Mitochondrial associated programmed cell death patterns in predicting the prognosis of non-small cell lung cancer

doi:10.21203/rs.3.rs-4179658/v1

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Mitochondrial associated programmed cell death patterns in predicting the prognosis of non-small cell lung cancer

https://doi.org/10.21203/rs.3.rs-4179658/v1

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Mitochondrion is the convergence point of multiple pathways that trigger programmed cell death (PCD), and mitochondrial associated PCD (mtPCD) is involved in the pathogenesis of several diseases. However, the roles of mtPCD in cancer pathogenesis and prognosis prediction in many cancers including non-small cell lung cancer (NSCLC), remain to be investigated. Here, 12 mtPCD patterns (necroptosis, autophagy, pyroptosis, ferroptosis, apoptosis, NETotic cell death, alkaliptosis, entotic cell death, cuproptosis, oxeiptosis, parthanatos, and lysosome-dependent cell death) were analyzed in a large number of transcriptomes, genomics, and clinical data collected from The Cancer Genome Atlas (TCGA)-NSCLC, GSE29013, GSE31210, and GSE37745 datasets, and a risk score assessment system was established with 18 genes (AP3S1, CCK, EIF2AK3, ERO1A, KRT8, PEBP1, PIK3CD, PPIA, PPP3CC, RAB39B, RIPK2, RUBCNL, SELENOK, SQLE, STK3, TRIM6, VDAC1, and VPS13D) included in the system. The NSCLC patients were divided into high- and low-risk groups. We found that NSCLC patients with a mtPCD high-risk score had a worse prognosis. A nomogram with high predictive performance on overall survival was constructed by incorporating the risk score with clinical features. Furthermore, the risk score was associated with clinicopathological information, tumor mutation frequency, and key tumor microenvironment components based on bulk transcriptome analysis. NSCLC patients with high-risk score had more Treg cells infiltration; however, these patients had higher tumor microenvironment and tumor mutation burden scores, and might be more sensitive to immunotherapy. These results indicated that mtPCD genes may have important roles in NSCLC carcinogenesis, and ptPCD patterns can predict clinical prognosis of NSCLC patients.

Mitochondrial associated programmed cell death

NSCLC

Prognosis

Gene signature

Lung cancer is the most commonly occurring cancer worldwide and the leading cause of cancer deaths, with 2.5 million new cases and 1.8 million deaths each year (1). Lung cancer comprises of non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC), whereas NSCLC accounts for 85% of all lung cancer cases and can be further divided into lung adenocarcinoma (LUAD, 40%), lung squamous cell carcinoma (LUSC, 30%), and large cell carcinoma (LCC) (2). In the past two decades, targeted therapies and immunotherapies have been used to treat NSCLC and significantly improve clinical outcome of the patients (3, 4). However, the therapeutic efficacy of NSCLC remains unsatisfactory due to primary or acquired drug resistance and the heterogeneity and complexity of tumor tissues (5–8). Moreover, current tumor staging systems cannot accurately predict the prognosis of patients with different molecular features (9). Therefore, biomarkers for prediction of response to individualized treatment is urgently needed.

Cell death can be classified as accident cell death or programmed cell death (PCD), with the latter as a complex regulatory process that involves multiple mechanisms in vivo (10). To date, at least 12 modes of PCD have been reported, including necroptosis, autophagy, pyroptosis, ferroptosis, apoptosis, NETotic cell death, alkaliptosis, entotic cell death, cuproptosis, oxeiptosis, parthanatos, and lysosome-dependent cell death (11–13). Apoptosis is the most extensively studied PCD modality that leads to immunogenicity or induces no immunogenic response in different context (14). Necroptosis is initiated by the activation of cell surface death receptors and RNA- and DNA-sensing molecules in cells, and is characterized by permeabilization of the plasma membrane, cell swelling, and loss of cellular and organelle integrity (15, 16). Pyroptosis, a recently discovered type of PCD, is characterized by caspase-1 dependence and the release of numerous pro-inflammatory factors (17, 18). Ferroptosis is a mode of cell death in which iron-dependent accumulation of lipid peroxidation reaches lethal levels (19, 20). Cuprotosis is characterized by excessive accumulation of copper ions, leading to abnormal aggregation of lipoacylated proteins, interference with mitochondrial respiration, and ultimately cell death (21). Autophagy is the major intracellular degradation pathway through which cytoplasmic materials are delivered and degraded in lysosome (22). NETotic cell death is an inflammatory cell death mode of neutrophils, that captures and kills pathogens by releasing cell-trapping nets into the extracellular (23). Parthanatos is a novel form of PCD caused by DNA damage and activation of poly (ADP-ribose) polymerase PARP-1 (24). Lysosome-dependent cell death occurs through the cathepsin-mediated cleavage and activation of key regulators of apoptosis (25). Alkaliptosis is a form of cell death that is driven by intracellular alkalization (26). Entotic cell death is a mode of cell death that involves the death of epithelial cells and the formation of glandular cavities (27). Oxeiptosis is a caspase-independent apoptosis-like cell death process induced by reactive oxygen species (28). Multiple forms of PCD work together to regulate cell fate. Mitochondria, organelles that have pivotal roles in various cellular processes including metabolism, energy production, and calcium homeostasis, serve as convergence points of multiple pathways that trigger PCD (29). Mitochondrial associated PCD (mtPCD) is involved in the pathogenesis of several diseases (30, 31), but its roles in cancer pathogenesis and prognosis prediction in many cancers including NSCLC, remain to be investigated.

This study aimed to evaluate the mtPCD status in NSCLC and the association between mtPCD and the efficacy of therapeutic interventions of the patients, using 4 datasets containing 977 NSCLC patients and 1253 mtPCD genes. We found the ability of mtPCD genes in prediction of the heterogeneity and clinical prognosis, and in selection of appropriate treatment options for patients with NSCLC.

Candidate genes and data collection

The mitochondria-related gene set was derived from two databases, MitoProteome (1705 genes) and MitoCarta3.0 (1135 genes). After merging the two databases and removing duplicate genes, the total number of genes was 1903. The gene set related to PCD was derived from the literature (13), and the total number of genes was 1253 after duplication removal.

The correlation between mitochondria-related gene sets and PCD gene sets was analyzed using The Cancer Genome Atlas (TCGA)-NSCLC dataset, Gene Expression Omnibus GSE29013, GSE31210, and GSE37745 datasets. The mtPCD gene selection criteria were | R | > 0.3 and P < 0.05. Univariate Cox regression analysis was performed to select mtPCD genes related to the prognosis of NSCLC. The screening criterion were P < 0.05 of overall survival (OS) based on survival curve analysis, and 1-, 3-, and 5-year area under the Receiver Operating Characteristic Curve (ROC) Curve (AUC) values analysis ≥ 0.55.

Construction of prognostic model and external validation

First, all the samples in TCGA-NSCLC dataset were randomly divided into two groups (1:1, training group and validation group). Second, the glmnet (32) package was used to conduct LASSO regression analysis according to the gene expression and clinical data related to prognosis obtained from the above screening in the training group. Finally, the marker genes were determined according to the LASSO regression results, and the regression coefficients of each gene, the risk values of each sample, and the differences in the risk values for different pathological types were calculated. The GSE29013, GSE31210, and GSE37745 datasets were used to verify model stability. The predict.cv.glmnet function was used to calculate risk values for each sample.

Clinical characteristics and genomic and transcriptomic variations

Demographic characteristics of patients were obtained from the corresponding datasets. Initially all samples were divided into high- and low-risk groups according to the above-mentioned median risk values. Differences in other clinical factors between the groups were compared using the chi-square test (categorical data) or the Wilcoxon test (continuous data). The maftools (33) package was used to explore the differences in the high and low risk groups at the genomic level and the gene mutation frequency between the samples in the two groups, and the limma package was used to explore the differences at the transcription levels between the high- and low-risk groups and to identify differentially expressed genes. The screening criteria were fold change (FC) > 2 and P < 0.05, followed by using the cluterprofiler (34) package for functional enrichment analysis. Based on these risk values, a prediction model was developed to describe the impact of these influencing factors on patient survival at 1-, 3-, and 5-year respectively, via multivariate regression models.

Tumor microenvironment and immune cell infiltration

According to the expression of immune cell marker genes in each sample, cibersort (35) was used to score the immune cell infiltration. Statistical differences between the scores of different cells in the high- and low-risk groups were calculated using Wilcoxon test. TMEscore (36) package was used to estimate the specific tumor microenvironment of each sample, and the Wilcoxon test was used to calculate the score difference between the high- and low-risk groups.

Statistical analysis

All statistical analyses were conducted using R software (v.4.1.0). Student’s t-test was used to analyze the differences between two groups. Pearson correlation analysis was conducted to analyze the correlations between two continuous variables. Univariate and multivariate Cox regression analysis were performed for prognostic analysis. Survival curves were described using Kaplan-Meier plots and compared using the log-rank test. P < 0.05 was considered statistically significant.

mtPCD genes associated with prognosis of NSCLC patients

A flow-chart of the study is shown in Fig. 1A. Patients of 4 datasets, including 500 from TCGA, 55 from GSE29013, 196 from GSE37745, and 226 from GSE31210, were included in this study. Meanwhile, 12 PCD patterns with 1253 genes were included in the analysis. To filter the mtPCD-related genes, correlation analysis was carried out based on the mitochondrial- and PCD-related gene sets in the TCGA-NSCLC dataset, and the genetic screening criteria was |R| > 0.3. Through correlation analysis of the genes, we found that most genes were associated with each other (Fig. 1B). A total of 1210 mtPCD genes were identified, and univariate Cox regression analysis was used to select candidate mtPCD genes related to the prognosis of NSCLC. The screening criteria were P < 0.05 of OS based on survival curve analysis, and 1-, 3-, and 5-year AUC values of ROC analysis ≥ 0.55. A total of 76 prognostic mtPCD genes were identified, including ERO1A, VDAC1, BMP5, KRT8, PRKCD, YWHAG, PPP1R13B, AP3S1, YWHAZ, and others (Table 1). Univariate COX regression analysis presents results for only a subset of genes were indeed related to the prognosis of NSCLC (Table 2).

Table 1

**The 76 mtPCD genes that are related to the prognosis of NSCLC**
Gene	OS P value	OS HR	DFS P value	DFS HR
AATF ADRB2 AP1S3 AP3S1 BLK BMP5 BTK CASP12 CCK CD27 CD28 CD5 CD74 CD84 CISD2 CTSG CTSL CTSV CX3CR1 CYCS CYLD EIF2AK3 ELAPOR1 ERO1A FADD FANCD2 FGF10 FYCO1 HERPUD1 HGF HSP90AA1 IL10RA IL33 KRT8 LCK MIF NEDD4 NLRP1 NUPR1 PDCD5 PEBP1 PERP PGD PIK3CD PIK3CG PIK3R1 PLA2G3 PLAUR PPARD PPIA PPP1R13B PPP3CC PPT2 PRKCD PSME3 PTGDS PTPRC RAB39B RELA RIPK2 RUBCNL SAT2 SELENOK SEPTIN4 SLC3A2 SQLE STK3 TFEB TNFRSF10C TREM2 TRIM38 TRIM6 VDAC1 VPS13D YWHAG YWHAZ	0.001463608 0.000604248 0.00013801 0.00035052 0.000450324 1.69661E-05 1.25387E-05 0.006229491 0.011364623 0.008530174 0.005346537 0.000600383 0.000346467 0.00148217 7.43282E-05 0.000515787 0.000269075 0.000360776 0.002339807 0.000141932 0.000105289 0.00248305 4.69416E-05 1.72991E-08 2.89707E-06 0.001224555 0.025853834 4.22601E-06 1.18948E-05 0.00028576 0.002356783 0.002443754 0.000148129 1.87106E-05 0.001033044 5.73635E-05 0.000222921 0.000619688 0.001491361 0.001350781 1.6554E-05 4.12061E-06 0.013766209 6.75863E-05 0.001048732 0.000198072 0.003314469 6.62913E-05 0.002029731 0.000124364 4.04041E-06 0.000440629 3.70653E-05 6.49679E-06 7.90856E-05 5.25695E-05 0.00037879 0.000162437 0.001160967 7.70016E-05 0.000386259 0.002404408 6.77735E-06 0.007817973 0.000597705 9.04135E-05 0.004662917 9.86438E-05 0.000143015 0.00688257 0.00250484 3.76238E-05 8.61136E-07 6.00218E-05 8.21208E-06 2.98876E-05	1.637884674 0.586783451 1.830730598 1.734925563 0.586853299 0.531269978 0.509625905 0.651146909 1.518838712 0.677279977 0.655666245 0.581476488 0.564492975 0.624228122 2.009400428 0.599836204 1.753617433 1.692902906 0.588709383 1.746765318 0.493023575 0.566536084 0.545048077 2.308761917 1.980236422 1.639740584 0.704876974 0.394491337 0.525266669 0.482082888 1.568128873 0.597140434 0.492728938 1.921793553 0.610992157 2.327339437 1.78520144 0.604227418 0.576194935 1.604207552 0.527650949 1.974890343 1.46899841 0.549643338 0.613834402 0.572962756 0.620287441 1.86356012 1.607640345 1.816781299 0.50780252 0.57444117 1.844849901 0.513349668 1.813614351 0.450174895 0.59186307 0.538762159 1.634242426 2.16028144 0.586121403 0.622071555 0.509478831 0.670255718 1.65705208 1.77770124 1.648804417 0.564766402 0.570517944 0.670983659 0.624506528 1.889016798 2.047677084 0.461774392 1.925603247 1.851819866	0.308581662 0.09361295 0.002734592 0.001713077 0.002182238 0.001824955 0.025954598 0.048065211 0.056254449 0.058667338 0.058064371 0.035232879 0.00489124 0.061604723 0.002206274 0.005028482 0.000404122 0.000459878 0.002071365 0.0003543 0.03530879 0.207309978 0.000934998 1.23606E-05 0.001009753 0.034833108 0.062574899 0.070303826 0.000720863 0.017639155 0.007582392 0.06968509 0.108362227 0.000279772 0.004407788 0.034781264 0.007459699 0.010107393 0.004747265 0.000312787 0.024595099 0.001553448 0.039115509 0.008899778 0.07046049 0.003355229 0.016750356 0.000575737 0.02402129 0.005836115 6.19671E-05 0.252768019 0.106183563 0.001541809 0.016393077 0.02148952 0.028969459 0.001554661 0.040872423 0.001022995 0.017276013 0.011311364 0.019207581 0.068829314 0.005777514 0.186146891 0.09611093 0.007177001 0.012811751 0.074978929 0.012401389 0.000110454 0.000369061 0.017240195 2.01286E-05 0.001211367	1.186694663 0.767756786 1.593666428 1.6322063 0.61832252 0.632696875 0.718247452 0.742370407 1.333347404 0.756363075 0.708197058 0.725172421 0.660616756 0.750903573 1.660091375 0.660809563 1.699105363 1.764772129 0.615655051 1.698684889 0.716448187 0.813244057 0.602100327 1.940484527 1.624430941 1.365869545 0.748375998 0.739952069 0.608649778 0.650467375 1.4933947 0.757928567 0.775247982 2.001893688 0.650872687 1.368812363 1.54465793 0.679309587 0.643667134 1.695257878 0.676331833 1.628653439 1.380257852 0.679616211 0.76133073 0.648708142 0.702103012 1.829533697 1.399782249 1.700710968 0.551762435 0.838139045 1.305604363 0.623699034 1.43675576 0.661734867 0.722366829 0.607919379 1.359960781 1.828553237 0.703229609 0.670387286 0.699598887 0.746313656 1.523037507 1.227462497 1.321966677 0.668665465 0.692614953 0.763089942 0.648360985 1.790602722 1.864258245 0.661682298 1.882515802 1.721306015

Table 2

The top 9 genes that are related to the prognosis of NSCLC
Gene	OS P value	OS HR	DFS P value	DFS HR
ERO1A	1.81E-07	1.494367831	0.000801571	1.305294
VDAC1	1.72E-06	1.874563126	0.001354623	1.557407
BMP5	7.49E-06	0.858564858	0.002208029	0.893505
KRT8	1.21E-05	1.468094655	0.000599661	1.36051
PRKCD	3.96E-05	0.625692388	0.031784316	0.763687
YWHAG	4.57E-05	1.669970398	0.012667647	1.361472
PPP1R13B	6.57E-05	0.722842665	0.000345529	0.730916
AP3S1	9.49E-05	1.887382109	0.000655791	1.751243
YWHAZ	0.000155	1.616013334	0.000937533	1.550296

Construction of a prognostic model for NSCLC patients

We explored whether a prognostic model of mtPCD genes could be constructed using a machine learning method by performing LASSO regression analysis based on the expression of the 76 prognostic mtPCD genes and clinical data, and cross-validated the LASSO regression analysis (Fig. 2A-B). Interestingly, a prognostic model containing 18 genes was constructed. These genes were AP3S1, CCK, EIF2AK3, ERO1A, KRT8, PEBP1, PIK3CD, PPIA, PPP3CC, RAB39B, RIPK2, RUBCNL, SELENOK, SQLE, STK3, TRIM6, VDAC1, and VPS13D. The risk score of each sample was calculated according to the fitted regression model and expression of each gene. All the samples were divided into high- and low-risk groups according to the above-mentioned median risk value. We then compared overall survival among patients with NSCLC in high- and low-risk groups. Our results showed that patients with a high-risk score had a lower survival rate than those with a low risk-score (Fig. 2C). To verify the accuracy of the model, ROC curve analysis was performed on the internal training set, validation set, and all the 500 NSCLC patients. Based on the ROC curve, the prognostic model constructed in this study can be used to predict the prognosis of patients with NSCLC. Moreover, the risk of dead was high, and the survival time was relatively short in high-risk group (Fig. 2D-F). To explore whether the prognostic model of these 18 genes could independently predict NSCLC prognosis, univariate and multivariate COX regression analyses were performed based on their expression levels of these 18 genes and clinical information, respectively. The results suggested that this model could work well independently in forecasting the prognosis of patients with NSCLC (Fig. 2G-H).

Correlation analysis between prognostic models and clinical features in NSCLC patients

To explore whether the risk scores were statistically significant among patients with different physiological and pathological information, the Wilcoxon test was used to test the difference in risk scores between different groups. The results showed that the risk score significantly correlated with sex, survival status (alive or dead), tumor stage T (T1-T4, T1/2, T3/4), N (N0-N3), M(M0-M1), and clinical stage (I-IV, I/II, III/IV) (Fig. 3A-F). Moreover, an obvious positive correlation between risk score and tumor purity was observed (Fig. 3G). Thes results suggest that the risk value may be used to indicate different stages of progression in patients with NSCLC, and that a higher risk score implies an advanced state of tumor.

Establishment and assessment of the nomogram survival model

To verify the accuracy of the model in external datasets, the stability of the model was verified using the GSE29013, GSE31210, and GSE37745 datasets, according to the expression of the genes used in the LASSO regression model construction (Fig. 4A-C). The results showed that the model still had high accuracy in the external validation set, further proving the accuracy and robustness of the prognostic model. To further improve the prediction accuracy of the prognostic model and enhance the possibility of applying this model to clinical treatment, we combined the above risk scores and pathological stage information to construct a nomogram model (Fig. 4D). Based on the risk values, a prediction model was established using a multivariate regression analysis to evaluate the impact of these influencing factors on the 1-, 3-, and 5-year survival rates of patients. Calibration curves showed the accuracy of the model in predicting survival at different follow-up periods. Based on the nomogram score, there was a significant difference in overall survival between the high- and low-risk groups (Fig. 4E), exhibiting a high accuracy in predicting overall survival in patients with NSCLC.

Genomic variation landscape of mtPCD genes in NSCLC patients

All samples were divided into high- and low-risk groups based on the median risk value of the prognostic model described above. We evaluated variants in mtPCD-related genes in patients with NSCLC from TCGA cohort. Our results showed that 440 (88.89%) of the 495 patients harbored mutations in their genome. The top 10 mutations of mtPCD-related genes were shown in Fig. 5A, among which TP53 had the highest mutation frequency (48%), and the frequencies of the others ranged from 25–45%. The higher frequency of TP53 mutation occurred in higher-risk patients with poor prognosis, and the mutations of TP53 were often associated with a poor prognosis, which is consistent with the clinical result that high-risk patients had a poor prognosis. Considering the high-risk group as an example, we presented the mutation state of mtPCD genes in the following six ways: variant classification, variant type, single nucleotide variants (SNV) class, variants per sample, variant classification summary, and the top 10 mutated genes (Fig. 5B). We summarized the top 10 gene mutation frequencies and the differences in mutation frequency between the high- and low-risk groups, and found that the mutation frequency of the high-risk group was significantly higher than that of the low-risk group (Fig. 5C-D). Simultaneously, we analyzed the correlation among the top 30 mutant genes and found that most of them were interrelated to varying degrees (Fig. 5E). We predicted and classified genes that could be targeted by drugs, and found that mutated p53 could be targeted by multiple drug types (tumor suppressor, transcription factor complex, RNA directed DNA polymerase, histone modification, etc; Fig. 5F), which may provide clues for future drug development.

Transcriptome variation landscape of mtPCD genes in NSCLC patients

In TCGA-NSCLC cohort, a total of 829 differentially expressed genes (DEGs) were obtained by differential enrichment analysis between high- and low- risk groups. Among them, 379 genes were upregulated and 450 genes were downregulated in the NSCLC group. The scaled RNA levels of DEGs were shown as volcano plots (Fig. 6A), and the top 20 differential genes were shown in the heat map (Fig. 6B). These included MUC2, NTSR1, FABP7, SPANXB1, IGFBP1, GCG, DKK4, PSG1, TAC3, MSTN, and others (Fig. 6B). Gene ontology (GO) enrichment analysis showed the top 15 entries in the three categories of biological process (BP), cellular component (CC), and molecular function (MF), and a circle diagram was used to display highly enriched entries and their corresponding genes, revealing that DEGs were involved in a variety of biological pathways (Fig. 6C-D). Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed that these DEGs were involved in neuroactive ligand-receptor interaction, PI3K-AKT, AMPK, and other biological pathways (Fig. 6E). A circular diagram shows the genes corresponding to the top 5 entries enriched, metabolic pathways, neuroactive ligand-receptor interaction, PI3K-Akt signaling pathway, pathways in cancer, and cytokine-cytokine receptor interaction (Fig. 6F).

Tumor microenvironment and mtPCD signature

We used the CIBERSORT algorithm to analyze the differences in the tumor immune microenvironment (TME) between the high- and low-risk groups. First, we analyzed the expression of various immune cells in the two groups, and found that these cell populations were widely present in all the samples (Fig. 7A). We then analyzed the differences in the distribution characteristics of immune cells between the high- and low-risk groups and presented the result in the form of heat map and box plot, respectively (Fig. 7B-C). The results showed that the infiltration of Treg cells in the high-risk group was significantly higher than that in the low-risk group, which might cause immune escape (Fig. 7D). Subsequently, we analyzed the correlation between various types of immune cells and found that there were more or less certain correlations between these immune cells (Fig. 7E). Furthermore, we found a significant difference in TME scores between the high- and low-risk groups (Fig. 7F-I), and a high TME score was associated with a favorable response to immune checkpoint inhibitor treatment; therefore, high-risk patients may be more suitable for immunotherapy. A significant difference in TMB scores between the high- and low-risk groups was also observed (Fig. 7J), with higher TMB scores corresponding to a positive immune checkpoint inhibitor response, further illustrating that patients with high-risk scores were the ones who could truly benefit from immunotherapy.

This study is the first to provide the comprehensive analysis of 12 different mtPCD modes, constructed a cell death signature in TCGA NSCLC cohort, and further validated its performance in three other external cohorts (GSE29013, GSE31210, and GSE37745). A nomogram, combining the clinical characteristics of NSCLC patients and mtPCD-related gene expression pattern, was constructed and shown to perform well in predicting prognosis. Similarly, we determined that mtPCD genes were strongly associated with immune cell infiltration and the response to immunotherapy.

mtPCD is a cell death process mediated by molecular programs with the regulated of specific genes, and plays a key role in the normal development and maintenance of homeostasis in organism (37, 38). A signature with 18 mtPCD-related genes (AP3S1, CCK, EIF2AK3, ERO1A, KRT8, PEBP1, PIK3CD, PPIA, PPP3CC, RAB39B, RIPK2, RUBCNL, SELENOK, SQLE, STK3, TRIM6, VDAC1, and VPS13D) was identified and confirmed to predict the OS of NSCLC patients with extremely high accuracy. Notably, AP3S1 is overexpressed in most tumors and has a significant inverse correlation with survival (39), and we found that AP3S1 was a risk factor for NSCLC survival. CCK is a neuropeptide that regulates digestion, neurotransmitters, and memory (40). We found an inverse correlation between CCK expression and NSCLC survival. ERO1A can promote breast cancer metastasis (41), targeting ERO1A can activate anti-tumor immunity (42), and our study found that high ERO1A was associated with poor prognosis of NSCLCs. KRT8 is abnormally expressed in LUAD and is inversely associated with patient prognosis (43); this was confirmed by our study. VPS13D deletion leads to impaired mitophagy, while VPS13D mutation leads to VPS13D-related dyskinesia (44); VPS13D was a prognostic factor for NSCLC OS in our study. As a key regulator of the cell death pathway, RIPK2 has become a target for cancer therapy (45); the present study also identified RIPK2 as a risk factor for NSCLC. SQLE attenuates ER stress and promotes pancreatic cancer growth (46), which is consistent with our study showing that high SQLE expression level was associated with poor prognosis in NSCLC. STK3 promotes pancreatic cancer cell proliferation through the P13K/AKT pathway (47), and our results indicated that STK3 was negatively correlated with NSCLC OS. Notably, PPP3CC was proved to promote the apoptosis of epithelial cells and inhibit tumor proliferation in ovarian cancer (48); however, our results show that it was a risk factor for NSCLC OS through the prognostic model. VDAC1 is located on the outer mitochondrial membrane and regulates energy production. Studies have shown that VDAC1 is overexpressed in breast cancer and is associated with the suppression of tumor immunity (49); our data show that VDAC1 was a prognostic factor in NSCLC.

Immunotherapy has substantially improved overall survival of patients with unresectable tumors (50). In general, decreased infiltration of antitumor immune cells and increased infiltration of pro-tumor immune cells indicate that the tumor immune microenvironment is compromised (51–53). Tregs have immunosuppressive effects and inhibit immune responses of other immune cells (54). Our results showed that Treg cell infiltration was significantly higher in the high-risk group than in the low-risk group. The high-risk group patients may exhibit immune escape, resulting in poor patient prognosis. In addition, we found a significant difference in the TME scores between the high- and low-risk groups, and the high TME score corresponded to the positive response to immune checkpoint inhibitor treatment (55). Therefore, based on these results, we speculated that the high-risk patients in this study might be more suitable for immunotherapy. At the same time, we found a significant difference in the TMB scores between the high- and low-risk groups, in which higher TMB scores corresponded to a more active response to immune checkpoint inhibitors (56), suggesting that high-risk patients might benefit from immunotherapy.

Although mtPCD may be used as an independent prognostic factor for NSCLC, and our model showed good performance in both training and validation datasets, our study still has some limitations. First, the patients included in the study were recruited retrospectively, which may have inevitably led to bias. Second, the clinical information of our patients was obtained from public databases and lacked validation using our own independent external cohort. Therefore, more in-depth studies, such as prospective clinical studies with high quality, large sample sizes, and sufficient follow-up data for additional validation, are needed to investigate the feasibility of using this model to predict prognosis and guide NSCLC treatment.

A variety of agents targeting PCD are being developed for potential clinical applications. For example, a small compound resibufogenin inhibits colorectal cancer development and metastasis by inducing receptor-interacting protein kinase 3 (RIPK3) -mediated necrosis (57). Simvastatin induces pyroptosis and inhibits NSCLC cell migration by activation of NACHT, LRR, and PYD domains-containing protein 3 (NLRP3) inflammasome and caspase-1 (58). Artesunate, a derivative of artemisinin, inhibits ovarian cancer cell proliferation by increasing reactive oxygen species production and triggering ferroptosis (59). The administration of nanoparticles can reverse cisplatin resistance in cancer cells by inducing cuproptosis (60). Additionally, the induction of ferroptosis can improve the therapeutic efficacy of immune checkpoint inhibitors (61). More studies should be carried out to determine the therapeutic potentials of PCD-targeting agents in NSCLC.

Author Contributions:

Guang-Biao Zhou conceived the project. Xue-Yan Shi designed and performed the study. Si-Chong Han and Gui-Zhen Wang supervised the study. Xue-Yan Shi and Guang-Biao Zhou analyzed the results and wrote the manuscript. All authors discussed the results and reviewed the manuscript.

Competing interests

The authors declare no competing interests.

Ethical Approval

The study was approved by the research ethics committee of the Cancer Hospital, Chinese Academy of Medical Sciences and was conducted in accordance with the tenets of the Declaration of Helsinki. The use of the TCGA genome data was approved by the National Institutes of Health of the United States of America with the approval number of #24437-4.

Funding

This work was supported by the National Key Research and Development Program of China (2022YFA1103900), the CAMS Innovation Fund for Medical Sciences (CIFMS; 2021-1-I2M-014, 2021-1-I2M-012, 2022-I2M-2-001), and the National Natural Science Foundation of China (82372944, 82073092, 82273076).

Availability of data and materials

Public datasets were analyzed in this study. TCGA-NSCLC cohort was downloaded from TCGA database (http://cancergenome.nih.gov/). GSE29013, GSE31210, and GSE37745 were downloaded from the GEO database (http://www.ncbi.nlm.nih.gov/geo/).

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No competing interests reported.

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Mitochondrial associated programmed cell death patterns in predicting the prognosis of non-small cell lung cancer

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Abstract

Figures

Introduction

Materials and Methods

Candidate genes and data collection

Construction of prognostic model and external validation

Clinical characteristics and genomic and transcriptomic variations

Tumor microenvironment and immune cell infiltration

Statistical analysis

Results

mtPCD genes associated with prognosis of NSCLC patients

Construction of a prognostic model for NSCLC patients

Correlation analysis between prognostic models and clinical features in NSCLC patients

Establishment and assessment of the nomogram survival model

Genomic variation landscape of mtPCD genes in NSCLC patients

Transcriptome variation landscape of mtPCD genes in NSCLC patients

Tumor microenvironment and mtPCD signature

Discussion

Declarations

References

Additional Declarations

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