Background
Lung cancer is the most common tumor worldwide. The median age of lung cancer patients is 71 years, with approximately 90% of patients older than 55 [
1]. Moreover, most patients evolve into advanced stage at diagnosis. Thus, the prognostic outcomes for advanced lung cancer patients are unsatisfactory due to the decreased immunity [
2]. In the clinicopathological staging of lung cancer, the proportion of NSCLC has been as high as 85% [
3]. According to statistics, only about 15% of NSCLC patients survive longer than 5 years, and the prognosis of patients with advanced-stage is disappointing [
4]. In the past, the usual treatment modality for advanced patients was platinum-based doublet chemotherapy [
5]. However, most advanced NCSLC patients remain resistant to the current treatment [
6,
7]. Therefore, the research on the mechanism related to NSCLC still needs to be further explored.
Cell death is the ultimate fate of cells. The two crucial mechanisms of cell death are accidental cell death (ACD) and regulated cell death (RCD) [
8]. RCD is often considered a defense against cancer. Unlike autophagy, apoptosis, and cell necrosis, one of the regulated cell death modalities, ferroptosis is unique cell death form [
9]. Cancer cells have defects in some normal executive mechanisms, which is a major reason why cancer cells become resistant to treatment [
10]. The growth of cancer cells exhibits a greater iron requirement than normal cells [
11]. Therefore, ferroptosis is often considered an adaptive feature in eliminating malignancy and play an important role in tumor suppression [
11]. Previous studies also showed that many tumor suppressors regulate ferroptosis and tumors by affecting tumor metabolisms, such as BAP1 and P53 [
10,
12,
13]. Thus, the treatment of NSCLC by inducing ferroptosis in cancer cells holds great promise for research. It has been demonstrated that in NSCLC, ginkgetin may promote DDP-induced anticancer effects via ferroptosis induction [
14]. However, it remains a challenge that ferroptosis is applied to diagnosis and therapy in cancer research. Therefore, developing new targets and predictive models for cancer treatment is necessary.
CEP-9722 is an inhibitor of PARP1 and PARP2, a prodrug of CEP-8983 [
15]. CEP-9722 has been shown to inhibit cell growth in ovarian cancer, colon cancer, glioma, and urothelial cancer [
15‐
18]. However, the role of CEP-9722 in NSCLC has not been investigated. Scutellaria baicalensis Georgi belongs to the Lamiaceae family and is a flowering plant. The root of Scutellaria baicalensis Georgi is used as an herbal remedy for influenza, pneumonia, dysentery, and cancer. Its Chinese name is Hedysarum Multijugum Maxim (HMM) [
19,
20]. Recent studies showed that some ingredients of HMM could be used to treat NSCLC, including baicalein, wogonin, and oroxylin A [
21]. However, to date, 126 small-molecule compounds have been isolated from HMM, and the functions of most of them are unknown [
19]. Therefore, the components of HMM that can treat NSCLC warrant further investigation.
This study is devoted to investigate novel traditional and western drugs that could be combined to treat NSCLC. Firstly, we identified the 14 prognostic genes associated with ferroptosis in NSCLC patients. Then, single-cell sequencing results indicated the risk genes were mainly derived from lung tumor cells. Moreover, the drug sensitivity analysis showed 106 drugs correlating with ferroptosis-related genes. The traditional Chinese drugs of HMM were also identified in TCMSP database. Finally, the effects of concomitant drugs was validated by assays.
Methods
Data collection
The GSE31210 was downloaded from the GEO database as the test set, where contains 20 cases of normal samples and 226 cases of tumor samples were involved. The mRNA sequencing data on 1089 patients and the clinical information on corresponding samples were downloaded from the TCGA (LUAD and LUSC) database as the training set (involving 108 cases of normal samples and 1041 cases of tumor samples).
DEG identification
Rank sum tests were performed on the training set by R V4.1.0. 587 ferroptosis-related genes were screened from previous literature reports. he data type used for DEG identification was the log2 transformed new FPKM and TPM value, namely, log2(FPKM + 1) and log2(TPM + 1). The data were normalized by using the limma package before DEG identification. Also, the new screening criteria were log FC ≥ 1 and adjusted P < 0.05. The intersection of the screened differential genes and iron death-related genes was taken, and the obtained results could be used for subsequent Univariate COX analysis and LASSO screening.
Gene enrichment analysis
GSEA is a computational method for calculating and comparing the consistent differences present in two biological states. The differential analysis of the enrichment of intersecting genes was performed on different signaling pathways obtained from differential analysis by using the R package clusterprofiler. The gene enrichment in the corresponding pathway was significant (P < 0.05).
Development and validation of prognostic risk models
The relationship between genes and overall survival (OS) was analyzed by using LASSO COX regression analysis. The software package was glmnet. A prognostic risk prediction model for NSCLC was established according to the LASSO risk score calculation formula. After the acquisition of the median risk score, NSCLC patients were divided into the high-risk and low-risk groups. The plotted KM curves were used for comparing OS between the two groups. The optimal R package for Survival analysis was the package SURVIVAL. The ROC curve analysis was adopted to assess the reliability of the prognostic model, and the evaluated standard for ROC curve analysis results (P < 0.05).
Single-cell sequencing data analysis
The downstream analysis of the downloaded scRNA-seq data (GSE131907) was performed by using the Seurat R package (version 3.0.2). The number of cells with genes present in less than 3 or when the number of genes in a single cell is less than 200 were filtered out, with a limit of 20% for the percentage of mitochondria. The data processing needed to be normalized by the LogNormalize method. Subsequently, the marker genes were clustered by using the "FindAllMarkers" function with the filter condition set to (FC) ≥ 1. The minimum cell ratio in either of the two populations was 0.25. In addition, the expression pattern of each marker gene in the cluster was visualized. The expression pattern of genes in clusters was also visualized by applying Seagate's "DotPlot" function. Meanwhile, the SingleR package (version 1.0.0) was used for marker-based cell-type annotation.
Protein level validation of central genes
HPA database, designed to create human proteome-wide maps by integrated OMC technology. To validate the protein expression levels of 14 genes, the protein expression of target genes from the HPA database was screened in different tissues of NSCLC patients.
Drug sensitivity screening of therapeutic targets
The CellMiner database, a drug sensitivity database based on the NCI-60 cell line, was built by the National Cancer Institute (NCI). The RNA-seq and NIC-60 drug z-score values were downloaded from the CellMiner database (
https://discover.nci.nih.gov/cellminer/home.do). Z-score values were positively correlated with drug sensitivity. FDA-approved drugs were obtained by screening, and Pearson correlation analysis was performed on the drug z-score values and characteristic gene expression. The threshold values were Pearson's correlation coefficient (PCC)|> 0.3 and
P < 0.05.
The collection of active ingredients of HMM
The Traditional Chinese Medicine Systems Pharmacology database (TCMSP) was investigated to acquire the ingredients of HMM (
https://old.tcmsp-e.com/tcmsp.php). This database includes a set of ingredients, targeted genes, and pharmacokinetic properties of natural compounds. To get the active ingredients of HMM in the database, the screening criteria are oral bioavailability (OB) ≥ 30% and drug-likeness (DL) ≥ 0.18. 20 active ingredients and 206 targeted genes of HMM were collected.
Molecular docking
Autodock vina (v1.2.0) was used to evaluate the molecular docking. The PDB files of proteins were downloaded from RSCB PDB (
https://www.rcsb.org/). The mol2 file of small molecules were obtained from TCMSP and transformed into PDB files through Open Babel (v3.1.1). Then, the structure was introduced to Autodock vina for removing water, hydrogenation, charge and rotational bond number calculation and the lowest energy posture is selected for research. Pymol (v2.5.3) was performed to get the 3D-2D model of high-quality small molecules and proteins.
Cell lines and cell culture
Lung cancer cell line A549 was provided by American type culture collection (ATCC). The A549 cells were cultured in McCoy’s 5A medium, supplemented with 10% bovine serum albumin (FBS), 100 μg/ml penicillin and 100 μg/ml streptomycin at 37 °C in 5% CO2.
CCK8 assay to detect cell viability
CCK8 assay was used for cell viability identification. 5 × 103 cells were inoculated into 96-well culture plates, and left adhering overnight. And then the cells were cultured in fresh medium containing different concentrations of CEP-9722 (MCE, HY-105303), RAF-265 (MCE, HY-10248), Gefitinib (MCE, HY-50895), BMS-599626 (MCE, HY-10251), and IMG (TargetMOI, CAS: 136087–29-1, Lot, 149,155) dissolved in DMSO (final concentration, 0.1%). After 48 h of incubation, CCK-8 was added and the absorbance at 450 nm was measured with an EnSpire® Multi-plate Reader (Perkin Elmer, USA). Each group of experiments was repeated 3 times.
The assay was conducted to analyze the effect of CEP-9722 on colony formation. Single cells were cultured in six-well plates with a cell count of 1 × 103 per well for 24 h. After 24 h, the medium was removed and different concentrations of CEP-9722 and IMG were added, and the cells were continued to be cultured until the colonies were clearly visible. Finally, the cells were stained with 0.1% crystal violet for counting.
Iron assay
The iron assay was conducted through the iron assay kit (ab83366, Abcam). The A549 cells were cultured in a 10 cm2 plate with a cell count of 5 × 106 for 24 h. After 24 h, the cells were treated with IMG, CEP-9722, erastin, RSL3, or DMSO for 12 h. Then, the cells were collected in the 5 × volumes of iron assay buffer on ice according to the manufacturer’s instructions. To remove insoluble material, the cells were centrifuged with 13000 g, 10 min at 4℃. The supernatant was added to the iron reducer and incubated at room temperature for 30 min. Next, 100 ul of the iron probe was added and incubated at room temperature for 1 h. The absorbance at 593 nm was detected using a colorimetric microplate reader.
Statistical analysis
Statistical analyses were performed with KM curves to compare the difference in survival between two risk groups. The predictive power of the model was determined by ROC curves analysis. Univariate COX regression analysis was adopted to assess the prognostic value of risk scores, with hazard ratios (HR) and 95% confidence intervals (CI) set for each variable. All parameters were default and there was a significant difference (P < 0.05).
Discussion
NSCLC is a common subtype of lung cancer. In current clinical treatment, advanced patients with poor prognosis and short survival time, and lack of effective targeted therapy are the most serious challenges [
23,
24]. As a type of regulated death, the role of ferroptosis should not be ignored in regulating cell development. It has been already reported that ferroptosis-related genes have been investigated as disease prediction models in multiple cancers [
25]. However, the therapy of combining traditional and western medicine by ferroptosis in NSCLC patients have not yet been fully elucidated. Therefore, based on LASSO regression, it is necessary to establish a ferroptosis-related risk score model to evaluate the prognosis of NSCLC patients. Then, the expression of risk scores in various subtypes of NSCLC was obtained by analyzing the single-cell sequencing data. Finally, the drug sensitivity analysis of potential targets through the CellMiner database showed that CEP9722 had the most significant tumor-suppressive effect. Meanwhile, network pharmacology and molecular docking analysis were performed to investigate the pontential active ingredients of HMM targeting ferroptosis-related genes, and the combination of traditional and western drugs were used to examine the effects for treating NSCLC. In conclusion, this study provides a potent strategy of concomitant drugs with traditional and western medicine for treating NSCLC.
Ferroptosis has long been considered as an iron-dependent death caused by membrane damage mediated by an imbalance of redox reactions in vivo [
26]. It has become a new therapeutic direction that tumor cell growth is regulated by developing new drugs to trigger the ferroptosis pathway [
27]. Ferroptosis-related genes have been reported as biomarkers in many studies. For example, Ren and Han et al. have investigated ferroptosis genes as biomarkers in both lung adenocarcinoma and NSCLC [
25,
28]. However, in their study, the drugs targeting predicted genes and single-cell data analysis were not elucidated. In NSCLC patients, 259 ferroptosis-related genes were clustered and the grouping effect was found to be the best when k = 2. Through differential gene expression analysis of patients, 587 differential genes were obtained. Then, by using LASSO linear regression model to perform Univariate COX and LASSO analyses on the ferroptosis-associated genes, a prognostic model of 14 ferroptosis-related genes was obtained. And 14 genes were found to be significantly associated with clinical prognosis. SPP1 can act as a hub gene for methylation and enhance colorectal cancer (CRC) metastasis through the mesenchymal transition in CRC cell lines [
29]. Additionally, silencing SPP1 expression was found to inhibit the growth, migration, and cell cycle of tumors. Furthermore, SPP1-encoded proteins, including fibronectin 1 and osteopontin (OPN) [
30], are involved in processes such as wound healing and angiogenesis and they are closely associated with tumor prognosis [
29]. Finally, the predictive function of the model was comprehensively evaluated by ROC curve, PCA analysis. In summary, we comprehensively evaluate and verify the performance of the model through a variety of methods.
The rise of single-cell sequencing technologies based on tumor cell heterogeneity is a technological advance, which provides a powerful tool for further revealing molecular mechanisms. Advances in single-cell sequencing technologies have provided the possibility of identifying novel or rare cell types, analyzing single-cell trajectory construction, and comparing healthy and disease-related tissue at single-cell resolution [
31]. Meanwhile, single-cell sequencing has great development potential for promoting the diagnosis, targeted therapy, and prognosis prediction of various tumors [
32]. Cell subtypes have been grouped and validated by single-cell sequencing based on prognostic models in tumors. However, previous ferroptosis-related prediction models have not been validated by single-cell sequencing. Therefore, cell clustering and trajectory analyses were performed on 14 prognostic genes based on single-cell sequencing data from NSCLC. First, according to the single-cell sequencing data on NSCLC, the cells were divided into seven categories: T lymphocytes, epithelial cells, myeloid cells, B lymphocytes, MAST cells, fibroblasts, and endothelial cells. Among them, the epithelial cells were found to have the highest risk score. The feasibility of 14 ferroptosis-related genes for prognostic models was further verified.
Through the CellMiner database, a chemotherapeutic drug sensitivity analysis was conducted, and the results indicates that these signature genes were positively correlated with 98 chemotherapeutic drugs. This suggested that our model can be used as a postoperative adjuvant chemotherapy model for prediction. In order to further improve the clinical predictive value, the most relevant drug CEP-9722 was selected for verification at the cellular level, confirming that the drug CEP-9722 has a significant inhibitory effect on NSCLC. The current treatment for NSCLC patients is mainly immunotherapy based on antibodies against PD-1 or PD-L1 [
33]. Although the current treatment methods for NSCLC patients are greatly improved, patients with advanced cancer will still develop resistance to the tumor treatment. Thus, it is important to find a new therapy. Our prediction model was used to perform the screening for CEP-9722, a prodrug of CEP-8983, which is a potent inhibitor of PARP-1 and PARP-2 [
17]. Both PARP-1 and PARP-2 are the major pathways for DNA repair in tumor cells [
17]. CEP-9722 can further promote the effect of DNA-damaging chemotherapy by inhibiting two major pathways. Besides, CEP-9722 can enhance the sensitivity of chemotherapy-resistant tumor cells as monotherapy or combined with other drugs [
34]. Moreover, the active ingredients of HMM were collected from the TCMSP database and their targeted genes were evaluated. Through overlapping these targeted genes with ferroptosis-related signature genes of NSCLC, we identified 3 genes targeted by active ingredients of HMM and correlated with ferroptosis and prognosis. Altogether, the findings in this study provide a potential strategy for concomitant drugs to treat NSCLC.
Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit
http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (
http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.