Integrative machine learning identifies lactylation-related gene signature prognostic for chemotherapeutic efficacy in colorectal carcinoma.

Background: The survival rate for late-stage colorectal cancer (CRC) is extremely low (4%-12%). The prognosis assessment for CRC pose numerous challenges. Recently, the role of the immune microenvironment in CRC progression has increasingly garnered attention, with immune cell infiltration and molecular expression closely related to patient prognosis. Therefore, this study aims to deeply analyze the molecular mechanisms underlying CRC and identify novel immune-related biomarkers for prognosis evaluation. Methods: High-throughput sequencing technology was utilized to compare and analyze the differential gene expression between CRC tumor tissues and normal tissues, as well as between patients with long and short survival durations. Univariate Cox analysis and Least Absolute Shrinkage and Selection Operator (Lasso) regression analysis were combined to screen hub genes closely related to CRC prognosis. A prognostic signature based on these hub genes was constructed to predict the prognosis of CRC patients. RNA sequencing (RNA-Seq) data from five paired CRC patients were collected to validate the expression patterns of the hub genes in CRC samples and their relationship with the immune microenvironment. Results: We successfully identified 13 hub genes (HOXC6, IGF2BP3, SGCG, HTR2C, ADCY5, SHC2, SUCLG2, PDHB, TSLP, CCR9, TNFRSF19, TGFB2, and LEP) that compose the prognostic signature. In the Cancer Genome Atlas (TCGA) dataset, this signature accurately classified patients into high- and low-risk groups with high precision (p < 0.001). Similar results were obtained in the Gene Expression Omnibus (GEO) dataset (p < 0.001 and p = 0.036). The study also found that when patients were stratified into high-risk and low-risk groups using the prognostic signature, there were significant differences in the immune environment between the two groups, including the infiltration degree of immune cells (p < 0.001) and the expression levels of immune molecules(p < 0.01). Additionally, when validated using RNA-Seq data from five paired CRC patients, the expression patterns of these hub genes in the five CRC samples were consistent with those observed in the TCGA analysis, further confirming the important roles of the 13 hub genes in CRC patients and their association with the immune microenvironment. Conclusion: In summary, this research has developed a novel signature that can predict both the survival outcomes and the immune infiltration status of CRC patients. The clinical application of this signature has the potential to enhance survival rates and individualize therapy approaches for CRC based on their risk score. Citation Format: Hui Zhang, Yu Lang, Zhaoqi Tang, Jialin Lin, Lu Yang, Jiapeng Kang, Changshun Yang, Qin Lin, Qiyuan Li, Feng Ye, Weiwei Tang. Establishment and validation of a prognostic signature based on thirteen hub genes in colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5350.

Background: Colorectal cancer (CRC) is a leading cause of cancer mortality globally. The molecular mechanisms of CRC and the accumulating immune cell infiltration in the tumor microenvironment (TME) are essential for enhancing the treatment strategy and evaluation of the prognosis. In this study, the authors applied machine learning techniques to single-cell RNA sequencing data to investigate the gene expression characteristics of immune cells in CRC and their association with immune cell infiltration. Methods: Differentially expressed genes (DEGs) in CRC were identified by machine learning methods, including clustering analysis, survival analysis, and gene enrichment analysis, and prognostic models were constructed. CIBERSORT and ESTIMATE algorithms were used to evaluate the abundance of infiltrating immune cells and UMAP and t-SNE techniques were used for dimensionality reduction and visualization of the data. Results: Specific gene expression patterns are closely related to immune cell infiltration in CRC patients. Clustering analysis demonstrated two unique subgroups in the CRC samples, characterized by significant differences in survival outcomes (p = 0.049). These DEGs are enriched in various biological processes, according to gene enrichment analysis. The prognostic models of the receiver operating characteristic curves had good predictive accuracy, with area under the curve values. Single-cell data analysis also showed the intricate associations of immune cells with tumor cells in the TME. Conclusions: This study reveals the complex relationship between gene expression and immune infiltration in CRC using machine learning techniques, and establishes prognostic models with potential value in the clinic. These findings reveal the new potential biomarkers for CRC desensitization and immunotherapy.

Simple SummaryColorectal cancer remains a major health challenge worldwide, highlighting the need for new diagnostic and prognostic biomarkers. In this study, we investigated the role of PAK6, a protein kinase, in colorectal cancer through integrative analysis of transcriptomic and clinical data. We found that PAK6 is significantly upregulated in tumor tissues and is associated with aggressive disease features and poorer patient survival. Importantly, PAK6 expression correlates with immune cell infiltration and chemokine signaling, suggesting its involvement in shaping the tumor immune microenvironment. Our findings indicate that PAK6 emerges as a candidate biomarker worthy of further investigation in colorectal cancer, with potential implications for guiding immunotherapy strategies in the future.Background: Colorectal cancer (CRC) represents a major global health challenge, characterized by rising incidence and mortality rates, necessitating improved diagnostic and therapeutic approaches. This study aimed to elucidate the expression and functional role of PAK6, a protein linked to cancer progression, as a potential biomarker for CRC. Methods: Utilizing comprehensive analyses of transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), we performed differential expression assessments, survival analyses, and functional enrichment studies. Results: Our findings demonstrate a significant upregulation of PAK6 in CRC tissues compared to adjacent normal tissues (p < 0.001), with a diagnostic AUC of 0.855, indicating its potential utility as a reliable biomarker for early detection. High PAK6 expression was significantly associated with aggressive clinicopathological features, including poor differentiation, residual tumor presence and reduced overall survival (HR = 1.72, p = 0.004). Functional enrichment analyses revealed PAK6’s involvement in critical biological processes such as cell cycle regulation, alongside its correlation with immune infiltration, particularly NK and CD8+ T cells. Moreover, PAK6 expression positively correlated with chemokines involved in immune cell recruitment, suggesting its role in modulating the tumor immune microenvironment. Conclusions: Our study underscores the significance of PAK6 as a diagnostic and prognostic biomarker in CRC, with the potential to inform targeted therapeutic strategies and enhance patient outcomes. Future research should focus on validating these findings in larger cohorts and exploring PAK6-targeted interventions to improve immunotherapeutic responses in CRC patients

Tumors that resist anoikis, a programmed cell death triggered by detachment from the extracellular matrix, promote metastasis; however, the role of anoikis-related genes (ARGs) in colorectal cancer (CRC) stratification, prognosis, and biological functions remains unclear. We obtained transcriptomic profiles of CRC and 27 ARGs from The Cancer Genome Atlas, the Gene Expression Omnibus, and MSigDB databases, respectively. CRC tissue samples were classified into two clusters based on the expression pattern of ARGs, and their functional differences were explored. Hub genes were screened using weighted gene co-expression network analysis, univariate analysis, and least absolute selection and shrinkage operator analysis, and validated in cell lines, tissues, or the Human Protein Atlas database. We constructed an ARG-risk model and nomogram to predict prognosis in patients with CRC, which was validated using an external cohort. Multifaceted landscapes, including stemness, tumor microenvironment (TME), immune landscape, and drug sensitivity, between high- and low-risk groups were examined. Patients with CRC were divided into C1 and C2 clusters. Cluster C1 exhibited higher TME scores, whereas cluster C2 had favorable outcomes and a higher stemness index. Eight upregulated hub ARGs (TIMP1, P3H1, SPP1, HAMP, IFI30, ADAM8, ITGAX, and APOC1) were utilized to construct the risk model. The qRT-PCR, Western blotting, and immunohistochemistry results were consistent with those of the bioinformatics analysis. Patients with high risk exhibited worse overall survival (p < 0.01), increased stemness, TME, immune checkpoint expression, immune infiltration, tumor mutation burden, and drug susceptibility compared with the patients with low risk. Our results offer a novel CRC stratification based on ARGs and a risk-scoring system that could predict the prognosis, stemness, TME, immunophenotypes, and drug susceptibility of patients with CRC, thereby improving their prognosis. This stratification may facilitate personalized therapies.

BackgroundLymphangiogenesis plays an important role in tumor progression and is significantly associated with tumor immune infiltration. However, the role and mechanisms of lymphangiogenesis in colorectal cancer (CRC) are still unknown. Thus, the objective is to identify the lymphangiogenesis-related genes associated with immune infiltration and investigation of their prognosis value.MethodsmRNA expression profiles and corresponding clinical information of CRC samples were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The lymphangiogenesis-related genes (LymRGs) were collected from the Molecular Signatures database (MSigDB). Lymphangiogenesis score (LymScore) and immune cell infiltrating levels were quantified using ssGSEA. LymScore) and immune cell infiltrating levels-related hub genes were identified using weighted gene co-expression network analysis (WGCNA). Univariate Cox and LASSO regression analyses were performed to identify the prognostic gene signature and construct a risk model. Furthermore, a predictive nomogram was constructed based on the independent risk factor generated from a multivariate Cox model.ResultsA total of 1076 LymScore and immune cell infiltrating levels-related hub genes from three key modules were identified by WGCNA. Lymscore is positively associated with natural killer cells as well as regulator T cells infiltrating. These modular genes were enriched in extracellular matrix and structure, collagen fibril organization, cell-substrate adhesion, etc. NUMBL, TSPAN11, PHF21A, PDGFRA, ZNF385A, and RIMKLB were eventually identified as the prognostic gene signature in CRC. And patients were divided into high-risk and low-risk groups based on the median risk score, the patients in the high-risk group indicated poor survival and were predisposed to metastasis and advanced stages. NUMBL and PHF21A were upregulated but PDGFRA was downregulated in tumor samples compared with normal samples in the Human Protein Atlas (HPA) database.ConclusionOur finding highlights the critical role of lymphangiogenesis in CRC progression and metastasis and provides a novel gene signature for CRC and novel therapeutic strategies for anti-lymphangiogenic therapies in CRC.

Identification of the prognostic biomarkers and their correlations with immune infiltration in colorectal cancer through bioinformatics analysis and in vitro experiments

Previous studies have shown that asthma is a risk factor for lung cancer, while the mechanisms involved remain unclear. We attempted to further explore the association between asthma and non-small cell lung cancer (NSCLC) via bioinformatics analysis. We obtained GSE143303 and GSE18842 from the GEO database. Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) groups were downloaded from the TCGA database. Based on the results of differentially expressed genes (DEGs) between asthma and NSCLC, we determined common DEGs by constructing a Venn diagram. Enrichment analysis was used to explore the common pathways of asthma and NSCLC. A protein-protein interaction (PPI) network was constructed to screen hub genes. KM survival analysis was performed to screen prognostic genes in the LUAD and LUSC groups. A Cox model was constructed based on hub genes and validated internally and externally. Tumor Immune Estimation Resource (TIMER) was used to evaluate the association of prognostic gene models with the tumor microenvironment (TME) and immune cell infiltration. Nomogram model was constructed by combining prognostic genes and clinical features. 114 common DEGs were obtained based on asthma and NSCLC data, and enrichment analysis showed that significant enrichment pathways mainly focused on inflammatory pathways. Screening of 5 hub genes as a key prognostic gene model for asthma progression to LUAD, and internal and external validation led to consistent conclusions. In addition, the risk score of the 5 hub genes could be used as a tool to assess the TME and immune cell infiltration. The nomogram model constructed by combining the 5 hub genes with clinical features was accurate for LUAD. Five-hub genes enrich our understanding of the potential mechanisms by which asthma contributes to the increased risk of lung cancer.

Background Heat shock protein B8 (HSPB8) is implicated in autophagy, and its aberrant expression has been linked to both the initiation and progression of tumors. However, the role and function of HSPB8 in colorectal cancer (CRC) and across multiple cancer types remain unclear. This study aimed to map the transcriptome of autophagy-related genes in CRC and to conduct a pan-cancer analysis of HSPB8 as both a prognostic and immunological biomarker. Methods We performed bioinformatics analyses on GSE113513 and GSE74602 to identify differentially expressed genes (DEGs) in CRC. These DEGs were then compared with autophagy-related genes to identify critical overlapping genes. The Kaplan-Meier plotter was used to verify the expression of autophagy-linked DEGs and evaluate its prognostic value. The protein expression of Hub gene in CRC was analyzed using the Human Protein Atlas database. The cBioPortal was used to analyze the type and frequency of Hub gene mutations. The TIMER (Tumor Immune Estimation Resource) database was used to study the correlation between HSPB8 and immune infiltration in CRC. Results In total, 825 DEGs were identified, including 8 autophagy-linked DEGs: ATIC, MYC, HSPB8, TNFSF10, BCL2, TP53INP2, ITPR1, and NKX2-3. Survival analysis showed that increased HSPB8 expression significantly correlates with poor prognosis in patients with CRC (p &lt; 0.05). HSPB8 was also found to be differentially expressed in various cancer types, correlating with both prognosis and immune infiltration. Further, changes in HSPB8 methylation and phosphorylation status were observed across several cancers, suggesting potential regulatory mechanisms. Therefore, HSPB8 may serve as a crucial prognostic and immunological biomarker in CRC and other cancers. Conclusions This study provides new insights into the role of autophagy-related genes in cancer progression and highlights HSPB8 as a potential target for cancer diagnostics and therapy.

The aim of this study was to identify genes with clinical significance in colorectal cancer (CRC). Gene expression profiles of 585 CRC tissues and 61 normal colorectal tissues from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases were used to identify differentially expressed genes (DEGs) between CRC and normal colorectal tissues. DAVID and KOBAS tools were used to explore Gene Ontology (GO) and KEGG pathways enriched by DEGs, respectively. In addition, TCGA data sets were also used to identify prognostic factors and develop a prognostic prediction model for CRC. A total of 353 DEGs including 117 upregulated and 236 downregulated genes in CRC were identified based on GSE32323 data set. These DEGs were significantly enriched in the biological process related to the regulation of cell proliferation and 50 signaling pathways, such as "TGF-beta signaling pathway," "Wnt signaling pathway," and "Jak-STAT signaling pathway." GCG, ADH1B, SLC4A4, ZG16, and CLCA4 were the top five downregulated in CRC. FOXQ1, LGR5, CLDN1, KRT23, and DPEP1 were the top five upregulated in CRC. KRT23 expression could affect tumor stage and regional lymph node metastasis in CRC patients. FOXQ1 expression could affect tumor distant metastasis in CRC patients. Survival analysis indicated that SLC4A4 expression was associated with the prognosis of CRC patients. Prognostic prediction model developed based on age, tumor stage, and SLC4A4 expression exhibited an efficient performance in predicting 1-, 3-, and 5-year overall survival of CRC patients. In conclusion, the current study identified several genes and pathways related to CRC, which provided new insight in understanding molecular mechanism of tumorigenesis and development of CRC.

Colorectal cancer (CRC)is responsible for a significant number of cancer-related fatalities worldwide. Researchers are investigating the therapeutic potential of ferroptosis, a type of iron-dependent controlled cell death, in the context of CRC. Curcumin, a natural compound found in turmeric, exhibits anticancer properties. This study explores the effects of curcumin on genes related to ferroptosis (FRGs)in CRC. To gather CRC data, we used the Gene Expression Profiling Interactive Analysis (GEPIA)and Gene Expression Omnibus (GEO)databases, while FRGs were obtained from the FerrDb database and PubMed. We identified 739 CRC differentially expressed genes (DEGs)in CRC and discovered 39 genes that were common genes between FRGs and CRC DEGs. The DEGs related to ferroptosis were enriched with various biological processes and molecular functions, including the regulation of signal transduction and glucose metabolism. Using the Drug Gene Interaction Database (DGIdb),we predicted drugs targeting CRC-DEGsand identified 17 potential drug targets. Additionally, we identified eight essential proteins related to ferroptosis in CRC, including MYC, IL1B, and SLC1A5. Survival analysis revealed that alterations in gene expression of CDC25A, DDR2, FABP4, IL1B, SNCA, and TFAM were associated with prognosis in CRC patients. In SW480 human CRC cells, treatment with curcumin decreased the expression of MYC, IL1B, and EZH2 mRNA, while simultaneously increasing the expression of SLCA5 and CAV1. The findings of this study suggest that curcumin could regulate FRGs in CRC and have the potential to be utilized as a therapeutic agent for treating CRC.

Colorectal cancer (CRC) is one of the most common malignancies worldwide; however, the potentially possible molecular biological mechanism of CRC is still not completely comprehended. This study aimed to confirm candidate key hub genes involved in the growth and development of CRC and their connection with immune infiltration as well as the related pathways. Gene expression data were selected from the GEO dataset. Hub genes for CRC were identified on the basis of differential expression analysis, weighted gene co-expression network analysis (WGCNA), and LASSO regression. Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), and Gene Set Enrichment Analysis (GSEA) were applied to reveal possible functions of the differential genes. Single-sample GSEA (ssGSEA) was implemented to identify the relationship between immune cells infiltration and hub genes. Two hundred and sixty-two differentially expressed genes (DEGs) were identified. Three modules were acquired based on WGCNA, and the blue module presented the highest relevance with CRC. Ten hub genes (AQP8, B3GALT5, CDH3, CEMIP, CPM, FOXQ1, PLAC8, SCNN1B, SPINK5, and SST) were acquired with LASSO analysis as underlying biomarkers for CRC. Compared with normal tissues, CRC tissues presented significantly higher numbers of CD4 T cells, CD8 T cells, B cells, natural regulatory T (Treg) cells, and monocytes. The functional enrichment analyses demonstrated that hub genes were primarily enriched in metabolic process, inflammatory-related, and immune-related response. Ten hub genes were identified to be involved in the occurrence and development of CRC and may be deemed as novel biomarkers for clinical diagnosis and treatment.

Chromatin regulators (CRs) are implicated in the development of cancer, but a comprehensive investigation of their role in colon adenocarcinoma (COAD) is inadequate. The purpose of this study is to find CRs that can provide recommendations for clinical diagnosis and treatment, and to explore the reasons why they serve as critical CRs. We obtained data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Weighted Gene Co-Expression Network Analysis (WGCNA) screened tumor-associated CRs. LASSO-Cox regression was used to construct the model and to screen key CRs together with support vector machine (SVM), the univariate Cox regression. We used single-cell data to explore the expression of CRs in cells and their communication. Immune infiltration, immune checkpoints, mutation, methylation, and drug sensitivity analyses were performed. Gene expression was verified by quantitative real-time reverse transcription-polymerase chain reaction (qRT-PCR). Pan-cancer analysis was used to explore the importance of hub CRs. We finally obtained 32 tumor-associated CRs. The prognostic model was constructed based on RCOR2, PPARGC1A, PKM, RAC3, PHF19, MYBBP1A, ORC1, and EYA2 by the LASSO-Cox regression. Single-cell data revealed that the model was immune-related. Combined with immune infiltration analysis, immune checkpoint analysis, and tumor immune dysfunction and exclusion (TIDE) analysis, the low-score risk group had more immune cell infiltration and better immune response. Mutation and methylation analysis showed that multiple CRs may be mutated and methylated in colon cancer. Drug sensitivity analysis revealed that the low-risk group may be more sensitive to several drugs and PKM was associated with multiple drugs. Combined with machine learning, PKM is perhaps the most critical gene in CRs. Pan-cancer analysis showed that PKM plays a role in the prognosis of cancers. We developed a prognostic model for COAD based on CRs. Increased expression of the core gene PKM is linked with a poor prognosis in several malignancies.

Dysregulated cellular metabolism is one of the major causes of colorectal cancer (CRC), including mitochondrial fission. Therefore, this study focuses on the specific regulatory mechanisms of mitochondrial dysfunction on CRC, which will provide theoretical guidance for CRC in the future. The Cancer Genome Atlas (TCGA)-CRC dataset, GSE103479 dataset and 40 mitochondrial fission-related genes (MFRGs) were downloaded in this study. The differentially expressed genes (DEGs) were analyzed in TCGA-CRC samples. Using MFRGs scores as traits, key module genes associated with its scores were screened by weighted gene co-expression network analysis (WGCNA). Then, differentially expressed MFRGs (DE-MFRGs) were obtained by intersecting DEGs and key module genes. Next, DE-MFRGs were subjected to univariate Cox, least absolute shrinkage and selection operator (LASSO), multivariate Cox and stepwise regression analysis to scree hub genes and to construct the risk model. The risk model was validated in GSE103479. Finally, the hub genes were comprehensively investigated through a multi-faceted approach encompassing clinical characteristic analysis, Gene Set Enrichment Analysis (GSEA), immune infiltration analysis, and drug sensitivity prediction. Subsequently, the expression levels of the identified key genes were validated utilizing quantitative real-time fluorescence PCR (qRT-PCR), reinforcing the findings and ensuring their accuracy. The 49 DE-MFRGs were gained by intersecting 3,310 DEGs and 1,952 key module genes. Then, CCDC68, FAM151A and MC1R were screened as hub genes. Also, the risk model validated in GSE103479 showed that the higher the risk score, the worse the survival of CRC patients. Furthermore, T/N/M stages were differences in risk scores between subgroups of clinical characteristics. The memory CD4+ T cell and plasma cell were more significant differences in the low-risk group samples. The 51 drugs were showed a better response in the high-risk group patients. RT-qPCR validation results showed that CCDC68 and FAM151A were down-regulated in CRC, while MC1R was up-regulated, consistent with the validation set results. And FAM151A and MC1R showed highly significant difference between CRC and normal samples (P<0.0001). In this study, we found CCDC68, FAM151A and MC1R as potential hub genes in CRC, and analyzed the molecular mechanism of mitochondrial affecting CRC, which would provide theoretical reference value for CRC.

The expression level of SLC35A3 is associated with the prognosis of many cancers, but its role in colorectal cancer (CRC) is unclear. The purpose of our study was to elucidate the role of SLC35A3 in CRC. The expression levels of SLC35A3 in CRC were evaluated through tumor immune resource assessment (TIMER), The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), International Cancer Genome Consortium (ICGC), Human Protein Atlas (HPA), qRT-PCR, and immunohistochemical evaluation. TCGA, GEO, and ICGC databases were used to analyze the diagnostic and prognostic value of SLC35A3 in CRC. A overall survival (OS) model was constructed and validated based on the expression level of SLC35A3 and multivariable analysis results. The cBioPortal tool was used to analyze SLC35A3 mutation in CRC. The UALCAN tool was used to analyze the promoter methylation level of SLC35A3 in colorectal cancer. In addition, the role of SLC35A3 in CRC was determined through GO analysis, KEGG analysis, gene set enrichment analysis (GSEA), immune infiltration analysis, and immune checkpoint correlation analysis. In vitro experiments validated the function of SLC35A3 in colorectal cancer cells. Compared with adjacent normal tissues and colonic epithelial cells, the expression of SLC35A3 was decreased in CRC tissues and CRC cell lines. Low expression of SLC35A3 was associated with N stage, pathological stage, and lymphatic infiltration, and it was unfavorable for OS, disease-specific survival (DSS), recurrence-free survival (RFS), and post-progression survival (PPS). According to the Receiver Operating Characteristic (ROC) analysis, SLC35A3 is a potential important diagnostic biomarker for CRC patients. The nomograph based on the expression level of SLC35A3 showed a better predictive model for OS than single prognostic factors and TNM staging. SLC35A3 has multiple types of mutations in CRC, and its promoter methylation level is significantly decreased. GO and KEGG analysis indicated that SLC35A3 may be involved in transmembrane transport protein activity, cell communication, and interaction with neurotransmitter receptors. GSEA revealed that SLC35A3 may be involved in energy metabolism, DNA repair, and cancer pathways. In addition, SLC35A3 was closely related to immune cell infiltration and immune checkpoint expression. Immunohistochemistry confirmed the positive correlation between SLC35A3 and helper T cell infiltration. In vitro experiments showed that overexpression of SLC35A3 inhibited the proliferation and invasion capability of colorectal cancer cells and promoted apoptosis. The results of this study indicate that decreased expression of SLC35A3 is closely associated with poor prognosis and immune cell infiltration in colorectal cancer, and it can serve as a promising independent prognostic biomarker and potential therapeutic target.

Endometriosis (EMs) is a chronic inflammatory disease characterized by the presence of endometrial tissue in the non-uterine cavity, resulting in dysmenorrhea, pelvic pain, and infertility. Epidemiologic data have suggested the correlation between EMs and recurrent pregnancy loss (RPL), but the pathological mechanism is unclear. This study aims to investigate the potential biomarkers and immune infiltration in EMs and RPL, providing a basis for early detection and treatment of the two diseases. Two RPL and six EMs transcriptomic datasets from the Gene Expression Omnibus (GEO) database were used for differential analysis via limma package, followed by weighted gene co-expression network analysis (WGCNA) for key modules screening. Protein-protein interaction (PPI) network and two machine learning algorithms were applied to identify the common core genes in both diseases. The diagnostic capabilities of the core genes were assessed by receiver operating characteristic (ROC) curves. Moreover, immune cell infiltration was estimated using CIBERSORTx, and the Cancer Genome Atlas (TCGA) database was employed to elucidate the role of key genes in endometrial carcinoma (EC). 26 common differentially expressed genes (DEGs) were screened in both diseases, three of which were identified as common core genes (MAN2A1, PAPSS1, RIBC2) through the combination of WGCNA, PPI network, and machine learning-based feature selection. The area under the curve (AUC) values generated by the ROC indicates excellent diagnostic powers in both EMs and RPL. The key genes were found to be significantly associated with the infiltration of several immune cells. Interestingly, MAN2A1 and RIBC2 may play a predominant role in the development and prognostic stratification of EC. We identified three key genes linking EMs and RPL, emphasizing the heterogeneity of immune infiltration in the occurrence of both diseases. These findings may provide new mechanistic insights or therapeutic targets for further research of EMs and RPL.