Integrating Single-Cell Sequencing and Machine Learning to Uncover the Role of Mitophagy in Subtyping and Prognosis of Esophageal Cancer

Integrating Single-Cell Sequencing and Machine Learning to Uncover the Role of Mitophagy in Subtyping and Prognosis of Esophageal Cancer | 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 Research Article Integrating Single-Cell Sequencing and Machine Learning to Uncover the Role of Mitophagy in Subtyping and Prognosis of Esophageal Cancer Feng Tian, Xinyang He, Saiwei Wang, Yiwei Liang, Zijie Wang, Minxuan Hu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4917245/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Feb, 2025 Read the published version in Apoptosis → Version 1 posted 9 You are reading this latest preprint version Abstract Globally, esophageal cancer stands as a prominent contributor to cancer-related fatalities, distinguished by its grim prognosis. Mitophagy has a significant impact on the process of cancer progression. This study investigates the prognostic significance of mitophagy-related genes (MRGs) in esophageal carcinoma (ESCA) with the aim of elucidating molecular subtypes. By analyzing RNA-seq data from The Cancer Genome Atlas (TCGA), 6451 differentially expressed genes (DEGs) were identified. Cox regression analysis narrowed this list to 14 MRGs with notable prognostic implications. ESCA patients were classified into two distinct subtypes (C1 and C2) based on these genes. Furthermore, leveraging the differentially expressed genes between Cluster 1 and Cluster 2, ESCA patients were classified into two novel subtypes (CA and CB). Notably, patients in C2 and CA subtypes exhibited inferior prognosis compared to those in C1 and CB (p < 0.05). Functional enrichments and immune microenvironments varied significantly among these subtypes, with C1 and CB demonstrating higher immune checkpoint expression levels. Employing machine learning algorithms like LASSO regression and Random Forest, alongside multivariate COX regression analysis, two core genes: HSPD1 and MAP1LC3B were identified. A robust prognostic model based on these genes was developed and validated in two external cohorts. Additionally, single-cell sequencing analysis provided novel insights into esophageal cancer microenvironment heterogeneity. Through Coremine database screening, Icaritin emerged as a potential therapeutic candidate to improve esophageal cancer prognosis. Molecular docking results indicated favorable binding efficacies of Icaritin with HSPD1 and MAP1LC3B, enhancing the comprehension of the underlying molecular mechanisms of esophageal cancer and offering therapeutic avenues. esophageal cancer mitophagy single-cell sequencing machine learning prognosis subtype Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 1 Introduction Esophageal carcinoma, a malignant tumor, predominantly comprises esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC), originating from the epithelial cells of the esophageal lining [ 1 ] . This disease imposes a considerable global health challenge, ranking as the sixth foremost contributor to cancer-related mortality and the ninth in the incidence of cancers globally, underscoring its widespread and severe nature [ 2 ] . Despite some therapeutic advancements in endoscopic interventions, surgical resections, chemotherapy, radiotherapy, and multimodal treatment regimens, clinical outcomes for ESCA patients remain discouraging, with mortality rates persistently high [ 3 ] . Therefore, there is an urgent requirement for identifying prognostic biomarkers and construct robust prognostic models to enhance patient status, implement personalized treatment strategies, and ultimately improve the survival rates of those suffering from this serious disease. The infiltration of immune cells has a well-established correlation with cancer prognosis, particularly in hard-to-treat tumors like esophageal cancer [ 4 ] . Statistical evidence suggests that immunotherapy has significantly improved treatment outcomes in these refractory cancers [ 5 ] . However, individual responses to cancer immunotherapy vary greatly [ 6 ] . Consequently, identifying novel and distinctive genes holds promise for advancing more targeted and effective treatment strategies, thereby offering valuable prospects for ESCA. Under stressors such as nutrient deprivation, reactive oxygen species (ROS), and cellular senescence, mitochondria can experience varying degrees of damage [ 7 ] . Mitophagy, a targeted autophagy mechanism that specifically eliminates damaged mitochondria within autophagosomes for fusion with lysosomes and subsequent disposal, is essential for preserving both mitochondrial integrity and cellular stability [ 8 ] . This concept was first formally proposed by Lemasters in 2005, who observed that the onset of mitophagy is triggered by the depletion of mitochondrial membrane potential and the subsequent opening of the mitochondrial permeability transition pore [ 9 ] . Several diseases exhibit a close correlation with aberrant mitophagy, implicating its involvement in their underlying pathogenesis, including neurodegenerative disorders, hematological malignancies, cardiovascular diseases, and even cancer [ 10 – 11 ] . Notably, mitophagy exhibits a dual and complex part in the development of tumors, both promoting and inhibiting cancer progression. Distinct cancer types, such as ovarian, lung, colorectal, and breast cancers, display heterogeneous levels of mitophagy, indicating its diverse involvement across various malignancies. Among the four main mitophagy pathways in mammals, the PINK1/Parkin-mediated mitophagy is considered a potential tumor suppression mechanism. Research has shown that 25% of colorectal cancer tumors exhibit focal PARK2 loss [ 12 ] , and specific PARK2 mutations or deletions have been detected across multiple cancer types, suggesting that PARK2 mutations may drive tumor progression by eliminating the E3 ubiquitin ligase activity of Parkin. However, the role of PARK2 loss in cancer may depend on the specific context and could even be contradictory. For instance, in melanoma, Parkin loss inhibits Mfn2 ubiquitination, inducing apoptosis and suppressing melanoma development and metastasis. BNIP3, a protein with pro-apoptotic properties, augments mitophagy by impeding the fusion of dysfunctional mitochondria, thereby expediting their elimination from the cell. The function of BNIP3 as a tumor inhibitor or promoter remains controversial in various cancer contexts. Similarly, the role of Nix-mediated mitophagy may exhibit complexity and diversity across different cancer types and stages [ 13 ] . The dual nature of mitophagy emphasizes its potential as a novel therapeutic target. Inducing mitophagy may provide an alternative mechanism for cancer cell death, creating hopeful avenues for cancer treatment. Inhibitory strategies targeting mitophagy regulators such as PINK1, Parkin, BNIP3, and FUNDC1 may offer new therapeutic options for cancer intervention [ 14 ] . Current research has primarily concentrated on the role of mitophagy in ovarian, lung, as well as breast cancers, while studies on esophageal cancer (ESCA) remain relatively limited. Therefore, there is an urgent need to explore the relationship between mitophagy and esophageal cancer to uncover new therapeutic strategies for this malignancy. This study aims to identify unique genes associated with mitophagy by comparing ESCA patients with a control group. We then employ univariate and multivariable Cox regression analyses, along with machine learning algorithms, to determine two feature genes with prognostic value. These findings provide a basis for establishing a prognostic scale for predicting outcomes in esophageal cancer patients. Furthermore, we validate the reliability of the model through ROC curves, DCA curves, and calibration curves. Additionally, we utilize consensus clustering methods to identify two distinct subtypes of esophageal cancer patients and explore their potential characteristics through enrichment analysis, immune infiltration analysis and immune checkpoints comparison. Moreover, using single-cell sequencing technology, we investigate the expression patterns of these two feature genes in esophageal cancer cells. Eventually, we conducted traditional Chinese medicine prediction and molecular docking for the core genes. This comprehensive approach not only enhances our comprehension of the molecular mechanisms driving esophageal cancer but also provides insightful perspectives for tailoring individualized therapeutic approaches to this condition. 2 Materials and Methods 2.1 Data Collection and Preprocessing The flowchart of this study is visually depicted in Fig. 1 ,outlining its methodology and progression. RNA sequencing (RNA-seq) data for 173 individuals diagnosed with esophageal carcinoma (ESCA) was obtained from The Cancer Genome Atlas (TCGA) public database ( https://portal.gdc.cancer.gov ). In addition, a collection of mitophagy-related genes (MRGs) was extracted from the GeneCards database ( https://www.genecards.org/ ), yielding a total of 385 genes for further analysis. For external validation, datasets GSE26886 and GSE20347 were sourced from the Gene Expression Omnibus (GEO) ( https://www.ncbi.nlm.nih.gov/geo/ ). The characteristics of these validation datasets are summarized in Table 1 . Table 1 External validation data set from a GEO information (GSE26886 and GSE20347) Datatsets Platform Total sample number Normal sample number ESCA sample number GSE26886 GPL570 69 20 49 GSE20347 GPL571 34 17 17 2.2 Identification of Differentially Expressed Genes Related to Mitophagy The "limma" package [ 15 ] were utilized for differential expression analysis. The parameters for differential expression analysis were set as follows: |log2FC| > 1.2, and FDR < 0.05. The genes identified through this screening were cross-referenced with mitophagy-related genes (MRGs) sourced from the GeneCards database to determine the common genes, designated as differentially expressed mitophagy-related genes (DEMRGs). 2.3 Cox regression analysis Univariate and multivariate Cox proportional hazards regression analyses were employed to screen critical genes closely associated with ESCA prognosis. Additionally, we applied forest plots to visually represent the results. 2.4 Machine Learning During the construction of the prognostic model, two advanced machine learning techniques were employed: LASSO [ 16 ] regression and Random Forest [ 17 ] . Lasso regression, an extension of logistic regression, incorporates a shrinkage parameter to minimize classification error while selecting significant variables, thereby providing an effective approach to filter through numerous feature variables for optimal model performance. Random forest, as an ensemble of decision trees, excel in survival analysis by adeptly identifying important features and elucidating patient outcomes, illustrating their extensive applicability in this domain. 2.5 Development and verification of a risk assessment model To develop a risk model, we employed LASSO regression to calculate the coefficients of each gene, as shown in the formula \(\:\:\text{R}\text{i}\text{s}\text{k}\:\text{s}\text{c}\text{o}\text{r}\text{e}=\varSigma\:\left(gene1\:expression\times\:coefficient1+gene2\:expression\times\:coefficient2\right)\) ,where represents the expression of MRDEGs. Patients were segregated into high- and low-risk cohorts, utilizing the median risk score as the demarcation point. To validate this prognostic model, external datasets, GSE26886 and GSE20347, sourced from the GEO database, were analyzed. To further assess the predictive precision of the risk model, decision curve analysis (DCA )[ 18 ] and time-dependent receiver operating characteristic (ROC) curve [ 19 ] evaluations were performed using the "ggDCA" and "timeROC" R packages. Kaplan-Meier curves were generated and log-rank tests administered to evaluate the distinction in overall survival (OS) between the two risk groups, subsequently assessing the clinical outcome predictive potential of the risk model in ESCA. Utilizing the "rms" and "survival" R packages, a comprehensive nomogram was developed that integrated risk score and clinicopathological characteristics to facilitate personalized predictions. A calibration curve was generated to assess the predictive accuracy of the nomogram. 2.6 Consensus Clustering Using the "ConsensusClusterPlus" R package [ 20 ] unsupervised clustering of DEMRGs was conducted to identify distinct subtypes of ESCA patients. To visualize the distribution of patients across these clusters, Uniform Manifold Approximation and Projection (UMAP) analysis was performed utilizing the "umap" R package. Subsequently, principal component analysis (PCA) was applied to the subtypes derived from the secondary clustering analysis, expounding upon their inherent structure. Both UMAP and PCA serve as effective dimensionality reduction techniques, enabling the mapping of high-dimensional data into lower-dimensional space, thereby making it convenient for a clearer representation of the data's characteristics and insights into ESCA subtypes. 2.7 Immune Infiltration To investigate the distribution of immune cells within ESCA patient subtypes and the association between risk score and tumor-infiltrating immune cells, three algorithms were employed: TIMER, CIBERSORT and ESTIMATE [ 21 ] . These algorithms aided in estimating the relevance between the prognostic model and immune cell infiltration patterns within the tumor microenvironment (TME). Data on immune infiltration was sourced from TIMER [ 22 ] ( https://cistrome.shinyapps.io/timer/ ), with results from TIMER, CIBERSORT compared. Moreover, the ESTIMATE algorithm evaluated the immune and stromal components of the TME, generating stromal scores, immune scores, and ESTIMATED scores [ 23 ] . An increase in these scores indicates a greater presence of corresponding components within the TME, providing critical insights into the tumor's immunological landscape and potential implications for patient prognosis. 2.8 Enrichment Analysis The "ClusterProfiler" R package was employed to conduct Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses to elucidate the biological processes (BP), molecular functions (MF),cellular components (CC), and potential signaling pathways associated with ESCA subtypes which was visualized in Metascape. To capture subtle alterations in pathway activity, Gene Set Variation Analysis (GSVA) was conducted, transforming gene expression profiles across samples into gene set enrichment scores, thus facilitating the assessment of gene set activity [ 24 ] . Complementarily, Gene Set Enrichment Analysis (GSEA) was conducted to evaluate the differential expression of predefined gene sets between distinct biological states, providing a comprehensive overview of the transcriptional landscape in ESCA [ 25 ] . 2.9 Mitophagy Expression in Single-Cell RNA Sequencing Tumor Data TISCH2 ( http://tisch.compgenomics.org ), a robust single-cell RNA-seq TME database, enables an exhaustive analysis of heterogeneity across datasets and cell types [ 26 ] . This study explores the immune-related prognostic features of ESCA at the single-cell level, utilizing data from TISCH2, specifically GSE160269 and GSE173950. 2.10 Prediction of Traditional Chinese medicine and molecular docking Map the mitophagy key genes obtained in the aforesaid steps onto the Coremine medical ontology information retrieval platform ( www.coremine.com/medical/ ), and select the top 30 traditional Chinese medicine drugs that may have a regulatory effect on the mitophagy key genes in patients suffering from esophageal cancer. Furthermore, to procure the target protein result files, searches were conducted on the PDB database ( https://www.rcsb.org/ ) and the AlphaFold database ( https://alphafold.ebi.ac.uk/ ). From the PubChem database ( https://pubchem.ncbi.nlm.nih.gov/ ), the active ingredient structure files were retrieved and converted into PDB format utilizing Open Babel 2.3.2 software. PYMOL 2.3.4 software was then employed to meticulously prepare the receptor protein by eliminating water molecules and ligands. Following this, the receptor protein underwent essential modifications, including hydrogenation and charge balancing, using AutoDockTools software. Subsequently, molecular docking simulations were conducted between the receptor protein and small molecule ligands utilizing AutoDock Vina 1.1.2 [ 27 ] . Finally, Pymol was utilized to visualize the docking outcomes, focusing on those with favorable binding energies. 3 Results 3.1 Identification of Prognostic Mitophagy-Related Genes (MRGs) Differential expression analysis of tumor and normal samples sourced from the TCGA database revealed significant alterations in gene expression profiles in esophageal squamous cell carcinoma (ESCA). Specifically, a total of 6451 differentially expressed genes (DEGs) were identified, with 1455 genes downregulated and 4996 genes upregulated (Fig. 2A) . Furthermore, we identified 136 genes common to both the mitophagy-related genes and differentially expressed genes (DEGs) (Fig. 2B) . Subsequently, a profound KEGG enrichment analysis of these genes prominently converged on mitophagy and autophagy pathways (Fig. 2C) .To evaluate the prognostic value of these genes, we conducted a univariate Cox regression analysis incorporating survival status, survival duration, and gene expression profiles, ultimately identifying 14 genes with significant prognostic value, the results were visually presented in a forest plot (Fig. 2D) .Moreover, a correlation heatmap was also generated to illustrate the intricate relationships among these 14 genes (Fig. 2E) . 3.2 Identification of MRG-related Subtypes Based on 14 MRGs Consensus clustering of 153 ESCA patients from the TCGA cohort was conducted utilizing the expression profiles of the 14 genes identified via univariate Cox regression analysis. Adjusting the clustering parameter (k) within a range from 2 to 10, we identified the optimal clustering configuration at k equals 2 (Fig. 3A-C) , effectively stratifying patients into two distinct clusters: Cluster 1 (n = 71) and Cluster 2 (n = 82). UMAP visualizations corroborated the clear demarcation between these clusters (Fig. 3D) . The Kaplan-Meier survival analysis revealed a statistically significant enhancement in the overall survival rate among patients belonging to Cluster 1, in contrast to those in Cluster 2 (p < 0.05). (Fig. 3E) , underscoring the capability of MRGs to stratify ESCA patients into subtypes with notable survival disparities. 3.3 Immunological Landscape of the Two Subtypes Our comprehensive immunological analyses elucidated substantial disparities in immune landscape between the two molecular subtypes. Utilizing the Estimate algorithm, we discerned notably heightened stromal, immune, and overall estimate scores among ESCA patients in Cluster 1 (Fig. 4A–C) , indicating a more favorable prognosis. The CIBERSORT algorithm further identified an increased proportion of T cells CD4 + naive (p < 0.05) and activated natural killer (NK) cells (p < 0.05) in Cluster 1; however, no significant differences were detected in other immune cell infiltrations (Fig. 4D) . Additionally, TIMER algorithm analysis confirmed higher infiltration levels of neutrophils and myeloid dendritic cells in Cluster 1, while no significant variation in CD8 + T cell infiltration was noted (Fig. 4E-G) , accentuating the distinct immunological profiles of these two subtypes. 3.4 Differentially Expressed Genes (DEGs) and their Functional Annotations Distinct sets of differentially expressed genes (DEGs) were discerned between the two clusters., uncovering a total of 164 DEGs. Among these, 70 genes exhibited reduced expression in Cluster 1 compared to Cluster 2, while 94 genes showed increased expression in Cluster 1 (Fig. 5A) . Subsequent KEGG enrichment analysis elucidated several prominent signaling pathways, including the p53 signaling pathway, ECM-receptor interaction, and protein digestion and absorption (Fig. 5B) . Correspondingly, GO enrichment analysis indicated that DEGs were significantly enriched in biological processes such as the cell cycle, extracellular matrix organization, and the positive regulation of cell migration (Fig. 5C) . Furthermore, enrichments of crucial molecular functions were observed (Fig. 5D) . Protein-protein interaction (PPI) analysis unveiled three sub-modules, all tightly linked to tumor development and immunity, suggesting a potential interplay between immune response and the role of mitophagy in esophageal cancer (Fig. 5E) . In order to gain deeper insights into the relationships between enriched pathways and the prognostic outcomes of ESCA patients, Gene Set Enrichment Analysis (GSEA) was conducted, demonstrating that focal adhesion and ECM receptor interaction were remarkably expressed in Cluster 1, whereas lysine degradation and homologous recombination were notably enriched in Cluster 2 (Fig. 5F) . 3.5 Further Identification of ESCA Based on DEGs Between Subtypes As noted previously, differential analysis between clusters 1 and 2 led to the identification of 164 genes exhibiting differential expression patterns (Fig. 6A) . Based on these genetic markers, ESCA patients were categorized into distinct subgroups. The optimal clustering was achieved at k = 2 (Fig. 6B-D) . Principal Component Analysis (PCA) revealed that these genes effectively differentiated the two subtypes—Cluster A (CA) and Cluster B (CB) (Fig. 6E) . 3.6 Discerning biological functions and immune microenvironments across distinct gene clusters. Our investigation into the expression of 14 genes with notable prognostic significance across the two subtypes revealed distinctive expression profiles. ( Fig. 7 A ) .The results of Gene Set Variation Analysis (GSVA) highlighted the predominant involvement of CA in NADPH oxidase H2O2 forming activity. Conversely, CB associates mainly with negative regulation of the vitamin D biosynthetic process and pathways, including the TGF-Beta signaling pathway and WNT signaling pathway ( Fig. 7 B, C ). The CIBERSORT algorithm disclosed higher infiltration levels of CD4 + naive T cells, follicular helper T cells, and activated myeloid dendritic cells in Cluster B, alongside lower infiltration levels of CD4 + memory resting T cells, CD4 + memory activated T cells, and resting NK cells ( Fig. 7 D ) . Additionally, the TIMER algorithm indicated higher infiltration levels of CD4 + T cells in Cluster A, along with lower levels of B cells, Neutrophils and Myeloid dendritic cells ( Fig. 7 E ) . 3.7 Construction of Risk Model We initially applied LASSO regression analysis, utilizing 10-fold cross-validation to identify the optimal model. The optimal λ value was determined, leading to the identification of 7 genes ( Fig. 8 A-B) . Furthermore, the random forest algorithm identified six genes with importance scores exceeding 1.4 as central features ( Fig. 8 C) . Notably, three genes—TMEM43, HSPD1, and MAP1LC3B—were common to both machine learning approaches ( Fig. 8 D) . Following this, multivariate Cox regression analysis revealed that HSPD1 (HR=1.58, 95% CI: 1.05-2.38) and MAP1LC3B (HR=2.86, 95% CI: 1.45-5.65) emerged as pivotal differentially expressed mitophagy-related genes (DEMRGs) ( Fig. 8 E) . Based on these two critical DEMRGs, a risk model was constructed, with the risk score calculated as follows: Risk score=(0.366419869×HSPD1 expression)+(0.397483984×MAP1LC3B expression). Patients in the TCGA cohort were subsequently stratified into high-risk (n=77) and low-risk (n=76) groups based on the median risk score. Moreover, the coefficient value of the model was determined ( Fig. 8 F) . The relationship between risk score and survival status was further investigated, with scatter plots illustrating that patient mortality increased alongside rising risk score ( Fig. 8 G) . To emphasize the prognostic accuracy of the model, a time-dependent ROC analysis was conducted, yielding AUC values of 0.74, 0.71, and 0.83 for 1, 3, and 5 years, respectively ( Fig. 8 H) , indicating the model's robust predictive capability. Kaplan-Meier analysis demonstrated that the high-risk group experienced a poorer prognosis compared to the low-risk group in the TCGA cohort ( Fig. 8 I) . 3.8 Independence of the Established Risk Model We further delved into the intricate relationship between the risk score and various clinical characteristics through rigorous subgroup and regression analyses, assessing the independence of our developed risk model. Our findings indicated no significant differences in risk score across different age cohorts (Fig. 9A) , genders (Fig. 9B) , M stages (Fig. 9C) , T stages (Fig. 9D) , N stages (Fig. 9E) , or overall staging (Fig. 9F) , suggesting a lack of correlation between the risk score and these clinical factors. Notably, even when stratifying patients based on age (Fig. 9G-H) and Gender (Fig. 9I-J) , our risk model maintained its robust predictive power, with individuals exhibiting lower risk score showing a more favorable prognosis. These outcomes highlight the exceptional utility of our risk model in prognosticating the outcomes of esophageal cancer patients. 3.9 Construction and Calibration of an Integrated Nomogram To enhance predictive performance, we constructed a nomogram model for evaluate the prognosis of esophageal squamous cell carcinoma (ESCA) ( Fig. 10 A) , which allows for individualized predictions of survival probabilities at 1-year, 3-year, and 5-year intervals. Each risk factor was assigned a score, and the cumulative scores of these indicators were utilized to calculate the total score for predicting ESCA prognosis for each patient. The calibration plot revealed a high degree of concordance between observed and ideal values ( Fig. 10 B) . Examination of the calibration curve provides a comprehensive evaluation of the model's efficacy, substantiating its applicability for clinical application. According to the model, the c-index value was determined to be 0.68 ( Fig. 10 C) . Decision Curve Analysis (DCA) revealed that the nomogram surpasses in delivering superior clinical net benefit ( Fig. 10 D) . These discoveries imply that the risk score-based nomogram can function as a potent prognostic instrument for prediction in clinical scenarios. Furthermore, to authenticate the accuracy of the model, validation was conducted utilizing the GSE26886 and GSE20347 datasets ( Fig. 10 E - F) , with all AUC values exceeding 0.7. 3.10 Comparison of Functional Enrichment and Immune Infiltration distinction Between High and Low-Risk Cohort To investigate the underlying mechanisms of the risk model, Gene Set Enrichment Analysis (GSEA) was employed to examine the functional enrichment profiles within high-risk and low-risk cohorts. The high-risk group exhibited significant enrichment in spliceosome activity, Alzheimer disease pathways, and the pentose phosphate pathway, while the VEGF, Notch, and ERBB signaling pathways were more prominent in the low-risk group ( Fig. 11 A) . Additionally, we conducted GSEA to elucidate the roles of the two hub genes, HSPD1 and MAP1LC3B, in esophageal carcinoma. In esophageal cancer, GSEA revealed diverse roles for HSPD1 ( Fig. 11 B) , including enrichment in critical processes such as "apoptosis," "DNA replication," and "Adherens junction formation," while also being involved in essential signaling pathways, including the p53 signaling pathway and focal adhesion pathways. For MAP1LC3B ( Fig. 11 C) , it was implicated in the immune process of "natural killer cell mediated cytotoxicity" and displayed enrichment in the "mismatch repair" process, contributing to critical signaling mechanisms, encompassing focal adhesion and the ERBB signaling pathway. Additionally, the differences in genetic mutation profiles between the high-risk and low-risk groups were unveiled through the illustration of the mutation waterfall plot ( Fig. 11 D) . CIBERSORT analysis indicated substantial disparities in immune cell infiltration patterns across high-risk and low-risk groups ( Fig. 11 E) . Correlation analysis demonstrated a significant positive correlation between Mast cell activated and Macrophage in the high-risk group, while a negative correlation was observed between resting myeloid dendritic cells and Monocyte (p < 0.05, Fig. 11 F ). Conversely, in the low-risk group, Macrophage M0, M2 and Neutrophil exhibited positive correlations, while Neutrophil displayed a negative correlation with Monocyte (p < 0.05, Fig. 11 G ). 3.11 Exploring the Correlation Between Three Types of Grouping Patterns We employed the risk score obtained from our risk model to construct box plots for contrasting risk score among the MRGs cluster and the DEGs patterns ( Fig. 12 A - B) . The results revealed that the C2 and CA groups demonstrated notably elevated risk score in comparison to the C1 and CB groups. Additionally, a Sankey diagram was employed as a visual tool to illustrate the correlation between distinct clusters and risk score ( Fig. 12 C) . Furthermore, we assessed the association between various immune checkpoints, revealing that levels of CD274 and TGFB1 were enhanced in the C1 and CB groups in comparison to the C2 and CA groups ( Fig. 12 D - E) . 3.12 Single-Cell Sequencing Data Search for Gene Expression in Tumors A comprehensive search of single-cell sequencing data for tumor gene expression was performed. We initially downloaded esophageal cancer single-cell datasets GSE160269 and GSE173950 from the TISCH2 database. The GSE160269 dataset, after dimension reduction, was annotated into 31 cell clusters, revealing 13 distinct cell types. In contrast, the GSE173950 dataset was annotated into 22 cell clusters, identifying 12 cell types, including both tumor and immune cells, and illustrating the proportional representation of different cell components in each patient ( Fig. 13 A-D) . UMAP plots and violin plots were utilized to depict the expression profiles of relevant genes across different annotated cell types ( Fig. 13 E-J) . The results demonstrated the expression of genes such as HSPD1 and MAP1LC3B across various annotated cell types, including tumor cells. 3.13 Prediction of Potential Traditional Chinese medicine and molecular docking The two screened pivotal genes were imported into the Coremine database, and the top 30 corresponding traditional Chinese medicines were selected. Among them, the traditional Chinese medicine where both genes appeared simultaneously was Icaritin, suggesting that this traditional Chinese medicine has considerable potential for improving the prognosis of patients with esophageal cancer. Icaritin (Epimedium flavonoids) was selected to perform molecular docking with HSPD1 and MAP1LC3B proteins respectively ( Fig. 14 A - B) . A binding energy value of less than -4.25 kcal·mol-1 signifies a certain level of binding capability between the two entities, while a value less than -5.0 kcal·mol-1 indicates a more superior binding ability [28] . The results showed that the core targets HSPD1 and MAP1LC3B had a better binding ability with Icaritin. The binding energy of Icaritin with HSPD1 was -9.3, and the binding energy with MAP1LC3B was -6.8, as shown in the Table 2 . Table 2 Binding Energy of Molecular Docking Numerator ID Name Receptor protein PDB ID Binding ability[a] 5318980 Icaritin HSPD1 4PJ1 -9.3 5318980 caritin MAP1LC3B 6LAN -6.8 [a] kcal·mol-1 4 Discussion Esophageal cancer ranks among the deadliest cancers globally, which has seriously endangered human health [ 29 ] . It is detrimental for those suffering from esophageal cancer to wait for treatment to begin, regardless of whether the disease is primary or metastatic [ 30 ] . Although immunotherapy for ESCA have been launched around the world in recent years, immunotherapies do not work well or benefit all patients in the majority of cases [ 31 ] . The problems such as the high invasiveness of esophageal cancer, the unsatisfactory clinical treatment effect and the poor prognosis are still huge challenges. Mitophagy is thought to be significant to recycle mitochondrial mass and remove damaged mitochondria [ 32 ] . Especially, the potential role in esophageal cancer, which is a highly invasive cancer, still needs to be further clarified. This study is the first to systematically evaluate the role of mitophagy in esophageal cancer. We identified mitophagy genes related to the prognosis of ESCA based on various advanced algorithms and screened 14 differentially expressed genes for related subtype identification. Consensus clustering was performed for ESCA patients in the TCGA cohort, and patients were divided into C1 and C2 with significantly different prognoses with k = 2 as the boundary. Notable disparities in immune infiltration were observed between the two clusters when analyzed using the CIBERSORT, TIMER, and ESTIMATE algorithms. Cluster 1 had a higher proportion of immune cell infiltration levels, which also implied a better prognosis for Cluster 1. After that, esophageal cancer patients were categorized into two distinct molecular subtypes, CA and CB, on the basis of the differentially expressed genes between C1 and C2. Comprehensive immunological analysis and biological enrichment analysis showed that the variations observed in immune infiltration levels and levels of pathway enrichment between the two clusters suggested the prospect of immunotherapy for ESCA. evaluated the differential expression of immune checkpoint genes across the two subtypes. The results revealed that the two subtypes, C1 and CB, had higher immune checkpoint expression levels, which provided new insights for immune checkpoint-based therapies in esophageal cancer. All in all, we combined mitophagy with the prognosis and classification of esophageal cancer, a fresh insight into the complexities of esophageal cancer heterogeneity. Our initial findings prompted the employment of two machine learning algorithms - LASSO regression and random forest - in conjunction with univariate and multivariate COX regression analysis. Ultimately, two pivotal genes, HSPD1 and MAP1LC3B, were identified and a prognostic model was constructed based on these findings. Our prognostic model was proved by the external validation set to have good predictive efficacy. The findings indicated a direct correlation between an elevated risk score and a poorer prognosis for patients. At the same time, through functional enrichment analysis, immune infiltration analysis and visualization of gene mutations, we revealed the complex differences between the high-risk and low-risk groups. In addition, we also constructed a nomogram for forecasting the prognosis of esophageal cancer patients, validated by calibration curves to exhibit robust predictive accuracy. HSPD1 is a gene encoding the chaperonin family. During the cancer development process, the downregulation of HSPD1 is closely related to cancer cell apoptosis [ 33 ] . The risk score's rise coincides with heightened mortality in ESCA patients, supporting the model's validity. In addition, as an effective prognostic marker, HSPD1 serves as a pivotal factor in cancer progression and has survival-promoting or apoptosis-inducing functions depending on the tumor type [ 34 ] . Beatrice Parma et al. discovered that mitochondrial HSPD1 targeting is related to the metabolic damage of non-small cell lung cancer, and HSPD1 is widely expressed in NSCLC tumors and cells [ 35 ] . Yu Zhang et al. identified the correlation between HSPD1 and mitochondrial autophagy in pituitary adenoma [ 36 ] . Seon-Kyu Kim et al. found that the prognostic marker EHMT2 inhibits apoptosis by controlling the expression of HSPD1 [ 37 ] ; The other hub gene MAP1LC3B, as a ubiquitin-like modifier involved in the formation of autophagosomes, meets the cellular energy requirements and prevents excessive ROS production by eliminating mitochondria to the basal level. In the study on the association between the expression pattern of MAP1LC3B and malignancy, an increase in expression level was related to a decrease in patient survival time. The viewpoint of our study is consistent with the histological experimental results [ 38 ] . Haishun Qu et al. integrated CTH and MAP1LC3B to construct a prognostic model for gastric cancer. Survival analysis revealed a marked decline in survival rates among the high-risk group. In the last few years, There has been a boom in single-cell sequencing in recent years [ 39 ] . Single-cell sequencing technology and analytical tools have allowed oncologists to gain a better understanding of the tumor immune microenvironment and how it affects the antitumor immune response [ 40 ] . Additionally, we further utilized the advanced single-cell sequencing technology to visualize the expression patterns of the two genes, HSPD1 and MAP1LC3B, in esophageal cancer tissues. The results suggested that HSPD1 and MAP1LC3B might be closely related to the immune microenvironment of esophageal cancer. Based on the above discoveries, we see hope in the treatment through immunotherapy in the new therapeutic approaches. Chinese traditional medicine has a firmly established position in preventing and treating cancer. [ 41 ] . At the end of this study, based on the two genes, HSPD1 and MAP1LC3B, we predicted the traditional Chinese medicine - Icaritin through the Coremine database, which is greatly important for improving the prognosis of esophageal cancer patients. And the effective component - Icaritin was used to perform molecular docking with the two molecules, HSPD1 and MAPL1C3B. The results indicated a favorable docking interaction. In the study of Yang et al, Icaritin could be considered a promising agent for treating and preventing OSCC [ 42 ] . It is suggested that Icaritin may have a positive effect in improving the prognosis of patients with esophageal cancer, but its clinical effect still needs further study. This study has made significant progress on multiple levels. First of all, we deeply analyzed the core role of mitophagy in the development of esophageal cancer. A novel interplay among immune cells, tumor cells, and cancer stem cells is revealed within the ESCA tumor microenvironment [ 43 ] , which provides a novel perspective for the deeper comprehension of the disease. Secondly, we applied advanced machine learning techniques, which not only improved the identification accuracy of pivotal genes and signaling pathways, but also significantly enhanced the accuracy and reliability of prognosis assessment [ 44 ] . Furthermore, combined with standardized single-cell sequencing data, we provided a detailed molecular map for revealing the heterogeneity of the tumor microenvironment [ 45 ] . Nevertheless, this research also has several constraints. Our conclusion is based on the secondary bioinformatics analysis of public database data. Although the model performed well in the external validation set, there is a lack of direct evidence from animal experiments and cell experiments. Because the samples were derived from a retrospective study, there may be selective bias, which may affect the universality of the analysis results [ 46 ] . Therefore, well-designed prospective studies are needed in the future to further validate our findings. Meanwhile, to understand the clinical significance of mitophagy more comprehensively, more clinical variables need to be considered. However, the collection of relevant data in the current public databases is still not perfect. Although we have preliminarily revealed the mechanism of mitophagy, its detailed molecular mechanism, regulatory network and interaction with other biological processes still need to be further explored. To sum up, through the comprehensive application of advanced technical means and methods, this study systematically revealed the important role of mitophagy in esophageal cancer and its close relationship with prognosis and classification. This discovery not only broadens our knowledge of the molecular mechanism of esophageal cancer, but also provides important molecular markers and theoretical basis for achieving precision medicine for esophageal cancer. Future studies will further explore the mechanism of mitophagy, optimize technical methods and promote clinical transformation and application, in order to bring more effective treatment options and longer survival periods for patients. 5 Conclusion By leveraging advanced technological approaches, this study systematically uncovered the pivotal role of mitophagy in esophageal cancer progression and its close association with prognosis and subtyping. The constructed prognostic model, featuring the key genes HSPD1 and MAP1LC3B, exhibited excellent predictive efficacy in external validation sets, while marked discrepancies were observed in gene expression patterns, functional enrichment, and immune cell infiltration between risk groups. We also successfully identified two subtypes (C1, C2 and CA, CB). Furthermore, single-cell sequencing illuminated the expression profiles of target genes in the tumor microenvironment, offering novel perspectives for immunotherapy applications. The predicted traditional Chinese medicines and their molecular docking outcomes presented promising avenues for improving esophageal cancer prognosis. This study significantly enriched our comprehension of esophageal cancer molecular mechanisms and provided essential molecular markers and theoretical frameworks for advancing precision medicine in esophageal cancer. Future endeavors will further explore mitophagy mechanisms, refine technical methodologies, and expedite clinical translations to offer more effective therapeutic options and prolonged survival for patients. Declarations Funding This study was funded by Detail Project of Precision Medicine Joint Fund of Natural Science Foundation of Hebei Province (Project No.H2021406066); National Natural Science Foundation Project Incubation Fund of Chengde Medical College (Project No.202416) Conflict of interest The authors declare no pertinent conflicts of interest with regard to the matter presented in this article. Author Contribution The conception and manuscript drafting of this study were conducted by FT. XH, SW, and ZW, MH were responsible for the data acquisition process. The comprehensive review and final approval of the manuscript were carried out by YL. YG oversaw all aspects of the manuscript including data interpretation, critical evaluation of the article, and ensuring its scientific rigor. Following a rigorous examination of the findings , all authors endorsed the manuscript for submission in its definitive version . Data Availability In this research, public datasets were scrutinized, including the TCGA-ESCA cohort sourced from the TCGA repository (accessible via http://cancergenome.nih.gov/) and GSE26886 along with GSE20347 retrieved from the GEO database (located at http://www.ncbi.nlm.nih.gov/geo/). The methodologies and software tools utilized for analysis are comprehensively detailed in the "Materials and Methods" section. The necessary data and materials were readily accessible when required. References Akhuj A, Athawale V, Fating T (2024) A Combat Journey of Rehabilitation in Pre- and Post-chemotherapy for Esophagus Carcinoma. Cureus 16(4):e58202. https://doi.org/10.7759/cureus.58202 Reijneveld EAE, Bor P, Dronkers JJ et al (2022) Impact of curative treatment on the physical fitness of patients with esophageal cancer: A systematic review and meta-analysis. Eur J Surg Oncol 48(2):391–402. https://doi.org/10.1016/j.ejso.2021.08.015 Qu HT, Li Q, Hao L et al (2024) Esophageal cancer screening, early detection and treatment: Current insights and future directions. World J Gastrointest Oncol 16(4):1180–1191. https://doi.org/10.4251/wjgo.v16.i4.1180 Sui X, Chen C, Zhou X et al (2023) Integrative analysis of bulk and single-cell gene expression profiles to identify tumor-associated macrophage-derived CCL18 as a therapeutic target of esophageal squamous cell carcinoma. J Exp Clin Cancer Res 42(1):51. https://doi.org/10.1186/s13046-023-02612-5 Silk AW, Barker CA, Bhatia S et al (2022) Society for Immunotherapy of Cancer (SITC) clinical practice guideline on immunotherapy for the treatment of nonmelanoma skin cancer. J Immunother Cancer 10(7):e004434. https://doi.org/10.1136/jitc-2021-004434 Liu X, Chen Y, Zhang S, Dong L (2021) Gut microbiota-mediated immunomodulation in tumor. J Exp Clin Cancer Res 40(1):221. https://doi.org/10.1186/s13046-021-01983-x Lu Y, Li Z, Zhang S, Zhang T, Liu Y, Zhang L (2023) Cellular mitophagy: Mechanism, roles in diseases and small molecule pharmacological regulation. Theranostics 13(2):736–766. https://doi.org/10.7150/thno.79876 D'Arcy MS (2024) Mitophagy in health and disease. Molecular mechanisms, regulatory pathways, and therapeutic implications. Apoptosis. https://doi.org/10.1007/s10495-024-01977-y Lemasters JJ (2005) Selective mitochondrial autophagy, or mitophagy, as a targeted defense against oxidative stress, mitochondrial dysfunction, and aging. Rejuvenation Res 8(1):3–5. https://doi.org/10.1089/rej.2005.8.3 Abeliovich H (2007) Mitophagy: the life-or-death dichotomy includes yeast. Autophagy 3(3):275–277. https://doi.org/10.4161/auto.3915 Youle RJ, Narendra DP (2011) Mechanisms of mitophagy. Nat Rev Mol Cell Biol 12(1):9–14. https://doi.org/10.1038/nrm3028 Su L, Zhang J, Gomez H et al (2023) Mitochondria ROS and mitophagy in acute kidney injury. Autophagy 19(2):401–414. https://doi.org/10.1080/15548627.2022.2084862 Song C, Pan S, Zhang J et al (2022) Mitophagy: A novel perspective for insighting into cancer and cancer treatment. Cell Prolif 55(12):e13327. https://doi.org/10.1111/cpr.13327 Panigrahi DP, Praharaj PP, Bhol CS et al (2020) The emerging, multifaceted role of mitophagy in cancer and cancer therapeutics. Semin Cancer Biol 66:45–58. https://doi.org/10.1016/j.semcancer.2019.07.015 Ritchie ME, Phipson B, Wu D et al (2015) limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 43(7):e47. https://doi.org/10.1093/nar/gkv007 Engebretsen S, Bohlin J (2019) Statistical predictions with glmnet. Clin Epigenetics . ;11(1):123. Published 2019 Aug 23. https://doi.org/10.1186/s13148-019-0730-1 Alderden J, Pepper GA, Wilson A et al (2018) Predicting Pressure Injury in Critical Care Patients: A Machine-Learning Model. Am J Crit Care 27(6):461–468. https://doi.org/10.4037/ajcc2018525 Vickers AJ, Cronin AM, Elkin EB et al (2008) Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers. BMC Med Inf Decis Mak 8:53 Published 2008 Nov 26. https://doi.org/10.1186/1472-6947-8-53 Miao Y, Yuan Q, Wang C et al (2023) Comprehensive Characterization of RNA-Binding Proteins in Colon Adenocarcinoma Identifies a Novel Prognostic Signature for Predicting Clinical Outcomes and Immunotherapy Responses Based on Machine Learning. Comb Chem High Throughput Screen 26(1):163–182. https://doi.org/10.2174/1386207325666220404125228 Zhang Y, Liu N, Wang S (2018) A differential privacy protecting K-means clustering algorithm based on contour coefficients. PLoS ONE 13(11):e0206832. https://doi.org/10.1371/journal.pone.0206832 Yoshihara K, Shahmoradgoli M, Martínez E et al (2013) Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun 4:2612. https://doi.org/10.1038/ncomms3612 Li T, Fan J, Wang B et al (2017) TIMER: A Web Server for Comprehensive Analysis of Tumor-Infiltrating Immune Cells. Cancer Res 77(21):e108–e110. https://doi.org/10.1158/0008-5472.CAN-17-0307 Becht E, Giraldo NA, Lacroix L et al (2016) Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression [published correction. appears Genome Biol 17(1):249. https://doi.org/10.1186/s13059-016-1070-5 Hänzelmann S, Castelo R, Guinney J (2013) GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics 14:7 Published 2013 Jan 16. https://doi.org/10.1186/1471-2105-14-7 Subramanian A, Tamayo P, Mootha VK et al (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102(43):15545–15550. https://doi.org/10.1073/pnas.0506580102 Han Y, Wang Y, Dong X et al (2023) TISCH2: expanded datasets and new tools for single-cell transcriptome analyses of the tumor microenvironment. Nucleic Acids Res 51(D1):D1425–D1431. https://doi.org/10.1093/nar/gkac959 Maleeva G, Wutz D, Rustler K et al (2019) A photoswitchable GABA receptor channel blocker. Br J Pharmacol 176(15):2661–2677. https://doi.org/10.1111/bph.14689 Jiang Wenjie L, Jiaqing N, Yang et al To explore the mechanism of pine-rhubarb-patchcompatibility in renal clear cell carcinoma based on network pharmacology and molecular docking. J Ningxia Med Univ (04), 413–421. 10.16050/j.cnki.issn1674-6309.2024.04.014 Moon H, Zhu J, Donahue LR et al (2019) Krt5 + /Krt15 + foregut basal progenitors give rise to cyclooxygenase-2-dependent tumours in response to gastric acid stress. Nat Commun 10(1):2225 Published 2019 May 20. https://doi.org/10.1038/s41467-019-10194-0 Foster JM, Sleightholm R, Patel A et al (2019) Morbidity and Mortality Rates Following Cytoreductive Surgery Combined With Hyperthermic Intraperitoneal Chemotherapy Compared With Other High-Risk Surgical Oncology Procedures. JAMA Netw Open 2(1):e186847 Published 2019 Jan 4. https://doi.org/10.1001/jamanetworkopen.2018.6847 Li Y, Yang C, Liu Z et al (2022) Integrative analysis of CRISPR screening data uncovers new opportunities for optimizing cancer immunotherapy. Mol Cancer 21(1):2 Published 2022 Jan 2. https://doi.org/10.1186/s12943-021-01462-z Li R, Xin T, Li D et al (2018) Therapeutic effect of Sirtuin 3 on ameliorating nonalcoholic fatty liver disease: The role of the ERK-CREB pathway and Bnip3-mediated mitophagy. Redox Biol 18:229–243. https://doi.org/10.1016/j.redox.2018.07.011 Kim SK, Kim K, Ryu JW et al (2019) The novel prognostic marker, EHMT2, is involved in cell proliferation via HSPD1 regulation in breast cancer. Int J Oncol 54(1):65–76. https://doi.org/10.3892/ijo.2018.4608 Yun CW, Kim HJ, Lim JH et al (2019) Heat Shock Proteins: Agents of Cancer Development and Therapeutic Targets in Anti-Cancer Therapy. Cells 9(1):60 Published 2019 Dec 24. https://doi.org/10.3390/cells9010060 Parma B, Ramesh V, Gollavilli PN et al (2021) Metabolic impairment of non-small cell lung cancers by mitochondrial HSPD1 targeting. J Exp Clin Cancer Res 40(1):248. https://doi.org/10.1186/s13046-021-02049-8 Zhang Y, Ma X, Liu C et al (2024) Identification of HSPD1 as a novel invasive biomarker associated with mitophagy in pituitary adenomas. Transl Oncol 41:101886. https://doi.org/10.1016/j.tranon.2024.101886 Kim SK, Kim K, Ryu JW et al (2019) The novel prognostic marker, EHMT2, is involved in cell proliferation via HSPD1 regulation in breast cancer. Int J Oncol 54(1):65–76. https://doi.org/10.3892/ijo.2018.4608 Spirina LV, Avgustinovich AV, Bakina OV et al (2022) LC3B, mTOR, AMPK Are Molecular Targets for Neoadjuvant Chemotherapy in Gastric Cancers. Curr Issues Mol Biol 44(7):2772–2782. https://doi.org/10.3390/cimb44070190 Xu J, Liao K, Yang X et al (2022) Correction to: Using single-cell sequencing technology to detect circulating tumor cells in solid tumors. Mol Cancer 21(1):100. https://doi.org/10.1186/s12943-022-01564-2 Jia Q, Chu H, Jin Z et al (2022) High-throughput single-сell sequencing in cancer research. Signal Transduct Target Ther 7(1):145. https://doi.org/10.1038/s41392-022-00990-4 Liu H, Huang M, Xin D, Wang H, Yu H, Pu W (2024) Natural products with anti-tumorigenesis potential targeting macrophage. Phytomedicine 131:155794. https://doi.org/10.1016/j.phymed.2024.155794 Yang JG, Lu R, Ye XJ et al (2017) Icaritin Reduces Oral Squamous Cell Carcinoma Progression via the Inhibition of STAT3 Signaling. Int J Mol Sci 18(1):132 Published 2017 Jan 12. https://doi.org/10.3390/ijms18010132 Yang K, Xie Y, Xue L et al (2023) M2 tumor-associated macrophage mediates the maintenance of stemness to promote cisplatin resistance by secreting TGF-β1 in esophageal squamous cell carcinoma. J Transl Med 21(1):26. https://doi.org/10.1186/s12967-022-03863-0 Romero-Brufau S, Whitford D, Johnson MG et al (2021) Using machine learning to improve the accuracy of patient deterioration predictions: Mayo Clinic Early Warning Score (MC-EWS). J Am Med Inf Assoc 28(6):1207–1215. https://doi.org/10.1093/jamia/ocaa347 Xie S, Cai Y, Chen D et al (2023) Single-cell transcriptome analysis reveals heterogeneity and convergence of the tumor microenvironment in colorectal cancer. Front Immunol 13:1003419 Published 2023 Jan 4. https://doi.org/10.3389/fimmu.2022.1003419 Hughes MSA, Beganovic M (2019) Introduction of Selection Biases Due to Loss to Follow-Up in Infectious Disease Retrospective Outcomes Studies. Antimicrob Agents Chemother 63(12):e01681–e01619. https://doi.org/10.1128/AAC.01681-19 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 13 Feb, 2025 Read the published version in Apoptosis → Version 1 posted Editorial decision: Revision requested 03 Nov, 2024 Reviews received at journal 02 Nov, 2024 Reviewers agreed at journal 22 Oct, 2024 Reviewers agreed at journal 15 Oct, 2024 Reviewers agreed at journal 14 Sep, 2024 Reviewers invited by journal 14 Sep, 2024 Editor assigned by journal 16 Aug, 2024 Submission checks completed at journal 16 Aug, 2024 First submitted to journal 15 Aug, 2024 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-4917245","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":344445853,"identity":"e45d8144-67d9-4e24-9017-dfb80c42709e","order_by":0,"name":"Feng Tian","email":"","orcid":"","institution":"Clinical College of Chengde Medical University","correspondingAuthor":false,"prefix":"","firstName":"Feng","middleName":"","lastName":"Tian","suffix":""},{"id":344445854,"identity":"479f9168-414e-4048-ab97-6e19e867bbcf","order_by":1,"name":"Xinyang He","email":"","orcid":"","institution":"Nursing College of Chengde Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xinyang","middleName":"","lastName":"He","suffix":""},{"id":344445855,"identity":"9c2f1e78-9cf5-4b57-bbe8-654819d9878f","order_by":2,"name":"Saiwei Wang","email":"","orcid":"","institution":"Nursing College of Chengde Medical University","correspondingAuthor":false,"prefix":"","firstName":"Saiwei","middleName":"","lastName":"Wang","suffix":""},{"id":344445856,"identity":"664b60bc-9de9-4e71-98f7-757b763c88f1","order_by":3,"name":"Yiwei Liang","email":"","orcid":"","institution":"Nursing College of Chengde Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yiwei","middleName":"","lastName":"Liang","suffix":""},{"id":344445857,"identity":"1c87159e-c907-4396-9f6d-826675ef843f","order_by":4,"name":"Zijie Wang","email":"","orcid":"","institution":"Nursing College of Chengde Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zijie","middleName":"","lastName":"Wang","suffix":""},{"id":344445858,"identity":"0d679e28-80ec-4e24-9ada-2220cd73cc52","order_by":5,"name":"Minxuan Hu","email":"","orcid":"","institution":"Clinical College of Chengde Medical University","correspondingAuthor":false,"prefix":"","firstName":"Minxuan","middleName":"","lastName":"Hu","suffix":""},{"id":344445860,"identity":"982b800a-2aad-4344-88b1-674408ca67bd","order_by":6,"name":"Yaxian Gao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYPACCQYG9gYQCQQHiNbCc4A0LSBdCURqkXc/fEziZ5uFvLnk44e3btQwyPHdSGD8XIBHi+GZtDTJnjMShjtnpxlb5xxjMJa8kcAsPQOfloYcMwmeCgnGDbdz2KRzGxgSN9xIYGPmwael/42Z5B8DCfsNN8+AtdQT1CIvkWMmDbQFaDgPWEuCASEtBhLPkq1lzkgkbzgD9ouE4cwzD5ul8drSn3zw5tu2OtsNxw8/vJ1TYyPPdzz54Ge8thxgYJFA4oPYjA14NABtaWBg/oBXxSgYBaNgFIwCAOnkSeWAxv1YAAAAAElFTkSuQmCC","orcid":"","institution":"Basic Medical Institute Immunology of Chengde Medical College","correspondingAuthor":true,"prefix":"","firstName":"Yaxian","middleName":"","lastName":"Gao","suffix":""}],"badges":[],"createdAt":"2024-08-15 05:46:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4917245/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4917245/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10495-024-02061-1","type":"published","date":"2025-02-13T15:57:53+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":66535697,"identity":"8ff4ac53-193e-46c7-83bc-3a9c0bfd3403","added_by":"auto","created_at":"2024-10-14 06:59:48","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3071540,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe data analysis process\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4917245/v1/ab2861c9a59926ca988685c0.jpg"},{"id":66536157,"identity":"231e7b3d-4bdf-4db4-a7e7-52e01d6b7c40","added_by":"auto","created_at":"2024-10-14 07:07:48","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1154415,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScreen the mitophagy-related genes that are linked to the prognosis of esophageal cancer patients \u0026nbsp;A\u003c/strong\u003e Volcano plot depicting differentially expressed genes (DEGs) between cancer and normal samples, with blue triangles marking significant upregulation \u003cstrong\u003eB\u003c/strong\u003e The Venn diagram illustrated the common genes shared between differentially expressed genes and mitophagy-related genes\u003cstrong\u003e C\u003c/strong\u003e The bubble chart visualized the KEGG enrichment results of the above 136 genes \u003cstrong\u003eD\u003c/strong\u003e The forest plot illustrated the p values and Hazard ratio of 14 genes with prognostic significance\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4917245/v1/238bc55ce55c8523aac81812.jpg"},{"id":66535689,"identity":"89489005-2fac-4023-9d40-cd2bc9079e69","added_by":"auto","created_at":"2024-10-14 06:59:48","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1113290,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of MRG-related subgroups based on 14 MRGs with significant prognostic value A\u003c/strong\u003e Consensus Clustering Outcome at k=2 \u003cstrong\u003eB\u003c/strong\u003eThe cumulative distribution function (CDF) plots portray the variation in k values, spanning from 2 to 10 \u003cstrong\u003eC\u003c/strong\u003e Visualization of Characteristic CDF Delta Area Curves \u003cstrong\u003eD\u003c/strong\u003e Visualization of the two clusters' distribution using Uniform Manifold Approximation and Projection (UMAP)\u003cstrong\u003e E \u003c/strong\u003eSurvival Analysis Highlighting Subtype Disparity (p \u0026lt; 0.001)\u003c/p\u003e","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4917245/v1/7f3eee81cb6accca982958df.jpg"},{"id":66536163,"identity":"ed7e9cb9-2318-429f-910c-576941fe422c","added_by":"auto","created_at":"2024-10-14 07:07:48","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1076160,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe distinctive variations of the immune microenvironment between Cluster 1 and Cluster 2 A\u003c/strong\u003e Comparison of stromal scores between C1 and C2 groups, revealing differences in stromal cell abundance within the tumor microenvironment\u003cstrong\u003e B\u003c/strong\u003e Analysis of immune scores for C1 versus C2, showcasing variations in immune cell infiltration patterns across the two groups \u003cstrong\u003eC\u003c/strong\u003e Estimated scores for C1 and C2, assessing the overall tumor-stroma-immune composition, highlighting disparities in tumor microenvironment complexity \u003cstrong\u003eD\u003c/strong\u003e CIBERSORT analysis distinguished 22 types of immune cell infiltrations in C1 versus C2 subtypes \u003cstrong\u003eE\u003c/strong\u003e TIMER analysis comparing neutrophil infiltration in C1 and C2 \u003cstrong\u003eF\u003c/strong\u003e Myeloid dendritic cell infiltration contrast by TIMER in C1 and C2\u003cstrong\u003e G\u003c/strong\u003e CD4+ T cell infiltration levels analyzed by TIMER in C1 and C2\u003c/p\u003e","description":"","filename":"Fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4917245/v1/4a57f6a1b98aa2c0c79540bf.jpg"},{"id":66535686,"identity":"c5e35f5b-6165-46e9-b035-65926e198ebf","added_by":"auto","created_at":"2024-10-14 06:59:47","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1572571,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of differentially expressed genes (DEGs) and functional analyses A\u003c/strong\u003e Volcano plot displaying DEGs between the two subtypes \u003cstrong\u003eB\u003c/strong\u003e Circle plot representing signaling pathways identified by Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis \u003cstrong\u003eC\u003c/strong\u003e Crafting a network visualization that illuminates biological processes enriched through the application of Gene Ontology (GO) analysis. \u003cstrong\u003eD\u003c/strong\u003e A bubble diagram illustrating enriched molecular functions from GO analysis\u003cstrong\u003e E\u003c/strong\u003e Exploration of Differentially Expressed Genes (DEGs) through Protein-Protein Interaction (PPI) analysis \u003cstrong\u003eF\u003c/strong\u003e Gene Set Enrichment Analysis (GSEA) plots uncovered distinct enrichment profiles between C1 and C2 subtypes\u003c/p\u003e","description":"","filename":"Fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4917245/v1/ba0129a72c30b4f25a4bb765.jpg"},{"id":66535687,"identity":"b9ef746f-9e00-4d9f-a622-b3d3cd95bf3e","added_by":"auto","created_at":"2024-10-14 06:59:47","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1534649,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDetecting gene clusters by scrutinizing distinct mitophagy-associated clusters to pinpoint genes displaying differential expression patterns A\u003c/strong\u003e The heatmap illustrating the expression patterns of 164 differentially expressed genes (DEGs) between clusters C1 and C2 \u003cstrong\u003eB \u003c/strong\u003eConsensus clustering analysis for k=2 revealed two distinct gene clusters among the 164 DEGs \u003cstrong\u003eC\u003c/strong\u003e A cumulative distribution function (CDF) curve that visualized the outcomes of the clustering analysis \u003cstrong\u003eD\u003c/strong\u003e Consensus ratings across various k values, spanning from 2 to 10, serve as indicators of cluster stability \u003cstrong\u003eE\u003c/strong\u003e Principal component analysis (PCA) demonstrating the distributional landscapes of the two distinguished gene cohorts\u003c/p\u003e","description":"","filename":"Fig6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4917245/v1/5e0bc89b28e8b8c311f5f615.jpg"},{"id":66535694,"identity":"d823a5e0-6409-4240-bf12-b727fb6b2222","added_by":"auto","created_at":"2024-10-14 06:59:48","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1135008,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCharacterization of immune infiltration and biological function features in distinct clusters of mitophagy A\u003c/strong\u003e Boxplots illustrating the expression variability of 14 differentially expressed mitophagy-related genes (DEMRGs) across the two clusters \u003cstrong\u003eB\u003c/strong\u003e A bar graph presenting the results of Gene Set Variation Analysis (GSVA) for Gene Ontology (GO) gene sets, comparing the two gene clusters \u003cstrong\u003eC\u003c/strong\u003e A bar chart depicting the GSVA results for KEGG gene sets across the two gene clusters \u003cstrong\u003eD\u003c/strong\u003eBoxplots displaying a comprehensive analysis of immune cell infiltration profiles between clusters CA and CB, based on the CIBERSORT algorithm \u003cstrong\u003eE\u003c/strong\u003eTIMER analysis revealed differences in immune cell infiltration between CA and CB\u003c/p\u003e","description":"","filename":"Fig7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4917245/v1/0726ef510512cf12a03bf7c3.jpg"},{"id":66536158,"identity":"2e04e1b1-cc63-462b-acc9-7f0feb9bbd9d","added_by":"auto","created_at":"2024-10-14 07:07:48","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1199411,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction of risk model A\u003c/strong\u003e The seven pivotal genes were identified through the LASSO analysis, with individual curves illustrating their respective contributions, based on altered parameters \u003cstrong\u003eB\u003c/strong\u003e The optimal parameter (lambda) was determined by vertical dashed lines\u003cstrong\u003e C\u003c/strong\u003e Random Forest analysis highlighted 14 genes based on MeanDecreaseGini values \u003cstrong\u003eD\u003c/strong\u003e A Venn diagram illustrated the convergence of three mitophagy-associated genes shared by both LASSO and Random Forest, underscoring their biological and prognostic significance \u003cstrong\u003eE\u003c/strong\u003e Forest plot visualizing three significant prognosis model genes, with p-values and Hazard Ratios listed \u003cstrong\u003eF \u003c/strong\u003eThe coefficients of the prognostic model were displayed \u003cstrong\u003eG\u003c/strong\u003e Heatmap depicting overall survival decline associated with increasing risk score and shifts in gene expression\u003cstrong\u003e H\u003c/strong\u003eTime-dependent ROC curves with AUC values consistently \u0026gt;0.7 at 1, 3, and 5 years \u003cstrong\u003eI\u003c/strong\u003e Kaplan-Meier survival curves showcasing overall survival differences between high and low-risk groups (p \u0026lt; 0.05)\u003c/p\u003e","description":"","filename":"Fig8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4917245/v1/5d0f5fdd7c248a4c6833d4c4.jpg"},{"id":66537481,"identity":"c778beb6-a451-4de0-9e3d-8d8afdb80423","added_by":"auto","created_at":"2024-10-14 07:15:48","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":661272,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBoxplots illustrating the association between risk score and clinical attributes A\u003c/strong\u003e Boxplot analysis revealed a lack of significant disparity in risk score across age groups, indicating independence from age\u003cstrong\u003e B\u003c/strong\u003eComparison by gender showed no discernible pattern in risk score, suggesting gender is not a significant factor \u003cstrong\u003eC\u003c/strong\u003e M Stages: Risk score remained consistent regardless of metastatic status \u003cstrong\u003eD\u003c/strong\u003e T Stages: Primary tumor characteristics did not significantly influence risk score \u003cstrong\u003eE\u003c/strong\u003e N Stages: Lymph node involvement did not correlate with risk score \u003cstrong\u003eF \u003c/strong\u003eOverall staging analysis confirmed the lack of a significant association between clinical staging and risk score\u003cstrong\u003e GH\u003c/strong\u003e Survival curves by age groups did not reveal a significant impact of age on survival outcomes, consistent with the independence of risk score from age\u003cstrong\u003e IJ\u003c/strong\u003e Gender-stratified survival curves further supported the absence of a significant gender effect on survival or risk score interpretation\u003c/p\u003e","description":"","filename":"Fig9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4917245/v1/08a525620abe731b56063c7c.jpg"},{"id":66536159,"identity":"a64feb46-e6d0-46ea-a558-950d6821a581","added_by":"auto","created_at":"2024-10-14 07:07:48","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":597302,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDevelopment and assessment of a nomogram model for prognosis of ESCA A \u003c/strong\u003eIntegrated Nomogram combined risk score and clinical features for ESCA prognosis \u003cstrong\u003eB\u003c/strong\u003e Calibration curve demonstrated predictive precision of the nomogram \u003cstrong\u003eC\u003c/strong\u003e The C-index curve quantified the nomogram's discriminative ability \u003cstrong\u003eD \u003c/strong\u003eDecision curve analysis illustrated clinical benefits of using the nomogram \u003cstrong\u003eE\u003c/strong\u003e ROC Curve Validation (GSE26886) confirmed model robustness in an independent dataset\u003cstrong\u003e F\u003c/strong\u003e ROC Curve Validation (GSE20347) further strengthened evidence with another dataset validation\u003c/p\u003e","description":"","filename":"Fig10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4917245/v1/96f3c01cc764b5ef689f038f.jpg"},{"id":66536160,"identity":"bfe595c4-b66f-40ad-b90f-1ca68b0177aa","added_by":"auto","created_at":"2024-10-14 07:07:48","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":1175890,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEnrichment analysis, mutation profiling, and immune infiltration analysis A \u003c/strong\u003eGSEA visualizing enrichment differences between high and low-risk groups\u003cstrong\u003e B\u003c/strong\u003e Single-gene GSEA analysis indicating enrichment differences based on high and low expression levels of the HSPD1 gene \u003cstrong\u003eC \u003c/strong\u003eSingle-gene GSEA analysis indicating enrichment differences based on high and low expression levels of the MAP1LC3B gene \u003cstrong\u003eD\u003c/strong\u003e Mutation waterfall plot showcasing distinct mutational landscapes between high and low-risk groups\u003cstrong\u003e E\u003c/strong\u003eCIBERSORT distinguishing 22 immune cell infiltrations between high and low-risk groups \u003cstrong\u003eF \u003c/strong\u003eCorrelation heatmap reflecting relationships among immune cells in the high-risk group \u003cstrong\u003eG\u003c/strong\u003e Correlation heatmap illuminating associations among immune cells in the low-risk cohort\u003c/p\u003e","description":"","filename":"Fig11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4917245/v1/dc02eb6eeb169619eac232d1.jpg"},{"id":66535691,"identity":"1807e3df-041e-465e-a44c-3dedc3fff5c7","added_by":"auto","created_at":"2024-10-14 06:59:48","extension":"jpg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":923669,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDevelopment of a DEMRGs Scoring System A\u003c/strong\u003e The DEMRGs-Scoring system discriminated between MRGs clusters 1 and 2, unveiling distinct risk profiles through its risk score calculation\u003cstrong\u003e B\u003c/strong\u003e Risk score revealed disparity across clusters A and B \u003cstrong\u003eC\u003c/strong\u003eA Sankey plot elegantly illustrated the intricate connections between the risk score and distinct clusters, revealing their synergistic effects on risk assessment \u003cstrong\u003eD \u003c/strong\u003eLevels of CD274, TGFB1, TLR4 and HMGB1 varied between MRGs clusters 1 and 2, with statistical significance denoted by *** (P \u0026lt; 0.001) and **** (P \u0026lt; 0.0001) \u003cstrong\u003eE\u003c/strong\u003e Distinct expression patterns of CD274, TGFB1, TLR4, and HMGB1 were identified between gene clusters A and B, showcasing significant differences as indicated by *** (P \u0026lt; 0.001) and **** (P \u0026lt; 0.0001)\u003c/p\u003e","description":"","filename":"Fig12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4917245/v1/e8353bb7a005ba600e3fcc70.jpg"},{"id":66535692,"identity":"e637c06b-1a1d-443e-9153-f250008ef1ef","added_by":"auto","created_at":"2024-10-14 06:59:48","extension":"jpg","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":1394633,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle-cell sequencing data unravel cellular expression levels of HSPD1 and MAP1LC3B genes ABCD\u003c/strong\u003e Distinct clustering patterns observed in GSE160269 (A, B) and GSE173950 (C, D) datasets \u003cstrong\u003eEFGH\u003c/strong\u003e UMAP plots visualizing expression profiles of HSPD1 and MAP1LC3B genes in GSE160269 (E, F) and GSE173950 (G, H) datasets\u003cstrong\u003e IJ\u003c/strong\u003e Violin plots depicting the distribution of HSPD1 and MAP1LC3B gene expression in GSE160269 (I) and GSE173950 (J) datasets\u003c/p\u003e","description":"","filename":"Fig13.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4917245/v1/2b99d84ec7a898cc8fd0ab12.jpg"},{"id":66536161,"identity":"479b30f6-309c-48b1-9a36-683a91eae46f","added_by":"auto","created_at":"2024-10-14 07:07:48","extension":"jpg","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":1075698,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMolecule docking A \u003c/strong\u003eA sophisticated molecular docking schematic delineated the intricate interaction between Icaritin and HSPD1, elucidating their binding affinity and mode of engagement \u003cstrong\u003eB \u003c/strong\u003eA precise molecular docking diagram captured the binding dynamics between Icaritin and MAP1LC3B, offering insights into their molecular recognition and potential functional consequences\u003c/p\u003e","description":"","filename":"Fig14.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4917245/v1/a5639bd52bb40c8221d9a368.jpg"},{"id":76487719,"identity":"19ddca2d-ea40-4931-a0f2-60b173fdbf30","added_by":"auto","created_at":"2025-02-17 16:11:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":19399490,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4917245/v1/7b9d06b1-20e3-4fa9-ada5-ce045b9d522f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrating Single-Cell Sequencing and Machine Learning to Uncover the Role of Mitophagy in Subtyping and Prognosis of Esophageal Cancer","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eEsophageal carcinoma, a malignant tumor, predominantly comprises esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC), originating from the epithelial cells of the esophageal lining\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. This disease imposes a considerable global health challenge, ranking as the sixth foremost contributor to cancer-related mortality and the ninth in the incidence of cancers globally, underscoring its widespread and severe nature\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Despite some therapeutic advancements in endoscopic interventions, surgical resections, chemotherapy, radiotherapy, and multimodal treatment regimens, clinical outcomes for ESCA patients remain discouraging, with mortality rates persistently high\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Therefore, there is an urgent requirement for identifying prognostic biomarkers and construct robust prognostic models to enhance patient status, implement personalized treatment strategies, and ultimately improve the survival rates of those suffering from this serious disease. The infiltration of immune cells has a well-established correlation with cancer prognosis, particularly in hard-to-treat tumors like esophageal cancer\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Statistical evidence suggests that immunotherapy has significantly improved treatment outcomes in these refractory cancers\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. However, individual responses to cancer immunotherapy vary greatly\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Consequently, identifying novel and distinctive genes holds promise for advancing more targeted and effective treatment strategies, thereby offering valuable prospects for ESCA.\u003c/p\u003e \u003cp\u003eUnder stressors such as nutrient deprivation, reactive oxygen species (ROS), and cellular senescence, mitochondria can experience varying degrees of damage\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Mitophagy, a targeted autophagy mechanism that specifically eliminates damaged mitochondria within autophagosomes for fusion with lysosomes and subsequent disposal, is essential for preserving both mitochondrial integrity and cellular stability\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. This concept was first formally proposed by Lemasters in 2005, who observed that the onset of mitophagy is triggered by the depletion of mitochondrial membrane potential and the subsequent opening of the mitochondrial permeability transition pore \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Several diseases exhibit a close correlation with aberrant mitophagy, implicating its involvement in their underlying pathogenesis, including neurodegenerative disorders, hematological malignancies, cardiovascular diseases, and even cancer\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Notably, mitophagy exhibits a dual and complex part in the development of tumors, both promoting and inhibiting cancer progression. Distinct cancer types, such as ovarian, lung, colorectal, and breast cancers, display heterogeneous levels of mitophagy, indicating its diverse involvement across various malignancies. Among the four main mitophagy pathways in mammals, the PINK1/Parkin-mediated mitophagy is considered a potential tumor suppression mechanism. Research has shown that 25% of colorectal cancer tumors exhibit focal PARK2 loss\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e, and specific PARK2 mutations or deletions have been detected across multiple cancer types, suggesting that PARK2 mutations may drive tumor progression by eliminating the E3 ubiquitin ligase activity of Parkin. However, the role of PARK2 loss in cancer may depend on the specific context and could even be contradictory. For instance, in melanoma, Parkin loss inhibits Mfn2 ubiquitination, inducing apoptosis and suppressing melanoma development and metastasis. BNIP3, a protein with pro-apoptotic properties, augments mitophagy by impeding the fusion of dysfunctional mitochondria, thereby expediting their elimination from the cell. The function of BNIP3 as a tumor inhibitor or promoter remains controversial in various cancer contexts. Similarly, the role of Nix-mediated mitophagy may exhibit complexity and diversity across different cancer types and stages\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. The dual nature of mitophagy emphasizes its potential as a novel therapeutic target. Inducing mitophagy may provide an alternative mechanism for cancer cell death, creating hopeful avenues for cancer treatment. Inhibitory strategies targeting mitophagy regulators such as PINK1, Parkin, BNIP3, and FUNDC1 may offer new therapeutic options for cancer intervention\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Current research has primarily concentrated on the role of mitophagy in ovarian, lung, as well as breast cancers, while studies on esophageal cancer (ESCA) remain relatively limited. Therefore, there is an urgent need to explore the relationship between mitophagy and esophageal cancer to uncover new therapeutic strategies for this malignancy.\u003c/p\u003e \u003cp\u003eThis study aims to identify unique genes associated with mitophagy by comparing ESCA patients with a control group. We then employ univariate and multivariable Cox regression analyses, along with machine learning algorithms, to determine two feature genes with prognostic value. These findings provide a basis for establishing a prognostic scale for predicting outcomes in esophageal cancer patients. Furthermore, we validate the reliability of the model through ROC curves, DCA curves, and calibration curves. Additionally, we utilize consensus clustering methods to identify two distinct subtypes of esophageal cancer patients and explore their potential characteristics through enrichment analysis, immune infiltration analysis and immune checkpoints comparison. Moreover, using single-cell sequencing technology, we investigate the expression patterns of these two feature genes in esophageal cancer cells. Eventually, we conducted traditional Chinese medicine prediction and molecular docking for the core genes. This comprehensive approach not only enhances our comprehension of the molecular mechanisms driving esophageal cancer but also provides insightful perspectives for tailoring individualized therapeutic approaches to this condition.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data Collection and Preprocessing\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe flowchart of this study is visually depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e,outlining its methodology and progression.\u003c/p\u003e \u003cp\u003eRNA sequencing (RNA-seq) data for 173 individuals diagnosed with esophageal carcinoma (ESCA) was obtained from The Cancer Genome Atlas (TCGA) public database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). In addition, a collection of mitophagy-related genes (MRGs) was extracted from the GeneCards database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genecards.org/\u003c/span\u003e\u003cspan address=\"https://www.genecards.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), yielding a total of 385 genes for further analysis. For external validation, datasets GSE26886 and GSE20347 were sourced from the Gene Expression Omnibus (GEO) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The characteristics of these validation datasets are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eExternal validation data set from a GEO information (GSE26886 and GSE20347)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDatatsets\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlatform\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal sample number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNormal sample number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eESCA sample number\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE26886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGPL570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE20347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGPL571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Identification of Differentially Expressed Genes Related to Mitophagy\u003c/h2\u003e \u003cp\u003eThe \"limma\" package\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e were utilized for differential expression analysis. The parameters for differential expression analysis were set as follows: |log2FC| \u0026gt; 1.2, and FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The genes identified through this screening were cross-referenced with mitophagy-related genes (MRGs) sourced from the GeneCards database to determine the common genes, designated as differentially expressed mitophagy-related genes (DEMRGs).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Cox regression analysis\u003c/h2\u003e \u003cp\u003eUnivariate and multivariate Cox proportional hazards regression analyses were employed to screen critical genes closely associated with ESCA prognosis. Additionally, we applied forest plots to visually represent the results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Machine Learning\u003c/h2\u003e \u003cp\u003eDuring the construction of the prognostic model, two advanced machine learning techniques were employed: LASSO\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e regression and Random Forest\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Lasso regression, an extension of logistic regression, incorporates a shrinkage parameter to minimize classification error while selecting significant variables, thereby providing an effective approach to filter through numerous feature variables for optimal model performance. Random forest, as an ensemble of decision trees, excel in survival analysis by adeptly identifying important features and elucidating patient outcomes, illustrating their extensive applicability in this domain.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Development and verification of a risk assessment model\u003c/h2\u003e \u003cp\u003eTo develop a risk model, we employed LASSO regression to calculate the coefficients of each gene, as shown in the formula \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\text{R}\\text{i}\\text{s}\\text{k}\\:\\text{s}\\text{c}\\text{o}\\text{r}\\text{e}=\\varSigma\\:\\left(gene1\\:expression\\times\\:coefficient1+gene2\\:expression\\times\\:coefficient2\\right)\\)\u003c/span\u003e\u003c/span\u003e,where represents the expression of MRDEGs. Patients were segregated into high- and low-risk cohorts, utilizing the median risk score as the demarcation point. To validate this prognostic model, external datasets, GSE26886 and GSE20347, sourced from the GEO database, were analyzed. To further assess the predictive precision of the risk model, decision curve analysis (DCA\u003csup\u003e)[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003eand time-dependent receiver operating characteristic (ROC) curve \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003eevaluations were performed using the \"ggDCA\" and \"timeROC\" R packages. Kaplan-Meier curves were generated and log-rank tests administered to evaluate the distinction in overall survival (OS) between the two risk groups, subsequently assessing the clinical outcome predictive potential of the risk model in ESCA. Utilizing the \"rms\" and \"survival\" R packages, a comprehensive nomogram was developed that integrated risk score and clinicopathological characteristics to facilitate personalized predictions. A calibration curve was generated to assess the predictive accuracy of the nomogram.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Consensus Clustering\u003c/h2\u003e \u003cp\u003eUsing the \"ConsensusClusterPlus\" R package\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e unsupervised clustering of DEMRGs was conducted to identify distinct subtypes of ESCA patients. To visualize the distribution of patients across these clusters, Uniform Manifold Approximation and Projection (UMAP) analysis was performed utilizing the \"umap\" R package. Subsequently, principal component analysis (PCA) was applied to the subtypes derived from the secondary clustering analysis, expounding upon their inherent structure. Both UMAP and PCA serve as effective dimensionality reduction techniques, enabling the mapping of high-dimensional data into lower-dimensional space, thereby making it convenient for a clearer representation of the data's characteristics and insights into ESCA subtypes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Immune Infiltration\u003c/h2\u003e \u003cp\u003eTo investigate the distribution of immune cells within ESCA patient subtypes and the association between risk score and tumor-infiltrating immune cells, three algorithms were employed: TIMER, CIBERSORT and ESTIMATE\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. These algorithms aided in estimating the relevance between the prognostic model and immune cell infiltration patterns within the tumor microenvironment (TME). Data on immune infiltration was sourced from TIMER\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cistrome.shinyapps.io/timer/\u003c/span\u003e\u003cspan address=\"https://cistrome.shinyapps.io/timer/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), with results from TIMER, CIBERSORT compared. Moreover, the ESTIMATE algorithm evaluated the immune and stromal components of the TME, generating stromal scores, immune scores, and ESTIMATED scores\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. An increase in these scores indicates a greater presence of corresponding components within the TME, providing critical insights into the tumor's immunological landscape and potential implications for patient prognosis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Enrichment Analysis\u003c/h2\u003e \u003cp\u003eThe \"ClusterProfiler\" R package was employed to conduct Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses to elucidate the biological processes (BP), molecular functions (MF),cellular components (CC), and potential signaling pathways associated with ESCA subtypes which was visualized in Metascape. To capture subtle alterations in pathway activity, Gene Set Variation Analysis (GSVA) was conducted, transforming gene expression profiles across samples into gene set enrichment scores, thus facilitating the assessment of gene set activity\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Complementarily, Gene Set Enrichment Analysis (GSEA) was conducted to evaluate the differential expression of predefined gene sets between distinct biological states, providing a comprehensive overview of the transcriptional landscape in ESCA\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Mitophagy Expression in Single-Cell RNA Sequencing Tumor Data\u003c/h2\u003e \u003cp\u003eTISCH2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tisch.compgenomics.org\u003c/span\u003e\u003cspan address=\"http://tisch.compgenomics.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), a robust single-cell RNA-seq TME database, enables an exhaustive analysis of heterogeneity across datasets and cell types\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. This study explores the immune-related prognostic features of ESCA at the single-cell level, utilizing data from TISCH2, specifically GSE160269 and GSE173950.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Prediction of Traditional Chinese medicine and molecular docking\u003c/h2\u003e \u003cp\u003eMap the mitophagy key genes obtained in the aforesaid steps onto the Coremine medical ontology information retrieval platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.coremine.com/medical/\u003c/span\u003e\u003cspan address=\"http://www.coremine.com/medical/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and select the top 30 traditional Chinese medicine drugs that may have a regulatory effect on the mitophagy key genes in patients suffering from esophageal cancer. Furthermore, to procure the target protein result files, searches were conducted on the PDB database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.rcsb.org/\u003c/span\u003e\u003cspan address=\"https://www.rcsb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and the AlphaFold database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://alphafold.ebi.ac.uk/\u003c/span\u003e\u003cspan address=\"https://alphafold.ebi.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). From the PubChem database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://pubchem.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), the active ingredient structure files were retrieved and converted into PDB format utilizing Open Babel 2.3.2 software. PYMOL 2.3.4 software was then employed to meticulously prepare the receptor protein by eliminating water molecules and ligands. Following this, the receptor protein underwent essential modifications, including hydrogenation and charge balancing, using AutoDockTools software. Subsequently, molecular docking simulations were conducted between the receptor protein and small molecule ligands utilizing AutoDock Vina 1.1.2\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Finally, Pymol was utilized to visualize the docking outcomes, focusing on those with favorable binding energies.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Identification of Prognostic Mitophagy-Related Genes (MRGs)\u003c/h2\u003e \u003cp\u003eDifferential expression analysis of tumor and normal samples sourced from the TCGA database revealed significant alterations in gene expression profiles in esophageal squamous cell carcinoma (ESCA). Specifically, a total of 6451 differentially expressed genes (DEGs) were identified, with 1455 genes downregulated and 4996 genes upregulated \u003cb\u003e(Fig.\u0026nbsp;2A)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e Furthermore, we identified 136 genes common to both the mitophagy-related genes and differentially expressed genes (DEGs) \u003cb\u003e(Fig.\u0026nbsp;2B)\u003c/b\u003e. Subsequently, a profound KEGG enrichment analysis of these genes prominently converged on mitophagy and autophagy pathways\u003cb\u003e(Fig.\u0026nbsp;2C)\u003c/b\u003e.To evaluate the prognostic value of these genes, we conducted a univariate Cox regression analysis incorporating survival status, survival duration, and gene expression profiles, ultimately identifying 14 genes with significant prognostic value, the results were visually presented in a forest plot\u003cb\u003e(Fig.\u0026nbsp;2D)\u003c/b\u003e.Moreover, a correlation heatmap was also generated to illustrate the intricate relationships among these 14 genes\u003cb\u003e(Fig.\u0026nbsp;2E)\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Identification of MRG-related Subtypes Based on 14 MRGs\u003c/h2\u003e \u003cp\u003eConsensus clustering of 153 ESCA patients from the TCGA cohort was conducted utilizing the expression profiles of the 14 genes identified via univariate Cox regression analysis. Adjusting the clustering parameter (k) within a range from 2 to 10, we identified the optimal clustering configuration at k equals 2 \u003cb\u003e(Fig.\u0026nbsp;3A-C)\u003c/b\u003e, effectively stratifying patients into two distinct clusters: Cluster 1 (n\u0026thinsp;=\u0026thinsp;71) and Cluster 2 (n\u0026thinsp;=\u0026thinsp;82). UMAP visualizations corroborated the clear demarcation between these clusters \u003cb\u003e(Fig.\u0026nbsp;3D)\u003c/b\u003e. The Kaplan-Meier survival analysis revealed a statistically significant enhancement in the overall survival rate among patients belonging to Cluster 1, in contrast to those in Cluster 2 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). \u003cb\u003e(Fig.\u0026nbsp;3E)\u003c/b\u003e, underscoring the capability of MRGs to stratify ESCA patients into subtypes with notable survival disparities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Immunological Landscape of the Two Subtypes\u003c/h2\u003e \u003cp\u003eOur comprehensive immunological analyses elucidated substantial disparities in immune landscape between the two molecular subtypes. Utilizing the Estimate algorithm, we discerned notably heightened stromal, immune, and overall estimate scores among ESCA patients in Cluster 1\u003cb\u003e(Fig.\u0026nbsp;4A\u0026ndash;C)\u003c/b\u003e, indicating a more favorable prognosis. The CIBERSORT algorithm further identified an increased proportion of T cells CD4\u0026thinsp;+\u0026thinsp;naive (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and activated natural killer (NK) cells (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in Cluster 1; however, no significant differences were detected in other immune cell infiltrations \u003cb\u003e(Fig.\u0026nbsp;4D)\u003c/b\u003e. Additionally, TIMER algorithm analysis confirmed higher infiltration levels of neutrophils and myeloid dendritic cells in Cluster 1, while no significant variation in CD8\u0026thinsp;+\u0026thinsp;T cell infiltration was noted \u003cb\u003e(Fig.\u0026nbsp;4E-G)\u003c/b\u003e, accentuating the distinct immunological profiles of these two subtypes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Differentially Expressed Genes (DEGs) and their Functional Annotations\u003c/h2\u003e \u003cp\u003eDistinct sets of differentially expressed genes (DEGs) were discerned between the two clusters., uncovering a total of 164 DEGs. Among these, 70 genes exhibited reduced expression in Cluster 1 compared to Cluster 2, while 94 genes showed increased expression in Cluster 1 \u003cb\u003e(Fig.\u0026nbsp;5A)\u003c/b\u003e. Subsequent KEGG enrichment analysis elucidated several prominent signaling pathways, including the p53 signaling pathway, ECM-receptor interaction, and protein digestion and absorption \u003cb\u003e(Fig.\u0026nbsp;5B)\u003c/b\u003e. Correspondingly, GO enrichment analysis indicated that DEGs were significantly enriched in biological processes such as the cell cycle, extracellular matrix organization, and the positive regulation of cell migration \u003cb\u003e(Fig.\u0026nbsp;5C)\u003c/b\u003e. Furthermore, enrichments of crucial molecular functions were observed \u003cb\u003e(Fig.\u0026nbsp;5D)\u003c/b\u003e. Protein-protein interaction (PPI) analysis unveiled three sub-modules, all tightly linked to tumor development and immunity, suggesting a potential interplay between immune response and the role of mitophagy in esophageal cancer \u003cb\u003e(Fig.\u0026nbsp;5E)\u003c/b\u003e. In order to gain deeper insights into the relationships between enriched pathways and the prognostic outcomes of ESCA patients, Gene Set Enrichment Analysis (GSEA) was conducted, demonstrating that focal adhesion and ECM receptor interaction were remarkably expressed in Cluster 1, whereas lysine degradation and homologous recombination were notably enriched in Cluster 2 \u003cb\u003e(Fig.\u0026nbsp;5F)\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Further Identification of ESCA Based on DEGs Between Subtypes\u003c/h2\u003e \u003cp\u003e As noted previously, differential analysis between clusters 1 and 2 led to the identification of 164 genes exhibiting differential expression patterns \u003cb\u003e(Fig.\u0026nbsp;6A)\u003c/b\u003e. Based on these genetic markers, ESCA patients were categorized into distinct subgroups. The optimal clustering was achieved at k\u0026thinsp;=\u0026thinsp;2 \u003cb\u003e(Fig.\u0026nbsp;6B-D)\u003c/b\u003e. Principal Component Analysis (PCA) revealed that these genes effectively differentiated the two subtypes\u0026mdash;Cluster A (CA) and Cluster B (CB) \u003cb\u003e(Fig.\u0026nbsp;6E)\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Discerning biological functions and immune microenvironments across distinct gene clusters.\u003c/h2\u003e \u003cp\u003eOur investigation into the expression of 14 genes with notable prognostic significance across the two subtypes revealed distinctive expression profiles. \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e7\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e.The results of Gene Set Variation Analysis (GSVA) highlighted the predominant involvement of CA in NADPH oxidase H2O2 forming activity. Conversely, CB associates mainly with negative regulation of the vitamin D biosynthetic process and pathways, including the TGF-Beta signaling pathway and WNT signaling pathway \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e7\u003c/span\u003eB, C\u003cb\u003e).\u003c/b\u003e The CIBERSORT algorithm disclosed higher infiltration levels of CD4\u0026thinsp;+\u0026thinsp;naive T cells, follicular helper T cells, and activated myeloid dendritic cells in Cluster B, alongside lower infiltration levels of CD4\u0026thinsp;+\u0026thinsp;memory resting T cells, CD4\u0026thinsp;+\u0026thinsp;memory activated T cells, and resting NK cells \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e7\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e. Additionally, the TIMER algorithm indicated higher infiltration levels of CD4\u0026thinsp;+\u0026thinsp;T cells in Cluster A, along with lower levels of B cells, Neutrophils and Myeloid dendritic cells \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e7\u003c/span\u003eE\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e\n \u003cp\u003e3.7 Construction of Risk Model\u003c/p\u003e\n\u003cp\u003eWe initially applied LASSO regression analysis, utilizing 10-fold cross-validation to identify the optimal model. The optimal \u0026lambda; value was determined,\u0026nbsp;leading to the identification of 7 genes\u003cstrong\u003e\u0026nbsp;(\u003c/strong\u003e\u003cstrong\u003eFig. 8\u003c/strong\u003e\u003cstrong\u003eA-B)\u003c/strong\u003e. Furthermore, the random forest algorithm identified six genes with importance scores exceeding 1.4 as central features\u003cstrong\u003e\u0026nbsp;(\u003c/strong\u003e\u003cstrong\u003eFig. 8\u003c/strong\u003e\u003cstrong\u003eC)\u003c/strong\u003e. Notably, three genes\u0026mdash;TMEM43, HSPD1, and MAP1LC3B\u0026mdash;were common to both machine learning approaches\u003cstrong\u003e\u0026nbsp;(\u003c/strong\u003e\u003cstrong\u003eFig. 8\u003c/strong\u003e\u003cstrong\u003eD)\u003c/strong\u003e. Following this, multivariate Cox regression analysis revealed that HSPD1 (HR=1.58, 95% CI: 1.05-2.38) and MAP1LC3B (HR=2.86, 95% CI: 1.45-5.65) emerged as pivotal differentially expressed mitophagy-related genes (DEMRGs) \u003cstrong\u003e(\u003c/strong\u003e\u003cstrong\u003eFig. 8\u003c/strong\u003e\u003cstrong\u003eE)\u003c/strong\u003e. Based on these two critical DEMRGs, a risk model was constructed, with the risk score calculated as follows: Risk score=(0.366419869\u0026times;HSPD1 expression)+(0.397483984\u0026times;MAP1LC3B expression). Patients in the TCGA cohort were subsequently stratified into high-risk (n=77) and low-risk (n=76) groups based on the median risk score. Moreover, the coefficient value of the model was determined\u003cstrong\u003e\u0026nbsp;(\u003c/strong\u003e\u003cstrong\u003eFig. 8\u003c/strong\u003e\u003cstrong\u003eF)\u003c/strong\u003e. The relationship between risk score and survival status was further investigated, with scatter plots illustrating that patient mortality increased alongside rising risk score\u003cstrong\u003e\u0026nbsp;(\u003c/strong\u003e\u003cstrong\u003eFig. 8\u003c/strong\u003e\u003cstrong\u003eG)\u003c/strong\u003e. To emphasize the prognostic accuracy of the model, a time-dependent ROC analysis was conducted, yielding AUC values of 0.74, 0.71, and 0.83 for 1, 3, and 5 years, respectively\u003cstrong\u003e\u0026nbsp;(\u003c/strong\u003e\u003cstrong\u003eFig. 8\u003c/strong\u003e\u003cstrong\u003eH)\u003c/strong\u003e, indicating the model\u0026apos;s robust predictive capability. Kaplan-Meier analysis demonstrated that the high-risk group experienced a poorer prognosis compared to the low-risk group in the TCGA cohort\u003cstrong\u003e\u0026nbsp;(\u003c/strong\u003e\u003cstrong\u003eFig. 8\u003c/strong\u003e\u003cstrong\u003eI)\u003c/strong\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Independence of the Established Risk Model\u003c/h2\u003e \u003cp\u003eWe further delved into the intricate relationship between the risk score and various clinical characteristics through rigorous subgroup and regression analyses, assessing the independence of our developed risk model. Our findings indicated no significant differences in risk score across different age cohorts \u003cb\u003e(Fig.\u0026nbsp;9A)\u003c/b\u003e, genders \u003cb\u003e(Fig.\u0026nbsp;9B)\u003c/b\u003e, M stages \u003cb\u003e(Fig.\u0026nbsp;9C)\u003c/b\u003e, T stages \u003cb\u003e(Fig.\u0026nbsp;9D)\u003c/b\u003e, N stages \u003cb\u003e(Fig.\u0026nbsp;9E)\u003c/b\u003e, or overall staging \u003cb\u003e(Fig.\u0026nbsp;9F)\u003c/b\u003e, suggesting a lack of correlation between the risk score and these clinical factors. Notably, even when stratifying patients based on age \u003cb\u003e(Fig.\u0026nbsp;9G-H)\u003c/b\u003e and Gender \u003cb\u003e(Fig.\u0026nbsp;9I-J)\u003c/b\u003e, our risk model maintained its robust predictive power, with individuals exhibiting lower risk score showing a more favorable prognosis. These outcomes highlight the exceptional utility of our risk model in prognosticating the outcomes of esophageal cancer patients.\u003c/p\u003e \u003cp\u003e3.9 \u0026nbsp;Construction and Calibration of an Integrated Nomogram\u003c/p\u003e\n\u003cp\u003eTo enhance predictive performance, we constructed a nomogram model for evaluate the prognosis of esophageal squamous cell carcinoma (ESCA)\u003cstrong\u003e\u0026nbsp;(\u003c/strong\u003e\u003cstrong\u003eFig. 10\u003c/strong\u003e\u003cstrong\u003eA)\u003c/strong\u003e, which allows for individualized predictions of survival probabilities at 1-year, 3-year, and 5-year intervals. Each risk factor was assigned a score, and the cumulative scores of these indicators were utilized to calculate the total score for predicting ESCA prognosis for each patient. The calibration plot revealed a high degree of concordance between observed and ideal values \u003cstrong\u003e(\u003c/strong\u003e\u003cstrong\u003eFig. 10\u003c/strong\u003e\u003cstrong\u003eB)\u003c/strong\u003e.\u0026nbsp;Examination\u0026nbsp;of the calibration curve provides a comprehensive\u0026nbsp;evaluation\u0026nbsp;of the model\u0026apos;s efficacy, substantiating its applicability for clinical application. According to the model, the c-index value was determined to be 0.68\u003cstrong\u003e\u0026nbsp;(\u003c/strong\u003e\u003cstrong\u003eFig. 10\u003c/strong\u003e\u003cstrong\u003eC)\u003c/strong\u003e. Decision Curve Analysis (DCA) revealed that the nomogram surpasses in delivering superior clinical net benefit \u003cstrong\u003e(\u003c/strong\u003e\u003cstrong\u003eFig. 10\u003c/strong\u003e\u003cstrong\u003eD)\u003c/strong\u003e. These discoveries imply that the risk score-based nomogram can function as a potent prognostic instrument for prediction in clinical scenarios. Furthermore, to authenticate the accuracy of the model, validation was conducted utilizing the GSE26886 and GSE20347 datasets\u003cstrong\u003e\u0026nbsp;(\u003c/strong\u003e\u003cstrong\u003eFig. 10\u003c/strong\u003e\u003cstrong\u003eE\u003c/strong\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003cstrong\u003eF)\u003c/strong\u003e, with all AUC values exceeding 0.7.\u003c/p\u003e\n\u003cp\u003e3.10\u0026nbsp; \u0026nbsp; \u0026nbsp;Comparison of Functional Enrichment and Immune Infiltration\u0026nbsp;distinction\u0026nbsp;Between High and Low-Risk\u0026nbsp;Cohort\u003c/p\u003e\n\u003cp\u003eTo investigate the underlying mechanisms of the risk model, Gene Set Enrichment Analysis (GSEA) was employed to examine the functional enrichment profiles\u0026nbsp;within high-risk and low-risk\u0026nbsp;cohorts. The high-risk group exhibited significant enrichment in spliceosome activity, Alzheimer disease pathways, and the pentose phosphate pathway, while the VEGF, Notch, and ERBB signaling pathways were more prominent in the low-risk group\u003cstrong\u003e\u0026nbsp;(\u003c/strong\u003e\u003cstrong\u003eFig. 11\u003c/strong\u003e\u003cstrong\u003eA)\u003c/strong\u003e. Additionally, we conducted GSEA to elucidate the roles of the two hub genes, HSPD1 and MAP1LC3B, in esophageal carcinoma. In esophageal cancer, GSEA revealed diverse roles for HSPD1 \u003cstrong\u003e(\u003c/strong\u003e\u003cstrong\u003eFig. 11\u003c/strong\u003e\u003cstrong\u003eB)\u003c/strong\u003e, including enrichment in critical processes such as \u0026quot;apoptosis,\u0026quot; \u0026quot;DNA replication,\u0026quot; and \u0026quot;Adherens junction formation,\u0026quot; while also being involved in essential signaling pathways, including the p53 signaling pathway and focal adhesion pathways. For MAP1LC3B\u003cstrong\u003e\u0026nbsp;(\u003c/strong\u003e\u003cstrong\u003eFig. 11\u003c/strong\u003e\u003cstrong\u003eC)\u003c/strong\u003e, it was implicated in the immune process of \u0026quot;natural killer cell mediated cytotoxicity\u0026quot; and displayed enrichment in the \u0026quot;mismatch repair\u0026quot; process, contributing to critical signaling mechanisms, encompassing\u0026nbsp;focal adhesion and the ERBB signaling pathway. Additionally,\u0026nbsp;the differences in genetic mutation profiles between the high-risk and low-risk groups were unveiled through the illustration of the mutation waterfall plot\u003cstrong\u003e\u0026nbsp;(\u003c/strong\u003e\u003cstrong\u003eFig. 11\u003c/strong\u003e\u003cstrong\u003eD)\u003c/strong\u003e. CIBERSORT analysis indicated substantial disparities in immune cell infiltration\u0026nbsp;patterns across\u0026nbsp;high-risk and low-risk groups\u003cstrong\u003e\u0026nbsp;(\u003c/strong\u003e\u003cstrong\u003eFig. 11\u003c/strong\u003e\u003cstrong\u003eE)\u003c/strong\u003e. Correlation analysis demonstrated a significant positive correlation between Mast cell activated and Macrophage in the high-risk group, while a negative correlation was observed between resting myeloid dendritic cells and Monocyte (p \u0026lt; 0.05,\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFig. 11\u003c/strong\u003e\u003cstrong\u003eF\u003c/strong\u003e). Conversely, in the low-risk group, Macrophage M0, M2 and Neutrophil exhibited positive correlations, while Neutrophil displayed a negative correlation with Monocyte (p \u0026lt; 0.05,\u0026nbsp;\u003cstrong\u003eFig. 11\u003c/strong\u003e\u003cstrong\u003eG\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3.11\u0026nbsp; \u0026nbsp; \u0026nbsp;Exploring the Correlation Between Three Types of Grouping Patterns\u003c/p\u003e\n\u003cp\u003eWe employed the risk score obtained from our risk model to construct box plots for contrasting risk score among the MRGs cluster and the DEGs patterns\u003cstrong\u003e\u0026nbsp;(\u003c/strong\u003e\u003cstrong\u003eFig. 12\u003c/strong\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003cstrong\u003eB)\u003c/strong\u003e. The results revealed that the C2 and CA groups demonstrated notably elevated risk score in comparison to the C1 and CB groups. Additionally,\u0026nbsp;a Sankey diagram was employed as a visual tool to illustrate the correlation between\u0026nbsp;distinct clusters\u0026nbsp;and risk score \u003cstrong\u003e(\u003c/strong\u003e\u003cstrong\u003eFig. 12\u003c/strong\u003e\u003cstrong\u003eC)\u003c/strong\u003e. Furthermore, we assessed the association between various immune checkpoints, revealing that levels of CD274 and TGFB1 were\u0026nbsp;enhanced\u0026nbsp;in the C1 and CB groups\u0026nbsp;in comparison to\u0026nbsp;the C2 and CA groups\u003cstrong\u003e\u0026nbsp;(\u003c/strong\u003e\u003cstrong\u003eFig. 12\u003c/strong\u003e\u003cstrong\u003eD\u003c/strong\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003cstrong\u003eE)\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3.12\u0026nbsp; \u0026nbsp; \u0026nbsp;Single-Cell Sequencing Data Search for Gene Expression in Tumors\u003c/p\u003e\n\u003cp\u003eA comprehensive search of single-cell sequencing data for tumor gene expression was performed. We initially downloaded esophageal cancer single-cell datasets GSE160269 and GSE173950 from the TISCH2 database. The GSE160269 dataset, after dimension reduction, was annotated into 31 cell clusters, revealing 13 distinct cell types. In contrast, the GSE173950 dataset was annotated into 22 cell clusters, identifying 12 cell types, including both tumor and immune cells, and illustrating the proportional representation of different cell components in each patient \u003cstrong\u003e(\u003c/strong\u003e\u003cstrong\u003eFig. 13\u003c/strong\u003e\u003cstrong\u003eA-D)\u003c/strong\u003e. UMAP plots and violin plots were utilized to depict the expression profiles of relevant genes across different annotated cell types \u003cstrong\u003e(\u003c/strong\u003e\u003cstrong\u003eFig. 13\u003c/strong\u003e\u003cstrong\u003eE-J)\u003c/strong\u003e. The results demonstrated the expression of genes such as HSPD1 and MAP1LC3B across various annotated cell types, including tumor cells.\u003c/p\u003e\n\u003cp\u003e3.13 \u0026nbsp; \u0026nbsp; Prediction of Potential Traditional Chinese medicine and molecular docking\u003c/p\u003e\n\u003cp\u003eThe two screened pivotal genes were imported into the Coremine database, and the top 30 corresponding traditional Chinese medicines were selected. Among them, the traditional Chinese medicine where both genes appeared simultaneously was Icaritin, suggesting that this traditional Chinese medicine has considerable potential for improving the prognosis of patients with esophageal cancer. Icaritin (Epimedium flavonoids) was selected to perform molecular docking with HSPD1 and MAP1LC3B proteins respectively \u003cstrong\u003e(\u003c/strong\u003e\u003cstrong\u003eFig. 14\u003c/strong\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003cstrong\u003eB)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eA binding energy value of less than -4.25 kcal\u0026middot;mol-1 signifies a certain level of binding capability between the two entities, while a value less than -5.0 kcal\u0026middot;mol-1 indicates a more superior binding ability\u003csup\u003e[28]\u003c/sup\u003e. The results showed that the core targets HSPD1 and MAP1LC3B had a better binding ability with Icaritin. The binding energy of Icaritin with HSPD1 was -9.3, and the binding energy with MAP1LC3B was -6.8, as shown in the \u003cstrong\u003eTable 2\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Binding Energy of Molecular Docking\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003eNumerator ID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003eName\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003eReceptor protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003ePDB ID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003eBinding ability[a]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e5318980\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003eIcaritin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003eHSPD1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e4PJ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e-9.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e5318980\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003ecaritin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003eMAP1LC3B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e6LAN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e-6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv id=\"ftn1\"\u003e\n \u003cp\u003e[a] kcal\u0026middot;mol-1\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eEsophageal cancer ranks among the deadliest cancers globally, which has seriously endangered human health\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. It is detrimental for those suffering from esophageal cancer to wait for treatment to begin, regardless of whether the disease is primary or metastatic\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. Although immunotherapy for ESCA have been launched around the world in recent years, immunotherapies do not work well or benefit all patients in the majority of cases\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. The problems such as the high invasiveness of esophageal cancer, the unsatisfactory clinical treatment effect and the poor prognosis are still huge challenges. Mitophagy is thought to be significant to recycle mitochondrial mass and remove damaged mitochondria\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. Especially, the potential role in esophageal cancer, which is a highly invasive cancer, still needs to be further clarified.\u003c/p\u003e \u003cp\u003eThis study is the first to systematically evaluate the role of mitophagy in esophageal cancer. We identified mitophagy genes related to the prognosis of ESCA based on various advanced algorithms and screened 14 differentially expressed genes for related subtype identification. Consensus clustering was performed for ESCA patients in the TCGA cohort, and patients were divided into C1 and C2 with significantly different prognoses with k\u0026thinsp;=\u0026thinsp;2 as the boundary. Notable disparities in immune infiltration were observed between the two clusters when analyzed using the CIBERSORT, TIMER, and ESTIMATE algorithms. Cluster 1 had a higher proportion of immune cell infiltration levels, which also implied a better prognosis for Cluster 1. After that, esophageal cancer patients were categorized into two distinct molecular subtypes, CA and CB, on the basis of the differentially expressed genes between C1 and C2. Comprehensive immunological analysis and biological enrichment analysis showed that the variations observed in immune infiltration levels and levels of pathway enrichment between the two clusters suggested the prospect of immunotherapy for ESCA. evaluated the differential expression of immune checkpoint genes across the two subtypes. The results revealed that the two subtypes, C1 and CB, had higher immune checkpoint expression levels, which provided new insights for immune checkpoint-based therapies in esophageal cancer. All in all, we combined mitophagy with the prognosis and classification of esophageal cancer, a fresh insight into the complexities of esophageal cancer heterogeneity.\u003c/p\u003e \u003cp\u003eOur initial findings prompted the employment of two machine learning algorithms - LASSO regression and random forest - in conjunction with univariate and multivariate COX regression analysis. Ultimately, two pivotal genes, HSPD1 and MAP1LC3B, were identified and a prognostic model was constructed based on these findings. Our prognostic model was proved by the external validation set to have good predictive efficacy. The findings indicated a direct correlation between an elevated risk score and a poorer prognosis for patients. At the same time, through functional enrichment analysis, immune infiltration analysis and visualization of gene mutations, we revealed the complex differences between the high-risk and low-risk groups. In addition, we also constructed a nomogram for forecasting the prognosis of esophageal cancer patients, validated by calibration curves to exhibit robust predictive accuracy.\u003c/p\u003e \u003cp\u003eHSPD1 is a gene encoding the chaperonin family. During the cancer development process, the downregulation of HSPD1 is closely related to cancer cell apoptosis\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. The risk score's rise coincides with heightened mortality in ESCA patients, supporting the model's validity. In addition, as an effective prognostic marker, HSPD1 serves as a pivotal factor in cancer progression and has survival-promoting or apoptosis-inducing functions depending on the tumor type\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. Beatrice Parma et al. discovered that mitochondrial HSPD1 targeting is related to the metabolic damage of non-small cell lung cancer, and HSPD1 is widely expressed in NSCLC tumors and cells\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. Yu Zhang et al. identified the correlation between HSPD1 and mitochondrial autophagy in pituitary adenoma\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. Seon-Kyu Kim et al. found that the prognostic marker EHMT2 inhibits apoptosis by controlling the expression of HSPD1\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e; The other hub gene MAP1LC3B, as a ubiquitin-like modifier involved in the formation of autophagosomes, meets the cellular energy requirements and prevents excessive ROS production by eliminating mitochondria to the basal level. In the study on the association between the expression pattern of MAP1LC3B and malignancy, an increase in expression level was related to a decrease in patient survival time. The viewpoint of our study is consistent with the histological experimental results\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. Haishun Qu et al. integrated CTH and MAP1LC3B to construct a prognostic model for gastric cancer. Survival analysis revealed a marked decline in survival rates among the high-risk group.\u003c/p\u003e \u003cp\u003eIn the last few years, There has been a boom in single-cell sequencing in recent years\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. Single-cell sequencing technology and analytical tools have allowed oncologists to gain a better understanding of the tumor immune microenvironment and how it affects the antitumor immune response\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. Additionally, we further utilized the advanced single-cell sequencing technology to visualize the expression patterns of the two genes, HSPD1 and MAP1LC3B, in esophageal cancer tissues. The results suggested that HSPD1 and MAP1LC3B might be closely related to the immune microenvironment of esophageal cancer. Based on the above discoveries, we see hope in the treatment through immunotherapy in the new therapeutic approaches.\u003c/p\u003e \u003cp\u003eChinese traditional medicine has a firmly established position in preventing and treating cancer. \u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. At the end of this study, based on the two genes, HSPD1 and MAP1LC3B, we predicted the traditional Chinese medicine - Icaritin through the Coremine database, which is greatly important for improving the prognosis of esophageal cancer patients. And the effective component - Icaritin was used to perform molecular docking with the two molecules, HSPD1 and MAPL1C3B. The results indicated a favorable docking interaction. In the study of Yang et al, Icaritin could be considered a promising agent for treating and preventing OSCC\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e. It is suggested that Icaritin may have a positive effect in improving the prognosis of patients with esophageal cancer, but its clinical effect still needs further study.\u003c/p\u003e \u003cp\u003eThis study has made significant progress on multiple levels. First of all, we deeply analyzed the core role of mitophagy in the development of esophageal cancer. A novel interplay among immune cells, tumor cells, and cancer stem cells is revealed within the ESCA tumor microenvironment\u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e, which provides a novel perspective for the deeper comprehension of the disease. Secondly, we applied advanced machine learning techniques, which not only improved the identification accuracy of pivotal genes and signaling pathways, but also significantly enhanced the accuracy and reliability of prognosis assessment\u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e. Furthermore, combined with standardized single-cell sequencing data, we provided a detailed molecular map for revealing the heterogeneity of the tumor microenvironment\u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNevertheless, this research also has several constraints. Our conclusion is based on the secondary bioinformatics analysis of public database data. Although the model performed well in the external validation set, there is a lack of direct evidence from animal experiments and cell experiments. Because the samples were derived from a retrospective study, there may be selective bias, which may affect the universality of the analysis results\u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e. Therefore, well-designed prospective studies are needed in the future to further validate our findings. Meanwhile, to understand the clinical significance of mitophagy more comprehensively, more clinical variables need to be considered. However, the collection of relevant data in the current public databases is still not perfect. Although we have preliminarily revealed the mechanism of mitophagy, its detailed molecular mechanism, regulatory network and interaction with other biological processes still need to be further explored.\u003c/p\u003e \u003cp\u003eTo sum up, through the comprehensive application of advanced technical means and methods, this study systematically revealed the important role of mitophagy in esophageal cancer and its close relationship with prognosis and classification. This discovery not only broadens our knowledge of the molecular mechanism of esophageal cancer, but also provides important molecular markers and theoretical basis for achieving precision medicine for esophageal cancer. Future studies will further explore the mechanism of mitophagy, optimize technical methods and promote clinical transformation and application, in order to bring more effective treatment options and longer survival periods for patients.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eBy leveraging advanced technological approaches, this study systematically uncovered the pivotal role of mitophagy in esophageal cancer progression and its close association with prognosis and subtyping. The constructed prognostic model, featuring the key genes HSPD1 and MAP1LC3B, exhibited excellent predictive efficacy in external validation sets, while marked discrepancies were observed in gene expression patterns, functional enrichment, and immune cell infiltration between risk groups. We also successfully identified two subtypes (C1, C2 and CA, CB). Furthermore, single-cell sequencing illuminated the expression profiles of target genes in the tumor microenvironment, offering novel perspectives for immunotherapy applications. The predicted traditional Chinese medicines and their molecular docking outcomes presented promising avenues for improving esophageal cancer prognosis. This study significantly enriched our comprehension of esophageal cancer molecular mechanisms and provided essential molecular markers and theoretical frameworks for advancing precision medicine in esophageal cancer. Future endeavors will further explore mitophagy mechanisms, refine technical methodologies, and expedite clinical translations to offer more effective therapeutic options and prolonged survival for patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was funded by Detail Project of Precision Medicine Joint Fund of Natural Science Foundation of Hebei Province (Project No.H2021406066); National Natural Science Foundation Project Incubation Fund of Chengde Medical College (Project No.202416)\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConflict of interest\u003c/strong\u003e \u003cp\u003eThe authors declare no pertinent conflicts of interest with regard to the matter presented in this article.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThe conception and manuscript drafting of this study were conducted by FT. XH, SW, and ZW, MH were responsible for the data acquisition process. The comprehensive review and final approval of the manuscript were carried out by YL. YG oversaw all aspects of the manuscript including data interpretation, critical evaluation of the article, and ensuring its scientific rigor. Following a rigorous examination of the findings , all authors endorsed the manuscript for submission in its definitive version .\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eIn this research, public datasets were scrutinized, including the TCGA-ESCA cohort sourced from the TCGA repository (accessible via\u0026nbsp;http://cancergenome.nih.gov/) and GSE26886 along with GSE20347 retrieved from the GEO database (located at\u0026nbsp;http://www.ncbi.nlm.nih.gov/geo/). The methodologies and software tools utilized for analysis are comprehensively detailed in the \"Materials and Methods\" section. The necessary data and materials were readily accessible when required.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAkhuj A, Athawale V, Fating T (2024) A Combat Journey of Rehabilitation in Pre- and Post-chemotherapy for Esophagus Carcinoma. Cureus 16(4):e58202. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7759/cureus.58202\u003c/span\u003e\u003cspan address=\"10.7759/cureus.58202\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReijneveld EAE, Bor P, Dronkers JJ et al (2022) Impact of curative treatment on the physical fitness of patients with esophageal cancer: A systematic review and meta-analysis. Eur J Surg Oncol 48(2):391\u0026ndash;402. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ejso.2021.08.015\u003c/span\u003e\u003cspan address=\"10.1016/j.ejso.2021.08.015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQu HT, Li Q, Hao L et al (2024) Esophageal cancer screening, early detection and treatment: Current insights and future directions. World J Gastrointest Oncol 16(4):1180\u0026ndash;1191. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4251/wjgo.v16.i4.1180\u003c/span\u003e\u003cspan address=\"10.4251/wjgo.v16.i4.1180\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSui X, Chen C, Zhou X et al (2023) Integrative analysis of bulk and single-cell gene expression profiles to identify tumor-associated macrophage-derived CCL18 as a therapeutic target of esophageal squamous cell carcinoma. J Exp Clin Cancer Res 42(1):51. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13046-023-02612-5\u003c/span\u003e\u003cspan address=\"10.1186/s13046-023-02612-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSilk AW, Barker CA, Bhatia S et al (2022) Society for Immunotherapy of Cancer (SITC) clinical practice guideline on immunotherapy for the treatment of nonmelanoma skin cancer. J Immunother Cancer 10(7):e004434. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/jitc-2021-004434\u003c/span\u003e\u003cspan address=\"10.1136/jitc-2021-004434\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu X, Chen Y, Zhang S, Dong L (2021) Gut microbiota-mediated immunomodulation in tumor. J Exp Clin Cancer Res 40(1):221. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13046-021-01983-x\u003c/span\u003e\u003cspan address=\"10.1186/s13046-021-01983-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu Y, Li Z, Zhang S, Zhang T, Liu Y, Zhang L (2023) Cellular mitophagy: Mechanism, roles in diseases and small molecule pharmacological regulation. Theranostics 13(2):736\u0026ndash;766. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7150/thno.79876\u003c/span\u003e\u003cspan address=\"10.7150/thno.79876\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD'Arcy MS (2024) Mitophagy in health and disease. Molecular mechanisms, regulatory pathways, and therapeutic implications. Apoptosis. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10495-024-01977-y\u003c/span\u003e\u003cspan address=\"10.1007/s10495-024-01977-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLemasters JJ (2005) Selective mitochondrial autophagy, or mitophagy, as a targeted defense against oxidative stress, mitochondrial dysfunction, and aging. Rejuvenation Res 8(1):3\u0026ndash;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1089/rej.2005.8.3\u003c/span\u003e\u003cspan address=\"10.1089/rej.2005.8.3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbeliovich H (2007) Mitophagy: the life-or-death dichotomy includes yeast. Autophagy 3(3):275\u0026ndash;277. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4161/auto.3915\u003c/span\u003e\u003cspan address=\"10.4161/auto.3915\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoule RJ, Narendra DP (2011) Mechanisms of mitophagy. Nat Rev Mol Cell Biol 12(1):9\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nrm3028\u003c/span\u003e\u003cspan address=\"10.1038/nrm3028\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSu L, Zhang J, Gomez H et al (2023) Mitochondria ROS and mitophagy in acute kidney injury. Autophagy 19(2):401\u0026ndash;414. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/15548627.2022.2084862\u003c/span\u003e\u003cspan address=\"10.1080/15548627.2022.2084862\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSong C, Pan S, Zhang J et al (2022) Mitophagy: A novel perspective for insighting into cancer and cancer treatment. Cell Prolif 55(12):e13327. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/cpr.13327\u003c/span\u003e\u003cspan address=\"10.1111/cpr.13327\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePanigrahi DP, Praharaj PP, Bhol CS et al (2020) The emerging, multifaceted role of mitophagy in cancer and cancer therapeutics. Semin Cancer Biol 66:45\u0026ndash;58. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.semcancer.2019.07.015\u003c/span\u003e\u003cspan address=\"10.1016/j.semcancer.2019.07.015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRitchie ME, Phipson B, Wu D et al (2015) limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 43(7):e47. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/nar/gkv007\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkv007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEngebretsen S, Bohlin J (2019) Statistical predictions with glmnet. \u003cem\u003eClin Epigenetics\u003c/em\u003e. ;11(1):123. Published 2019 Aug 23. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13148-019-0730-1\u003c/span\u003e\u003cspan address=\"10.1186/s13148-019-0730-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlderden J, Pepper GA, Wilson A et al (2018) Predicting Pressure Injury in Critical Care Patients: A Machine-Learning Model. Am J Crit Care 27(6):461\u0026ndash;468. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4037/ajcc2018525\u003c/span\u003e\u003cspan address=\"10.4037/ajcc2018525\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVickers AJ, Cronin AM, Elkin EB et al (2008) Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers. BMC Med Inf Decis Mak 8:53 Published 2008 Nov 26. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1472-6947-8-53\u003c/span\u003e\u003cspan address=\"10.1186/1472-6947-8-53\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiao Y, Yuan Q, Wang C et al (2023) Comprehensive Characterization of RNA-Binding Proteins in Colon Adenocarcinoma Identifies a Novel Prognostic Signature for Predicting Clinical Outcomes and Immunotherapy Responses Based on Machine Learning. Comb Chem High Throughput Screen 26(1):163\u0026ndash;182. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2174/1386207325666220404125228\u003c/span\u003e\u003cspan address=\"10.2174/1386207325666220404125228\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Y, Liu N, Wang S (2018) A differential privacy protecting K-means clustering algorithm based on contour coefficients. PLoS ONE 13(11):e0206832. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0206832\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0206832\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoshihara K, Shahmoradgoli M, Mart\u0026iacute;nez E et al (2013) Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun 4:2612. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/ncomms3612\u003c/span\u003e\u003cspan address=\"10.1038/ncomms3612\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi T, Fan J, Wang B et al (2017) TIMER: A Web Server for Comprehensive Analysis of Tumor-Infiltrating Immune Cells. Cancer Res 77(21):e108\u0026ndash;e110. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1158/0008-5472.CAN-17-0307\u003c/span\u003e\u003cspan address=\"10.1158/0008-5472.CAN-17-0307\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBecht E, Giraldo NA, Lacroix L et al (2016) Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression [published correction. appears Genome Biol 17(1):249. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13059-016-1070-5\u003c/span\u003e\u003cspan address=\"10.1186/s13059-016-1070-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eH\u0026auml;nzelmann S, Castelo R, Guinney J (2013) GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics 14:7 Published 2013 Jan 16. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1471-2105-14-7\u003c/span\u003e\u003cspan address=\"10.1186/1471-2105-14-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSubramanian A, Tamayo P, Mootha VK et al (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102(43):15545\u0026ndash;15550. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.0506580102\u003c/span\u003e\u003cspan address=\"10.1073/pnas.0506580102\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan Y, Wang Y, Dong X et al (2023) TISCH2: expanded datasets and new tools for single-cell transcriptome analyses of the tumor microenvironment. Nucleic Acids Res 51(D1):D1425\u0026ndash;D1431. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/nar/gkac959\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkac959\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaleeva G, Wutz D, Rustler K et al (2019) A photoswitchable GABA receptor channel blocker. Br J Pharmacol 176(15):2661\u0026ndash;2677. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/bph.14689\u003c/span\u003e\u003cspan address=\"10.1111/bph.14689\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang Wenjie L, Jiaqing N, Yang et al To explore the mechanism of pine-rhubarb-patchcompatibility in renal clear cell carcinoma based on network pharmacology and molecular docking. J Ningxia Med Univ (04), 413\u0026ndash;421. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.16050/j.cnki.issn1674-6309.2024.04.014\u003c/span\u003e\u003cspan address=\"10.16050/j.cnki.issn1674-6309.2024.04.014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoon H, Zhu J, Donahue LR et al (2019) Krt5\u003csup\u003e+\u003c/sup\u003e/Krt15\u003csup\u003e+\u003c/sup\u003e foregut basal progenitors give rise to cyclooxygenase-2-dependent tumours in response to gastric acid stress. Nat Commun 10(1):2225 Published 2019 May 20. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41467-019-10194-0\u003c/span\u003e\u003cspan address=\"10.1038/s41467-019-10194-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFoster JM, Sleightholm R, Patel A et al (2019) Morbidity and Mortality Rates Following Cytoreductive Surgery Combined With Hyperthermic Intraperitoneal Chemotherapy Compared With Other High-Risk Surgical Oncology Procedures. JAMA Netw Open 2(1):e186847 Published 2019 Jan 4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1001/jamanetworkopen.2018.6847\u003c/span\u003e\u003cspan address=\"10.1001/jamanetworkopen.2018.6847\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Y, Yang C, Liu Z et al (2022) Integrative analysis of CRISPR screening data uncovers new opportunities for optimizing cancer immunotherapy. Mol Cancer 21(1):2 Published 2022 Jan 2. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12943-021-01462-z\u003c/span\u003e\u003cspan address=\"10.1186/s12943-021-01462-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi R, Xin T, Li D et al (2018) Therapeutic effect of Sirtuin 3 on ameliorating nonalcoholic fatty liver disease: The role of the ERK-CREB pathway and Bnip3-mediated mitophagy. Redox Biol 18:229\u0026ndash;243. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.redox.2018.07.011\u003c/span\u003e\u003cspan address=\"10.1016/j.redox.2018.07.011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim SK, Kim K, Ryu JW et al (2019) The novel prognostic marker, EHMT2, is involved in cell proliferation via HSPD1 regulation in breast cancer. Int J Oncol 54(1):65\u0026ndash;76. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3892/ijo.2018.4608\u003c/span\u003e\u003cspan address=\"10.3892/ijo.2018.4608\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYun CW, Kim HJ, Lim JH et al (2019) Heat Shock Proteins: Agents of Cancer Development and Therapeutic Targets in Anti-Cancer Therapy. Cells 9(1):60 Published 2019 Dec 24. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/cells9010060\u003c/span\u003e\u003cspan address=\"10.3390/cells9010060\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParma B, Ramesh V, Gollavilli PN et al (2021) Metabolic impairment of non-small cell lung cancers by mitochondrial HSPD1 targeting. J Exp Clin Cancer Res 40(1):248. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13046-021-02049-8\u003c/span\u003e\u003cspan address=\"10.1186/s13046-021-02049-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Y, Ma X, Liu C et al (2024) Identification of HSPD1 as a novel invasive biomarker associated with mitophagy in pituitary adenomas. Transl Oncol 41:101886. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.tranon.2024.101886\u003c/span\u003e\u003cspan address=\"10.1016/j.tranon.2024.101886\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim SK, Kim K, Ryu JW et al (2019) The novel prognostic marker, EHMT2, is involved in cell proliferation via HSPD1 regulation in breast cancer. Int J Oncol 54(1):65\u0026ndash;76. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3892/ijo.2018.4608\u003c/span\u003e\u003cspan address=\"10.3892/ijo.2018.4608\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpirina LV, Avgustinovich AV, Bakina OV et al (2022) LC3B, mTOR, AMPK Are Molecular Targets for Neoadjuvant Chemotherapy in Gastric Cancers. Curr Issues Mol Biol 44(7):2772\u0026ndash;2782. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/cimb44070190\u003c/span\u003e\u003cspan address=\"10.3390/cimb44070190\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu J, Liao K, Yang X et al (2022) Correction to: Using single-cell sequencing technology to detect circulating tumor cells in solid tumors. Mol Cancer 21(1):100. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12943-022-01564-2\u003c/span\u003e\u003cspan address=\"10.1186/s12943-022-01564-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJia Q, Chu H, Jin Z et al (2022) High-throughput single-сell sequencing in cancer research. Signal Transduct Target Ther 7(1):145. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41392-022-00990-4\u003c/span\u003e\u003cspan address=\"10.1038/s41392-022-00990-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu H, Huang M, Xin D, Wang H, Yu H, Pu W (2024) Natural products with anti-tumorigenesis potential targeting macrophage. Phytomedicine 131:155794. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.phymed.2024.155794\u003c/span\u003e\u003cspan address=\"10.1016/j.phymed.2024.155794\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang JG, Lu R, Ye XJ et al (2017) Icaritin Reduces Oral Squamous Cell Carcinoma Progression via the Inhibition of STAT3 Signaling. Int J Mol Sci 18(1):132 Published 2017 Jan 12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijms18010132\u003c/span\u003e\u003cspan address=\"10.3390/ijms18010132\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang K, Xie Y, Xue L et al (2023) M2 tumor-associated macrophage mediates the maintenance of stemness to promote cisplatin resistance by secreting TGF-β1 in esophageal squamous cell carcinoma. J Transl Med 21(1):26. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12967-022-03863-0\u003c/span\u003e\u003cspan address=\"10.1186/s12967-022-03863-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRomero-Brufau S, Whitford D, Johnson MG et al (2021) Using machine learning to improve the accuracy of patient deterioration predictions: Mayo Clinic Early Warning Score (MC-EWS). J Am Med Inf Assoc 28(6):1207\u0026ndash;1215. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/jamia/ocaa347\u003c/span\u003e\u003cspan address=\"10.1093/jamia/ocaa347\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXie S, Cai Y, Chen D et al (2023) Single-cell transcriptome analysis reveals heterogeneity and convergence of the tumor microenvironment in colorectal cancer. Front Immunol 13:1003419 Published 2023 Jan 4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fimmu.2022.1003419\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2022.1003419\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHughes MSA, Beganovic M (2019) Introduction of Selection Biases Due to Loss to Follow-Up in Infectious Disease Retrospective Outcomes Studies. Antimicrob Agents Chemother 63(12):e01681\u0026ndash;e01619. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1128/AAC.01681-19\u003c/span\u003e\u003cspan address=\"10.1128/AAC.01681-19\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"apoptosis","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"appt","sideBox":"Learn more about [Apoptosis](http://link.springer.com/journal/10495)","snPcode":"10495","submissionUrl":"https://submission.nature.com/new-submission/10495/3","title":"Apoptosis","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"esophageal cancer, mitophagy, single-cell sequencing, machine learning, prognosis, subtype","lastPublishedDoi":"10.21203/rs.3.rs-4917245/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4917245/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGlobally, esophageal cancer stands as a prominent contributor to cancer-related fatalities, distinguished by its grim prognosis. Mitophagy has a significant impact on the process of cancer progression. This study investigates the prognostic significance of mitophagy-related genes (MRGs) in esophageal carcinoma (ESCA) with the aim of elucidating molecular subtypes. By analyzing RNA-seq data from The Cancer Genome Atlas (TCGA), 6451 differentially expressed genes (DEGs) were identified. Cox regression analysis narrowed this list to 14 MRGs with notable prognostic implications. ESCA patients were classified into two distinct subtypes (C1 and C2) based on these genes. Furthermore, leveraging the differentially expressed genes between Cluster 1 and Cluster 2, ESCA patients were classified into two novel subtypes (CA and CB). Notably, patients in C2 and CA subtypes exhibited inferior prognosis compared to those in C1 and CB (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Functional enrichments and immune microenvironments varied significantly among these subtypes, with C1 and CB demonstrating higher immune checkpoint expression levels. Employing machine learning algorithms like LASSO regression and Random Forest, alongside multivariate COX regression analysis, two core genes: HSPD1 and MAP1LC3B were identified. A robust prognostic model based on these genes was developed and validated in two external cohorts. Additionally, single-cell sequencing analysis provided novel insights into esophageal cancer microenvironment heterogeneity. Through Coremine database screening, Icaritin emerged as a potential therapeutic candidate to improve esophageal cancer prognosis. Molecular docking results indicated favorable binding efficacies of Icaritin with HSPD1 and MAP1LC3B, enhancing the comprehension of the underlying molecular mechanisms of esophageal cancer and offering therapeutic avenues.\u003c/p\u003e","manuscriptTitle":"Integrating Single-Cell Sequencing and Machine Learning to Uncover the Role of Mitophagy in Subtyping and Prognosis of Esophageal Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-14 06:59:42","doi":"10.21203/rs.3.rs-4917245/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-03T08:45:13+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-02T09:41:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"328916094497309422866042931954210220408","date":"2024-10-22T15:45:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"122631572267486044587877652113908829984","date":"2024-10-15T07:21:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"288979473017878164339452701999304550567","date":"2024-09-14T10:58:58+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-09-14T08:36:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-16T05:43:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-16T05:43:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"Apoptosis","date":"2024-08-15T05:44:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"apoptosis","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"appt","sideBox":"Learn more about [Apoptosis](http://link.springer.com/journal/10495)","snPcode":"10495","submissionUrl":"https://submission.nature.com/new-submission/10495/3","title":"Apoptosis","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"00e45075-9b83-4f0e-b99c-c12a9bd5a77d","owner":[],"postedDate":"October 14th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-02-17T16:06:33+00:00","versionOfRecord":{"articleIdentity":"rs-4917245","link":"https://doi.org/10.1007/s10495-024-02061-1","journal":{"identity":"apoptosis","isVorOnly":false,"title":"Apoptosis"},"publishedOn":"2025-02-13 15:57:53","publishedOnDateReadable":"February 13th, 2025"},"versionCreatedAt":"2024-10-14 06:59:42","video":"","vorDoi":"10.1007/s10495-024-02061-1","vorDoiUrl":"https://doi.org/10.1007/s10495-024-02061-1","workflowStages":[]},"version":"v1","identity":"rs-4917245","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4917245","identity":"rs-4917245","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}