NecroX Improves Polyhexamethylene Guanidine-induced Lung Injury by Regulating Mitochondrial Oxidative Stress and Endoplasmic Reticulum Stress.

Various environmental compounds are inducers of lung injury. Mitochondria are crucial organelles that can be affected by many lung diseases. NecroX is an indole-derived antioxidant that specifically targets mitochondria. We aimed to evaluate the therapeutic potential and related molecular mechanisms of NecroX in preclinical models of fatal lung injury. We investigated the therapeutic effects of NecroX on two different experimental models of lung injury induced by polyhexamethylene guanidine (PHMG) and bleomycin, respectively. We also performed transcriptome analysis of lung tissues from PHMG-exposed mice and compared the expression profiles with those from dozens of bleomycin-induced fibrosis public data sets. Respiratory exposure to PHMG and bleomycin led to fatal lung injury manifesting extensive inflammation followed by fibrosis. These specifically affected mitochondria regarding biogenesis, mitochondrial DNA integrity, and the generation of mitochondrial reactive oxygen species in various cell types. NecroX significantly improved the pathobiologic features of the PHMG- and bleomycin-induced lung injuries through regulation of mitochondrial oxidative stress. Endoplasmic reticulum stress was also implicated in PHMG-associated lung injuries of mice and humans, and NecroX alleviated PHMG-induced lung injury and the subsequent fibrosis, in part, via regulation of endoplasmic reticulum stress in mice. Gene expression profiles of PHMG-exposed mice were highly consistent with public data sets of bleomycin-induced lung injury models. Pathways related to mitochondrial activities, including oxidative stress, oxidative phosphorylation, and mitochondrial translation, were upregulated, and these patterns were significantly reversed by NecroX. These findings demonstrate that NecroX possesses therapeutic potential for fatal lung injury in humans.

BEAR: A Novel Virtual Screening Method Based on Large-Scale Bioactivity Data.

Data-driven drug discovery exploits a comprehensive set of big data to provide an efficient path for the development of new drugs. Currently, publicly available bioassay data sets provide extensive information regarding the bioactivity profiles of millions of compounds. Using these large-scale drug screening data sets, we developed a novel in silico method to virtually screen hit compounds against protein targets, named BEAR (Bioactive compound Enrichment by Assay Repositioning). The underlying idea of BEAR is to reuse bioassay data for predicting hit compounds for targets other than their originally intended purposes, i.e., "assay repositioning". The BEAR approach differs from conventional virtual screening methods in that (1) it relies solely on bioactivity data and requires no physicochemical features of either the target or ligand. (2) Accordingly, structurally diverse candidates are predicted, allowing for scaffold hopping. (3) BEAR shows stable performance across diverse target classes, suggesting its general applicability. Large-scale cross-validation of more than a thousand targets showed that BEAR accurately predicted known ligands (median area under the curve = 0.87), proving that BEAR maintained a robust performance even in the validation set with additional constraints. In addition, a comparative analysis demonstrated that BEAR outperformed other machine learning models, including a recent deep learning model for ABC transporter family targets. We predicted P-gp and BCRP dual inhibitors using the BEAR approach and validated the predicted candidates using in vitro assays. The intracellular accumulation effects of mitoxantrone, a well-known P-gp/BCRP dual substrate for cancer treatment, confirmed nine out of 72 dual inhibitor candidates preselected by primary cytotoxicity screening. Consequently, these nine hits are novel and potent dual inhibitors for both P-gp and BCRP, solely predicted by bioactivity profiles without relying on any structural information of targets or ligands.

Identification of MYC as an antinecroptotic protein that stifles RIPK1-RIPK3 complex formation.

The underlying mechanism of necroptosis in relation to cancer is still unclear. Here, MYC, a potent oncogene, is an antinecroptotic factor that directly suppresses the formation of the RIPK1-RIPK3 complex. Gene set enrichment analyses reveal that the MYC pathway is the most prominently down-regulated signaling pathway during necroptosis. Depletion or deletion of MYC promotes the RIPK1-RIPK3 interaction, thereby stabilizing the RIPK1 and RIPK3 proteins and facilitating necroptosis. Interestingly, MYC binds to RIPK3 in the cytoplasm and inhibits the interaction between RIPK1 and RIPK3 in vitro. Furthermore, MYC-nick, a truncated form that is mainly localized in the cytoplasm, prevented TNF-induced necroptosis. Finally, down-regulation of MYC enhances necroptosis in leukemia cells and suppresses tumor growth in a xenograft model upon treatment with birinapant and emricasan. MYC-mediated suppression of necroptosis is a mechanism of necroptosis resistance in cancer, and approaches targeting MYC to induce necroptosis represent an attractive therapeutic strategy for cancer.

Comprehensive Survey of Recent Drug Discovery Using Deep Learning.

Drug discovery based on artificial intelligence has been in the spotlight recently as it significantly reduces the time and cost required for developing novel drugs. With the advancement of deep learning (DL) technology and the growth of drug-related data, numerous deep-learning-based methodologies are emerging at all steps of drug development processes. In particular, pharmaceutical chemists have faced significant issues with regard to selecting and designing potential drugs for a target of interest to enter preclinical testing. The two major challenges are prediction of interactions between drugs and druggable targets and generation of novel molecular structures suitable for a target of interest. Therefore, we reviewed recent deep-learning applications in drug-target interaction (DTI) prediction and de novo drug design. In addition, we introduce a comprehensive summary of a variety of drug and protein representations, DL models, and commonly used benchmark datasets or tools for model training and testing. Finally, we present the remaining challenges for the promising future of DL-based DTI prediction and de novo drug design.

Large-scale pharmacogenomics based drug discovery for ITGB3 dependent chemoresistance in mesenchymal lung cancer.

Even when targets responsible for chemoresistance are identified, drug development is often hampered due to the poor druggability of these proteins. We systematically analyzed therapy-resistance with a large-scale cancer cell transcriptome and drug-response datasets and predicted the candidate drugs based on the gene expression profile. Our results implicated the epithelial-mesenchymal transition as a common mechanism underlying resistance to chemotherapeutic drugs. Notably, we identified ITGB3, whose expression was abundant in both drug resistance and mesenchymal status, as a promising target to overcome chemoresistance. We also confirmed that depletion of ITGB3 sensitized cancer cells to conventional chemotherapeutic drugs by modulating the NF-κB signaling pathway. Considering the poor druggability of ITGB3 and the lack of feasible drugs to directly inhibit this protein, we took an in silico screening for drugs mimicking the transcriptome-level changes caused by knockdown of ITGB3. This approach successfully identified atorvastatin as a novel candidate for drug repurposing, paving an alternative path to drug screening that is applicable to undruggable targets.

miRseqViewer: multi-panel visualization of sequence, structure and expression for analysis of microRNA sequencing data.

SUMMARY: Deep sequencing of small RNAs has become a routine process in recent years, but no dedicated viewer is as yet available to explore the sequence features simultaneously along with secondary structure and gene expression of microRNA (miRNA). We present a highly interactive application that visualizes the sequence alignment, secondary structure and normalized read counts in synchronous multipanel windows. This helps users to easily examine the relationships between the structure of precursor and the sequences and abundance of final products and thereby will facilitate the studies on miRNA biogenesis and regulation. The project manager handles multiple samples of multiple groups. The read alignment is imported in BAM file format. Implemented features comprise sorting, zooming, highlighting, editing, filtering, saving, exporting, etc. Currently, miRseqViewer supports 84 organisms whose annotation is available at miRBase. AVAILABILITY AND IMPLEMENTATION: miRseqViewer, implemented in Java, is available at https://github.com/insoo078/mirseqviewer or at http://msv.kobic.re.kr. CONTACT: sanghyuk@ewha.ac.kr.

lncRNAtor: a comprehensive resource for functional investigation of long non-coding RNAs.

MOTIVATION: A number of long non-coding RNAs (lncRNAs) have been identified by deep sequencing methods, but their molecular and cellular functions are known only for a limited number of lncRNAs. Current databases on lncRNAs are mostly for cataloging purpose without providing in-depth information required to infer functions. A comprehensive resource on lncRNA function is an immediate need. RESULTS: We present a database for functional investigation of lncRNAs that encompasses annotation, sequence analysis, gene expression, protein binding and phylogenetic conservation. We have compiled lncRNAs for six species (human, mouse, zebrafish, fruit fly, worm and yeast) from ENSEMBL, HGNC, MGI and lncRNAdb. Each lncRNA was analyzed for coding potential and phylogenetic conservation in different lineages. Gene expression data of 208 RNA-Seq studies (4995 samples), collected from GEO, ENCODE, modENCODE and TCGA databases, were used to provide expression profiles in various tissues, diseases and developmental stages. Importantly, we analyzed RNA-Seq data to identify coexpressed mRNAs that would provide ample insights on lncRNA functions. The resulting gene list can be subject to enrichment analysis such as Gene Ontology or KEGG pathways. Furthermore, we compiled protein-lncRNA interactions by collecting and analyzing publicly available CLIP-seq or PAR-CLIP sequencing data. Finally, we explored evolutionarily conserved lncRNAs with correlated expression between human and six other organisms to identify functional lncRNAs. The whole contents are provided in a user-friendly web interface. AVAILABILITY AND IMPLEMENTATION: lncRNAtor is available at http://lncrnator.ewha.ac.kr/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

GEPdb: a database for investigating the ternary association of genotype, gene expression and phenotype.

UNLABELLED: GEPdb integrates both genome-wide association studies and expression quantitative trait loci information, the two primary sources of genome-wide mapping for genotype-phenotype and genotype-expression associations together with phenotype-associated gene lists. The GEPdb provides simultaneous interpretation of both genetic risks and potential gene regulatory pathways toward phenotypic outcome by establishing the ternary relationship of genotype-expression-phenotype (GEP). The analytic scope is further extended by linkage disequilibrium from five different populations of the international HapMap Project. AVAILABILITY AND IMPLEMENTATION: http://ercsbweb.ewha.ac.kr/gepdb.

MiRGator v3.0: a microRNA portal for deep sequencing, expression profiling and mRNA targeting.

Biogenesis and molecular function are two key subjects in the field of microRNA (miRNA) research. Deep sequencing has become the principal technique in cataloging of miRNA repertoire and generating expression profiles in an unbiased manner. Here, we describe the miRGator v3.0 update (http://mirgator.kobic.re.kr) that compiled the deep sequencing miRNA data available in public and implemented several novel tools to facilitate exploration of massive data. The miR-seq browser supports users to examine short read alignment with the secondary structure and read count information available in concurrent windows. Features such as sequence editing, sorting, ordering, import and export of user data would be of great utility for studying iso-miRs, miRNA editing and modifications. miRNA-target relation is essential for understanding miRNA function. Coexpression analysis of miRNA and target mRNAs, based on miRNA-seq and RNA-seq data from the same sample, is visualized in the heat-map and network views where users can investigate the inverse correlation of gene expression and target relations, compiled from various databases of predicted and validated targets. By keeping datasets and analytic tools up-to-date, miRGator should continue to serve as an integrated resource for biogenesis and functional investigation of miRNAs.

gsGator: an integrated web platform for cross-species gene set analysis.

BACKGROUND: Gene set analysis (GSA) is useful in deducing biological significance of gene lists using a priori defined gene sets such as gene ontology (GO) or pathways. Phenotypic annotation is sparse for human genes, but is far more abundant for other model organisms such as mouse, fly, and worm. Often, GSA needs to be done highly interactively by combining or modifying gene lists or inspecting gene-gene interactions in a molecular network. DESCRIPTION: We developed gsGator, a web-based platform for functional interpretation of gene sets with useful features such as cross-species GSA, simultaneous analysis of multiple gene sets, and a fully integrated network viewer for visualizing both GSA results and molecular networks. An extensive set of gene annotation information is amassed including GO & pathways, genomic annotations, protein-protein interaction, transcription factor-target (TF-target), miRNA targeting, and phenotype information for various model organisms. By combining the functionalities of Set Creator, Set Operator and Network Navigator, user can perform highly flexible and interactive GSA by creating a new gene list by any combination of existing gene sets (intersection, union and difference) or expanding genes interactively along the molecular networks such as protein-protein interaction and TF-target. We also demonstrate the utility of our interactive and cross-species GSA implemented in gsGator by several usage examples for interpreting genome-wide association study (GWAS) results. gsGator is freely available at http://gsGator.ewha.ac.kr. CONCLUSIONS: Interactive and cross-species GSA in gsGator greatly extends the scope and utility of GSA, leading to novel insights via conserved functional gene modules across different species.

5-aminosalicylic acid suppresses osteoarthritis through the OSCAR-PPARγ axis.

Osteoarthritis (OA) is a progressive and irreversible degenerative joint disease that is characterized by cartilage destruction, osteophyte formation, subchondral bone remodeling, and synovitis. Despite affecting millions of patients, effective and safe disease-modifying osteoarthritis drugs are lacking. Here we reveal an unexpected role for the small molecule 5-aminosalicylic acid (5-ASA), which is used as an anti-inflammatory drug in ulcerative colitis. We show that 5-ASA competes with extracellular-matrix collagen-II to bind to osteoclast-associated receptor (OSCAR) on chondrocytes. Intra-articular 5-ASA injections ameliorate OA generated by surgery-induced medial-meniscus destabilization in male mice. Significantly, this effect is also observed when 5-ASA was administered well after OA onset. Moreover, mice with DMM-induced OA that are treated with 5-ASA at weeks 8-11 and sacrificed at week 12 have thicker cartilage than untreated mice that were sacrificed at week 8. Mechanistically, 5-ASA reverses OSCAR-mediated transcriptional repression of PPARγ in articular chondrocytes, thereby suppressing COX-2-related inflammation. It also improves chondrogenesis, strongly downregulates ECM catabolism, and promotes ECM anabolism. Our results suggest that 5-ASA could serve as a DMOAD.

miRGator v2.0: an integrated system for functional investigation of microRNAs.

miRGator is an integrated database of microRNA (miRNA)-associated gene expression, target prediction, disease association and genomic annotation, which aims to facilitate functional investigation of miRNAs. The recent version of miRGator v2.0 contains information about (i) human miRNA expression profiles under various experimental conditions, (ii) paired expression profiles of both mRNAs and miRNAs, (iii) gene expression profiles under miRNA-perturbation (e.g. miRNA knockout and overexpression), (iv) known/predicted miRNA targets and (v) miRNA-disease associations. In total, >8000 miRNA expression profiles, ∼300 miRNA-perturbed gene expression profiles and ∼2000 mRNA expression profiles are compiled with manually curated annotations on disease, tissue type and perturbation. By integrating these data sets, a series of novel associations (miRNA-miRNA, miRNA-disease and miRNA-target) is extracted via shared features. For example, differentially expressed genes (DEGs) after miRNA knockout were systematically compared against miRNA targets. Likewise, differentially expressed miRNAs (DEmiRs) were compared with disease-associated miRNAs. Additionally, miRNA expression and disease-phenotype profiles revealed miRNA pairs whose expression was regulated in parallel in various experimental and disease conditions. Complex associations are readily accessible using an interactive network visualization interface. The miRGator v2.0 serves as a reference database to investigate miRNA expression and function (http://miRGator.kobic.re.kr).

Targeting Na-H exchanger 1 overcomes nuclear factor kappa B-mediated tumor resistance to radiotherapy.

Intrinsic or acquired radioresistance often limits the efficacy of radiation therapy (RT), thereby leading to local control failure. Cancerous cells have abnormal pH dynamics due to high metabolic demands, but it is unclear how pH dynamics contribute to radioresistance. In this study, we investigated the role of Na-H exchange 1 (NHE1), the major intracellular pH (pHi) regulator, in RT response. We observed that RT increased NHE1 expression and modulated pHi in MDA-MB-231 human breast cancer cells. When combined with RT, pharmacological NHE1 inhibition by 5-(N-Ethyl-N-isopropyl)amiloride (EIPA) reduced pHi and clonogenic survival. EIPA attenuated radiation-damaged DNA repair, increasing G2/M cell cycle arrest. The combination of EIPA and RT increased apoptotic cell death while decreasing phosphorylation of NF-κB p65. Similarly, the knockdown of NHE1 increased radiosensitivity with lower pHi and increased apoptosis. Consistent with in vitro data, the EIPA plus RT inhibited the growth of MDA-MB-231 xenograft tumors in mice to a greater extent than either EIPA or RT alone. EIPA abrogated the RT-induced increase in NHE1 and phospho-NF-κB p65 expression in tumor tissues. Such coincidence of increased NHE1 level, pHi, and NF-κB activation was also found in radioresistant MDA-MB-231 cells, which were reversed by EIPA treatment. Bioinformatics analysis of RNA sequencing data revealed that inhibiting NHE1 reversed three core gene networks that were up-regulated in radioresistant cells and correlated with high NHE1 expression in patient samples: NF-κB, senescence, and extracellular matrix. Taken together, our findings suggest that NHE1 contributes to RT resistance via NF-κB-mediated signaling networks, and NHE1 may be a promising target for improving RT outcomes.

Discovery of dipeptidyl peptidase-4 inhibitor specific biomarker in non-alcoholic fatty liver disease mouse models using modified basket trial.

BACKGROUND/AIMS: We aimed to define an optimal target population and drug-specific biomarkers that may predict dipeptidyl peptidase (DPP)-4 inhibitor responses in non-alcoholic fatty liver disease (NAFLD). METHODS: An exploration study (study I) was performed using three different NAFLD models (basket study design; high-fat diet [HFD], methionine choline-deficient diet [MCD], and high-cholesterol Western diet [WD] models). RNA transcriptome analysis was performed on pre-studied liver tissues to identify biomarkers that could predict the response to DPP-4 inhibitors. In the validation study (study II), the HFD-induced NAFLD model was divided into high and low hepatic insulin-like growth factor binding protein 1 (Igfbp-1) groups based on the pre-study liver biopsy. RESULTS: DPP-4 inhibitor attenuated the NAFLD activity score and fibrosis stage in the HFD model but not in the WD and MCD models. The overall response rate was 19% across the modified basket NAFLD trial and 42%, 25%, and 0% in the HFD, WD, and MCD models. Hepatic Igfbp-1 expression was higher in the responder group than in the non-responder group in pre-study biopsy samples. In contrast, hepatic Igfbp-1 expression was lower in the responder group than in the non-responder group in the end-study biopsy samples. DPP-4 inhibitor response rates were 83% and 17% in the baseline hepatic high Igfbp-1 and low Igfbp-1 groups, respectively. Hepatic messenger RNA Igfbp-1 expression was positively correlated with serum IGFBP-1 levels. CONCLUSION: The DPP-4 inhibitor response was higher in the HFD phenotype and pre-treatment levels of hepatic or serum IGFBP-1 were high.

Crosstalk between YAP and TGFß regulates SERPINE1 expression in mesenchymal lung cancer cells.

Serpin family E member 1 (SERPINE1), a serine proteinase inhibitor, serves as an important regulator of extracellular matrix remodeling. Emerging evidence suggests that SERPINE1 has diverse roles in cancer and is associated with poor prognosis. However, the mechanism via which SERPINE1 is induced in cancer has not been fully determined. In order to examine the molecular mechanism of SERPINE1 expression, the present study took advantage of the isogenic pair of lung cancer cells with epithelial or mesenchymal features. Using genetic perturbation and following biochemical analysis, the present study demonstrated that SERPINE1 expression was upregulated in mesenchymal lung cancer cells and promoted cellular invasiveness. Yes­associated protein (YAP)­dependent SERPINE1 expression was modulated by treatment with a Rho­associated protein kinase inhibitor, Y27632. Moreover, TGFß treatment supported YAP­dependent SERPINE1 expression, and an enhanced TGFß response in mesenchymal lung cancer cells promoted SERPINE1 expression. TGFß­mediated SERPINE1 expression was significantly attenuated by knockdown of YAP or transcriptional co­activator with PDZ­binding motif, suggesting that crosstalk between the TGFß and YAP pathways underlies SERPINE1 expression in mesenchymal cancer cells.

Circulating Small Extracellular Vesicles Activate TYRO3 to Drive Cancer Metastasis and Chemoresistance.

Extracellular vesicles (EV) in the tumor microenvironment have emerged as crucial mediators that promote proliferation, metastasis, and chemoresistance. However, the role of circulating small EVs (csEV) in cancer progression remains poorly understood. In this study, we report that csEV facilitate cancer progression and determine its molecular mechanism. csEVs strongly promoted the migration of cancer cells via interaction with phosphatidylserine of csEVs. Among the three TAM receptors, TYRO3, AXL, and MerTK, TYRO3 mainly interacted with csEVs. csEV-mediated TYRO3 activation promoted migration and metastasis via the epithelial-mesenchymal transition and stimulation of RhoA in invasive cancer cells. Additionally, csEV-TYRO3 interaction induced YAP activation, which led to increased cell proliferation and chemoresistance. Combination treatment with gefitinib and KRCT-6j, a selective TYRO3 inhibitor, significantly reduced tumor volume in xenografts implanted with gefitinib-resistant non-small cell lung cancer cells. The results of this study show that TYRO3 activation by csEVs facilitates cancer cell migration and chemoresistance by activation of RhoA or YAP, indicating that the csEV/TYRO3 interaction may serve as a potential therapeutic target for aggressive cancers in the clinic. SIGNIFICANCE: These findings demonstrate that circulating extracellular vesicles are a novel driver in migration and survival of aggressive cancer cells via TYRO3 activation. GRAPHICAL ABSTRACT: http://cancerres.aacrjournals.org/content/canres/81/13/3539/F1.large.jpg.

MiR-4435 is an UQCRB-related circulating miRNA in human colorectal cancer.

Ubiquinol-cytochrome c reductase (UQCRB), a subunit of the mitochondrial complex III, is highly expressed in tissues from colorectal cancer patients. Since UQCRB is highly expressed in colorectal cancer, we investigated miRNAs from mutant UQCRB-expressing cell lines to identify new miRNA biomarkers. After sequencing miRNAs in the mutant UQCRB-expressing cell lines, miR-4435 was selected as a potential biomarker candidate from the six up-regulated miRNAs. The expression level of miR-4435 in the mutant UQCRB-expressing cell lines and colon cancer was increased. Notably, the expression level of miR-4435 was increased in exosomes isolated from cell culture medium, suggesting that miR-4435 is closely related to colon cancer and that large amounts of miR-4435 may be secreted outside of the cells through exosomes. Additionally, exosomes extracted from the serum samples of colorectal cancer patients showed increased miR-4435 levels depending on the cancer progression stage. Moreover, analyses of a miRNA database and mRNA-sequencing data of the mutant UQCRB-expressing cell lines revealed that TIMP3, a tumor suppressor, could be a target of miR-4435. Additionally, the expression of miR-4435 was suppressed by UQCRB inhibitor treatment whereas TIMP3 was up-regulated. Upregulation of TIMP3 decreased proliferation of the mutant UQCRB-expressing cell lines and a colorectal cancer cell line. TIMP3 was also upregulated in response to miR-4435 inhibitor and UQCRB inhibitor treatments. Furthermore, these findings suggest that miR-4435 is related to an oncogenic function in UQCRB related disease, CRC, and that effects migration and invasion on mutant UQCRB-expressing cell lines and colorectal cancer cell. In conclusion, our results identified miR-4435 as a potential circulating miRNA biomarker of colorectal cancer associated with UQCRB.

Safe scarless cassette-free selection of genome-edited human pluripotent stem cells using temporary drug resistance.

An efficient gene-editing technique for use in human pluripotent stem cells (hPSCs) has great potential value in regenerative medicine, as well as in drug discovery based on isogenic human disease models. However, the extremely low efficiency of gene editing in hPSCs remains as a major technical hurdle. Previously, we demonstrated that YM155, a survivin inhibitor developed as an anti-cancer drug, induces highly selective cell death in undifferentiated hPSCs. In this study, we demonstrated that the high cytotoxicity of YM155 in hPSCs, which is mediated by selective cellular uptake of the drug, is due to the high expression of SLC35F2 in these cells. Knockout of SLC35F2 with CRISPR-Cas9, or depletion with siRNAs, made the hPSCs highly resistant to YM155. Simultaneous editing of a gene of interest and transient knockdown of SLC35F2 following YM155 treatment enabled the survival of genome-edited hPSCs as a result of temporary YM155 resistance, thereby achieving an enriched selection of clonal populations with gene knockout or knock-in. This precise and efficient genome editing approach took as little as 3 weeks and required no cell sorting or the introduction of additional genes, to be a more feasible approach for gene editing in hPSCs due to its simplicity.

Systematic identification of a nuclear receptor-enriched predictive signature for erastin-induced ferroptosis.

Erastin, a synthetic lethal compound against cancer expressing an oncogenic RAS, inhibits cystine/glutamate antiporters and causes ferroptosis. However, despite recent evidence for the mechanisms underlying ferroptosis, molecular biomarkers of erastin-dependent ferroptosis have not been identified. Here, we employed isogenic lung cancer cell models to show that a redox imbalance leads to glutathione depletion and ferroptosis. Subsequent transcriptome analysis of pan-cancer cell lines revealed that the activity of transcription factors, including NRF2 and AhR, serve as important markers of erastin resistance. Based on the integrated expression of genes in the nuclear receptor meta-pathway (NRM), we constructed an NRM model and validated its robustness using an independent pharmacogenomics dataset. The NRM model was further evaluated by sensitivity tests on nine cancer cell lines for which erastin sensitivities had not been determined. Our pharmacogenomics approach has the potential to pave the way for the efficient classification of patients for therapeutic intervention using erastin.