Identification of prognostic immune-related genes in the tumor microenvironment of endometrial cancer

Characterization and validation of fatty acid metabolism-related genes predicting prognosis, immune infiltration, and drug sensitivity in endometrial cancer

Endometrial cancer is considered to be the second most common tumor of the female reproductive system, and patients diagnosed with advanced endometrial cancer have a poor prognosis. The influence of fatty acid metabolism in the prognosis of patients with endometrial cancer remains unclear. We constructed a prognostic risk model using transcriptome sequencing data of endometrial cancer and clinical information of patients from The Cancer Genome Atlas (TCGA) database via least absolute shrinkage and selection operator regression analysis. The tumor immune microenvironment was analyzed using the CIBERSORT algorithm, followed by functional analysis and immunotherapy efficacy prediction by gene set variation analysis. The role of model genes in regulating endometrial cancer in vitro was verified by CCK-8, colony formation, wound healing, and transabdominal invasion assays, and verified in vivo by subcutaneous tumor transplantation in nude mice. A prognostic model containing 14 genes was constructed and validated in 3 cohorts and clinical samples. The results showed differences in the infiltration of immune cells between the high-risk and low-risk groups, and that the high-risk group may respond better to immunotherapy. Experiments in vitro confirmed that knockdown of epoxide hydrolase 2 (EPHX2) and acyl-CoA oxidase like (ACOXL) had an inhibitory effect on EC cells, as did overexpression of hematopoietic prostaglandin D synthase (HPGDS). The same results were obtained in experiments in vivo. Prognostic models related to fatty acid metabolism can be used for the risk assessment of endometrial cancer patients. Experiments in vitro and in vivo confirmed that the key genes HPGDS, EPHX2, and ACOXL in the prognostic model may affect the development of endometrial cancer.

Unveiling the Roles of Cysteine Proteinases F and W: From Structure to Pathological Implications and Therapeutic Targets

Cysteine cathepsins F and W are members of the papain-like cysteine protease family, which have distinct structural features and functional roles in various physiological and pathological processes. This review provides a comprehensive overview of the current understanding of the structure, biological functions, and pathological implications of cathepsins F and W. Beginning with an introduction to these proteases, we delve into their structural characteristics and elucidate their unique features that dictate their enzymatic activities and substrate specificity. We also explore the intricate involvement of cathepsins F and W in malignancies, highlighting their role as potential biomarkers and therapeutic targets in cancer progression. Furthermore, we discuss the emerging roles of these enzymes in immune response modulation and neurological disorders, shedding light on their implications in autoimmune and neurodegenerative diseases. Finally, we review the landscape of inhibitors targeting these proteases, highlighting their therapeutic potential and challenges in clinical translation. This review brings together the diverse facets of cysteine cathepsins F and W, providing insights into their roles in health and disease and guiding future investigations for therapeutic advances.

The Deep Proteomics Approach Identified Extracellular Vesicular Proteins Correlated to Extracellular Matrix in Type One and Two Endometrial Cancer

Among gynecological cancers, endometrial cancer is the most common in developed countries. Extracellular vesicles (EVs) are cell-derived membrane-surrounded vesicles that contain proteins involved in immune response and apoptosis. A deep proteomic approach can help to identify dysregulated extracellular matrix (ECM) proteins in EVs correlated to key pathways for tumor development. In this study, we used a proteomics approach correlating the two acquisitions-data-dependent acquisition (DDA) and data-independent acquisition (DIA)-on EVs from the conditioned medium of four cell lines identifying 428 ECM proteins. After protein quantification and statistical analysis, we found significant changes in the abundance (p < 0.05) of 67 proteins. Our bioinformatic analysis identified 26 pathways associated with the ECM. Western blotting analysis on 13 patients with type 1 and type 2 EC and 13 endometrial samples confirmed an altered abundance of MMP2. Our proteomics analysis identified the dysregulated ECM proteins involved in cancer growth. Our data can open the path to other studies for understanding the interaction among cancer cells and the rearrangement of the ECM.

Research on the toxicological prognostic significance of age-related genes in endometrial cancer unveiling key factors in patient prognosis

This study investigates the influence of aging-related genes on endometrial cancer, a prominent gynecological malignancy with rising incidence and mortality. By analyzing gene expression differences between cancerous and normal endometrial tissues, 42 aging-related genes were identified as differentially expressed. Utilizing the TCGA-UCEC sample, consensus clustering divided the samples into two molecular subgroups, Aging low and Aging high, based on their expression profiles. These subgroups showed distinct prognoses and survival rates, with the Aging high group associated with DNA repair and cell cycle pathways, and the Aging low group showing suppressed metabolic pathways and increased immune cell infiltration, suggesting a potential for better immunotherapy outcomes. Mutation analysis did not find significant differences in mutation frequencies between the groups, but a high Tumor Mutation Burden (TMB) correlated with better prognosis. A risk score model was also developed, showcasing significant prognostic power. Further analysis of the SIX1 gene revealed its overexpression in cancer cells. Drug sensitivity tests indicated that the low-risk group might respond better to chemotherapy. This research underscores the significance of aging-related genes in endometrial cancer, offering insights into their prognostic value and therapeutic potential, which could lead to personalized treatment approaches and enhanced patient management.

Comprehensive analysis of a NAD+ metabolism-derived gene signature to predict the prognosis and immune landscape in endometrial cancer

As a crucial regulator influencing tumor progression, nicotinamide adenine dinucleotide (NAD+) is widely acknowledged. However, its role in endometrial cancer (EC) is not completely understood. In this study, we aimed to develop an NAD+metabolic-related genes (NMRGs) risk signature that could reflect the prognosis of EC patients and their responsiveness to immunotherapy and chemotherapy. Data from The Cancer Genome Atlas (TCGA) databases and the Molecular Signatures Database (MSigDB) confirmed two distinct NMRG subtypes in EC patients using consensus clustering, and a risk score was constructed utilizing an NAD+-related prognostic signature depending on the least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Receiver operating characteristic (ROC) curves were employed to assess the model's precision. Additionally, we used Gene Set Enrichment Analysis (GSEA) to predict the biological signaling pathways that might be involved. We also explored the role of the risk score in immune cell infiltration, tumor mutation burden (TMB), immunotherapy, and chemotherapy. Our study established a prognostic risk signature based on six NMRGs, and we observed that the high-risk group was associated with a poorer prognosis. Furthermore, we identified a strong correlation between the high-risk group and several pathways, including DNA replication, cell cycle, and mismatch repair. Lastly, our findings highlighted the influence of NMRGs on the regulation of immune infiltration in EC. Therefore, this signature holds potential value in predicting the prognosis of EC patients and guiding their management, including decisions regarding immunotherapy and chemotherapy, ultimately improving the accuracy of EC patient care.

Identification of stemness subtypes and features to improve endometrial cancer treatment using machine learning

Endometrial cancer is one of the most common malignant tumours in women, and cancer stem cells are known to play an important role in its growth, invasion, metastasis, and drug resistance. Immunotherapy for endometrial cancer is still under research. In this study, a total of 547 endometrial cancer cases were randomly divided into training set (351 cases) set and test set (196 cases). The stemness index of patients was calculated using the One-Class Logistic Regression (OCLR) machine learning algorithm to explore the clinicopathological differences between index levels. Stemness subtypes were determined according to the characteristics of cancer stemness and their clinicopathological characteristics, immune features, and therapeutic effects were described. Our study suggests that endometrial cancer is classified into two stemness subtypes. Stemness subtypes, which are associated with its clinical features, may be independent prognostic factors for endometrial cancer. The stemness subtypes differed significantly in immune activity, immune cell infiltration, and the immune microenvironment, including sensitivity to chemotherapeutic drugs and potential therapeutic compounds. Algorithms were utilised to construct a stemness subtype prediction model and predictor. These findings will provide guidance for the clinical diagnosis, treatment, and prognosis of endometrial cancer.

PTPRC functions as a prognosis biomarker in the tumor microenvironment of cutaneous melanoma

Cutaneous melanoma is one of the most malignant types of skin cancer, with an extremely poor prognosis. Immune cells infiltrated in the tumor microenvironment (TME) affects melanoma initiation, progression, prognosis and immunotherapy strategies in melanoma. The potential utility of TME-related genes as a prognostic model for melanoma and as a predictor of immunotherapeutic response merits further exploration. In this study, we determined that an immune-related gene, protein tyrosine phosphatase receptor type C (PTPRC), was positively correlated with the positive prognosis of melanoma patients. Integration of this gene with TNM classification created a predictive model that showed better performance in determining overall survival than others. PTPRC expression was positively correlated with the levels of immune checkpoint molecules, and PTPRC knockdown significantly enhanced the migration, invasion, and proliferation of melanoma cells. Finally, immunohistochemical results from HPA and Real-time quantitative PCR of clinical tissues confirmed that PTPRC expression was higher in melanoma than in normal skin. In conclusion, PTPRC served as a potential predictor of survival and response to immunotherapy in melanoma patients. The risk model combining the PTPRC and TNM classifications holds the potential to be a promising tool for prognostic prediction of cutaneous melanoma. This will help in the effective clinical management of melanoma patients.

Re-stratification of patients with copy-number low endometrial cancer by clinicopathological characteristics

OBJECTIVE

To stratify patients with copy-number low (CNL) endometrial cancer (EC) by clinicopathological characteristics.

METHODS

EC patients who underwent surgery between June 2018 and June 2022 at Peking University People's Hospital were included and further classified according to TCGA molecular subtyping: POLE ultramutated, microsatellite instability high (MSI-H), CNL, and copy-number high (CNH). Clinicopathological characteristics and prognosis of CNL patients were retrospectively reviewed. The Cox proportional hazards regression model was applied to perform univariate and multivariate analysis, and independent risk factors were identified. Differentially expressed genes (DEGs) according to overall survival (OS) were screened based on the transcriptome of CNL cases from the TCGA program. Finally, a nomogram was established, with an accuracy analysis performed.

RESULTS

(1) A total of 279 EC patients were included, of whom 168 (60.2%) were in the CNL group. A total of 21 patients had recurrence and 6 patients deceased, and no significant difference in recurrence-free survival (RFS) was exhibited among the four molecular subtypes (P = 0.104), but that in overall survival (OS) was statistically significant (P = 0.036). (2) CNL patients were divided into recurrence and non-recurrence groups, and significant differences (P < 0.05) were found between the two groups in terms of pathological subtype, FIGO stage, ER, PR, glycated hemoglobin (HbA1c), and high-density lipoprotein cholesterol (HDL-C). All the above factors were included in univariate and multivariate Cox regression models, among which pathological subtype, PR, and HDL-C were statistically different (P < 0.05), resulting in three independent risk factors for the prognosis of patients in the CNL group. (3) By comparing the transcriptome of tumor tissues between living and deceased CNL patients from the TCGA database, 903 (4.4%) DEGs were screened, with four lipid metabolism pathways significantly enriched. Finally, a nomogram was established, and internal cross-validation was performed, showing good discrimination accuracy with an AUC of 0.831 and a C-index of 0.748 (95% CI 0.444-1.052). (4) According to the established nomogram and the median total score (85.89), patients were divided into the high score group (n = 85) and low score group (n = 83), and the 8 patients with recurrence were all in the high score group. Survival analysis was performed between the two groups, and the difference in RFS was statistically significant (P = 0.010).

CONCLUSION

In the CNL group of EC patients, pathological subtype, PR, and HDL-C were independent prognostic risk factors, the nomogram established based upon which had a good predictive ability for the recurrence risk of patients with CNL EC.

Features of the immunosuppressive tumor microenvironment in endometrial cancer based on molecular subtype

Endometrial cancer (EC) is one of the three most prevalent gynecological tumors affecting women and is the most prevalent gynecological malignancy in the developed world. Its incidence is rapidly increasing worldwide, mostly affecting postmenopausal women, whereas recently its prevalence has increased in younger people. EC is an immune gene disease and many studies have shown that the tumor-immunosuppressive microenvironment plays an important role in cancer progression. In recent years, findings regarding the immunosuppressive tumor microenvironment (ITME) of EC have included immune evasion mechanisms and immunotherapy, which are mostly immune checkpoint inhibitors (ICI) for EC. Recently studies on the ITME of different molecular types of EC have found that different molecular types may have different ITME. With the research on the immune microenvironment of EC, a new immunophenotype classification based on the immune microenvironment has been carried out in recent years. However, the impact of the ITME on EC remains unclear, and the immunophenotype of EC remains limited to the research stage. Our review describes recent findings regarding the ITME features of different EC molecular types. The advent of immunotherapy has brought hope for improved efficacy and prognosis in patients with advanced or recurrent EC. The efficacy and safety of ICIs combination therapy remains the focus of future research.

Down regulation of Cathepsin W is associated with poor prognosis in pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is associated with a very poor prognosis. Therefore, there has been a focus on identifying new biomarkers for its early diagnosis and the prediction of patient survival. Genome-wide RNA and microRNA sequencing, bioinformatics and Machine Learning approaches to identify differentially expressed genes (DEGs), followed by validation in an additional cohort of PDAC patients has been undertaken. To identify DEGs, genome RNA sequencing and clinical data from pancreatic cancer patients were extracted from The Cancer Genome Atlas Database (TCGA). We used Kaplan-Meier analysis of survival curves was used to assess prognostic biomarkers. Ensemble learning, Random Forest (RF), Max Voting, Adaboost, Gradient boosting machines (GBM), and Extreme Gradient Boosting (XGB) techniques were used, and Gradient boosting machines (GBM) were selected with 100% accuracy for analysis. Moreover, protein-protein interaction (PPI), molecular pathways, concomitant expression of DEGs, and correlations between DEGs and clinical data were analyzed. We have evaluated candidate genes, miRNAs, and a combination of these obtained from machine learning algorithms and survival analysis. The results of Machine learning identified 23 genes with negative regulation, five genes with positive regulation, seven microRNAs with negative regulation, and 20 microRNAs with positive regulation in PDAC. Key genes BMF, FRMD4A, ADAP2, PPP1R17, and CACNG3 had the highest coefficient in the advanced stages of the disease. In addition, the survival analysis showed decreased expression of hsa.miR.642a, hsa.mir.363, CD22, BTNL9, and CTSW and overexpression of hsa.miR.153.1, hsa.miR.539, hsa.miR.412 reduced survival rate. CTSW was identified as a novel genetic marker and this was validated using RT-PCR. Machine learning algorithms may be used to Identify key dysregulated genes/miRNAs involved in the disease pathogenesis can be used to detect patients in earlier stages. Our data also demonstrated the prognostic and diagnostic value of CTSW in PDAC.

Landscape of immunocytes infiltration and prognostic immune-related genes in hepatocellular carcinoma

BACKGROUND

/Objective: The therapeutic efficacy and prognosis of patients with hepatocellular carcinoma (HCC) remain unsatisfactory, and further studies are supposed to provide more effective treatment strategies. Immune cells in extracellular matrix play an important role in the therapeutic effect and prognosis of hepatocellular carcinoma.

METHOD

To explore the mechanism of immune cells, we analysed the relationship between immune infiltration and gene expression profile through public databases. Patients were divided into high and low groups according to immune score by the ESTIMATE algorithm. It was found that the prognosis and clinical data of patients with liver cancer were correlated with immune scores.

RESULTS

Using the CIBERSORT method, we identified 21 types of immunocytes infiltration in HCC and demonstrated that these immune cells have clinical and prognostic implications. According to immune score, 590 differentially expressed genes were identified, and 6 key immune-related genes were screened by univariate COX analysis and lasso regression. In addition, the expression of EGLN3, KLRB1, MSC and HMOX1 was found to be correlated with immune cells (B cells, CD4+ T cells, CD8+ T cells, neutrophils, macrophages and dendritic cells) in HCC by the TIMER algorithm.

CONCLUSIONS

These findings provide new clues to understanding the mechanisms of immune cells in HCC and lay the foundation for the development of new prognostic strategies in HCC.

MRI-based radiomics model for distinguishing Stage I endometrial carcinoma from endometrial polyp: a multicenter study

BACKGROUND

Patients with early endometrial carcinoma (EC) have a good prognosis, but it is difficult to distinguish from endometrial polyps (EPs).

PURPOSE

To develop and assess magnetic resonance imaging (MRI)-based radiomics models for discriminating Stage I EC from EP in a multicenter setting.

MATERIAL AND METHODS

Patients with Stage I EC (n = 202) and EP (n = 99) who underwent preoperative MRI scans were collected in three centers (seven devices). The images from devices 1-3 were utilized for training and validation, and the images from devices 4-7 were utilized for testing, leading to three models. They were evaluated by the area under the receiver operating characteristic curve (AUC) and metrics including accuracy, sensitivity, and specificity. Two radiologists evaluated the endometrial lesions and compared them with the three models.

RESULTS

The AUCs of device 1, 2_ada, device 1, 3_ada, and device 2, 3_ada for discriminating Stage I EC from EP were 0.951, 0.912, and 0.896 for the training set, 0.755, 0.928, and 1.000 for the validation set, and 0.883, 0.956, and 0.878 for the external validation set, respectively. The specificity of the three models was higher, but the accuracy and sensitivity were lower than those of radiologists.

CONCLUSION

Our MRI-based models showed good potential in differentiating Stage I EC from EP and had been validated in multiple centers. Their specificity was higher than that of radiologists and may be used for computer-aided diagnosis in the future to assist clinical diagnosis.

Mirogabalin improves cancer-associated pain but increases the risk of malignancy in mice with pancreatic cancer

ABSTRACT

Mirogabalin, a selective voltage-gated calcium channel α2δ ligand, improves peripheral neuropathic pain; however, its effects on patients with cancers including pancreatic ductal adenocarcinoma (PDAC) remain unknown. We analyzed the effects of mirogabalin on a KPPC ( LSL-KrasG12D/+; Trp53flox/flox; Pdx-1cre/+ ) mouse model of PDAC. Six-week-old KPPC mice received oral mirogabalin (10 mg/kg/day) (n = 10) or vehicle water (n = 14) until the humane end point. Cancer-associated pain was evaluated using the scores of hunching and mouse grimace scale (MGS). Tumor status and plasma cytokine levels were determined using histopathological analysis and cytokine array, respectively. The effects of mirogabalin on the proliferative ability of PDAC cell lines were determined. The scores of the hunching and MGS improved after mirogabalin administration with a decrease in the plasma levels of inflammatory cytokines, such as tumor necrosis factor-α, interleukin-6, and interferon-γ. Although no significant difference in the survival rate was observed, mirogabalin significantly increased pancreatic tumor size and proliferative index of Ki-67 and cyclins. Local arginase-1 + M2-like tumor-associated macrophages and CD31 + tumor blood vessels increased after mirogabalin administration. By contrast, the number of α-smooth muscle actin + cancer-associated fibroblasts, desmoplastic stroma, and CD8 + T cells decreased. Local myeloperoxidase + tumor-associated neutrophils and CD45R + B cells were unaltered. Mirogabalin enhanced the proliferative ability of PDAC cell lines with the upregulation of cyclins and cyclin-dependent kinases; however, it inhibited the potential of pancreatic stellate cells in vitro. Therefore, our results suggest that mirogabalin improves cancer-associated pain but enhances the proliferative potential of PDAC in vitro and in vivo.

Construction of the novel immune risk scoring system related to CD8+ T cells in uterine corpus endometrial carcinoma

BACKGROUND

Uterine corpus endometrial carcinoma (UCEC) is a gynecological malignant tumor with high incidence and poor prognosis. Although immunotherapy has brought significant survival benefits to advanced UCEC patients, traditional evaluation indicators cannot accurately identify all potential beneficiaries of immunotherapy. Consequently, it is necessary to construct a new scoring system to predict patient prognosis and responsiveness of immunotherapy.

METHODS

CIBERSORT combined with weighted gene co-expression network analysis (WGCNA), non-negative matrix factorization (NMF), and random forest algorithms to screen the module associated with CD8⁺ T cells, and key genes related to prognosis were selected out by univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses to develop the novel immune risk score (NIRS). Kaplan-Meier (K-M) analysis was used to compare the difference of survival between high- and low- NIRS groups. We  also explored the correlations between NIRS, immune infiltration and immunotherapy, and three external validation sets were used to verify the predictive performance of NIRS. Furthermore, clinical subgroup analysis, mutation analysis, differential expression of immune checkpoints, and drug sensitivity analysis were performed to generate individualized treatments for patients with different risk scores. Finally, gene set variation analysis (GSVA) was conducted to explore the biological functions of NIRS, and qRT-PCR was applied to verify the differential expressions of three trait genes at cellular and tissue levels.

RESULTS

Among the modules clustered by WGCNA, the magenta module was most positively associated with CD8⁺ T cells. Three genes (CTSW, CD3D and CD48) were selected to construct NIRS after multiple screening procedures. NIRS was confirmed as an independent prognostic factor of UCEC, and patients with high NIRS had significantly worse prognosis compared to those with low NIRS. The high NIRS group showed lower levels of infiltrated immune cells, gene mutations, and expression of multiple immune checkpoints, indicating reduced sensitivity to immunotherapy. Three module genes were identified as protective factors positively correlated with the level of CD8⁺ T cells.

CONCLUSIONS

In this study, we constructed NIRS as a novel predictive signature of UCEC. NIRS not only differentiates patients with distinct prognoses and immune responsiveness, but also guides their therapeutic regimens.

Novel insights into tumorigenesis and prognosis of endometrial cancer through systematic investigation and validation on mitophagy-related signature

In-depth studies on the pathogenesis of endometrial cancer (EC) are critical because of the increasing global incidence of EC. Mitophagy, a mitochondrial quality control process, plays an important role in carcinogenesis and tumor progression. This study aimed to develop a novel mitophagy-based signature to predict the tumorigenesis and prognosis of EC. Data was downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases, and 29 mitophagy-related genes were downloaded from the Pathway Unification Database. EC patients were classified into two risk groups based on the two-key- gene signature, TOMM40 and MFN1, which were constructed using Cox regression analysis. A better prognosis was noted in the low-risk group. The model was validated for four aspects: clinical features, mutation status, clinical therapeutic response, and immune cell infiltration status. Moreover, according to the contribution to the risk model, TOMM40 was selected for further in vitro experiments. The silencing of TOMM40 inhibited mitochondrial degradation; suppressed cell proliferation; induced cell apoptosis and G1 phase cell cycle arrest; inhibited migration, invasion, and epithelial-mesenchymal transition; and suppressed cell stemness. In conclusion, the mitophagy-related risk score provides a novel perspective for survival and drug selection during the individual treatment of EC patients. TOMM40 serves as an oncogene in EC and promotes tumor progression via a mitophagy-related pathway. Thus, TOMM40 is a potential therapeutic target in EC.

A novel prognostic signatures based on metastasis- and immune-related gene pairs for colorectal cancer

Background

Metastasis remains the leading cause of mortality in patients diagnosed with colorectal cancer (CRC). The pivotal contribution of the immune microenvironment in the initiation and progression of CRC metastasis has gained significant attention.

Methods

A total of 453 CRC patients from The Cancer Genome Atlas (TCGA) were included as the training set, and GSE39582, GSE17536, GSE29621, GSE71187 were included as the validation set. The single-sample gene set enrichment analysis (ssGSEA) was performed to assess the immune infiltration of patients. Least absolute shrinkage and selection operator (LASSO) regression analysis, Time-dependent receiver operating characteristic (ROC) and Kaplan-Meier analysis were used to construct and validate risk models based on R package. CTSW and FABP4-knockout CRC cells were constructed via CRISPR-Cas9 system. Western-blot and Transwell assay were utilized to explore the role of fatty acid binding protein 4 (FABP4) / cathepsin W (CTSW) in CRC metastasis and immunity.

Results

Based on the normal/tumor, high-/low-immune cell infiltration, and metastatic/non-metastatic group, we identified 161 differentially expressed genes. After random assignment and LASSO regression analysis, a prognostic model containing 3 metastasis- and immune-related gene pairs was constructed and represented good prognostic prediction efficiency in the training set and 4 independent CRC cohorts. According to this model, we clustered patients and found that the high-risk group was associated with stage, T and M stage. In addition, the high-risk group also shown higher immune infiltration and high sensitivity to PARP inhibitors. Further, FABP4 and CTSW derived from the constitutive model were identified to be involved in metastasis and immunity of CRC.

Conclusion

In conclusion, a validated prognosis predictive model for CRC was constructed. CTSW and FABP4 are potential targets for CRC treatment.

PPP1R14B is a diagnostic prognostic marker in patients with uterine corpus endometrial carcinoma

Uterine corpus endometrial carcinoma (UCEC) is one of the most common malignancies of the female genital tract. A recently discovered protein-coding gene, PPP1R14B, can inhibit protein phosphatase 1 (PP1) as well as different PP1 holoenzymes, which are important proteins regulating cell growth, the cell cycle, and apoptosis. However, the association between PPP1R14B expression and UCEC remains undefined. The expression profiles of PPP1R14B in multiple cancers were analysed based on TCGA and GTE databases. Then, PPP1R14B expression in UCEC was investigated by gene differential analysis and single gene correlation analysis. In addition, we performed gene ontology term analysis, Kyoto Encyclopedia of Genes and Genomes pathway analysis, gene set enrichment analysis, and Kaplan-Meier survival analysis to predict the potential function of PPP1R14B and its role in the prognosis of UCEC patients. Then, a tool for predicting the prognosis of UCEC, namely, a nomogram model, was constructed. PPP1R14B expression was higher in UCEC tumour tissues than in normal tissues. The results revealed that PPP1R14B expression was indeed closely associated with tumour development. The results of Kaplan-Meier plotter data indicated that patients with high PPP1R14b expression had poorer overall survival, disease-specific survival, and progression-free interval than those with low expression. A nomogram based on the results of multifactor Cox regression was generated. PPP1R14B is a key player in UCEC progression, is associated with a range of adverse outcomes, and can serve as a prognostic marker in the clinic.

Gynecological cancer prognosis using machine learning techniques: A systematic review of last three decades (1990–2022)

OBJECTIVE

Many Computer Aided Prognostic (CAP) systems based on machine learning techniques have been proposed in the field of oncology. The objective of this systematic review was to assess and critically appraise the methodologies and approaches used in predicting the prognosis of gynecological cancers using CAPs.

METHODS

Electronic databases were used to systematically search for studies utilizing machine learning methods in gynecological cancers. Study risk of bias (ROB) and applicability were assessed using the PROBAST tool. 139 studies met the inclusion criteria, of which 71 predicted outcomes for ovarian cancer patients, 41 predicted outcomes for cervical cancer patients, 28 predicted outcomes for uterine cancer patients, and 2 predicted outcomes for gynecological malignancies broadly.

RESULTS

Random forest (22.30 %) and support vector machine (21.58 %) classifiers were used most commonly. Use of clinicopathological, genomic and radiomic data as predictors was observed in 48.20 %, 51.08 % and 17.27 % of studies, respectively, with some studies using multiple modalities. 21.58 % of studies were externally validated. Twenty-three individual studies compared ML and non-ML methods. Study quality was highly variable and methodologies, statistical reporting and outcome measures were inconsistent, preventing generalized commentary or meta-analysis of performance outcomes.

CONCLUSION

There is significant variability in model development when prognosticating gynecological malignancies with respect to variable selection, machine learning (ML) methods and endpoint selection. This heterogeneity prevents meta-analysis and conclusions regarding the superiority of ML methods. Furthermore, PROBAST-mediated ROB and applicability analysis demonstrates concern for the translatability of existing models. This review identifies ways that this can be improved upon in future works to develop robust, clinically translatable models within this promising field.

Molecular Characterization and Prognosis of Lactate-Related Genes in Lung Adenocarcinoma

Objective: To explore the lactate-related genes (LRGs) in lung adenocarcinoma (LUAD) by various methods, construct a prognostic model, and explore the relationship between lactate subtypes and the immune tumor microenvironment (TME). Methods: 24 LRGs were collected. The mutation landscape and the prognosis value of LRGs were explored by using The Cancer Genome Atlas (TCGA) data. Consensus clustering analysis was used for different lactate subtype identification. Based on the lactate subtypes, we explore the landscape of TME cell infiltration. A risk-score was calculated by using the LASSO-Cox analysis. A quantitative real-time PCR assay was utilized to validate the expression of characteristic genes in clinical cancer tissues and paracarinoma tissues from LUAD patients. Results: Comparing the normal samples, 18 LRGs were differentially expressed in tumor samples, which revealed that the differential expression of LRGs may be related to Copy Number Variation (CNV) alterations. The two distinct lactate subtypes were defined. Compared to patients in the LRGcluster A group, LUAD patients in the LRGcluster B group achieved better survival. The prognostic model was constructed based on differentially expressed genes (DEGs) via the LASSO-Cox analysis, which showed the accuracy of predicting the prognosis of LUAD patients using the ROC curve. A high-risk score was related to a high immune score, stromal score, and tumor mutation burden (TMB). Patients had better OS with low risk compared with those with high risk. The sensitivities of different risk groups to chemotherapeutic drugs were explored. Finally, the expression of characteristic genes in clinical cancer tissues and paracarinoma tissues from LUAD patients was verified via qRT-PCR. Conclusions: The lactate subtypes were independent prognostic biomarkers in LUAD. Additionally, the difference in the lactate subtypes was an indispensable feature for the individual TME. The comprehensive evaluation of the lactate subtypes in the single tumor would help us to understand the infiltration characteristics of TME and guide immunotherapy strategies.

Comprehensive analysis of PHGDH for predicting prognosis and immunotherapy response in patients with endometrial carcinoma

BACKGROUND

PHGDH (Phosphoglycerate Dehydrogenase) is the first branch enzyme in the serine biosynthetic pathway and plays a vital role in several cancers. However, little is known about the clinical significance of PHGDH in endometrial cancer.

METHODS

Clinicopathological data of endometrial cancer were downloaded from the Cancer Genome Atlas database (TCGA). First, the expression of PHGDH in pan-cancer was investigated, as well as the expression and prognostic value of PHGDH in endometrial cancer. The effect of PHGDH expression on the prognosis of endometrial cancer was analyzed by Kaplan-Meier plotter and Cox regression. The relationship between PHGDH expression and clinical characteristics of endometrial cancer was investigated by logistic regression. Receiver operating characteristic (ROC) curves and nomograms were developed. Possible cellular mechanisms were explored using the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, the Gene Ontology (GO), and gene set enrichment analysis (GSEA). Finally, TIMER and CIBERSORT were used to analyze the relationship between PHGDH expression and immune infiltration. CellMiner™ was used to analyze the drug sensitivity of PHGDH.

RESULTS

The results showed that PHGDH expression was significantly higher in endometrial cancer tissues than in normal tissues at mRNA and protein levels. Kaplan-Meier survival curves showed that patients in the high expression group had shorter overall survival (OS) and disease free survival (DFS) than patients in the low PHGDH expression group. Multifactorial COX regression analysis further supported that high PHGDH expression was an independent risk factor associated with prognosis in patients with endometrial cancer. The results showed estrogen response, mTOR, K-RAS, and epithelial mesenchymal transition (EMT) were differentially elevated in the high-expression group of the PHGDH group. CIBERSORT analysis showed that PHGDH expression is related to the infiltration of multiple immune cells. When PHGDH is highly expressed, the number of CD8⁺T cells decreases.

CONCLUSION

PHGDH plays a vital role in the development of endometrial cancer, which is related to tumor immune infiltration, and can be used as an independent diagnostic and prognostic marker for endometrial cancer.

CXCR3 predicts the prognosis of endometrial adenocarcinoma

OBJECTIVES

Currently, endometrial adenocarcinoma lacks an effective prognostic indicator. This study was to develop and validate a gene biomarker and a nomogram to predict the survival of endometrial adenocarcinoma, explore potential mechanisms and select sensitive drugs.

METHODS

425 endometrial adenocarcinoma cases with RNA sequencing data from TCGA were used to identify the most immune-related module by WGCNA. As an external test set, 103 cases from GSE17025 were used. Immune-related genes were downloaded from Innate DB. The three sets of data were used to identify the prognostic genes. Based on 397 cases with complete clinical data from TCGA, randomly divided into the training set (n = 199) and test set (n = 198), we identified CXCR3 as the prognostic gene biomarker. Age, grade, FIGO stage, and risk were used to develop and validate a predictive nomogram. AUC, C-index, calibration curve and K-M estimate evaluated the model's predictive performance. KEGG enrichment analysis, immune functions, TMB, the effectiveness of immunotherapy, and drug sensitivity between the high-risk and low-risk groups.

RESULTS

CXCR3 was identified as a prognostic biomarker. We calculated the risk score and divided the cases into the high-risk and low-risk groups by the median value of the risk score. The OS of the high-risk group was better than the low-risk group. The risk was the prognostic indicator independent of age, grade, and FIGO stage. We constructed the nomogram including age, grade, FIGO stage, and risk to predict the prognosis of endometrial adenocarcinoma. The top five KEGG pathways enriched by the DEGs between the high- and low-risk groups were viral protein interaction with cytokine and cytokine receptors, cytokine-cytokine receptor interaction, chemokine signaling pathway, natural killer cell-mediated cytotoxicity, and cell adhesion molecules. We analyzed the difference in immune cells and found that CD8+ T cells, activated CD4+ T cells, T helper cells, monocytes, and M1 macrophages were infiltrated more in the low-risk group. However, M0 macrophages and activated dendritic cells were more in the high-risk group. The immune function including APC coinhibition, APC costimulation, CCR, checkpoint, cytolytic activity, HLA, inflammation-promoting, MHC-I, parainflammation, T cell coinhibition, T cell costimulation, type I-IFN-response, and type II-IFN-response were better in the low-risk group. TMB and TIDE scores were both better in the low-risk group. By 'the pRRophetic' package, we found 56 sensitive drugs for different risk groups.

CONCLUSION

We identified CXCR3 as the prognostic biomarker. We also developed and validated a predictive nomogram model combining CXCR3, age, histological grade, and FIGO stage for endometrial adenocarcinoma, which could help explore the precise treatment.

Cysteine Cathepsins as Therapeutic Targets in Immune Regulation and Immune Disorders

Cysteine cathepsins, as the most abundant proteases found in the lysosomes, play a vital role in several processes-such as protein degradation, changes in cell signaling, cell morphology, migration and proliferation, and energy metabolism. In addition to their lysosomal function, they are also secreted and may remain functional in the extracellular space. Upregulation of cathepsin expression is associated with several pathological conditions including cancer, neurodegeneration, and immune-system dysregulation. In this review, we present an overview of cysteine-cathepsin involvement and possible targeting options for mitigation of aberrant function in immune disorders such as inflammation, autoimmune diseases, and immune response in cancer.

Synaptotagmin-like protein 1 is a potential diagnostic and prognostic biomarker in endometrial cancer based on bioinformatics and experiments

Endometrial cancer (EC) is one of the most common gynecologic malignancies. Identification of potential EC biomarkers is essential to improve the prognosis and development of therapies against EC. Synaptotagmin-like protein 1 (SYTL1), as a small GTPase Rab27 effector, mainly plays a role in vesicle trafficking and cytotoxic granule exocytosis in lymphocytes. However the role of SYTL1 in EC remains uncertain. We performed a comprehensive assessment of the relationship between SYTL1 and patient diagnosis and prognosis by analysis of EC patients' data from TCGA. We employed the LinkedOmics and Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database to analyze the biological function of SYTL1 in EC. In addition, the correlation between SYTL1 expression and its DNA methylation was performed by using cBioportal, UALCAN, TCGA Wanderer and MethSurv databases. We further assessed the link between SYTL1 and tumor-infiltrating immune cells by using gene set variation analysis (GSVA).Results We found that SYTL1 was highly expressed in EC patients and cell lines. And increased expression of SYTL1 was associated with age, clinical stage, histological type, histological grade and good overall survival (OS).SYTL1 DNA methylation is negatively associated with SYTL1 expression and UCEC patients' OS. SYTL1 expression is closely correlated with immune infiltration. Furthermore, we carried out in vitro experiments to verify the results of bioinformatic analysis.Conclusion Our results demonstrated that the elevation of SYTL1 expression is associated with good OS and SYTL1 might be a potential diagnostic and prognostic marker in EC.

Establishment and validation of an aging-related risk signature associated with prognosis and tumor immune microenvironment in breast cancer

BACKGROUND

Breast cancer (BC) is a highly malignant and heterogeneous tumor which is currently the cancer with the highest incidence and seriously endangers the survival and prognosis of patients. Aging, as a research hotspot in recent years, is widely considered to be involved in the occurrence and development of a variety of tumors. However, the relationship between aging-related genes (ARGs) and BC has not yet been fully elucidated.

MATERIALS AND METHODS

The expression profiles and clinicopathological data were acquired in the Cancer Genome Atlas (TCGA) and the gene expression omnibus (GEO) database. Firstly, the differentially expressed ARGs in BC and normal breast tissues were investigated. Based on these differential genes, a risk model was constructed composed of 11 ARGs via univariate and multivariate Cox analysis. Subsequently, survival analysis, independent prognostic analysis, time-dependent receiver operating characteristic (ROC) analysis and nomogram were performed to assess its ability to sensitively and specifically predict the survival and prognosis of patients, which was also verified in the validation set. In addition, functional enrichment analysis and immune infiltration analysis were applied to reveal the relationship between the risk scores and tumor immune microenvironment, immune status and immunotherapy. Finally, multiple datasets and real-time polymerase chain reaction (RT-PCR) were utilized to verify the expression level of the key genes.

RESULTS

An 11-gene signature (including FABP7, IGHD, SPIB, CTSW, IGKC, SEZ6, S100B, CXCL1, IGLV6-57, CPLX2 and CCL19) was established to predict the survival of BC patients, which was validated by the GEO cohort. Based on the risk model, the BC patients were divided into high- and low-risk groups, and the high-risk patients showed worse survival. Stepwise ROC analysis and Cox analyses demonstrated the good performance and independence of the model. Moreover, a nomogram combined with the risk score and clinical parameters was built for prognostic prediction. Functional enrichment analysis revealed the robust relationship between the risk model with immune-related functions and pathways. Subsequent immune microenvironment analysis, immunotherapy, etc., indicated that the immune status of patients in the high-risk group decreased, and the anti-tumor immune function was impaired, which was significantly different with those in the low-risk group. Eventually, the expression level of FABP7, IGHD, SPIB, CTSW, IGKC, SEZ6, S100B, CXCL1, IGLV6-57 and CCL19 was identified as down-regulated in tumor cell line, while CPLX2 up-regulated, which was mostly similar with the results in TCGA and Human Protein Atlas (HPA) via RT-PCR.

CONCLUSIONS

In summary, our study constructed a risk model composed of ARGs, which could be used as a solid model for predicting the survival and prognosis of BC patients. Moreover, this model also played an important role in tumor immunity, providing a new direction for patient immune status assessment and immunotherapy selection.

A comprehensive analysis focusing on cuproptosis to investigate its clinical and biological relevance in uterine corpus endometrial carcinoma and its potential in indicating prognosis

Cuproptosis, a novel copper-dependent cell death involving mitochondrial respiration, is distinct from other known death mechanisms, which inspires us to study further in uterine corpus endometrial carcinoma (UCEC). Herein, leveraging comprehensive data from TCGA-UCEC, we conducted transcriptional and genetic analyses of 13 recently identified cuproptosis genes. We discovered severe genetic instability of cuproptosis genes, extensive positive correlations among those genes with each other at the mRNA level, and their involvement in oncogenic pathways in UCEC samples. Next, WGCNA was performed to identify a potential module regulating cuproptosis, in which the hub genes, in addition to 13 cuproptosis genes, were drawn to construct a scoring system termed Cu. Score. Furthermore, its clinical and biological relevance and tumor immune landscape, genetic alterations, as well as predicted sensitivity of chemotherapy drugs in different Cu. Score subgroups had been discussed extensively and in detail. Additionally, univariate Cox and LASSO regression were performed to identify 13 cuproptosis-related prognostic genes to establish a prognostic signature, the Risk. Score. Integrating the Risk. Score and clinical parameters, we established a nomogram with excellent performance to predict the 1-/3-/5-year survival probabilities of UCEC patients. To conclude, we conducted a comprehensive analysis encompassing cuproptosis and developed a cuproptosis scoring system and a prognostic prediction model for UCEC, which may offer help with individualized assessment and treatment for UCEC patients from the perspective of a novel death mechanism.

Presence of regulatory T-cells in endometrial cancer predicts poorer overall survival and promotes progression of tumor cells

PURPOSE

Endometrial cancer (EC) is one of the most common gynaecologic malignancies. Tumor infiltrating regulatory T-cells (Treg) have been reported to have a prognostic impact in many malignancies. Immunotherapeutic strategies are gaining interest for advanced and recurrent EC cases, where treatment options are rare. Our study was aimed at determining the value of Treg in EC progression.

METHODS

EC specimens from 275 patients and 28 controls were screened immunohistochemically for the presence of Treg represented by FoxP3. Correlations with clinicopathological and survival parameters were performed. Functional assays were performed using EC cell lines Ishikawa + and RL95-2 after co-culturing with isolated CD4 + CD25 + CD127dim Treg. To assess the influence of EC on the composition of peripheral blood mononuclear cells (PBMC), flow cytometric analyses were performed.

RESULTS

We found that an increased infiltration of Treg was associated with high grades and a reduced overall survival. Treg were almost absent in endometrium tissues from healthy control patients. Co-culture of tumor cells with CD4 + CD25 + CD127dim Treg led to functional changes: enhanced invasion, migration and viability indicated that increased levels of Treg in the tumor microenvironment may promote tumor growth. Furthermore, we found that the presence of EC cells led to phenotypic changes in PBMC, showing significantly increased levels of CD25 and FoxP3.

CONCLUSION

Our results indicate that the presence of Treg in the EC tumor environment is associated with a poorer outcome. A remarkable impact of Treg on tumor cell behaviour and vice versa of tumor cells on PBMC subpopulations support this notion mechanistically. Our findings provide a basis for focusing on Treg as potential future therapeutic targets in EC.

Immune Subtypes and Immune Landscape Analysis of Endometrial Carcinoma

Some patients with endometrial cancer (EC) suffer from limited survival benefits after immunotherapy, suggesting that there may be a specific pattern associated with immunotherapy. Immune-related genes were extracted from The Cancer Genome Atlas databases. We analyzed the differences among immune subtypes (ISs) in the distribution of the tumor mutational burden, chemotherapy-induced immune response markers, immune checkpoint-related genes, immunotherapy, and chemotherapy. We applied dimensionality reduction and defined the immune landscape of EC. Then, we used the Weighted Gene Co-Expression Network Analysis package to identify the coexpression modules of these immune genes. Finally, hub genes were selected and detected by quantitative PCR and immunohistochemistry. We obtained three ISs. There were differences in the distribution of the tumor mutational burden, chemotherapy-induced immune response markers, and immune checkpoint-related genes among the ISs. Regarding immunotherapy and chemotherapy, the IS2 subtypes were more sensitive to programmed cell death protein 1 inhibitors. In addition, different positions in the immune landscape map exhibited different prognostic characteristics, providing further evidence of the ISs. The IS2 subtypes were significantly positively correlated with yellow module gene list, indicating a good prognosis with high score. SIRPG and SLAMF1 were identified as the final characteristic genes. The quantitative PCR and immunohistochemistry results showed that the expression levels of SIRPG and SLAMF1 were low in human EC tissue. In this study, we identified three reproducible ISs of EC. The immune landscape analysis further revealed the intraclass heterogeneity of the ISs. SIRPG and SLAMF1 were identified to be associated with progression, suggesting that they may be novel immune-related biomarkers of EC.

Dynamic Expression of Palmitoylation Regulators across Human Organ Development and Cancers Based on Bioinformatics

Protein palmitoylation is a reversible modification process that links palmitate to cysteine residues via a reversible thioester bond. Palmitoylation exerts an important role in human organ development and tumor progression. However, a comprehensive landscape regarding the dynamic expression of palmitoylation regulators in human organ development remains unclear. In this study, we analyzed the dynamic expression of palmitoylation regulators in seven organ development and eight cancer types based on bioinformatics. We found that the expression levels of most palmitoylation regulators were altered after birth. In particular, ZDHHC7/20/21 exhibited converse expression patterns in multiple cancer types. Survival analysis showed that the poor prognosis in patients with kidney renal clear carcinoma (KIRC) is related to low expression of ZDHHC7/20/21, and a high expression of ZDHHC7/20/21 is related to worse survival in patients with liver hepatocellular carcinoma (LIHC). Furthermore, we found that the expression of ZDHHC7 is associated with infiltration levels of some types of immune cells in the tumor microenvironment (TME), and we explored the relationship between ZDHHC7 expression and immune checkpoint (ICP) genes across 33 cancer types. In addition, gene set enrichment analysis (GSEA) results indicated that ZDHHC7 might regulate different genes to mediate the same pathway in different organs. In summary, the comprehensive analysis of palmitoylation regulators reveals their functions in human organ development and cancer, which may provide new insights for developing new tumor markers.

Pan-Cancer Gene Analysis of m6A Modification and Immune Infiltration in Uterine Corpus Endometrial Carcinoma

Objective

This investigation was to test the potential role of m6A-related long non-coding RNAs (lncRNAs) and immune infiltration as crucial factors in the diagnosis and treatment of uterine corpus endometrial cancer (UCEC).

Method

The UCEC RNA-seq data were downloaded in the Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/). There were 587 samples totally, containing 543 UCEC cases and 35 healthy cases. The clinical information of UCEC cases included survival time, survival status, gender, age, stage, and TMN stage. Twenty-three m6A-related genes were found in published journals. The RNA-seq documents of UCEC were downloaded in the Cancer Genome Atlas (TCGA). The hub gene data of UCEC were downloaded from GEPIA2 database. The different packages of R language were applied to calculate and analyze in this research.

Results

Among 587 cases in our study, we discovered 3039 lncRNAs in the TCGA-UCEC database. After the differential analysis, 23 m6A-associated genetics were screened and twenty-one m6A-associated differential genetics were found. In the end, we obtained 20 m6A-related lncRNAs. LNCTAM34A was considered as a predictive gene through univariate and multivariate Cox regression analysis. In addition to the above, patients with high LNCTAM34A expression had better outcomes than those with low LNCTAM34A expression. The high-risk cohort had greater scores of activated dendritic cells (aDCs), B cells, and T cell regulatory (Tregs) than low-risk cohort; in the meanwhile, high-risk cohort had lower scores of DCs and iDCs. Then, the high-risk cohort displayed greater scores in the immune functions of MHC class I, para-inflammation, and type I IFN response than those of low-risk cohort. Among 27 immune-inducible genes, the level of CD244, KIR3DLI, NRP1, PDCD1LG2, and TNFRSF8 was reduced in UCEC samples and the level of CD27, CD28, CD70, CD80, CD86, HAVCR2, ICOS, IDO1, LAIR1, PDCD1, TIGIT, TNFRSF18, -25, -9, -14, and VTCN1 was increased in UCEC samples.

Conclusion

The key role of M6A-related lncRNAs in immune microenvironment in high-risk patients of UCEC. The patients with strong expression of LNCTAM34A have a good prognosis, and LNCTAM34A can be used as a prognostic gene for UCEC. m6A-related lncRNAs can be used as a potential treatment for UCEC. Our observations can be used as a hypothetical basis for future in vitro and animal experiments.

High RIG-I and EFTUD2 expression predicts poor survival in endometrial cancer

PURPOSE

Endometrial cancer is the most common gynecological malignancy. The helicase RIG-I, a part of the innate immune system, and EFTUD2, a splicing factor which can upregulate RIG-I expression, are shown to influence tumor growth and disease progression in several malignancies. For endometrial cancer, an immunogenic cancer, data about RIG-I and EFTUD2 are still missing. The aim of this study was to examine the expression of RIG-I and EFTUD2 in endometrial cancer.

METHODS

225 specimen of endometrial cancer were immunohistochemically stained for RIG-I and EFTUD2. The results were correlated to clinicopathological data, overall survival (OS) and progression-free survival (PFS).

RESULTS

High RIG-I expression correlated with advanced tumor stages (FIGO: p = 0.027; pT: p = 0.010) and worse survival rates (OS: p = 0.009; PFS: p = 0.022). High EFTUD2 expression correlated to worse survival rates (OS: p = 0.026; PFS: p < 0.001) and was determined to be an independent marker for progression-free survival.

CONCLUSION

Our data suggest that the expression of RIG-I and EFTUD2 correlates with survival data, which makes both a possible therapeutic target in the future.

Machine Learning for Endometrial Cancer Prediction and Prognostication

Endometrial cancer (EC) is a prevalent uterine cancer that remains a major contributor to cancer-associated morbidity and mortality. EC diagnosed at advanced stages shows a poor therapeutic response. The clinically utilized EC diagnostic approaches are costly, time-consuming, and are not readily available to all patients. The rapid growth in computational biology has enticed substantial research attention from both data scientists and oncologists, leading to the development of rapid and cost-effective computer-aided cancer surveillance systems. Machine learning (ML), a subcategory of artificial intelligence, provides opportunities for drug discovery, early cancer diagnosis, effective treatment, and choice of treatment modalities. The application of ML approaches in EC diagnosis, therapies, and prognosis may be particularly relevant. Considering the significance of customized treatment and the growing trend of using ML approaches in cancer prediction and monitoring, a critical survey of ML utility in EC may provide impetus research in EC and assist oncologists, molecular biologists, biomedical engineers, and bioinformaticians to further collaborative research in EC. In this review, an overview of EC along with risk factors and diagnostic methods is discussed, followed by a comprehensive analysis of the potential ML modalities for prevention, screening, detection, and prognosis of EC patients.

Crosstalk of Redox-Related Subtypes, Establishment of a Prognostic Model and Immune Responses in Endometrial Carcinoma

Simple Summary

In order to explore the role of redox as a prognostic indicator in endometrial carcinoma (EC), we detected the expression patterns of 55 redox-related genes (RRGs) in EC cohorts from public databases. Performing consensus cluster algorithm, we determined four molecular subclusters based on RRGs which had significant differences in overall survival (OS) and immune activities of EC patients. Furthermore, we developed a prognostic risk model on the basis of the redox-related subtype by stepwise Cox regression analyses. All EC patients were divided into high-risk and low-risk groups according to the median value of risk score. Our proposed model could accurately assess the clinical outcome and had favorable independent ability in EC cases. Moreover, our signature can serve as a predictor for immune status and chemotherapy sensitivity.

Abstract

Redox plays a central part in the pathogeneses and development of tumors. We comprehensively determined the expression patterns of redox-related genes (RRGs) in endometrial carcinoma (EC) cohorts from public databases and identified four different RRG-related clusters. The prognosis and the characteristics of TME cell infiltration of RRGcluster C patients were worse than those of other RRG clusters. When it comes to the gene cluster, there were great differences in clinicopathology traits and immunocyte infiltration. The RRG score was calculated by Cox analyses, and an RRG-based signature was developed. The risk score performed well in the EC cohort. Samples were separated into two risk subgroups with the standard of the value of the median risk score. Low-risk patients had a better prognosis and higher immunogenicity. In addition, RRG score was closely associated with immunophenoscore, microsatellite instability, tumor mutation burden, tumor stem cell index, copy number variation and chemotherapy sensitivity. The nomogram accurately predicted the prognosis of patients, and our model showed better performance than other published models. In conclusion, we built a prognostic model of RRGs which can help to evaluate clinical outcomes and guide more effective treatment.

Integrative Bioinformatics Analysis Reveals CHEK1 and UBE2C as Luminal A Breast Cancer Subtype Biomarkers

In light of the limited number of targetable oncogenic drivers in breast cancer (BRCA), it is important to identify effective and druggable gene targets for the treatment of this devastating disease. Herein, the GSE102484 dataset containing expression profiling data from 683 BRCA patients was re-analyzed using weighted gene co-expression network analysis (WGCNA). The yellow module with the highest correlation to BRCA progression was screened out, followed by functional enrichment analysis and establishment of a protein–protein interaction (PPI) network. After further validation through survival analysis and expression evaluation, CHEK1 and UBE2C were finally identified as hub genes related to the progression of BRCA, especially the luminal A breast cancer subtype. Notably, both hub genes were found to be dysregulated in multiple types of immune cells and closely correlated with tumor infiltration, as revealed by Tumor Immune Estimation Resource (TIMER) along with other bioinformatic tools. Construction of transcription factors (TF)-hub gene network further confirmed the existence of 11 TFs which could regulate both hub genes simultaneously. Our present study may facilitate the invention of targeted therapeutic drugs and provide novel insights into the understanding of the mechanism beneath the progression of BRCA.

Integrated Bioinformatic Analysis of DNA Methylation and Immune Infiltration in Endometrial Cancer

Background

Endometrial cancer greatly threatens the health of female. Emerging evidences have demonstrated that DNA methylation and immune infiltration are involved in the occurrence and development of endometrial cancer. However, the mechanism and prognostic biomarkers of endometrial cancer are still unclear. In this study, we assess DNA methylation and immune infiltration via bioinformatic analysis.

Methods

The latest RNA-Seq, DNA methylation data, and clinical data related to endometrial cancer were downloaded from the UCSC Xena dataset. The methylation-driven genes were selected, and then the risk score was obtained using “MethylMix” and “corrplot” R packages. The connection between methylated genes and the expression of screened driven genes were explored using “survminer” and “beeswarm” packages, respectively. Finally, the role of VTCN1in immune infiltration was analyzed using “CIBERSORT” package.

Results

In this study, 179 upregulated genes, and 311 downregulated genes were identified and found to be related to extracellular matrix organization, cell–cell junctions, and cell adhesion molecular binding. The methylation-driven gene VTCN1 was selected, and patients classified to the hypomethylation and high expression group displayed poor prognosis. The VTCN1 gene exhibited highest correlation coefficient between methylation and expression. More importantly, the hypomethylation of promoter of VTCN1 led to its high expression, thereby induce tumor development by inhibiting CD8+ T cell infiltration.

Conclusions

Overall, our study was the first to reveal the mechanism of endometrial cancer by assessing DNA methylation and immune infiltration via integrated bioinformatic analysis. In addition, we found a pivotal prognostic biomarker for the disease. Our study provides potential targets for the diagnosis and prognosis of endometrial cancer in the future.

CX3CR1 Acts as a Protective Biomarker in the Tumor Microenvironment of Colorectal Cancer

The tumor microenvironment (TME) plays an important role in the pathogenesis of many cancers. We aimed to screen the TME-related hub genes of colorectal adenoma (CRAD) and identify possible prognostic biomarkers. The gene expression profiles and clinical data of 464 CRAD patients in The Cancer Genome Atlas (TCGA) database were downloaded. The Estimation of STromal and Immune cells in MAlignant Tumours using Expression data (ESTIMATE) algorithm was performed to calculate the ImmuneScore, StromalScore, and EstimateScore. Thereafter, differentially expressed genes (DEGs) were screened. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and protein–protein interaction (PPI) analysis were performed to explore the roles of DEGs. Furthermore, univariate and multivariate Cox analyses were accomplished to identify independent prognostic factors of CRAD. CX3CR1 was selected as a hub gene, and the expression was confirmed in colorectal cancer (CRC) patients and cell lines. The correlations between CX3CR1 and tumor-infiltrating immune cells were estimated by Tumor IMmune Estimation Resource database (TIMER) and CIBERSORT analysis. Besides, we investigated the effects of coculture with THP-1-derived macrophages with HCT8 cells with low CX3CR1 expression on immune marker expression, cell viability, and migration. There were significant differences in the ImmuneScore and EstimateScore among different stages. Patients with low scores presented significantly lower lifetimes than those in the high-score group. Moreover, we recognized 1,578 intersection genes in ImmuneScore and StromalScore, and these genes were mainly enriched in numerous immune-related biological processes. CX3CR1 was found to be associated with immune cell infiltration levels, immune marker expression, and macrophage polarization. Simultaneous silencing of CX3CR1 and coculture with THP-1 cells further regulated macrophage polarization and promoted the cell proliferation and migration of CRC cells. CX3CR1 was decreased in CRAD tissues and cell lines and was related to T and N stages, tumor differentiation, and prognosis. Our results suggest that CX3CR1 contributes to the recruitment and regulation of immune-infiltrating cells and macrophage polarization in CRC and TAM-induced CRC progression. CX3CR1 may act as a prognostic biomarker in CRC.

Identification and Validation of Immune-Related Prognostic Genes in the Tumor Microenvironment of Colon Adenocarcinoma

The tumor microenvironment (TME) has been shown to be involved in angiogenesis, tumor metastasis, and immune response, thereby affecting the treatment and prognosis of patients. This study aims to identify genes that are dysregulated in the TME of patients with colon adenocarcinoma (COAD) and to evaluate their prognostic value based on RNA omics data. We obtained 512 COAD samples from the Cancer Genome Atlas (TCGA) database and 579 COAD patients from the independent dataset (GSE39582) in the Gene Expression Omnibus (GEO) database. The immune/stromal/ESTIMATE score of each patient based on their gene expression was calculated using the ESTIMATE algorithm. Kaplan-Meier survival analysis, Cox regression analysis, gene functional enrichment analysis, and protein-protein interaction (PPI) network analysis were performed. We found that immune and stromal scores were significantly correlated with COAD patients' overall survival (log rank p < 0.05). By comparing the high immune/stromal score group with the low score group, we identified 688 intersection differentially expressed genes (DEGs) from the TCGA dataset (663 upregulated and 25 downregulated). The functional enrichment analysis of intersection DEGs showed that they were mainly enriched in the immune process, cell migration, cell motility, Toll-like receptor signaling pathway, and PI3K-Akt signaling pathway. The hub genes were revealed by PPI network analysis. Through Kaplan-Meier and Cox analysis, four TME-related genes that were significantly related to the prognosis of COAD patients were verified in GSE39582. In addition, we uncovered the relationship between the four prognostic genes and immune cells in COAD. In conclusion, based on the RNA expression profiles of 1091 COAD patients, we screened four genes that can predict prognosis from the TME, which may serve as candidate prognostic biomarkers for COAD.

Platelet-to-Lymphocyte Ratio as an Independent Factor Was Associated With the Severity of Ankylosing Spondylitis

The study was aimed to determine the association of the platelet-lymphocyte ratio (PLR) with the disease activity of ankylosing spondylitis (AS). A total of 275 patients, including 180 AS patients and 95 non-AS patients, participated in the study. We assessed a full blood count for each participant. Platelet to monocyte ratio (PMR), monocytes to lymphocyte ratio (MLR), monocyte to neutrophil ratio (MNR), platelet to lymphocyte ratio (PLR), neutrophil to lymphocyte ratio (NLR), and platelet to neutrophil ratio (PNR) were calculated. LASSO and logistic regression analyses were performed to establish the nomogram. Receiver operating characteristic (ROC) analysis was performed to evaluate the clinical value of the nomogram. We constructed a novel nomogram, which incorporated easily accessible clinical characteristics like sex, PLR, WBC, EOS, and ESR for AS diagnosis. The AUC value of this nomogram was 0.806; also, the calibration curves indicated a satisfactory agreement between nomogram prediction and actual probabilities. Furthermore, PLR was positively correlated with the severity of AS. PLR was identified as an independent factor for the diagnosis of AS and was associated with the severity of AS.

TYROBP, TLR4 and ITGAM regulated macrophages polarization and immune checkpoints expression in osteosarcoma

We established a relationship among the immune-related genes, tumor-infiltrating immune cells (TIICs), and immune checkpoints in patients with osteosarcoma. The gene expression data for osteosarcoma were downloaded from UCSC Xena and GEO database. Immune-related differentially expressed genes (DEGs) were detected to calculate the risk score. “Estimate” was used for immune infiltrating estimation and “xCell” was used to obtain 64 immune cell subtypes. Furthermore, the relationship among the risk scores, immune cell subtypes, and immune checkpoints was evaluated. The three immune-related genes (TYROBP, TLR4, and ITGAM) were selected to establish a risk scoring system based on their integrated prognostic relevance. The GSEA results for the Hallmark and KEGG pathways revealed that the low-risk score group exhibited the most gene sets that were related to immune-related pathways. The risk score significantly correlated with the xCell score of macrophages, M1 macrophages, and M2 macrophages, which significantly affected the prognosis of osteosarcoma. Thus, patients with low-risk scores showed better results with the immune checkpoints inhibitor therapy. A three immune-related, gene-based risk model can regulate macrophage activation and predict the treatment outcomes the survival rate in osteosarcoma.

CD169 Defines Activated CD14+ Monocytes With Enhanced CD8+ T Cell Activation Capacity

Monocytes are antigen-presenting cells (APCs) that play diverse roles in promoting or regulating inflammatory responses, but their role in T cell stimulation is not well defined. In inflammatory conditions, monocytes frequently show increased expression of CD169/Siglec-1, a type-I interferon (IFN-I)-regulated protein. However, little is known about the phenotype and function of these CD169⁺ monocytes. Here, we have investigated the phenotype of human CD169⁺ monocytes in different diseases, their capacity to activate CD8⁺ T cells, and the potential for a targeted-vaccination approach. Using spectral flow cytometry, we detected CD169 expression by CD14⁺ CD16^- classical and CD14⁺ CD16⁺ intermediate monocytes and unbiased analysis showed that they were distinct from dendritic cells, including the recently described CD14-expressing DC3. CD169⁺ monocytes expressed higher levels of co-stimulatory and HLA molecules, suggesting an increased activation state. IFNα treatment highly upregulated CD169 expression on CD14⁺ monocytes and boosted their capacity to cross-present antigen to CD8⁺ T cells. Furthermore, we observed CD169⁺ monocytes in virally-infected patients, including in the blood and bronchoalveolar lavage fluid of COVID-19 patients, as well as in the blood of patients with different types of cancers. Finally, we evaluated two CD169-targeting nanovaccine platforms, antibody-based and liposome-based, and we showed that CD169⁺ monocytes efficiently presented tumor-associated peptides gp100 and WT1 to antigen-specific CD8⁺ T cells. In conclusion, our data indicate that CD169⁺ monocytes are activated monocytes with enhanced CD8⁺ T cell stimulatory capacity and that they emerge as an interesting target in nanovaccine strategies, because of their presence in health and different diseases.

Identification of tumor microenvironment-related prognostic genes in colorectal cancer based on bioinformatic methods

Colorectal cancer (CRC) ranks fourth among the deadliest cancers globally, and the progression is highly affected by the tumor microenvironment (TME). This study explores the relationship between TME and colorectal cancer prognosis and identifies prognostic genes related to the CRC microenvironment. We collected the gene expression data from The Cancer Genome Atlas (TCGA) and calculated the scores of stromal/immune cells and their relations to clinical outcomes in colorectal cancer by the ESTIMATE algorithm. Lower immune scores were significantly related to the malignant progression of CRC (metastasis, p = 0.001). We screened 292 differentially expressed genes (DEGs) by dividing CRC cases into high and low stromal/immune score groups. Functional enrichment analyses and protein-protein interaction (PPI) networks illustrated that these DEGs were closely involved in immune response, cytokine-cytokine receptor interaction, and chemokine signaling pathway. Six DEGs (FABP4, MEOX2, MMP12, ERMN, TNFAIP6, and CHST11) with prognostic value were identified by survival analysis and validated in two independent cohorts (GSE17538 and GSE161158). The six DEGs were significantly related to immune cell infiltration levels based on the Tumor Immune Estimation Resource (TIMER). The results might contribute to discovering new diagnostic and prognostic biomarkers and new treatment targets for colorectal cancer.

NOL4L, a novel nuclear protein, promotes cell proliferation and metastasis by enhancing the PI3K/AKT pathway in ovarian cancer

Nucleolar protein 4-like (NOL4L) was first identified in acute myeloid leukaemia. Then, it was verified to be involved in cell progression in neuroblastoma. However, the functional role of NOL4L in tumor proliferation and metastasis and the underlying molecular mechanism(s) are not fully understood. Immunohistochemistry (IHC) assays were performed in patient tissues to reveal NOL4L expression profiles. Then, we knocked down NOL4L in two ovarian cancer cell lines (Skov3-ip1 and Hey), and cell-based in-vitro and in-vivo assays were subsequently conducted to gain insight into the underlying mechanism of NOL4L in ovarian cancer. We confirmed that the expression of NOL4L was higher in tumor tissues, especially in peritoneal metastatic tissues. Furthermore, we observed that NOL4L was related to prognosis in ovarian cancer patients. Next, we conducted CCK-8 assays, colony formation assays, migration and invasion experiments and wound healing assays and verified that NOL4L could promote proliferation and metastasis in ovarian cancer cells. In addition, NOL4L promoted tumor progression and metastasis in a nude mouse model. Mechanistically, we demonstrated that NOL4L influenced gene expression in the PI3K/AKT pathway. Overall, our study provides genetic and biochemical evidence that NOL4L is critical for tumor progression and metastasis in ovarian cancer cells. Thus, it could serve as a target for antimetastatic therapy in ovarian cancer.

Prognostic Biomarker-Based Identification of Drugs for Managing the Treatment of Endometrial Cancer

INTRODUCTION

Uterine corpus endometrial carcinoma (UCEC) causes thousands of deaths per year. To improve the overall survival of patients with UCEC, there is a need to identify prognostic biomarkers and potential drugs.

OBJECTIVES

The aim of this study was twofold: the identification of prognostic gene signatures from expression profiles of pattern recognition receptor (PRR) genes and identification of the most effective existing drugs using the prognostic gene signature.

METHODS

This study was based on the expression profile of PRR genes of 541 patients with UCEC obtained from The Cancer Genome Atlas. Key prognostic signatures were identified using various approaches, including survival analysis, network, and clustering. Hub genes were identified by constructing a co-expression network. Representative genes were identified using k-means and k-medoids-based clustering. Univariate Cox proportional hazard (PH) analysis was used to identify survival-associated genes. 'cmap2' was used to identify potential drugs that can suppress/enhance the expression of prognostic genes.

RESULTS

Models were developed using hub genes and achieved a maximum hazard ratio (HR) of 1.37 (p = 0.294). Then, a clustering-based model was developed using seven genes (HR 9.14; p = 1.49 × 10^-12). Finally, a nine gene-based risk stratification model was developed (CLEC1B, CLEC3A, IRF7, CTSB, FCN1, RIPK2, NLRP10, NLRP9, and SARM1) and achieved HR 10.70; p = 1.1 × 10^-12. The performance of this model improved significantly in combination with the clinical stage and achieved HR 15.23; p = 2.21 × 10^-7. We also developed a model for predicting high-risk patients (survival ≤ 4.3 years) and achieved an area under the receiver operating characteristic curve (AUROC) of 0.86.

CONCLUSION

We identified potential immunotherapeutic agents based on prognostic gene signature: hexamethonium bromide and isoflupredone. Several novel candidate drugs were suggested, including human interferon-α-2b, paclitaxel, imiquimod, MESO-DAP1, and mifamurtide. These biomolecules and repurposed drugs may be utilised for prognosis and treatment for better survival.

Elevated expression of LPCAT1 predicts a poor prognosis and is correlated with the tumour microenvironment in endometrial cancer

Background

Endometrial cancer (EC) is one of the three malignant reproductive tumours that threaten womens lives and health. Glycerophospholipids (GPLs) are important bioactive lipids involved in various physiological and pathological processes, including cancer. Immune infiltration of the tumour microenvironment (TME) is positively associated with the overall survival in EC. Exploring GPL-related factors associated with the TME in endometrial cancer can aid in the prognosis of patients and provide new therapeutic targets.

Methods

Differentially expressed GPL-related genes were identified from TCGA-UCEC datasets and the Molecular Signatures Database (MSigDB). Univariate Cox regression analysis was used to select GPL-related genes with prognostic value. The Random forest algorithm, LASSO algorithm and PPI network were used to identify critical genes. ESTIMATEScore was calculated to identify genes associated with the TME. Then, differentiation analysis and survival analysis of LPCAT1 were performed based on TCGA datasets. GSE17025 and immunohistochemistry (IHC) verified the results of the differentiation analysis. An MTT assay was then conducted to determine the proliferation of EC cells. GO and KEGG enrichment analyses were performed to explore the underlying mechanism of LPCAT1. In addition, we used the ssGSEA algorithm to explore the correlation between LPCAT1 and cancer immune infiltrates.

Results

Twenty-three differentially expressed GPL-related genes were identified, and eleven prognostic genes were selected by univariate Cox regression analysis. Four significant genes were identified by two different algorithms and the PPI network. Only LPCAT1 was significantly correlated with the tumour microenvironment. Then, we found that LPCAT1 was highly expressed in tumour samples compared with that in normal tissues, and lower survival rates were observed in the groups with high LPCAT1 expression. Silencing of LPCAT1 inhibited the proliferation of EC cells. Moreover, the expression of LPCAT1 was positively correlated with the histologic grades and types. The ROC curve indicated that LPCAT1 had good prognostic accuracy. Receptor ligand activity, pattern specification process, regionalization, anterior/posterior pattern specification and salivary secretion pathways were enriched as potential targets of LPCAT1. By using the ssGSEA algorithm, fifteen kinds of tumor-infiltrating cells (TICs) were found to be correlated with LPCAT1 expression.

Conclusion

These findings suggested that LPCAT1 may act as a valuable prognostic biomarker and be correlated with immune infiltrates in endometrial cancer, which may provide novel therapy options for and improved treatment of EC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-01965-1.

Prognostic value of tumour microenvironment-related genes by TCGA database in rectal cancer

Rectal cancer is a common malignant tumour and the progression is highly affected by the tumour microenvironment (TME). This study intended to assess the relationship between TME and prognosis, and explore prognostic genes of rectal cancer. The gene expression profile of rectal cancer was obtained from TCGA and immune/stromal scores were calculated by Estimation of Stromal and Immune cells in Malignant Tumors using Expression data (ESTIMATE) algorithm. The correlation between immune/stromal scores and survival time as well as clinical characteristics were evaluated. Differentially expressed genes (DEGs) were identified according to the stromal/immune scores, and the functional enrichment analyses were conducted to explore functions and pathways of DEGs. The survival analyses were conducted to clarify the DEGs with prognostic value, and the protein‐protein interaction (PPI) network was performed to explore the interrelation of prognostic DEGs. Finally, we validated prognostic DEGs using data from the Gene Expression Omnibus (GEO) database by PrognoScan, and we verified these genes at the protein levels using the Human Protein Atlas (HPA) databases. We downloaded gene expression profiles of 83 rectal cancer patients from The Cancer Genome Atlas (TCGA) database. The Kaplan‐Meier plot demonstrated that low‐immune score was associated with worse clinical outcome (P = .034), metastasis (M1 vs. M0, P = .031) and lymphatic invasion (+ vs. ‐, P < .001). A total of 540 genes were screened as DEGs with 539 up‐regulated genes and 1 down‐regulated gene. In addition, 60 DEGs were identified associated with overall survival. Functional enrichment analyses and PPI networks showed that the DEGs are mainly participated in immune process, and cytokine‐cytokine receptor interaction. Finally, 19 prognostic genes were verified by GSE17536 and GSE17537 from GEO, and five genes (ADAM23, ARHGAP20, ICOS, IRF4,MMRN1) were significantly different in tumour tissues compared with normal tissues at the protein level. In summary, our study demonstrated the associations between TME and prognosis as well as clinical characteristics of rectal cancer. Moreover, we explored and verified microenvironment‐related genes, which may be the potential key prognostic genes of rectal cancer. Further clinical samples and functional studies are needed to validate this finding.

Identification of Tumor Microenvironment-Related Alternative Splicing Events to Predict the Prognosis of Endometrial Cancer

Background

Endometrial cancer (EC) is one of the most common female malignant tumors. The immunity is believed to be associated with EC patients’ survival, and growing studies have shown that aberrant alternative splicing (AS) might contribute to the progression of cancers.

Methods

We downloaded the clinical information and mRNA expression profiles of 542 tumor tissues and 23 normal tissues from The Cancer Genome Atlas (TCGA) database. ESTIMATE algorithm was carried out on each EC sample, and the OS-related different expressed AS (DEAS) events were identified by comparing the high and low stromal/immune scores groups. Next, we constructed a risk score model to predict the prognosis of EC patients. Finally, we used unsupervised cluster analysis to compare the relationship between prognosis and tumor immune microenvironment.

Results

The prognostic risk score model was constructed based on 16 OS-related DEAS events finally identified, and then we found that compared with high-risk group the OS in the low-risk group was notably better. Furthermore, according to the results of unsupervised cluster analysis, we found that the better the prognosis, the higher the patient’s ESTIMATE score and the higher the infiltration of immune cells.

Conclusions

We used bioinformatics to construct a gene signature to predict the prognosis of patients with EC. The gene signature was combined with tumor microenvironment (TME) and AS events, which allowed a deeper understanding of the immune status of EC patients, and also provided new insights for clinical patients with EC.

Identification of a Metabolism-Related Signature for the Prediction of Survival in Endometrial Cancer Patients

Objective

Endometrial cancer (EC) is one of the most common gynecologic malignancies. The present study aims to identify a metabolism-related biosignature for EC and explore the molecular immune-related mechanisms underlying the tumorigenesis of EC.

Methods

Transcriptomics and clinical data of EC were retrieved from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Common differentially expressed metabolism-related genes were extracted and a risk signature was identified by using the least absolute shrinkage and selection operator (LASSO) regression analysis method. A nomogram integrating the prognostic model and the clinicopathological characteristics was established and validated by a cohort of clinical EC patients. Furthermore, the immune and stromal scores were observed and the infiltration of immune cells in EC cells was analyzed.

Results

Six genes, including CA3, HNMT, PHGDH, CD38, PSAT1, and GPI, were selected for the development of the risk prediction model. The Kaplan-Meier curve indicated that patients in the low-risk group had considerably better overall survival (OS) (P = 7.874e-05). Then a nomogram was constructed and could accurately predict the OS (AUC = 0.827, 0.821, 0.845 at 3-, 5-, and 7-year of OS). External validation with clinical patients showed that patients with low risk scores had a longer OS (p = 0.04). Immune/stromal scores and infiltrating density of six types of immune cells were lower in high-risk group.

Conclusions

In summary, our work provided six potential metabolism-related biomarkers as well as a nomogram for the prognosis of EC patients, and explored the underlying mechanism involved in the progression of EC.

Identification of an immune-related risk signature and nomogram predicting the overall survival in patients with endometrial cancer

Objective

Aimed to construct an immune-related risk signature and nomogram predicting endometrial cancer (EC) prognosis.

Methods

An immune-related risk signature in EC was constructed using the least absolute shrinkage and selection operator regression analysis based on The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A nomogram integrating the immune-related genes and the clinicopathological characteristics was established and validated using the Kaplan-Meier survival curve and receiver operating characteristic (ROC) curve to predict the overall survival (OS) of EC patients. The Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) R tool was used to explore the immune and stromal scores.

Results

CCL17, CTLA4, GPI, HDGF, HFE2, ICOS, IFNG, IL21R, KAL1, NR3C1, S100A2, and S100A9 were used in developing an immune-related risk signature evaluation model. The Kaplan-Meier curve indicated that patients in the low-risk group had better OS (p<0.001). The area under the ROC curve (AUC) values of this model were 0.737, 0.764, and 0.782 for the 3-, 5-, and 7-year OS, respectively. A nomogram integrating the immune-related risk model and clinical features could accurately predict the OS (AUC=0.772, 0.786, and 0.817 at 3-, 5-, and 7-year OS, respectively). The 4 immune cell scores were lower in the high-risk group. Forkhead box P3 (FOXP3) and basic leucine zipper ATF-like transcription factor (BATF) showed a potential significant role in the immune-related risk signature.

Conclusion

Twelve immune-related genes signature and nomogram for assessing the OS of patients with EC had a good practical value.

Development of a novel immune-related lncRNA signature as a prognostic classifier for endometrial carcinoma

Endometrial carcinoma (EnCa) is one of the deadliest gynecological malignancies. The purpose of the current study was to develop an immune-related lncRNA prognostic signature for EnCa. In the current research, a series of systematic bioinformatics analyses were conducted to develop a novel immune-related lncRNA prognostic signature to predict disease-free survival (DFS) and response to immunotherapy and chemotherapy in EnCa. Based on the newly developed signature, immune status and mutational loading between high‑ and low‑risk groups were also compared. A novel 13-lncRNA signature associated with DFS of EnCa patients was ultimately developed using systematic bioinformatics analyses. The prognostic signature allowed us to distinguish samples with different risks with relatively high accuracy. In addition, univariate and multivariate Cox regression analyses confirmed that the signature was an independent factor for predicting DFS in EnCa. Moreover, a predictive nomogram combined with the risk signature and clinical stage was constructed to accurately predict 1-, 2-, 3-, and 5-year DFS of EnCa patients. Additionally, EnCa patients with different levels of risk had markedly different immune statuses and mutational loadings. Our findings indicate that the immune-related 13-lncRNA signature is a promising classifier for prognosis and response to immunotherapy and chemotherapy for EnCa.

Screening and Identification of Prognostic Tumor-Infiltrating Immune Cells and Genes of Endometrioid Endometrial Adenocarcinoma: Based on The Cancer Genome Atlas Database and Bioinformatics

Background

Endometrioid endometrial adenocarcinoma (EEA) is one of the most common tumors in the female reproductive system. With the further understanding of immune regulation mechanism in tumor microenvironment, immunotherapy is emerging in tumor treatment. However, there are few systematic studies on EEA immune infiltration.

Methods

In this study, prognostic tumor-infiltrating immune cells (TIICs) and related genes of EEA were comprehensively analyzed for the first time through the bioinformatics method with CIBERSORT algorithm as the core. Gene expression profile data were downloaded from the TCGA database, and the abundance ratio of TIICs was obtained. Kaplan-Meier analysis and Cox regression analysis were used to identify prognostic TIICs. EEA samples were grouped according to the risk score in Cox regression model. Differential analysis and functional enrichment analyses were performed on high- and low-risk groups to find survival-related hub genes, which were verified by Tumor Immune Estimation Resource (TIMER).

Result

Four TIICs including memory CD4+ T cells, regulatory T cells, natural killer cells and dendritic cells were identified. And two hub gene modules were found, in which six hub genes including APOL1, CCL17, RBP4, KRT15, KRT71, and KRT79 were significantly related to overall survival and were closely correlated with some certain TIICs in the validation of TIMER.

Conclusion

In this study, four prognostic TIICs and six hub genes were found to be closely related to EEA. These findings provided new potential targets for EEA immunotherapy.