies for gastric cancer.

Inhibitor development of MTH1 via high-throughput screening with fragment based library and MTH1 substrate binding cavity.

MutT Homolog 1 (MTH1) has been proven to hydrolyze oxidized nucleotide triphosphates during DNA repair. It can prevent the incorporation of wrong nucleotides during DNA replication and mitigate cell apoptosis. In a cancer cell, abundant reactive oxygen species can lead to substantial DNA damage and DNA mutations by base-pairing mismatch. MTH1 could eliminate oxidized dNTP and prevent cancer cells from entering cell death. Therefore, inhibition of MTH1 activity is considered to be an anti-cancer therapeutic target. In this study, high-throughput screening techniques were combined with a fragment-based library containing 2,313 compounds, which were used to screen for lead compounds with MTH1 inhibitor activity. Four compounds with MTH1 inhibitor ability were selected, and compound MI0639 was found to have the highest effective inhibition. To discover the selectivity and specificity of this action, several derivatives based on the MTH1 and MI0639 complex structure were synthesized. We compared 14 complex structures of MTH1 and the various compounds in combination with enzymatic inhibition and thermodynamic analysis. Nanomolar-range IC50 inhibition abilities by enzyme kinetics and Kd values by thermodynamic analysis were obtained for two compounds, named MI1020 and MI1024. Based on structural information and compound optimization, we aim to provide a strategy for the development of MTH1 inhibitors with high selectivity and specificity.