A prognostic immune predictor, HLA-DRA, plays diverse roles in non-muscle invasive and muscle invasive bladder cancer

Causal relationship, shared genes between rheumatoid arthritis and pulp and periapical disease: evidence from GWAS and transcriptome data

Objective

Patients with rheumatoid arthritis (RA) have an increased risk of developing pulp and periapical disease (PAP), but the causal relationship and shared genetic factors between these conditions have not been explored. This study aimed to investigate the bidirectional causal relationship between RA and PAP and to analyze shared genes and pathogenic pathways.

Methods

We utilized GWAS data from the IEU Open GWAS Project and employed five Mendelian randomization methods (MR Egger, weighted median, inverse variance weighted, simple mode, and weighted mode) to investigate the bidirectional causal relationship between RA and PAP. Transcriptome data for RA and irreversible pulpitis (IRP) were obtained from the GEO database. Hub genes were identified through differential analysis, CytoHubba, machine learning (ML), and other methods. The immune infiltration of both diseases was analyzed using the ssGSEA method. Finally, we constructed a regulatory network for miRNAs, transcription factors, chemicals, diseases, and RNA-binding proteins based on the identified hub genes.

Results

RA was significantly associated with an increased risk of PAP (OR = 1.1284, 95% CI 1.0674-1.1929, p < 0.001). However, there was insufficient evidence to support the hypothesis that PAP increased the risk of RA. Integrating datasets and differential analysis identified 84 shared genes primarily involved in immune and inflammatory pathways, including the IL-17 signaling pathway, Th17 cell differentiation, and TNF signaling pathway. Using CytoHubba and three ML methods, we identified three hub genes (HLA-DRA, ITGAX, and PTPRC) that are significantly correlated and valuable for diagnosing RA and IRP. We then constructed a comprehensive regulatory network using the miRDB, miRWalk, ChipBase, hTFtarget, CTD, MalaCards, DisGeNET, and ENCORI databases.

Conclusion

RA may increase the risk of PAP. The three key genes, HLA-DRA, ITGAX, and PTPRC, have significant diagnostic value for both RA and IRP.

Molecular biomarkers of progression in non-muscle-invasive bladder cancer - beyond conventional risk stratification

The global incidence of bladder cancer is more than half a million diagnoses each year. Bladder cancer can be categorized into non-muscle-invasive bladder cancer (NMIBC), which accounts for ~75% of diagnoses, and muscle-invasive bladder cancer (MIBC). Up to 45% of patients with NMIBC develop disease progression to MIBC, which is associated with a poor outcome, highlighting a clinical need to identify these patients. Current risk stratification has a prognostic value, but relies solely on clinicopathological parameters that might not fully capture the complexity of disease progression. Molecular research has led to identification of multiple crucial players involved in NMIBC progression. Identified biomarkers of progression are related to cell cycle, MAPK pathways, apoptosis, tumour microenvironment, chromatin stability and DNA-damage response. However, none of these biomarkers has been prospectively validated. Reported gene signatures of progression do not improve NMIBC risk stratification. Molecular subtypes of NMIBC have improved our understanding of NMIBC progression, but these subtypes are currently unsuitable for clinical implementation owing to a lack of prospective validation, limited predictive value as a result of intratumour subtype heterogeneity, technical challenges, costs and turnaround time. Future steps include the development of consensus molecular NMIBC subtypes that might improve conventional clinicopathological risk stratification. Prospective implementation studies of biomarkers and the design of biomarker-guided clinical trials are required for the integration of molecular biomarkers into clinical practice.

Changes of mRNA, miRNA and lncRNA expression contributing to skeletal muscle differences between fetus and adult Mongolian horses

The growth and development of myofibers, as the fundamental units comprising muscle tissue, and their composition type are indeed among the most crucial factors influencing skeletal muscle types. Muscle fiber adaptation is closely associated with alterations in physiological conditions. Muscle fiber types undergo dynamic changes in fetus and adult horses. Our aim is to investigate the mechanisms influencing the differences in muscle fiber types between fetal and adult stages of Mongolian horses. The study investigated the distribution of muscle fiber types within longissimus dorsi muscle of fetus and adult Mongolian horses. A total of 652 differentially expressed genes (DEGs), 476 Differentially expressed lncRNAs (DELs), and 174 Differentially expressed miRNAs (DEMIRs) were identified using deep RNA-seq analysis. The results of functional analysis reveal the transformations in muscle fiber type from the fetal to adult stage in Mongolian horses. The up-regulated DEGs were implicated in the development and differentiation of muscle fibers, while down-regulated DEGs were associated with muscle fiber contraction, transformation, and metabolism. Additionally, connections between non-coding RNA and mRNA landscapes were identified based on their functional alterations, some non-coding RNA target genes may be associated with immunity. These data have broadened our understanding of the specific roles and interrelationships among regulatory molecules involved in Mongolian horse development, this provides new perspectives for selecting and breeding superior individuals and for disease prevention.

Comprehensive multiomics analysis reveals distinct differences between pediatric choroid plexus papilloma and carcinoma

Choroid plexus tumors (CPTs) are intraventricular tumors derived from the choroid plexus epithelium and occur frequently in children. The aim of this study was to investigate the genomic and epigenomic characteristics of CPT and identify the differences between choroid plexus papilloma (CPP) and choroid plexus carcinoma (CPC). We conducted multiomics analyses of 20 CPT patients including CPP and CPC. Multiomics analysis included whole-genome sequencing, whole-transcriptome sequencing, and methylation sequencing. Mutually exclusive TP53 and EPHA7 point mutations, coupled with the amplification of chromosome 1, were exclusively identified in CPC. In contrast, amplification of chromosome 9 was specific to CPP. Differential gene expression analysis uncovered a significant overexpression of genes related to cell cycle regulation and epithelial-mesenchymal transition pathways in CPC compared to CPP. Overexpression of genes associated with tumor metastasis and progression was observed in the CPC subgroup with leptomeningeal dissemination. Furthermore, methylation profiling unveiled hypomethylation in major repeat regions, including long interspersed nuclear elements, short interspersed nuclear elements, long terminal repeats, and retrotransposons in CPC compared to CPP, implying that the loss of epigenetic silencing of transposable elements may play a role in tumorigenesis of CPC. Finally, the differential expression of AK1, regulated by both genomic and epigenomic factors, emerged as a potential contributing factor to the histological difference of CPP against CPC. Our results suggest pronounced genomic and epigenomic disparities between CPP and CPC, providing insights into the pathogenesis of CPT at the molecular level.

Current look at the most promising proteomic and glycomic biomarkers of bladder cancer

BACKGROUND

Bladder cancer (BC) belongs to the most frequent cancer types. The diagnostic process is still long and costly, with a high percentage of false-positive or -negative results. Due to the cost and lack of effectiveness, older methods need to be supplemented or replaced by a newer more reliable method. In this regard, proteins and glycoproteins pose high potential.

METHODS

We performed an online search in PubMed/Medline, Scopus, and Web of Science databases to find relevant studies published in English up until May 2023. If applicable, we set the AUC threshold to 0.90 and sensitivity/specificity (SN/SP) to 90%.

FINDINGS

Protein and glycoprotein biomarkers are a demonstrably viable option in BC diagnostics. Cholinesterase shows promise in progression-free survival. BLCA-4, ORM-1 along with HTRA1 in the detection of BC. Matrix metallopeptidase 9 exhibits potential for stratification of muscle-invasive subtypes with high negative predictive value for aggressive phenotypes. Distinguishing non-muscle invasive subtypes benefits from Keratin 17. Neu5Gc-modified UMOD glycoproteins pose potential in BC diagnosis, while fibronectin, laminin-5, collagen type IV, and lamprey immunity protein in early detection of BC.

Identification of key immune cell-related genes involved in tumorigenesis and prognosis of cervical squamous cell carcinoma

The infiltration of immune cells can significantly affect the prognosis and immune therapy of patients with cervical squamous cell carcinoma (CSCC). This study aimed to explore key immune cell-related genes in the tumorigenesis and prognosis of CSCC. The module significantly related to immunity was screened by weighted gene co-expression network analysis (WGCNA) and ESTIMATE analysis, followed by correlation analysis with clinical traits. Key candidate genes were intersected with the protein-protein interaction (PPI) network genes for immune-related genes. The relationship between immune cell infiltration and key genes was analyzed. Tumor immune dysfunction and exclusion (TIDE) and immunophenoscore (IPS) predicted the response to immunotherapy in CSCC patients. Clinically, quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry were manipulated for analyzing the changes in mRNA and protein expression of key genes in cancer. Western blot was conducted to assess the correlation between key genes and immune infiltration. The brown module was notably associated with the immune microenvironment of CSCC, from which three immune-related key genes (TYROBP, CCL5, and HLA-DRA) were obtained. High expression of these genes was significantly positively associated with the infiltration abundance of T cells, B cells, and other immune cells. High expression levels of three key genes were confirmed in para-cancer tissue and correlated with the abundance of immune cells. The high-expression group of key genes was more sensitive to immunotherapy. We provide a theoretical basis for searching for potential targets for effective treatment and diagnosis of CSCC and provide new ideas for developing novel immunotherapy strategies.

Excavation of diagnostic biomarkers and construction of prognostic model for clear cell renal cell carcinoma based on urine proteomics

Purpose

Clear cell renal cell carcinoma (ccRCC) is the most common pathology type in kidney cancer. However, the prognosis of advanced ccRCC is unsatisfactory. Thus, early diagnosis becomes one of the most important research priorities of ccRCC. However, currently available studies about ccRCC lack urine-related further studies. In this study, we applied proteomics to search urinary biomarkers to assist early diagnosis of ccRCC. In addition, we constructed a prognostic model to assist judge patients' prognosis.

Materials and methods

Urine which was used to perform 4D label-free quantitative proteomics was collected from 12 ccRCC patients and 11 non-tumor patients with no urinary system diseases. The urine of 12 patients with ccRCC confirmed by pathological examination after surgery was collected before operatoin. Bioinformatics analysis was used to describe the urinary proteomics landscape of these patients with ccRCC. The top ten proteins with the highest expression content were selected as the basis for subsequent validation. Urine from 46 ccRCC patients and 45 control patients were collected to use for verification by enzyme linked immunosorbent assay (ELISA). In order to assess the prognostic value of urine proteomics, a prognostic model was constructed by COX regression analysis on the intersection of RNA-sequencing data in The Cancer Genome Atlas (TCGA) database and our urine proteomic data.

Results

133 proteins differentially expressed in the urinary samples were found and 85 proteins (Fold Change, FC>1.5) were identified up-regulated while 48 down-regulated (FC<0.5). Top 10 proteins including S100A14, PKHD1L1, FABP4, ITIH2, C3, C8G, C2, ATF6, ANGPTL6, F13B were performed ELISA to verify. The results showed that PKHD1L1, ANGPTL6, FABP4 and C3 were statistically significant (P<0.05). We performed multivariate logistic regression analysis and plotted a nomogram. Receiver operating characteristic (ROC) curve indicted that the diagnostic efficiency of combined indicators is satisfactory (Aare under curve, AUC=0.835). Furthermore, the prognostic value of the urine proteomics was explored through the intersection between urine proteomics and TCGA RNA-seq data. Thus, COX regression analysis showed that VSIG4, HLA-DRA, SERPINF1, and IGLV2-23 were statistically significant (P<0.05).

Conclusion

Our study indicated that the application of urine proteomics to explore diagnostic biomarkers and to construct prognostic models of renal clear cell carcinoma is of certain clinical value. PKHD1L1, ANGPTL6, FABP4 and C3 can assist to diagnose ccRCC. The prognostic model constituted of VSIG4, HLA-DRA, SERPINF1, and IGLV2-23 can significantly predict the prognosis of ccRCC patients, but this still needs more clinical trials to verify.

Construction and validation of a bladder cancer risk model based on autophagy-related genes

Autophagy has an important association with tumorigenesis, progression, and prognosis. However, the mechanism of autophagy-regulated genes on the risk prognosis of bladder cancer (BC) patients has not been fully elucidated yet. In this study, we created a prognostic model of BC risk based on autophagy-related genes, which further illustrates the value of genes associated with autophagy in the treatment of BC. We first downloaded human autophagy-associated genes and BC datasets from Human Autophagy Database and The Cancer Genome Atlas (TCGA) database, and finally obtained differential prognosis-associated genes for autophagy by univariate regression analysis and differential analysis of cancer versus normal tissues. Subsequently, we downloaded two datasets from Gene Expression Omnibus (GEO), GSE31684 and GSE15307, to expand the total number of samples. Based on these genes, we distinguished the molecular subtypes (C1, C2) and gene classes (A, B) of BC by consistent clustering analysis. Using the genes merged from TCGA and the two GEO datasets, we conducted least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis to obtain risk genes and construct autophagy-related risk prediction models. The accuracy of this risk prediction model was assessed by receiver operating characteristic (ROC) and calibration curves, and then nomograms were constructed to predict the survival of bladder cancer patients at 1, 3, and 5 years, respectively. According to the median value of the risk score, we divided BC samples into the high- and low-risk groups. Kaplan-Meier (K-M) survival analysis was performed to compare survival differences between subgroups. Then, we used single sample gene set enrichment analysis (ssGSEA) for immune cell infiltration abundance, immune checkpoint genes, immunotherapy response, gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis, and tumor mutation burden (TMB) analysis for different subgroups. We also applied quantitative real-time polymerase chain reaction (PCR) and immunohistochemistry (IHC) techniques to verify the expression of these six genes in the model. Finally, we chose the IMvigor210 dataset for external validation. Six risk genes associated with autophagy (SPOCD1, FKBP10, NAT8B, LDLR, STMN3, and ANXA2) were finally screened by LASSO regression algorithm and multivariate Cox regression analysis. ROC and calibration curves showed that the model established was accurate and reliable. Univariate and multivariate regression analyses were used to verify that the risk model was an independent predictor. K-M survival analysis indicated that patients in the high-risk group had significantly worse overall survival than those in the low-risk group. Analysis by algorithms such as correlation analysis, gene set variation analysis (GSVA), and ssGSEA showed that differences in immune microenvironment, enrichment of multiple biologically active pathways, TMB, immune checkpoint genes, and human leukocyte antigens (HLAs) were observed in the different risk groups. Then, we constructed nomograms that predicted the 1-, 3-, and 5-year survival rates of different BC patients. In addition, we screened nine sensitive chemotherapeutic drugs using the correlation between the obtained expression status of risk genes and drug sensitivity results. Finally, the external dataset IMvigor210 verified that the model is reliable and efficient. We established an autophagy-related risk prognostic model that is accurate and reliable, which lays the foundation for future personalized treatment of bladder cancer.

Non-muscle invasive bladder cancer biomarkers beyond morphology

Non-muscle invasive bladder cancer (NMIBC) still represents a challenge in decision-making and clinical management since prognostic and predictive biomarkers of response to treatment are still under investigation. In addition to the risk factors defined by EORTC guidelines, histological features have also been considered key variables able to impact on recurrence and progression in bladder cancer. Conversely, the role of genomic rearrangements or expression of specific proteins at tissue level need further assessment in NMIBC. As with muscle-invasive cancer, NMIBC is a heterogeneous disease, characterized by genomic instability, varying rates of mutation and a wide range of protein tissue expression. In this Review, we summarized the recent evidence on prognostic and predictive tissue biomarkers in NMIBC, beyond morphological parameters, outlining how they could affect tumor biology and consequently its behavior during clinical care. Our aim was to facilitate clinical evaluation of promising biomarkers that may be employed to better stratify patients. We described the most common molecular events and immunohistochemical protein expressions linked to recurrence and progression. Moreover, we discussed the link between available treatments and molecular drivers that could be predictive of clinical response. In conclusion, we foster further investigations with particular focus on immunohistochemical evaluation of tissue biomarkers, a promising and cost-effective tool for daily practice.

HLA class II molecule HLA-DRA identifies immuno-hot tumors and predicts the therapeutic response to anti-PD-1 immunotherapy in NSCLC

Background

Immune checkpoint blockade (ICB) only works well for a certain subset of patients with non-small cell lung cancer (NSCLC). Therefore, biomarkers for patient stratification are desired, which can suggest the most beneficial treatment.

Methods

In this study, three datasets (GSE126044, GSE135222, and GSE136961) of immunotherapy from the Gene Expression Omnibus (GEO) database were analyzed, and seven intersected candidates were extracted as potential biomarkers for ICB followed by validation with The Cancer Genome Atlas (TCGA) dataset and the in-house cohort data.

Results

Among these candidates, we found that human leukocyte antigen-DR alpha (HLA-DRA) was downregulated in NSCLC tissues and both tumor and immune cells expressed HLA-DRA. In addition, HLA-DRA was associated with an inflamed tumor microenvironment (TME) and could predict the response to ICB in NSCLC. Moreover, we validated the predictive value of HLA-DRA in immunotherapy using an in-house cohort. Furthermore, HLA-DRA was related to the features of inflamed TME in not only NSCLC but also in most cancer types.

Conclusion

Overall, HLA-DRA could be a promising biomarker for guiding ICB in NSCLC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09840-6.

Inclusion Body Myositis and Neoplasia: A Narrative Review

Inclusion body myositis (IBM) is an acquired, late-onset inflammatory myopathy, with both inflammatory and degenerative pathogenesis. Although idiopathic inflammatory myopathies may be associated with malignancies, IBM is generally not considered paraneoplastic. Many studies of malignancy in inflammatory myopathies did not include IBM patients. Indeed, IBM is often diagnosed only after around 5 years from onset, while paraneoplastic myositis is generally defined as the co-occurrence of malignancy and myopathy within 1 to 3 years of each other. Nevertheless, a significant association with large granular lymphocyte leukemia has been recently described in IBM, and there are reports of cancer-associated IBM. We review the pathogenic mechanisms supposed to be involved in IBM and outline the common mechanisms in IBM and malignancy, as well as the therapeutic perspectives. The terminally differentiated, CD8+ highly cytotoxic T cells expressing NK features are central in the pathogenesis of IBM and, paradoxically, play a role in some cancers as well. Interferon gamma plays a central role, mostly during the early stages of the disease. The secondary mitochondrial dysfunction, the autophagy and cell cycle dysregulation, and the crosstalk between metabolic and mitogenic pathways could be shared by IBM and cancer. There are intermingled subcellular mechanisms in IBM and neoplasia, and probably their co-existence is underestimated. The link between IBM and cancers deserves further interest, in order to search for efficient therapies in IBM and to improve muscle function, life quality, and survival in both diseases.

Identification of Hub Genes and Immune Infiltration in Pediatric Biliary Atresia by Comprehensive Bioinformatics Analysis

Background: Biliary atresia (BA) is the leading cause of pediatric liver failure and pediatric liver transplantation worldwide. Evidence suggests that the immune system plays a central role in the pathogenesis of BA. Methods: In this work, the novel immune-related genes between BA and normal samples were investigated based on weighted gene co-expression network analysis (WGCNA) and the deconvolution algorithm of CIBERSORT. Results: Specifically, 650 DEGs were identified between the BA and normal groups. The blue module was the most positively correlated with BA containing 3274 genes. Totally, 610 overlapping BA-related genes of DEGs and WGCNA were further used to identify IRGs. Three IRGs including VCAM1, HLA-DRA, and CD74 were finally identified as the candidate biomarkers. Particularly, the CD74 biomarker was discovered for the first as a potential immune biomarker for BA. Conclusions: Possibly, these 3 IRGs might serve as candidate biomarkers and guide the individualized treatment strategies for BA patients. Our results would provide great insights for a deeper understanding of both the occurrence and the treatment of BA.

Identification of Transcriptional Pattern Related to Immune Cell Infiltration With Gene Co-Expression Network in Papillary Thyroid Cancer

BACKGROUND

A growing body of evidence suggests that immune cell infiltration in cancer is closely related to clinical outcomes. However, there is still a lack of research on papillary thyroid cancer (PTC).

METHODS

Based on single-sample gene set enrichment analysis (SSGSEA) algorithm and weighted gene co-expression network analysis (WGCNA) tool, the infiltration level of immune cell and key modules and genes associated with the level of immune cell infiltration were identified using PTC gene expression data from The Cancer Genome Atlas (TCGA) database. In addition, the co-expression network and protein-protein interactions network analysis were used to identify the hub genes. Moreover, the immunological and clinical characteristics of these hub genes were verified in TCGA and GSE35570 datasets and quantitative real-time polymerase chain reaction (PCR). Finally, receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic value of hub genes.

RESULTS

Activated B cell, activated dendritic cell, CD56bright natural killer cell, CD56dim natural killer cell, Eosinophil, Gamma delta T cell, Immature dendritic cell, Macrophage, Mast cell, Monocyte, Natural killer cell, Neutrophil and Type 17 T helper cell were significantly changed between PTC and adjacent normal groups. WGCNA results showed that the black model had the highest correlation with the infiltration level of activated dendritic cells. We found 14 hub genes whose expression correlated to the infiltration level of activated dendritic cells in both TCGA and GSE35570 datasets. After validation in the TCGA dataset, the expression level of only 5 genes (C1QA, HCK, HLA-DRA, ITGB2 and TYROBP) in 14 hub genes were differentially expressed between PTC and adjacent normal groups. Meanwhile, the expression levels of these 5 hub genes were successfully validated in GSE35570 dataset. Quantitative real-time PCR results showed the expression of these 4 hub genes (except C1QA) was consistent with the results in TCGA and GSE35570 dataset. Finally, these 4 hub genes had diagnostic value to distinguish PTC and adjacent normal controls.

CONCLUSIONS

HCK, HLA-DRA, ITGB2 and TYROBP may be key diagnostic biomarkers and immunotherapy targets in PTC.

18F-Fluciclovine PET/CT performance in biochemical recurrence of prostate cancer: a systematic review

BACKGROUND

A systematic literature review of the performance of 18Fluorine-fluciclovine PET/CT for imaging of men with recurrent prostate cancer was performed.

METHODS

Scientific literature databases (MEDLINE, ScienceDirect and Cochrane Libraries) were searched systematically during Oct 2020 using PRISMA criteria. No limit was put on the date of publication. Prospective studies reporting a patient-level 18F-fluciclovine detection rate (DR) from ≥25 patients with recurrent prostate cancer were sought. Proceedings of relevant meetings held from 2018 through Oct 2020 were searched for abstracts meeting criteria.

RESULTS

Searches identified 321 unique articles. In total, nine articles (six papers and three conference abstracts), comprising a total of 850 patients met inclusion criteria. Most studies (n = 6) relied on ASTRO-Phoenix Criteria, EAU-ESTRO-SIOG, and/or ASTRO-AUA guidelines to identify patients with biochemical recurrence. Patients' PSA levels ranged from 0.02-301.7 ng/mL (median level per study, 0.34-4.10 ng/mL [n = 8]). Approximately 64% of patients had undergone prostatectomy, but three studies focused solely on post-prostatectomy patients. Adherence to imaging protocol guidelines was heterogeneous, with variance seen in administered activity, uptake and scan times. Overall patient-level DR varied between studies from 26% to 83%, with 78% of studies reporting a DR > 50%. DR was proportional to PSA, but even at PSA < 0.5 ng/mL DR of up to 53% were reported. Prostate/bed DR (n = 7) ranged from 18% to 78% and extra-prostatic rates (n = 6) from 8% to 72%. Pelvic node and bone lesion DR ranged from 8% to 47% and 0% to 26%, respectively (n = 5). 18F-Fluciclovine PET/CT was shown to impact patient management and outcomes. Two studies reported 59-63% of patients to have a management change post-scan. A further study showed significant increase in failure-free survival following 18F-fluciclovine-guided compared with conventional imaging-guided radiotherapy planning.

CONCLUSIONS

18F-Fluciclovine PET/CT shows good performance in patients with recurrent prostate cancer leading to measurable clinical benefits. Careful adherence to recommended imaging protocols may help optimize DR.

Identification of Genes Related to Immune Infiltration in the Tumor Microenvironment of Cutaneous Melanoma

Cutaneous melanoma (CM) is the leading cause of skin cancer deaths and is typically diagnosed at an advanced stage, resulting in a poor prognosis. The tumor microenvironment (TME) plays a significant role in tumorigenesis and CM progression, but the dynamic regulation of immune and stromal components is not yet fully understood. In the present study, we quantified the ratio between immune and stromal components and the proportion of tumor-infiltrating immune cells (TICs), based on the ESTIMATE and CIBERSORT computational methods, in 471 cases of skin CM (SKCM) obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were analyzed by univariate Cox regression analysis, least absolute shrinkage, and selection operator (LASSO) regression analysis, and multivariate Cox regression analysis to identify prognosis-related genes. The developed prognosis model contains ten genes, which are all vital for patient prognosis. The areas under the curve (AUC) values for the developed prognostic model at 1, 3, 5, and 10 years were 0.832, 0.831, 0.880, and 0.857 in the training dataset, respectively. The GSE54467 dataset was used as a validation set to determine the predictive ability of the prognostic signature. Protein–protein interaction (PPI) analysis and weighted gene co-expression network analysis (WGCNA) were used to verify “real” hub genes closely related to the TME. These hub genes were verified for differential expression by immunohistochemistry (IHC) analyses. In conclusion, this study might provide potential diagnostic and prognostic biomarkers for CM.