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

Establishment and verification of a prognostic signature associated with fatty acid metabolism in endometrial cancer

Endometrial carcinoma (EC) is one of the leading causes of mortality in women. Metabolic disorders, such as abnormal fatty acid metabolism (FAM), are considered to be indicators of tumorigenesis. However, to the best of our knowledge, the relationship between EC and FAM remains unclear. The process of FAM is associated with the function of immune cells, thus samples from The Cancer Genome Atlas were grouped according to immune infiltration levels. Subsequently, prognostic gene signatures were constructed based on selected FAM‑associated genes. The signature effect was validated, and enrichment analyses were conducted based on sample classification. Nomograms were used to predict survival, merging clinical data and the gene signature. Samples were divided into high‑ and low‑risk groups based on the gene signature. The survival status, clinical characteristics, enrichment analysis and immune infiltration were significantly different between high‑ and low‑risk groups. According to the nomogram, low microsatellite instability‑high as well as a high tumor mutation burden can be observed in the low‑nomo‑score group. Immune checkpoint inhibitor‑associated genes were differentially expressed between groups and 35 sensitive compounds were identified. Comprehensive bioinformatics analysis in EC revealed potential roles of FAM in tumorigenesis, the tumor microenvironment and prognosis, suggesting that FAM‑associated signatures are promising biomarkers for EC. These findings may improve the understanding of FAM in EC and pave the way for a more accurate assessment of prognosis and immunotherapy outcomes.

A machine learning-based immune response signature to facilitate prognosis prediction in patients with endometrial cancer

Endometrial cancer is the most prevalent form of gynecologic malignancy, with a significant surge in incidence among youngsters. Although the advent of the immunotherapy era has profoundly improved patient outcomes, not all patients benefit from immunotherapy; some patients experience hyperprogression while on immunotherapy. Hence, there is a pressing need to further delineate the distinct immune response profiles in patients with endometrial cancer to enhance prognosis prediction and facilitate the prediction of immunotherapeutic responses. The ssGSEA method was used to evaluate the activities of the immune response pathways in patients with endometrial cancer. Unsupervised clustering was employed to identify the different immune response patterns. WGCNA was employed to identify the genes that highly correlated with the immune response patterns observed. Ninety-five machine learning combinations were utilized to identify the optimal prognosis model and the novel biomarker, SLC38A3. Experiments such as cell invasion, migration, scratch, and in vivo tumorigenicity were performed to determine the function of SLC28A3. Molecular docking techniques were employed to determine the targeted action of periodate-oxidized adenosine on SLC38A3. Patients exhibited both immune response-suppressing C1 phenotypes and immune response-activating C2 phenotypes, with significant differences in prognosis between these two phenotypes. WGCNA identified 418 genes that highly correlated with the immune response phenotypes, of which 69 genes were associated with prognosis. The immune response-related score (IRRS) established by multiple machine learning frameworks demonstrated stability in predicting patient prognosis and immune status. High expression of SLC38A3 contributes to cellular malignant traits, and periodate-oxidized adenosine bound stably to SLC38A3. IRRS accurately predicts disease prognosis and immune status in patients with endometrial cancer. SLC38A3 serves as a prognostic marker for these patients and can be stably targeted by periodate-oxidized adenosine.

Magnesium Supplementation Modifies Arthritis Synovial and Splenic Transcriptomic Signatures Including Ferroptosis and Cell Senescence Biological Pathways

BACKGROUND

Rheumatoid arthritis (RA) is a common systemic autoimmune inflammatory disease that can cause joint damage. We have recently reported that oral magnesium supplementation significantly reduces disease severity and joint damage in models of RA.

METHODS

In the present study, we analyzed the transcriptome of spleens and synovial tissues obtained from mice with KRN serum-induced arthritis (KSIA) consuming either a high Mg supplemented diet (Mg2800; n = 7) or a normal diet (Mg500; n = 7). Tissues were collected at the end of a 15-day KSIA experiment. RNA was extracted and used for sequencing and analyses.

RESULTS

There was an enrichment of differentially expressed genes (DEGs) belonging to Reactome and Gene Ontology (GO) pathways implicated in RA pathogenesis such as RHO GTPases, the RUNX1 pathway, oxidative stress-induced senescence, and the senescence-associated secretory phenotype. Actc1 and Nr4a3 were among the genes with the highest expression, while Krt79 and Ffar2 were among the genes with the lowest expression in synovial tissues of the Mg2800 group compared with the Mg500 group. Spleens had an enrichment for the metabolism of folate and pterines and the HSP90 chaperone cycle for the steroid hormone receptor.

CONCLUSIONS

We describe the tissue transcriptomic consequences of arthritis-protecting Mg supplementation in KSIA mice. These results show that oral Mg supplementation may interfere with the response to oxidative stress and senescence and other processes known to participate in RA pathogenesis. We provide new evidence supporting the disease-suppressing effect of increased Mg intake in arthritis and its potential to become a new addition to the therapeutic options for RA and other autoimmune and inflammatory diseases.

FUNDC1 predicts Poor Prognosis and promotes Progression and Chemoresistance in Endometrial Carcinoma

Absence of effective prognostic biomarkers and therapeutic targets for reversing chemoresistance of endometrial carcinoma (EC) remains a huge challenge for clinicians. Mitophagy plays a crucial role in carcinogenesis and chemoresistance. FUN14 domain-containing protein 1 (FUNDC1) is a novel mitophagy receptor protein involved in tumorigenesis under hypoxic conditions. However, the implication of FUNDC1 in EC progression, chemoresistance in particular, remains unclear. Based on The Cancer Genome Atlas (TCGA) cohort, comprised of 403 EC patients, the association of FUNDC1 mRNA levels with hypoxia-inducible factor 1α (HIF-1α) expression, clinicopathologic features and prognosis in EC was analyzed, and subsequently verified utilizing immunohistochemistry of 288 EC specimens. Analysis of the cohort in TCGA showed that patients with higher FUNDC1 levels exhibited worse OS, with the shortest OS exhibited by patients with co-upregulated FUNDC1 and HIF-1α (P < 0.05). Analysis of the validation cohort indicated that OS and PFS rates of high-FUNDC1 patients were lower than that of low-FUNDC1 group (P < 0.05). Cases with co-downregulation of FUNDC1 and HIF-1α had higher OS and PFS rates than those with co-upregulation of these two proteins (88.8% vs. 71.2%, P = 0.002; 85.6% vs. 71.2%, P = 0.009). Higher FUNDC1 expression was observed in platinum-resistant patients. Multivariate Cox regression analysis revealed that FUNDC1 expression, FIGO stage, lymphatic invasion, depth of myometrial invasion, and ascites were independent risk factors for OS and PFS. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis showed that FUNDC1 was closely related to spliceosome, neurodegeneration pathways of multiple diseases, and cell cycle signaling pathways. Significantly enriched RNA splicing and ncRNA processing were identified in Gene Ontology (GO) analysis. Gene set enrichment analysis (GSEA) indicated that abnormal expression of FUNDC1 was involved in endometrial cancer, NOD-like receptor signaling pathway and cytokine signaling in the immune system. In addition, immune cell infiltration analysis by Tumor Immune Estimation Resources (TIMER) database and the Xiantao academic tool demonstrated that FUNDC1 expression was strongly associated with the infiltration of Th2, NK, Th17, Tem, pDC, neutrophil, MDSC, CD4+ T, and γδ T cells. Knockdown of FUNDC1 using shRNA in HEC-1B and Ishikawa EC cells inhibited proliferation, migration and invasion, accompanied by enhanced chemotherapeutic susceptibility to carboplatin and paclitaxel. Accordingly, FUNDC1 could be a prospective prognostic biomarker and potential therapeutic target for EC.

Keratin-15 high expression links with lymph node metastasis and poor survival prognosis in epithelial ovarian cancer patients

BACKGROUND

Keratin-15 (KRT15) involves in the progression and owns prognostic values in several solid cancers, whose clinical role in epithelial ovarian cancer (EOC) is rarely reported. This study aimed to identify the association of KRT15 expression with tumor features and survival of surgical EOC patients.

METHODS

Formalin-fixed paraffin-embedded tumor tissues of 140 EOC patients who underwent tumor resection were retrieved for KRT15 determination using immunohistochemistry (IHC) assay.

RESULTS

The median (interquartile range) KRT15 IHC score was 0.0 (0.0-1.0), ranging from 0.0 to 12.0. Among all, 36.4% of patients had positive KRT15 expression (IHC score > 0) and 15.0% of patients had high KRT15 expression (IHC score > 3). KRT15 was positively related to lymph node metastasis incidence (P = 0.027), and showed a tendency to correlate to FIGO stage but without statistical significance (P = 0.052), while it was not correlated with age, other tumor features, and tumor markers. Positive KRT15 expression was linked with poor disease-free survival (DFS) (P = 0.009) and overall survival (OS) (P = 0.032). Notably, high KRT15 expression showed an even stronger relationship with worse DFS (P = 0.001) and OS (P < 0.001). After adjustment of multivariable Cox's regression, high KRT15 expression was independently correlated with unfavorable DFS (hazard ratio (HR): 2.241, P = 0.007).

CONCLUSION

Even though KRT15 is insufficiently expressed in EOC tissues generally, its positive expression or high expression can predict the lymph node metastasis and poor survival prognosis in EOC patients who undergo tumor resection.

Apolipoprotein L1 is a tumor suppressor in clear cell renal cell carcinoma metastasis

The 5-year survival rate of kidney cancer drops dramatically from 93% to 15% when it is metastatic. Metastasis constitutes for 30% of kidney cancer cases, in which clear cell renal cell carcinoma (ccRCC) is the most prominent subtype. By sequencing mRNA of ccRCC patient samples, we found that apolipoprotein L1 (APOL1) was highly expressed in tumors compared to their adjacent normal tissues. This gene has been previously identified in a large body of kidney disease research and was reported as a potential prognosis marker in many types of cancers. However, the molecular function of APOL1 in ccRCC, especially in metastasis, remained unknown. In this study, we modulated the expression of APOL1 in various renal cancer cell lines and analyzed their proliferative, migratory, and invasive properties. Strikingly, APOL1 overexpression suppressed ccRCC metastasis both in vitro and in vivo. We then explored the mechanism by which APOL1 alleviated ccRCC malignant progression by investigating its downstream pathways. APOL1 overexpression diminished the activity of focal adhesive molecules, Akt signaling pathways, and EMT processes. Furthermore, in the upstream, we discovered that miR-30a-3p could inhibit APOL1 expression. In conclusion, our study revealed that APOL1 play a role as a tumor suppressor in ccRCC and inhibit metastasis, which may provide novel potential therapeutic approaches for ccRCC patients.

Construction of a fecal immune-related protein-based biomarker panel for colorectal cancer diagnosis: a multicenter study

Purpose

To explore fecal immune-related proteins that can be used for colorectal cancer (CRC) diagnosis.

Patients and methods

Three independent cohorts were used in present study. In the discovery cohort, which included 14 CRC patients and 6 healthy controls (HCs), label-free proteomics was applied to identify immune-related proteins in stool that could be used for CRC diagnosis. Exploring potential links between gut microbes and immune-related proteins by 16S rRNA sequencing. The abundance of fecal immune-associated proteins was verified by ELISA in two independent validation cohorts and a biomarker panel was constructed that could be used for CRC diagnosis. The validation cohort I included 192 CRC patients and 151 HCs from 6 different hospitals. The validation cohort II included 141 CRC patients, 82 colorectal adenoma (CRA) patients, and 87 HCs from another hospital. Finally, the expression of biomarkers in cancer tissues was verified by immunohistochemistry (IHC).

Results

In the discovery study, 436 plausible fecal proteins were identified. And among 67 differential fecal proteins (|log2 fold change| > 1, P< 0.01) that could be used for CRC diagnosis, 16 immune-related proteins with diagnostic value were identified. The 16S rRNA sequencing results showed a positive correlation between immune-related proteins and the abundance of oncogenic bacteria. In the validation cohort I, a biomarker panel consisting of five fecal immune-related proteins (CAT, LTF, MMP9, RBP4, and SERPINA3) was constructed based on the least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression. The biomarker panel was found to be superior to hemoglobin in the diagnosis of CRC in both validation cohort I and validation cohort II. The IHC result showed that protein expression levels of these five immune-related proteins were significantly higher in CRC tissue than in normal colorectal tissue.

Conclusion

A novel biomarker panel consisting of fecal immune-related proteins can be used for the diagnosis of CRC.

Computed tomography‐based radiomics prediction of CTLA4 expression and prognosis in clear cell renal cell carcinoma

OBJECTIVES

To predict CTLA4 expression levels and prognosis of clear cell renal cell carcinoma (ccRCC) by constructing a computed tomography-based radiomics model and establishing a nomogram using clinicopathologic factors.

METHODS

The clinicopathologic parameters and genomic data were extracted from 493 ccRCC cases of the Cancer Genome Atlas (TCGA)-KIRC database. Univariate and multivariate Cox regression and Kaplan-Meier analysis were performed for prognosis analysis. Cibersortx was applied to evaluate the immune cell composition. Radiomic features were extracted from the TCGA/the Cancer Imaging Archive (TCIA) (n = 102) datasets. The support vector machine (SVM) was employed to establish the radiomics signature for predicting CTLA4 expression. Receiver operating characteristic curve (ROC), decision curve analysis (DCA), and precision-recall curve were utilized to assess the predictive performance of the radiomics signature. Correlations between radiomics score (RS) and selected features were also evaluated. An RS-based nomogram was constructed to predict prognosis.

RESULTS

CTLA4 was significantly overexpressed in ccRCC tissues and was related to lower overall survival. A higher CTLA4 expression was independently linked to the poor prognosis (HR = 1.458, 95% CI 1.13-1.881, p = 0.004). The radiomics model for the prediction of CTLA4 expression levels (AUC = 0.769 in the training set, AUC = 0.724 in the validation set) was established using seven radiomic features. A significant elevation in infiltrating M2 macrophages was observed in the RS high group (p < 0.001). The predictive efficiencies of the RS-based nomogram measured by AUC were 0.826 at 12 months, 0.805 at 36 months, and 0.76 at 60 months.

CONCLUSIONS

CTLA4 mRNA expression status in ccRCC could be predicted noninvasively using a radiomics model based on nephrographic phase contrast-enhanced CT images. The nomogram established by combining RS and clinicopathologic factors could predict overall survival for ccRCC patients. Our findings may help stratify prognosis of ccRCC patients and identify those who may respond best to ICI-based treatments.

Importance of the endometrial immune environment in endometrial cancer and associated therapies

Endometrial cancer is rising in prevalence. The standard treatment modality of hysterectomy is becoming increasingly inadequate due primarily to the direct link between endometrial cancer and high BMI which increases surgical risks. This is an immunogenic cancer, with unique molecular subtypes associated with differential immune infiltration. Despite the immunogenicity of endometrial cancer, there is limited pre-clinical and clinical evidence of the function of immune cells in both the normal and cancerous endometrium. Immune checkpoint inhibitors for endometrial cancer are the most well studied type of immune therapy but these are not currently used as standard-of-care and importantly, they represent only one method of immune manipulation. There is limited evidence regarding the use of other immunotherapies as surgical adjuvants or alternatives. Levonorgestrel-loaded intra-uterine systems can also be effective for early-stage disease, but with varying success. There is currently no known reason as to what predisposes some patients to respond while others do not. As hormones can directly influence immune cell function, it is worth investigating the immune compartment in this context. This review assesses the immunological components of the endometrium and describes how the immune microenvironment changes with hormones, obesity, and in progression to malignancy. It also describes the importance of investigating novel pathways for immunotherapy.

Integrated Bioinformatics Analysis of Serine Racemase as an Independent Prognostic Biomarker in Endometrial Cancer

Endometrial cancer (EC) kills about 76,000 women worldwide, with the highest incidence in industrialized countries. Because of the rise in disease mortality and new diagnoses, EC is now a top priority for women’s health. Serine racemase (SRR) is thought to play a role in the central nervous system, but its role in cancers, particularly in EC, is largely unknown. The current study starts with a pan-cancer examination of SRR’s expression and prognostic value before delving into SRR’s potential cancer-suppressing effect in patients with EC. SRR may affect the endometrial tumor immune microenvironment, according to subsequent immune-related analysis. SRR expression is also linked to several genes involved in specific pathways such as ferroptosis, N6-methyladenosine methylation, and DNA damage repair. Finally, we used the expression, correlation, and survival analyses to investigate the upstream potential regulatory non-coding RNAs of SRR. Overall, our findings highlight the prognostic significance of SRR in patients with EC, and we can formulate a reasonable hypothesis that SRR influences metabolism and obstructs key carcinogenic processes in EC.

A 13-Gene Signature Based on Estrogen Response Pathway for Predicting Survival and Immune Responses of Patients With UCEC

Background: Accumulating evidence suggests that anti-estrogens have been effective against multiple gynecological diseases, especially advanced uterine corpus endometrial carcinoma (UCEC), highlighting the contribution of the estrogen response pathway in UCEC progression. This study aims to identify a reliable prognostic signature for potentially aiding in the comprehensive management of UCEC.

Methods: Firstly, univariate Cox and LASSO regression were performed to identify a satisfying UCEC prognostic model quantifying patients’ risk, constructed from estrogen-response-related genes and verified to be effective by Kaplan-Meier curves, ROC curves, univariate and multivariate Cox regression. Additionally, a nomogram was constructed integrating the prognostic model and other clinicopathological parameters. Next, UCEC patients from the TCGA dataset were divided into low- and high-risk groups according to the median risk score. To elucidate differences in biological characteristics between the two risk groups, pathway enrichment, immune landscape, genomic alterations, and therapeutic responses were evaluated to satisfy this objective. As for treatment, effective responses to anti-PD-1 therapy in the low-risk patients and sensitivity to six chemotherapy drugs in the high-risk patients were demonstrated.

Results: The low-risk group with a relatively favorable prognosis was marked by increased immune cell infiltration, higher expression levels of HLA members and immune checkpoint biomarkers, higher tumor mutation burden, and lower copy number alterations. This UCEC prognostic signature, composed of 13 estrogen-response-related genes, has been identified and verified as effective.

Conclusion: Our study provides molecular signatures for further functional and therapeutic investigations of estrogen-response-related genes in UCEC and represents a potential systemic approach to characterize key factors in UCEC pathogenesis and therapeutic responses.

A Bioinformatic Analysis of Immune-Related Prognostic Genes in Clear Cell Renal Cell Carcinoma Based on TCGA and GEO Databases

Background

Clear cell renal cell carcinoma (ccRCC) is a commonly occurring tumor. Through a deeper understanding of the immune regulatory mechanisms in the tumor microenvironment, immunotherapy may serve as a potential treatment for cancer patients. This study aimed at identifying the survival-related immune cells and hub genes, which could be potential targets for immunotherapy in ccRCC.

Methods

The gene expression profiles and clinical data of ccRCC patients were extracted from UCSC Xena and Gene Expression Omnibus (GEO) databases. Kaplan–Meier (KM) survival and Least Absolute Shrinkage and Selection Operator (LASSO) regression analyses were utilized to select the survival-related tumor-infiltrating immune cells. Multivariate Cox regression was utilized to develop a signature based on the tumor-infiltrating immune cells (TIICs). Based on the signature, the risk score was calculated, following which the samples were divided into high-risk and low-risk groups. Differentially expressed genes (DEGs) between the two risk groups were identified. Functional enrichment analysis was performed and cytoHubba plug-in of Cytoscape was used to identify the hub genes. Multiple datasets were utilized to validate these hub genes, including the Gene Expression Profiling Interactive Analysis (GEPIA), UALCAN, and the Human Protein Atlas (HPA), and the GEO datasets. Finally, a correlation analysis was performed to evaluate the relationship between the hub genes and TIICs.

Results

Four immune survival-related cells, including T cell CD4 memory-activated, T cell regulatory (Tregs), eosinophils, and mast cell resting were identified. Nine immune-specific hub genes were identified, which included APOE, CASR, CTLA4, CXCL8, EGF, F2, KNG1, MMP9, and IL6. Furthermore, these hub genes were significantly correlated with clinical traits and closely associated with some TIICs.

Conclusion

A total of four survival-related immune cell types and nine hub genes were found to be closely associated with ccRCC. These findings may have implications for the development of novel potential immunotherapeutic targets for ccRCC.

A 4-Gene Signature Associated With Recurrence in Low- and Intermediate-Risk Endometrial Cancer

Background

The molecular profile of endometrial cancer has become an important tool in determining patient prognosis and their optimal adjuvant treatment. In addition to The Cancer Genome Atlas (TCGA), simpler tools have been developed, such as the Proactive Molecular Risk Classifier for Endometrial Cancer (ProMisE). We attempted to determine a genetic signature to build a recurrence risk score in patients diagnosed with low- and intermediate-risk endometrial cancer.

Methods

A case-control study was conducted. The eligible patients were women diagnosed with recurrence low- and intermediate-risk endometrial cancer between January 2009 and December 2014 at a single institution; the recurrence patients were matched to two nonrecurrence patients with the same diagnosis by age and surgical staging. Following RNA isolation of 51 cases, 17 recurrence and 34 nonrecurrence patients, the expression profile was determined using the nCounter^® PanCancer Pathways Panel, which contains 770 genes.

Results

The expression profile was successfully characterized in 49/51 (96.1%) cases. We identified 12 genes differentially expressed between the recurrence and nonrecurrence groups. The ROC curve for each gene was generated, and all had AUCs higher than 0.7. After backward stepwise logistic regression, four genes were highlighted: FN1, DUSP4, LEF1, and SMAD9. The recurrence risk score was calculated, leading to a ROC curve of the 4-gene model with an AUC of 0.93, sensitivity of 100%, and specificity of 72.7%.

Conclusion

We identified a four-gene signature that may be associated with recurrence in patients with low- and intermediate-risk endometrial cancer. This finding suggests a new prognostic factor in this poorly explored group of patients with endometrial cancer.