Elucidating the pathway activity and prognostic significance of diverse cell-death patterns in idiopathic pulmonary fibrosis

Elucidating the pathway activity and prognostic significance of diverse cell-death patterns in idiopathic pulmonary fibrosis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Elucidating the pathway activity and prognostic significance of diverse cell-death patterns in idiopathic pulmonary fibrosis Jiazheng Sun, Yalu Sun, Hehua Guo, Yalan Nie, Sirui Zhou, Yulan Zeng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4195254/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Idiopathic pulmonary fibrosis (IPF) is one of the interstitial lung diseases (ILDs) with poor prognosis. Multiple regulated cell death (RCD) pathways are involved in regulating the progression of pulmonary fibrosis at different stages. Methods A total of 20 RCD pathways and crucial regulatory genes were collected from available literature. The study initially elucidated the profiling of 20 kinds of RCD pathways in normal and fibrotic lung tissues based on the scRNAseq dataset and bulk-RNAseq dataset. Targets associated with IPF were identified by Mendelian randomization analysis, and univariate Cox regression was used to further identify RCD-related genes significantly associated with overall survival (OS). A combination of 101 distinct machine-learning algorithms was utilized to develop a prognostic signature. In addition, we investigated the relationship between prognostic signature and clinical characteristics. Results By integrating scRNAseq data and bulk-RNAseq data, the study initially elucidated the pathway activity associated with distinct RCD patterns in IPF patients. In addition, following detailed research of various RCD patterns, the study developed the CDI signature with 13 genes, which combined with multiple machine learning methods to generate CDI signature has a strong predictive influence on the prognosis of IPF patients. As proven by independent datasets, IPF patients with high CDI had a poorer outcome. From the clinical characteristics, IPF patients with high CDI have impaired lung function. Finally, a nomogram with strong predictive ability was generated by integrating CDI with clinical characteristics. Conclusion In summary, we have developed a novel CDI model that effectively forecasts the clinical prognosis of patients with IPF by integrating various cell death patterns. Biological sciences/Biotechnology Health sciences/Medical research Idiopathic pulmonary fibrosis Regulatory cell death Immune microenvironment infiltration Prognostic signature Immunotherapy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Idiopathic pulmonary fibrosis (IPF) is a chronic interstitial lung disease characterized by progressive and irreversible fibrosis of the lung parenchyma. ( 1 ) This fibrosis leads to a gradual decline in lung function. IPF patients have a bleak outlook, with a median survival of approximately 2–3 years from diagnosis ( 2 ) and a 5-year survival rate of less than 40% ( 3 ). Therefore, it is crucial to delineate the heterogeneity of IPF patients and stratify them based on their characteristics to forecast patient outcomes and tailor treatment accordingly. Cell death patterns can be categorized into two groups based on the rate of occurrence and susceptibility to medications or genes: accidental cell death (ACD) and regulatory cell death (RCD). ( 4 ) ACD is an unregulated process when cells die due to excessive harmful stimuli that are beyond the cell's regulatory capacity. RCD is controlled by a set of pathways that have been preserved throughout evolution ( 5 ). These pathways are significant in the processes of development and immune response, as well as in maintaining homeostasis and contributing to the development of diseases. RCD can occur through a variety of mechanisms, including autophagy, alkaliptosis, anoikis, apoptosis, cuproptosis, disulfidptosis, entosis, entotic cell death, ferroptosis, immunogenic cell death, lysosome-dependent cell death, methuosis, mitochondrial permeability transition (MPT) driven necrosis, necroptosis, NETosis, netotic cell death, oxeiptosis, paraptosis, parthanatos, and pyroptosis. ( 6 – 9 ) Alkaliptosis is a specific kind of cell death that is triggered by intracellular alkalinization. ( 10 ) Anoikis is a type of programmed cell death (PCD) that occurs when cells lose or have inappropriate adherence to the extracellular matrix (ECM). PCD is regarded to be the RCD that occurs normally in the body. ( 11 ) Autophagy is a type of programmed cell death in which damaged proteins or organelles are wrapped in autophagic vesicles with a bilayer membrane structure and transported to lysosomes for breakdown and recycling. ( 12 ) Apoptosis is a biologically active "conscious suicide" of cells in an organism that occurs when the cell death pathway is engaged by particular endogenous and external signals and programmed death occurs under the control of the appropriate genes ( 13 ). Cuproptosis is a type of RCD that relies on copper and mitochondrial respiration for its occurrence ( 14 ). Disulfidoptosis is an accelerated form of death resulting from disulfide stress, which occurs due to an excessive buildup of cystine in cells ( 9 ). Entotic cell death activates phagocytosis and lysosomal degradation processes controlled by Cathepsin B(CTSB), engulfing and killing identical cells. Entosis induction is controlled by cell adhesion and cytoskeletal rearrangement pathways ( 15 ). Ferroptosis is an iron-dependent mode of programmed cell death, which is characterized by accumulated iron leading to lipid oxidation producing reactive oxygen species (ROS). ( 16 , 17 ) Immunogenic cell death is a distinct form of RCD that is triggered by stress and can stimulate adaptive immunity against antigens from dead cells. ( 18 ) Lysosome-dependent cell death is a form of planned cell death that is defined by the malfunction or rupture of lysosomes. ( 19 ) Methuosis induces the accumulation and enlargement of cytoplasmic vacuoles, resulting in the deceleration of cellular metabolism, rupture of the cell membrane, and ultimately, cell demise. ( 20 ) MPT-driven necrosis is defined by a notable elevation in the permeability of the mitochondrial membrane due to a rise in calcium concentration. This often results in the uncoupling of oxidative phosphorylation, loss of cellular energy, and ultimately, cell necrosis and death. ( 21 ) As an alternative to apoptosis, necroptosis is not dependent on the cysteine family protease pathway but triggers important physiological functions including inflammatory responses. ( 22 ) Netotic cell death is characteristic of chromatin decondensation and nuclear membrane disruption due to NET. ( 23 ) NETosis is the process by which activated neutrophils release neutrophil extracellular traps (NETs) into the extracellular environment. ( 24 ) Oxeiptosis is characteristic of being induced by reactive oxygen and is not dependent on cystathione. ( 25 ) Pyroptosis is a form of programmed cell death triggered by microbial infection, which is characterized by the release of large amounts of pro-inflammatory factors. ( 26 ) Paraptosis is characteristic of vacuolation of the cytoplasm and swelling of the mitochondria, without nuclear sequestration. ( 27 , 28 ) Parthanatos is a PARP1-dependent form of RCD that is activated by oxidative stress-induced DNA damage. ( 29 ) Recently, there has been a growing recognition among individuals that RCD can effectively control the progression of pulmonary fibrosis at different stages, thereby either facilitating or inhibiting the onset and advancement of the condition. There is scientific research indicating that Spermidine mitigates the development of lung fibrosis caused by bleomycin by promoting autophagy and suppressing cell death generated by endoplasmic reticulum stress (ERS) in mice. ( 30 ) Additionally, the suppression of ferroptosis and iron accumulation alleviated pulmonary fibrosis. ( 31 ) These results highlight the significance of researching RCD to enhance our understanding of IPF and develop novel treatments to tackle the disease. However, the majority of the aforementioned research only examines the effects of a single RCD mode on pulmonary fibrosis. There is still a lack of comprehensive understanding of the heterogeneity of RCD patterns in IPF. The study collected 20 RCD patterns and significant regulatory genes from existing works of literature. Figure 1 depicts the flow chart of the study. The study preliminarily depicted the gene expression landscape of RCD-related genes in several cell types in the scRNAseq dataset. Secondly, we explored the heterogeneity of RCD patterns between the IPF cohort and the control cohort in the bulk RNAseq dataset. The targets associated with IPF were identified by Mendelian randomization analysis, and the genes that showed a significant correlation with overall survival (OS) were further analyzed using univariate Cox regression. 101 distinct machine-learning algorithm combinations were employed to develop a dependable prognostic signature. In addition, we examined the relationship between the prognostic signature and clinical characteristics. 2. Materials and methods 2.1 Acquisition of datasets and RCD-related genes The datasets enrolled in the study were obtained from the Gene Expression Omnibus (GEO) database( http://www.ncbi.nlm.nih.gov/geo/ ) The scRNA-seq GSE128033 dataset ( 32 ) and bulk-RNAseq GSE150910 dataset ( 33 ) were used to elucidate the profiling of 20 kinds of RCD patterns in IPF patients. The study chose lower lobe samples from the GSE128033 dataset, including both normal and fibrotic lung tissue, because pulmonary fibrosis often begins in the lower lobe in clinical settings. The prognosis signature was constructed based on four datasets, including GSE27957 ( 34 ), GSE28042 ( 34 ), GSE70866 ( 35 ), and GSE93606 ( 36 ). GSE70866 was used as the training set, while the other three data sets were used as the validation sets. The screening criteria for samples were as follows: Criteria for screening:1. The sample was diagnosed with IPF. 2. The sample possessed comprehensive survival information. (refer to Supplementary Table 1 for detailed clinical parameters) In this study, we collected a total of 20 PCD patterns and 2013 key regulatory genes (refer to Supplementary Table 2 for details) from the existing published articles ( 7 – 9 ). We removed 449 duplicate gene symbols, resulting in 1564 RCD-related genes for subsequent analysis. 2.2 Single-cell analysis To ensure the utilization of scRNA-seq data of superior quality, the "Seurat" packages were employed for processing and analysis, along with the implementation of particular filtering methods. Cells of poor quality were excluded based on chosen quality markers, such as cell count and gene count. Only genes that are active in a minimum of five individual cells were considered, and cells that have less than 300 active genes were excluded. The scRNA-seq data was normalized by the "NormalizeData" function, which was then converted to Seurat objects and the first 1,500 highly variable genes were identified using the "FindVariableFeatures" function. Afterward, the "RunPCA" function of the "Seurat" R package was applied to perform principal component analysis (PCA) to reduce the dimensionality of the scRNA-seq data based on the top 1,500 genes. The functions "FindNeighbors" and "FindClusters" were used for cell clustering analysis. The "FindMarkers" function was used to identify differentially expressed genes within clusters. Subsequently, the known cell type marker genes were used to annotate each cell cluster. 2.3 Differential expression analysis The R package "DEseq2" was employed to extract differentially expressed genes (DEGs) between the IPF cohort and control cohort in the GSE150910 dataset (Parameter: adjusted. P value 1.5). 2.4 Functional enrichment analysis Further, the study performed the gene ontology (GO), and kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis of all the DEGs using the “ClusterProfiler” R package. 2.5 Mendelian randomization analysis The eQTLs of the RCD genome were selected as the exposure data. To generate IVs, we selected SNPs with FDR of < 0.05 and within ± 1000 kb from each gene’s transcriptional start site (TSS). SNPs in each eQTL were then clumped at r2 < 0.01 using European samples from the 1000 Genomes Project ( 37 , 38 ). For MR analysis, the "TwoSampleMR" R package was utilized. The RCD genome's eQTLs were chosen to serve as the exposure data. IPF GWAS dataset was chosen to serve as the outcome data. In the main analysis, a Wald ratio estimate was calculated for each genetic variant and summarized the estimates using the inverse-variance weighted (IVW) method. The IVW technique with multiplicative random effects offers a succinct calculation and considers the possibility of heterogeneity among the Wald ratio estimations from SNPs. Therefore, when there is heterogeneity, the random-effects inverse-variance weighted (IVW) model was applied. Otherwise, the fixed-effect IVW model was applied. In the subsequent stage of the MR analysis, two supplementary MR methods, namely MR-Egger ( 39 ) and weighted median ( 40 ), were employed to conduct sensitivity analysis. The IVW technique with multiplicative random effects offers a succinct calculation and considers the possibility of heterogeneity among the Wald ratio estimations from SNPs. 2.6 Integration of machine learning algorithms To enhance the precision and consistency of the CDI signature, the study incorporated ten machine-learning algorithms into our analysis. These algorithms encompass random survival forest (RSF) ( 41 ), elastic network (Enet), Lasso, Ridge, Stepwise Cox ( 45 ), CoxBoost ( 42 ), partial least squares regression for Cox ( 43 ), supervised principal components (SuperPC) ( 44 ), generalized boosted regression modeling (GBM) ( 45 ), and survival support vector machine (survival-SVM) ( 46 ). Several algorithms have demonstrated the capability of doing feature selection, including Lasso, stepwise Cox, CoxBoost, and RSF. Therefore, we integrated these algorithms to produce a consensus model. A total of 101 algorithm combinations were performed to construct prediction models using the 10-fold cross-validation technique. 2.7 Statistical analysis Statistical differences between groups were determined by Student's t-test for normally distributed variables, and for non-normally distributed variables, statistical differences between groups were determined by the Wilcoxon test. The statistical studies were conducted using the R project (version 4.3.3). 3. Results 3.1 The pathway activity profiling of 20 kinds of RCD patterns in normal and fibrotic lung tissues A total of 30,533 cells were screened and retrieved from five IPF samples and five normal samples from the GSE128033 dataset. The GSE128033 dataset was analyzed using the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm, which revealed the presence of thirteen distinct cell populations (Fig. 2 A). The "FindMarkers" function was employed to discover DEGs that characterize each cluster. (Fig. 2 B) Furthermore, 9 cell populations were identified using the "ScType" R package and cell type marker genes. (Fig. 2 C) Fig. 2 D displayed the cellular distribution in both the control cohort and the IPF cohort. Subsequently, the study analyzed the distribution of several cell types in distinct groups (Fig. 2 E) and recognized a significant diversity in the cell composition of both the control and IPF cohorts. These cells were subjected to gene set variation analysis (GSVA) based on the 20-RCD genesets. Analysis of pathway activity displayed heterogeneity in 19 RCD signaling pathways except the methuosis pathway between the control and IPF cohorts ( Supplementary Figure ). In the IPF cohort, the activity of netotic cell death and oxeiptosis signaling pathways was the lowest, which was significantly decreased compared with the control cohort, moreover. While parthanatos signal pathway had the highest activity, followed by entotic cell death signal pathway. Comparing multiple cell types longitudinally, RCD signaling pathway activity is mainly concentrated in macrophages and fibroblasts. (Fig. 2 F) In addition, the study explored the heterogeneity of RCD-related pathways between the IPF cohort and the control cohort in the bulk RNAseq dataset. According to the GSE150910 dataset, 890 DEGs were identified between the IPF cohort and the control cohort (Fig. 3 A). Moreover, these genes were mapped to specific locations in the human genome (Fig. 3 B). The KEGG enrichment analysis results (Fig. 3 C) indicated that the RCD-related DEGs were primarily involved in the neuroactive ligand − receptor interaction pathway, cyclic adenosine monophosphate(cAMP) signaling pathway, and calcium signaling pathway The GO enrichment analysis results (Fig. 3 D) indicated that the enrichment was primarily observed in extracellular structural organizations, specifically in terms of biological processes. The cilium was predominantly enriched in terms of cellular components. Regarding molecular function, it is mostly endowed with numerous peptidase regulator activities. ssGSEA algorithm was further used to compare immune infiltration between the control cohort and the IPF cohort. Compared to the control cohort, B cells memory, macrophages M0, mast cells resting, plasma cells, T cells follicular helper, T cells gamma delta, and T cells regulatory (Tregs) showed higher levels of infiltration in the IPF cohort. (Fig. 3 E) Dendritic cells activated, eosinophils, monocytes, neutrophils, NK cells resting, T cells CD4 memory resting is the opposite. The infiltration of T_cells_CD4_memory_activated and Neutrophils was significantly positively correlated with the activity of multiple RCD pathways, while plasma_cells was significantly negatively correlated with the activity of multiple RCD pathways. NK cells and monocytes are not associated with the activity of most RCD pathways. (Fig. 3 F) Additionally, there is heterogeneity of multiple immune functions in the control cohort and IPF cohort. (Fig. 3 G) 3.2 Identification of RCD-related genes associated with IPF In this study, we employed eQTLs from blood that exhibited overlap with RCD-related genes. These eQTLs were utilized as exposures, encompassing a total of 1186 gene symbols. SNPs with a transcription start site (TSS) range of ± 1000 kb were clumped. Wald Ratio or Inverse-variance Weighted method was used for MR Analysis. Ultimately, a total of 79 possible targets related to IPF were discovered. (Refer to Supplementary Tables 3 and 4 for the full results of the significantly expressed IV and MR) RCD-related eQTLs from lung tissue were acquired as exposures by employing criteria akin to blood eQTLs, encompassing 633 gene symbols, in order to conduct MR. Consequently, a total of 37 possible targets related to IPF were discovered. (Refer to Supplementary Tables 5 and 6 for the full results of IV and MR Significantly expressed) After eliminating duplicate target symbols in both results, a total of 108 unique IPF-related RCD targets were identified for follow-up studies. 3.3 Construction and validation of the CDI signature Following univariate Cox regression analysis of IPF-related RCD targets obtained by MR Analysis, 31 RCD-related genes associated with IPF prognosis were identified. ( Supplementary Table 7 ) Subsequently, we employed a total of 10 distinct machine-learning algorithms to develop and construct 101 prognostic signatures. The robustness of these signatures was evaluated using a ten-fold cross-validation approach in four distinct cohorts (GSE70866 as the training cohort, and three external validation cohorts including GSE27957, GSE28042, and GSE93606 cohort). The best-performing predictive signature was determined as the signature with the greatest mean C-index in three external validation cohorts, due to overfitting in the training cohort (Fig. 4 A). The findings indicated that the StepCox[backward] + SuperPC algorithm combination demonstrated the highest average C-index (0.663), making it the optimal algorithm combination for developing the CDI signature, which consisted of 14 genes (Fig. 4 B). The obtained equation is as follows: CDI score = CD15× (-1.094082) + GCC2× (-1.538944) + GCLC×1.114980 + MAP3K5× (1.016809) + NPC1×1.642120 + PPID×1.074283 + CLTC×1.046599 + BCL2L11×1.188298 + BMP6×3.057019 + DAPK2×1.988332 + FANCD2× (-1.522632) + SRC× (-1.558877) + STAT5A× ( -1.443486) + TMEM106B× (-1.196694) Afterward, the CDI score for each patient was calculated. The samples were then categorized into the low CDI score group and the high CDI score group based on the optimal threshold value of the CDI score determined using the R package "survminer". Subsequently, KM survival analysis and assessment of prognostic performance were conducted. Demonstrating a substantial difference in OS between the low CDI score group and the high CDI score group in all four cohorts (Fig. 4 C), the ROC analysis indicated that the CDI signature effectively predicted the prognosis of IPF patients (Fig. 4 D). Furthermore, due to the scarcity of prognostic models for non-tumor diseases compared to tumors, and the limited availability of datasets including comprehensive gene expression data for genes associated with signatures, a total of 10 prognostic models published in IPF were ultimately gathered from the existing literature. The features encompass a range of biological processes observed in the IPF cohort, such as hypoxia, autophagy, pyroptosis, epithelial-mesenchymal transition, 5-methylcytosine, and inflammation. ( Supplementary Table 8 ) These features were then compared to the C-index of the CDI signature. The findings indicated that the CDI signatures exhibited superior performance compared to the majority of the signatures within their respective categories (Fig. 4 E). 3.4 Association of CDI signature with pulmonary function In the GSE38958 and GSE93606 cohorts, the CDI score was significantly negatively correlated with FVC% prediction and Dlco% prediction, suggesting that the CDI signature could be used as a potential biomarker to assess IPF severity. ( Fig. 5 A ) To facilitate computation, a nomogram was created, integrating the factors of age, gender, and CDI score. (Fig. 5 B) 4. Discussion It is challenging to make an accurate prediction regarding the prognosis of individuals who have IPF since the disease is both complicated and variable. A proper prognosis of the progression of the disease, the selection of the treatment techniques that are the most suitable, and the assessment of the overall survival rate are all made possible by the utilization of prognostic signatures, which are of the utmost significance. The study employed scRNAseq and bulk-RNAseq to characterize the pathway activity profiling of RCD patterns in IPF patients. In addition, 101 distinct machine-learning algorithm combinations were utilized to develop a stable CDI signature, which was derived by analyzing comprehensive bulk-RNAseq datasets. The stability and reliability of the CDI signature are ensured by utilizing the advantages of each algorithm and adopting the set learning technique. By conducting validation on several datasets, the signature demonstrated exceptional performance in forecasting the prognosis of IPF patients. The RCD-related genes enrolled in the CDI signature are involved in regulating fibrotic processes in a variety of tissues. It is through the maintenance of epithelial integrity that tetraspanin CD151 can prevent pulmonary fibrosis ( 47 ). GCLC is involved in the synthesis of glutathione, which is an important pathological mechanism of pulmonary fibrosis by regulating the oxidation-antioxidant balance ( 48 , 49 ) The mitogen-activated protein kinase family, in which MAP3K5 is located, plays a key role in cytokines and stress-induced apoptosis and is closely associated with liver or kidney fibrosis ( 50 ). The mutation of the NPC1 gene activates the classical Wnt signaling pathway in mouse kidneys and promotes renal fibrosis. ( 50 ) BCL2L11 is involved in regulating myocardial fibrosis. ( 51 ) BMP6 knockdown enhances cardiac fibrosis in a mouse myocardial infarction model by upregulating AP-1/CEMIP expression. ( 52 ) BMP6 inhibits hepatic fibrosis in non-alcoholic fatty liver disease. Increased expression of BMP6 can inhibit nonalcoholic fatty liver fibrosis. ( 53 ) Mutations in the FANCD2 gene can lead to renal nuclear hypertrophy and fibrosis through incomplete repair of DNA damage. ( 54 , 55 ) Src family kinases are involved in regulating the process of pulmonary fibrosis through various pathological mechanisms such as myofibroblast activation and epithelial-mesenchymal transformation STAT5A: MEHP promotes liver fibrosis by down-regulating STAT5A in BRL-3A hepatocytes. ( 56 ) The study undoubtedly has numerous limitations. Initially, validation of the CDI signature requires an independent clinical cohort, and additional data gathering is necessary to verify its predictive capacity. In addition, the RCD-related genes enrolled in the CDI signature require further studies to determine their role in the progression of pulmonary fibrosis. 5. Conclusion In conclusion, the CDI signature proposed in this study is a potential prognostic predictor for IPF patients which can make a notable difference in the assessment of clinical outcomes. Declarations Acknowledgments All authors would like to express our sincere thanks for sharing the online databases. Data availability The blood eQTL data was acquired from the eQTLGen Consortium (https://eqtlgen.org/). The cis-eQTL data for lung tissue was acquired via the GTEx V.8 (https://gtexportal.org). The IPF GWAS dataset was obtained via the GWAS Catalog (https://www.ebi.ac.uk/gwas/). The GEO datasets were obtained from GEO database (http://www.ncbi.nlm.nih.gov/geo/). The code applied in the study is available from the corresponding author upon reasonable request. Conflict of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Author Contributions JS and YS designed and conducted the entire study. JS and HG performed the data collection, bioinformatics, and statistical data analysis. YN and SZ investigated the literature. YZ was responsible for the integrity of the entire study and manuscript review. All authors contributed to the article and approved the submitted version. Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. References Richeldi L, Collard HR, Jones MG. Idiopathic pulmonary fibrosis. 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Interpreting findings from Mendelian randomization using the MR-Egger method. Eur J Epidemiol. 2017;32(5):377–89. Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genet Epidemiol. 2016;40(4):304–14. Rigatti SJ. Random Forest. Journal of Insurance Medicine. 2017;47(1):31–9. Binder H, Allignol A, Schumacher M, Beyersmann J. Boosting for high-dimensional time-to-event data with competing risks. Bioinformatics. 2009;25(7):890–6. Núñez E, Steyerberg EW, Núñez J. Regression Modeling Strategies. Revista Española de Cardiología (English Edition). 2011;64(6):501–7. Bair E, Tibshirani R. Semi-Supervised Methods to Predict Patient Survival from Gene Expression Data. PLoS Biol. 2004;2(4):e108. Guo CY, Chang KH. A Novel Algorithm to Estimate the Significance Level of a Feature Interaction Using the Extreme Gradient Boosting Machine. Int J Environ Res Public Health. 2022;19(4):2338. Van Belle V, Pelckmans K, Van Huffel S, Suykens JAK. Improved performance on high-dimensional survival data by application of Survival-SVM. Bioinformatics. 2011;27(1):87–94. Tsujino K, Takeda Y, Arai T, Shintani Y, Inagaki R, Saiga H, et al. Tetraspanin CD151 protects against pulmonary fibrosis by maintaining epithelial integrity. Am J Respir Crit Care Med. 2012;186(2):170–80. Kinnula VL, Myllärniemi M. Oxidant-antioxidant imbalance as a potential contributor to the progression of human pulmonary fibrosis. Antioxid Redox Signal. 2008;10(4):727–38. Wang C, Hua S, Song L. Ferroptosis in pulmonary fibrosis: an emerging therapeutic target. Front Physiol. 2023;14:1205771. Guan L, Jia Z, Xu K, Yang M, Li X, Qiao L, et al. Npc1 gene mutation abnormally activates the classical Wnt signalling pathway in mouse kidneys and promotes renal fibrosis. Anim Genet. 2024;55(1):99–109. Wan J, Lin S, Yu Z, Song Z, Lin X, Xu R, et al. Protective Effects of MicroRNA-200b-3p Encapsulated by Mesenchymal Stem Cells-Secreted Extracellular Vesicles in Myocardial Infarction Via Regulating BCL2L11. J Am Heart Assoc. 2022;11(12):e024330. Lu G, Ge Z, Chen X, Ma Y, Yuan A, Xie Y, et al. BMP6 knockdown enhances cardiac fibrosis in a mouse myocardial infarction model by upregulating AP-1/CEMIP expression. Clin Transl Med. 2023;13(6):e1296. Arndt S, Wacker E, Dorn C, Koch A, Saugspier M, Thasler WE, et al. Enhanced expression of BMP6 inhibits hepatic fibrosis in non-alcoholic fatty liver disease. Gut. 2015;64(6):973–81. Shinzeki M, Takeyama Y, Ueda T, Yasuda T, Kishi S, Kuroda Y. Intraperitoneal administration of oxygenated perfluorochemical inhibits bacterial translocation associated with severe acute pancreatitis. Kobe J Med Sci. 2003;49(1–2):17–24. Kanj SS, Tapson V, Davis RD, Madden J, Browning I. Infections in patients with cystic fibrosis following lung transplantation. Chest. 1997;112(4):924–30. Zhang Y, Hui J, Xu Y, Ma Y, Sun Z, Zhang M, et al. MEHP promotes liver fibrosis by down-regulating STAT5A in BRL-3A hepatocytes. Chemosphere. 2022;295:133925. Additional Declarations No competing interests reported. Supplementary Files STable.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4195254","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":290325966,"identity":"9fb3fc81-50bf-42d3-9596-1cc8669c2f4d","order_by":0,"name":"Jiazheng Sun","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Jiazheng","middleName":"","lastName":"Sun","suffix":""},{"id":290325967,"identity":"07b762ca-9c0c-4373-b0fe-ffdd11b9087c","order_by":1,"name":"Yalu Sun","email":"","orcid":"","institution":"Affiliated Hospital of Jining Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yalu","middleName":"","lastName":"Sun","suffix":""},{"id":290325968,"identity":"af0be5f6-ca03-4695-9a5e-dc1efec761ea","order_by":2,"name":"Hehua Guo","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Hehua","middleName":"","lastName":"Guo","suffix":""},{"id":290325969,"identity":"d1e2f685-7acc-47b9-9bc0-2a15b3a69ef5","order_by":3,"name":"Yalan Nie","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Yalan","middleName":"","lastName":"Nie","suffix":""},{"id":290325971,"identity":"2a06ba91-dd5f-4ff4-b1f4-835bc682c104","order_by":4,"name":"Sirui Zhou","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Sirui","middleName":"","lastName":"Zhou","suffix":""},{"id":290325973,"identity":"4e91dba6-7e7a-44e0-93b2-56838e1d1b76","order_by":5,"name":"Yulan Zeng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIie3RMUvEMBTA8RcKqcOrWVMC9Ss8rlARD+6r9DjIFES4xc2DQm7xPoLfQRd19Ajc1A8guAiFTjeLBx3soZuSeptD/kMggR8PXgBCoX8Yj6t1w6y8nlTR5vut9JNj3MxyZsclxVz3dxommTSFglqXJLD4G+FSawVX7uI0wvcGuw5EbAh2Tx6CjTsHcvOzKnnME0uQ3myJrWoPifX0tSds4ZIHlSwI6MVQxKyHgCH1RbBV2BFMBsmRGfVET+8ccoW8nyKHyH7JQOM8rXiR3tocZd1erlcecrLsv/KDZCaEa+W2yzKxnN2/7TzkR7g/ng8AoVAoFPqlT1VXSGJkzq9BAAAAAElFTkSuQmCC","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Yulan","middleName":"","lastName":"Zeng","suffix":""}],"badges":[],"createdAt":"2024-03-31 10:44:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4195254/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4195254/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54999258,"identity":"5a33d89a-e6d7-4771-b993-eb75ebc7991e","added_by":"auto","created_at":"2024-04-19 18:28:41","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":284126,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe study's flowchart diagram\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4195254/v1/03ecfbff57cedcaf1c04f9c7.jpg"},{"id":54999259,"identity":"9370fdf4-d2b9-49a5-8b54-0dca19d498fa","added_by":"auto","created_at":"2024-04-19 18:28:41","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2976776,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe pathway activity profiling of 20 kinds of RCD patterns in normal and fibrotic lung tissues according to the scRNAseq dataset\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eUnbiased clustering clustering of 30,335 cells reveals 13 cellular clusters. Clusters are distinguished by different colors. \u003cstrong\u003e(B)\u003c/strong\u003e Volcano plot displaying representative differentially expressed genes between each cluster. \u003cstrong\u003e(C)\u003c/strong\u003e t-SNE plot displaying the cell composition in the microenvironment of IPF, colored according to cell types. \u003cstrong\u003e(D)\u003c/strong\u003e t-SNE plot displaying the cell composition colored as originating from control and IPF patients. \u003cstrong\u003e(E)\u003c/strong\u003e Barplot displaying the overall cell composition of control and IPF samples, colored by cell types. \u003cstrong\u003e(F)\u003c/strong\u003e t-SNE plot displaying the activity of RCD-related pathways.\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4195254/v1/6aaf1db08ae855fa81876828.jpg"},{"id":55003994,"identity":"0cfcbb3c-cc57-48f1-915f-9443a93ed9aa","added_by":"auto","created_at":"2024-04-19 18:44:41","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2274605,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe pathway activity profiling of 20 kinds of RCD patterns in normal and fibrotic lung tissues according to the bulk-RNAseq dataset.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Volcano plot of the RCD-related DEGs. Points with labels are obvious DEGs with adjusted. P value \u0026lt; 0.05 and |log2FC| \u0026gt; 1.5. \u003cstrong\u003e(B)\u003c/strong\u003e The location, and expression of RCD-related DEGs in the GSE150910 cohort. \u003cstrong\u003e(C)\u003c/strong\u003e KEGG enrichment analyses based on the DEGs. \u003cstrong\u003e(D)\u003c/strong\u003e GO enrichment analyses based on the DEGs. \u003cstrong\u003e(E)\u003c/strong\u003e The box plot of displaying a comparison of the infiltration of 22 immune cells between the IPF cohort and the control cohort.\u003cstrong\u003e (F)\u003c/strong\u003e The heatmap displaying the relationship between RCD signaling pathway activity and immune cell infiltration. \u003cstrong\u003e(G)\u003c/strong\u003e The heatmap displaying the comparison of the infiltration of 13 immune functions between the IPF cohort and the control cohort.\u003c/p\u003e","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4195254/v1/a4497a695de457ef9fa4fd0e.jpg"},{"id":55001003,"identity":"4e252437-9a80-4c14-8199-7896cd0af6a3","added_by":"auto","created_at":"2024-04-19 18:36:41","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3096552,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction and validation of the CDI signature\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) A total of 101 combinations of machine learning algorithms for the CDI signatures via a 10-fold cross-validation framework based on the TCGA-LUAD cohort. The C-index of each model was calculated across validation datasets, including the GSE29013 cohort, GSE30219 cohort, GSE3141 cohort, and GSE50081 cohort. \u003cstrong\u003e(B)\u003c/strong\u003e 14 model genes enrolled in CDI signature. \u003cstrong\u003e(C)\u003c/strong\u003e Kaplan-Meier survival curve of OS between patients with high CDI signature scores and with CDI signature scores in GSE27957, GSE28042, GSE70866, and GSE93606. \u003cstrong\u003e(D)\u003c/strong\u003e ROC analysis of CDI signature in GSE27957, GSE28042, GSE70866, and GSE93606. \u003cstrong\u003e(E)\u003c/strong\u003eC-index comparison of CDI signature and 10 previously published signatures in the validation cohort (GSE27957, GSE28042, and GSE93606).\u003c/p\u003e","description":"","filename":"Fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4195254/v1/e60c1f5c85fb0da8f279d90a.jpg"},{"id":54999261,"identity":"aa994598-4c6b-4c0f-b99a-1741c53f29c4","added_by":"auto","created_at":"2024-04-19 18:28:41","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":167006,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation of CDI signature with pulmonary function\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eScatter plot displaying the correlation between CDI signature and pulmonary function.\u003cstrong\u003e (B) \u003c/strong\u003eThe nomogram for OS is based on age, gender, and CDI score.\u003c/p\u003e","description":"","filename":"Fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4195254/v1/faeec5d6b19fb6723842a0ae.jpg"},{"id":86401428,"identity":"33a70a5b-cda9-46b2-a2c1-5551f59b01ed","added_by":"auto","created_at":"2025-07-10 08:54:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9733363,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4195254/v1/96c03b01-ff98-419a-9373-484357ab5f2f.pdf"},{"id":54999263,"identity":"fa993928-2e99-40ec-b243-3fb0d0429818","added_by":"auto","created_at":"2024-04-19 18:28:41","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2221162,"visible":true,"origin":"","legend":"","description":"","filename":"STable.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4195254/v1/b0cac3a3a12c192b9fa2a387.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Elucidating the pathway activity and prognostic significance of diverse cell-death patterns in idiopathic pulmonary fibrosis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIdiopathic pulmonary fibrosis (IPF) is a chronic interstitial lung disease characterized by progressive and irreversible fibrosis of the lung parenchyma. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) This fibrosis leads to a gradual decline in lung function. IPF patients have a bleak outlook, with a median survival of approximately 2\u0026ndash;3 years from diagnosis (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) and a 5-year survival rate of less than 40% (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Therefore, it is crucial to delineate the heterogeneity of IPF patients and stratify them based on their characteristics to forecast patient outcomes and tailor treatment accordingly.\u003c/p\u003e \u003cp\u003eCell death patterns can be categorized into two groups based on the rate of occurrence and susceptibility to medications or genes: accidental cell death (ACD) and regulatory cell death (RCD). (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) ACD is an unregulated process when cells die due to excessive harmful stimuli that are beyond the cell's regulatory capacity. RCD is controlled by a set of pathways that have been preserved throughout evolution (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). These pathways are significant in the processes of development and immune response, as well as in maintaining homeostasis and contributing to the development of diseases.\u003c/p\u003e \u003cp\u003eRCD can occur through a variety of mechanisms, including autophagy, alkaliptosis, anoikis, apoptosis, cuproptosis, disulfidptosis, entosis, entotic cell death, ferroptosis, immunogenic cell death, lysosome-dependent cell death, methuosis, mitochondrial permeability transition (MPT) driven necrosis, necroptosis, NETosis, netotic cell death, oxeiptosis, paraptosis, parthanatos, and pyroptosis. (\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) Alkaliptosis is a specific kind of cell death that is triggered by intracellular alkalinization. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) Anoikis is a type of programmed cell death (PCD) that occurs when cells lose or have inappropriate adherence to the extracellular matrix (ECM). PCD is regarded to be the RCD that occurs normally in the body. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) Autophagy is a type of programmed cell death in which damaged proteins or organelles are wrapped in autophagic vesicles with a bilayer membrane structure and transported to lysosomes for breakdown and recycling. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) Apoptosis is a biologically active \"conscious suicide\" of cells in an organism that occurs when the cell death pathway is engaged by particular endogenous and external signals and programmed death occurs under the control of the appropriate genes (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Cuproptosis is a type of RCD that relies on copper and mitochondrial respiration for its occurrence (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Disulfidoptosis is an accelerated form of death resulting from disulfide stress, which occurs due to an excessive buildup of cystine in cells (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Entotic cell death activates phagocytosis and lysosomal degradation processes controlled by Cathepsin B(CTSB), engulfing and killing identical cells. Entosis induction is controlled by cell adhesion and cytoskeletal rearrangement pathways (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Ferroptosis is an iron-dependent mode of programmed cell death, which is characterized by accumulated iron leading to lipid oxidation producing reactive oxygen species (ROS). (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) Immunogenic cell death is a distinct form of RCD that is triggered by stress and can stimulate adaptive immunity against antigens from dead cells. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) Lysosome-dependent cell death is a form of planned cell death that is defined by the malfunction or rupture of lysosomes. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) Methuosis induces the accumulation and enlargement of cytoplasmic vacuoles, resulting in the deceleration of cellular metabolism, rupture of the cell membrane, and ultimately, cell demise. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) MPT-driven necrosis is defined by a notable elevation in the permeability of the mitochondrial membrane due to a rise in calcium concentration. This often results in the uncoupling of oxidative phosphorylation, loss of cellular energy, and ultimately, cell necrosis and death. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) As an alternative to apoptosis, necroptosis is not dependent on the cysteine family protease pathway but triggers important physiological functions including inflammatory responses. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) Netotic cell death is characteristic of chromatin decondensation and nuclear membrane disruption due to NET. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) NETosis is the process by which activated neutrophils release neutrophil extracellular traps (NETs) into the extracellular environment. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e) Oxeiptosis is characteristic of being induced by reactive oxygen and is not dependent on cystathione. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) Pyroptosis is a form of programmed cell death triggered by microbial infection, which is characterized by the release of large amounts of pro-inflammatory factors. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) Paraptosis is characteristic of vacuolation of the cytoplasm and swelling of the mitochondria, without nuclear sequestration. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e) Parthanatos is a PARP1-dependent form of RCD that is activated by oxidative stress-induced DNA damage. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eRecently, there has been a growing recognition among individuals that RCD can effectively control the progression of pulmonary fibrosis at different stages, thereby either facilitating or inhibiting the onset and advancement of the condition. There is scientific research indicating that Spermidine mitigates the development of lung fibrosis caused by bleomycin by promoting autophagy and suppressing cell death generated by endoplasmic reticulum stress (ERS) in mice. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) Additionally, the suppression of ferroptosis and iron accumulation alleviated pulmonary fibrosis. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e) These results highlight the significance of researching RCD to enhance our understanding of IPF and develop novel treatments to tackle the disease.\u003c/p\u003e \u003cp\u003eHowever, the majority of the aforementioned research only examines the effects of a single RCD mode on pulmonary fibrosis. There is still a lack of comprehensive understanding of the heterogeneity of RCD patterns in IPF. The study collected 20 RCD patterns and significant regulatory genes from existing works of literature. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e depicts the flow chart of the study. The study preliminarily depicted the gene expression landscape of RCD-related genes in several cell types in the scRNAseq dataset. Secondly, we explored the heterogeneity of RCD patterns between the IPF cohort and the control cohort in the bulk RNAseq dataset. The targets associated with IPF were identified by Mendelian randomization analysis, and the genes that showed a significant correlation with overall survival (OS) were further analyzed using univariate Cox regression. 101 distinct machine-learning algorithm combinations were employed to develop a dependable prognostic signature. In addition, we examined the relationship between the prognostic signature and clinical characteristics.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Acquisition of datasets and RCD-related genes\u003c/h2\u003e \u003cp\u003eThe datasets enrolled in the study were obtained from the Gene Expression Omnibus (GEO) database(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) The scRNA-seq GSE128033 dataset (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e) and bulk-RNAseq GSE150910 dataset (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e) were used to elucidate the profiling of 20 kinds of RCD patterns in IPF patients. The study chose lower lobe samples from the GSE128033 dataset, including both normal and fibrotic lung tissue, because pulmonary fibrosis often begins in the lower lobe in clinical settings. The prognosis signature was constructed based on four datasets, including GSE27957 (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e), GSE28042 (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e), GSE70866 (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e), and GSE93606 (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). GSE70866 was used as the training set, while the other three data sets were used as the validation sets. The screening criteria for samples were as follows: Criteria for screening:1. The sample was diagnosed with IPF. 2. The sample possessed comprehensive survival information. (refer to \u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e for detailed clinical parameters)\u003c/p\u003e \u003cp\u003eIn this study, we collected a total of 20 PCD patterns and 2013 key regulatory genes (refer to \u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\u003e for details) from the existing published articles (\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). We removed 449 duplicate gene symbols, resulting in 1564 RCD-related genes for subsequent analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Single-cell analysis\u003c/h2\u003e \u003cp\u003eTo ensure the utilization of scRNA-seq data of superior quality, the \"Seurat\" packages were employed for processing and analysis, along with the implementation of particular filtering methods. Cells of poor quality were excluded based on chosen quality markers, such as cell count and gene count. Only genes that are active in a minimum of five individual cells were considered, and cells that have less than 300 active genes were excluded. The scRNA-seq data was normalized by the \"NormalizeData\" function, which was then converted to Seurat objects and the first 1,500 highly variable genes were identified using the \"FindVariableFeatures\" function. Afterward, the \"RunPCA\" function of the \"Seurat\" R package was applied to perform principal component analysis (PCA) to reduce the dimensionality of the scRNA-seq data based on the top 1,500 genes.\u003c/p\u003e \u003cp\u003eThe functions \"FindNeighbors\" and \"FindClusters\" were used for cell clustering analysis. The \"FindMarkers\" function was used to identify differentially expressed genes within clusters. Subsequently, the known cell type marker genes were used to annotate each cell cluster.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Differential expression analysis\u003c/h2\u003e \u003cp\u003eThe R package \"DEseq2\" was employed to extract differentially expressed genes (DEGs) between the IPF cohort and control cohort in the GSE150910 dataset (Parameter: adjusted. P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log2FC| \u0026gt; 1.5).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Functional enrichment analysis\u003c/h2\u003e \u003cp\u003eFurther, the study performed the gene ontology (GO), and kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis of all the DEGs using the \u0026ldquo;ClusterProfiler\u0026rdquo; R package.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Mendelian randomization analysis\u003c/h2\u003e \u003cp\u003eThe eQTLs of the RCD genome were selected as the exposure data. To generate IVs, we selected SNPs with FDR of \u0026lt;\u0026thinsp;0.05 and within \u0026plusmn;\u0026thinsp;1000 kb from each gene\u0026rsquo;s transcriptional start site (TSS). SNPs in each eQTL were then clumped at r2\u0026thinsp;\u0026lt;\u0026thinsp;0.01 using European samples from the 1000 Genomes Project (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor MR analysis, the \"TwoSampleMR\" R package was utilized. The RCD genome's eQTLs were chosen to serve as the exposure data. IPF GWAS dataset was chosen to serve as the outcome data. In the main analysis, a Wald ratio estimate was calculated for each genetic variant and summarized the estimates using the inverse-variance weighted (IVW) method. The IVW technique with multiplicative random effects offers a succinct calculation and considers the possibility of heterogeneity among the Wald ratio estimations from SNPs. Therefore, when there is heterogeneity, the random-effects inverse-variance weighted (IVW) model was applied. Otherwise, the fixed-effect IVW model was applied. In the subsequent stage of the MR analysis, two supplementary MR methods, namely MR-Egger (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e) and weighted median (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e), were employed to conduct sensitivity analysis. The IVW technique with multiplicative random effects offers a succinct calculation and considers the possibility of heterogeneity among the Wald ratio estimations from SNPs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Integration of machine learning algorithms\u003c/h2\u003e \u003cp\u003eTo enhance the precision and consistency of the CDI signature, the study incorporated ten machine-learning algorithms into our analysis. These algorithms encompass random survival forest (RSF) (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e), elastic network (Enet), Lasso, Ridge, Stepwise Cox (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e), CoxBoost (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e), partial least squares regression for Cox (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e), supervised principal components (SuperPC) (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e), generalized boosted regression modeling (GBM) (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e), and survival support vector machine (survival-SVM) (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). Several algorithms have demonstrated the capability of doing feature selection, including Lasso, stepwise Cox, CoxBoost, and RSF. Therefore, we integrated these algorithms to produce a consensus model. A total of 101 algorithm combinations were performed to construct prediction models using the 10-fold cross-validation technique.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Statistical analysis\u003c/h2\u003e \u003cp\u003eStatistical differences between groups were determined by Student's t-test for normally distributed variables, and for non-normally distributed variables, statistical differences between groups were determined by the Wilcoxon test. The statistical studies were conducted using the R project (version 4.3.3).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e \u003cb\u003e3.1 The pathway activity profiling of 20 kinds of RCD patterns in normal and fibrotic lung tissues\u003c/b\u003e \u003c/p\u003e \u003cp\u003eA total of 30,533 cells were screened and retrieved from five IPF samples and five normal samples from the GSE128033 dataset. The GSE128033 dataset was analyzed using the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm, which revealed the presence of thirteen distinct cell populations (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The \"FindMarkers\" function was employed to discover DEGs that characterize each cluster. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB) Furthermore, 9 cell populations were identified using the \"ScType\" R package and cell type marker genes. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC) Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD displayed the cellular distribution in both the control cohort and the IPF cohort. Subsequently, the study analyzed the distribution of several cell types in distinct groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE) and recognized a significant diversity in the cell composition of both the control and IPF cohorts.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThese cells were subjected to gene set variation analysis (GSVA) based on the 20-RCD genesets. Analysis of pathway activity displayed heterogeneity in 19 RCD signaling pathways except the methuosis pathway between the control and IPF cohorts (\u003cb\u003eSupplementary Figure\u003c/b\u003e). In the IPF cohort, the activity of netotic cell death and oxeiptosis signaling pathways was the lowest, which was significantly decreased compared with the control cohort, moreover. While parthanatos signal pathway had the highest activity, followed by entotic cell death signal pathway. Comparing multiple cell types longitudinally, RCD signaling pathway activity is mainly concentrated in macrophages and fibroblasts. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF)\u003c/p\u003e \u003cp\u003eIn addition, the study explored the heterogeneity of RCD-related pathways between the IPF cohort and the control cohort in the bulk RNAseq dataset. According to the GSE150910 dataset, 890 DEGs were identified between the IPF cohort and the control cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Moreover, these genes were mapped to specific locations in the human genome (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The KEGG enrichment analysis results (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC) indicated that the RCD-related DEGs were primarily involved in the neuroactive ligand\u0026thinsp;\u0026minus;\u0026thinsp;receptor interaction pathway, cyclic adenosine monophosphate(cAMP) signaling pathway, and calcium signaling pathway\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe GO enrichment analysis results (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD) indicated that the enrichment was primarily observed in extracellular structural organizations, specifically in terms of biological processes. The cilium was predominantly enriched in terms of cellular components. Regarding molecular function, it is mostly endowed with numerous peptidase regulator activities.\u003c/p\u003e \u003cp\u003essGSEA algorithm was further used to compare immune infiltration between the control cohort and the IPF cohort. Compared to the control cohort, B cells memory, macrophages M0, mast cells resting, plasma cells, T cells follicular helper, T cells gamma delta, and T cells regulatory (Tregs) showed higher levels of infiltration in the IPF cohort. (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE) Dendritic cells activated, eosinophils, monocytes, neutrophils, NK cells resting, T cells CD4 memory resting is the opposite.\u003c/p\u003e \u003cp\u003eThe infiltration of T_cells_CD4_memory_activated and Neutrophils was significantly positively correlated with the activity of multiple RCD pathways, while plasma_cells was significantly negatively correlated with the activity of multiple RCD pathways. NK cells and monocytes are not associated with the activity of most RCD pathways. (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF) Additionally, there is heterogeneity of multiple immune functions in the control cohort and IPF cohort. (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG)\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Identification of RCD-related genes associated with IPF\u003c/h2\u003e \u003cp\u003eIn this study, we employed eQTLs from blood that exhibited overlap with RCD-related genes. These eQTLs were utilized as exposures, encompassing a total of 1186 gene symbols.\u003c/p\u003e \u003cp\u003eSNPs with a transcription start site (TSS) range of \u0026plusmn;\u0026thinsp;1000 kb were clumped. Wald Ratio or Inverse-variance Weighted method was used for MR Analysis. Ultimately, a total of 79 possible targets related to IPF were discovered. (Refer to Supplementary Tables\u0026nbsp;3 and 4 for the full results of the significantly expressed IV and MR)\u003c/p\u003e \u003cp\u003eRCD-related eQTLs from lung tissue were acquired as exposures by employing criteria akin to blood eQTLs, encompassing 633 gene symbols, in order to conduct MR. Consequently, a total of 37 possible targets related to IPF were discovered. (Refer to Supplementary Tables\u0026nbsp;5 and 6 for the full results of IV and MR Significantly expressed)\u003c/p\u003e \u003cp\u003eAfter eliminating duplicate target symbols in both results, a total of 108 unique IPF-related RCD targets were identified for follow-up studies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Construction and validation of the CDI signature\u003c/h2\u003e \u003cp\u003eFollowing univariate Cox regression analysis of IPF-related RCD targets obtained by MR Analysis, 31 RCD-related genes associated with IPF prognosis were identified. (\u003cb\u003eSupplementary Table\u0026nbsp;7\u003c/b\u003e) Subsequently, we employed a total of 10 distinct machine-learning algorithms to develop and construct 101 prognostic signatures. The robustness of these signatures was evaluated using a ten-fold cross-validation approach in four distinct cohorts (GSE70866 as the training cohort, and three external validation cohorts including GSE27957, GSE28042, and GSE93606 cohort).\u003c/p\u003e \u003cp\u003eThe best-performing predictive signature was determined as the signature with the greatest mean C-index in three external validation cohorts, due to overfitting in the training cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). The findings indicated that the StepCox[backward]\u0026thinsp;+\u0026thinsp;SuperPC algorithm combination demonstrated the highest average C-index (0.663), making it the optimal algorithm combination for developing the CDI signature, which consisted of 14 genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). The obtained equation is as follows:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCDI score\u0026thinsp;=\u0026thinsp;CD15\u0026times; (-1.094082)\u0026thinsp;+\u0026thinsp;GCC2\u0026times; (-1.538944)\u0026thinsp;+\u0026thinsp;GCLC\u0026times;1.114980\u0026thinsp;+\u0026thinsp;MAP3K5\u0026times; (1.016809)\u0026thinsp;+\u0026thinsp;NPC1\u0026times;1.642120\u0026thinsp;+\u0026thinsp;PPID\u0026times;1.074283\u0026thinsp;+\u0026thinsp;CLTC\u0026times;1.046599\u0026thinsp;+\u0026thinsp;BCL2L11\u0026times;1.188298\u0026thinsp;+\u0026thinsp;BMP6\u0026times;3.057019\u0026thinsp;+\u0026thinsp;DAPK2\u0026times;1.988332\u0026thinsp;+\u0026thinsp;FANCD2\u0026times; (-1.522632)\u0026thinsp;+\u0026thinsp;SRC\u0026times; (-1.558877)\u0026thinsp;+\u0026thinsp;STAT5A\u0026times; ( -1.443486)\u0026thinsp;+\u0026thinsp;TMEM106B\u0026times; (-1.196694)\u003c/p\u003e \u003cp\u003eAfterward, the CDI score for each patient was calculated. The samples were then categorized into the low CDI score group and the high CDI score group based on the optimal threshold value of the CDI score determined using the R package \"survminer\". Subsequently, KM survival analysis and assessment of prognostic performance were conducted. Demonstrating a substantial difference in OS between the low CDI score group and the high CDI score group in all four cohorts (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC), the ROC analysis indicated that the CDI signature effectively predicted the prognosis of IPF patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eFurthermore, due to the scarcity of prognostic models for non-tumor diseases compared to tumors, and the limited availability of datasets including comprehensive gene expression data for genes associated with signatures, a total of 10 prognostic models published in IPF were ultimately gathered from the existing literature. The features encompass a range of biological processes observed in the IPF cohort, such as hypoxia, autophagy, pyroptosis, epithelial-mesenchymal transition, 5-methylcytosine, and inflammation. (\u003cb\u003eSupplementary Table\u0026nbsp;8\u003c/b\u003e) These features were then compared to the C-index of the CDI signature. The findings indicated that the CDI signatures exhibited superior performance compared to the majority of the signatures within their respective categories (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Association of CDI signature with pulmonary function\u003c/h2\u003e \u003cp\u003eIn the GSE38958 and GSE93606 cohorts, the CDI score was significantly negatively correlated with FVC% prediction and Dlco% prediction, suggesting that the CDI signature could be used as a potential biomarker to assess IPF severity. \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e To facilitate computation, a nomogram was created, integrating the factors of age, gender, and CDI score. (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIt is challenging to make an accurate prediction regarding the prognosis of individuals who have IPF since the disease is both complicated and variable. A proper prognosis of the progression of the disease, the selection of the treatment techniques that are the most suitable, and the assessment of the overall survival rate are all made possible by the utilization of prognostic signatures, which are of the utmost significance.\u003c/p\u003e \u003cp\u003eThe study employed scRNAseq and bulk-RNAseq to characterize the pathway activity profiling of RCD patterns in IPF patients. In addition, 101 distinct machine-learning algorithm combinations were utilized to develop a stable CDI signature, which was derived by analyzing comprehensive bulk-RNAseq datasets. The stability and reliability of the CDI signature are ensured by utilizing the advantages of each algorithm and adopting the set learning technique. By conducting validation on several datasets, the signature demonstrated exceptional performance in forecasting the prognosis of IPF patients.\u003c/p\u003e \u003cp\u003eThe RCD-related genes enrolled in the CDI signature are involved in regulating fibrotic processes in a variety of tissues. It is through the maintenance of epithelial integrity that tetraspanin CD151 can prevent pulmonary fibrosis (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). GCLC is involved in the synthesis of glutathione, which is an important pathological mechanism of pulmonary fibrosis by regulating the oxidation-antioxidant balance (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e) The mitogen-activated protein kinase family, in which MAP3K5 is located, plays a key role in cytokines and stress-induced apoptosis and is closely associated with liver or kidney fibrosis (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). The mutation of the NPC1 gene activates the classical Wnt signaling pathway in mouse kidneys and promotes renal fibrosis. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e) BCL2L11 is involved in regulating myocardial fibrosis. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e) BMP6 knockdown enhances cardiac fibrosis in a mouse myocardial infarction model by upregulating AP-1/CEMIP expression. (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e) BMP6 inhibits hepatic fibrosis in non-alcoholic fatty liver disease. Increased expression of BMP6 can inhibit nonalcoholic fatty liver fibrosis. (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e) Mutations in the FANCD2 gene can lead to renal nuclear hypertrophy and fibrosis through incomplete repair of DNA damage. (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e) Src family kinases are involved in regulating the process of pulmonary fibrosis through various pathological mechanisms such as myofibroblast activation and epithelial-mesenchymal transformation STAT5A: MEHP promotes liver fibrosis by down-regulating STAT5A in BRL-3A hepatocytes. (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThe study undoubtedly has numerous limitations. Initially, validation of the CDI signature requires an independent clinical cohort, and additional data gathering is necessary to verify its predictive capacity. In addition, the RCD-related genes enrolled in the CDI signature require further studies to determine their role in the progression of pulmonary fibrosis.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, the CDI signature proposed in this study is a potential prognostic predictor for IPF patients which can make a notable difference in the assessment of clinical outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors would like to express our sincere thanks for sharing the online databases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe blood eQTL data was acquired from the eQTLGen Consortium (https://eqtlgen.org/). The cis-eQTL data for lung tissue was acquired via the GTEx V.8 (https://gtexportal.org). The IPF GWAS dataset was obtained via the GWAS Catalog (https://www.ebi.ac.uk/gwas/). The GEO datasets were obtained from GEO database (http://www.ncbi.nlm.nih.gov/geo/). The code applied in the study is available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJS and YS designed and conducted the entire study. JS and HG performed the data collection, bioinformatics, and statistical data analysis. YN and SZ investigated the literature. YZ was responsible for the integrity of the entire study and manuscript review. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePublisher\u0026rsquo;s note\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRicheldi L, Collard HR, Jones MG. Idiopathic pulmonary fibrosis. Lancet. 2017;389(10082):1941\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKing TE, Albera C, Bradford WZ, Costabel U, du Bois RM, Leff JA, et al. All-cause mortality rate in patients with idiopathic pulmonary fibrosis. 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Chemosphere. 2022;295:133925.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Idiopathic pulmonary fibrosis, Regulatory cell death, Immune microenvironment infiltration, Prognostic signature, Immunotherapy","lastPublishedDoi":"10.21203/rs.3.rs-4195254/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4195254/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eIdiopathic pulmonary fibrosis (IPF) is one of the interstitial lung diseases (ILDs) with poor prognosis. Multiple regulated cell death (RCD) pathways are involved in regulating the progression of pulmonary fibrosis at different stages.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA total of 20 RCD pathways and crucial regulatory genes were collected from available literature. The study initially elucidated the profiling of 20 kinds of RCD pathways in normal and fibrotic lung tissues based on the scRNAseq dataset and bulk-RNAseq dataset. Targets associated with IPF were identified by Mendelian randomization analysis, and univariate Cox regression was used to further identify RCD-related genes significantly associated with overall survival (OS). A combination of 101 distinct machine-learning algorithms was utilized to develop a prognostic signature. In addition, we investigated the relationship between prognostic signature and clinical characteristics.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eBy integrating scRNAseq data and bulk-RNAseq data, the study initially elucidated the pathway activity associated with distinct RCD patterns in IPF patients. In addition, following detailed research of various RCD patterns, the study developed the CDI signature with 13 genes, which combined with multiple machine learning methods to generate CDI signature has a strong predictive influence on the prognosis of IPF patients. As proven by independent datasets, IPF patients with high CDI had a poorer outcome. From the clinical characteristics, IPF patients with high CDI have impaired lung function. Finally, a nomogram with strong predictive ability was generated by integrating CDI with clinical characteristics.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eIn summary, we have developed a novel CDI model that effectively forecasts the clinical prognosis of patients with IPF by integrating various cell death patterns.\u003c/p\u003e","manuscriptTitle":"Elucidating the pathway activity and prognostic significance of diverse cell-death patterns in idiopathic pulmonary fibrosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-19 18:28:36","doi":"10.21203/rs.3.rs-4195254/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2533bf14-7cff-42ce-abf2-98489323c2ba","owner":[],"postedDate":"April 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":30580277,"name":"Biological sciences/Biotechnology"},{"id":30580278,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2025-07-10T08:54:00+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-19 18:28:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4195254","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4195254","identity":"rs-4195254","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}