Prognostic significance of mutant-allele tumor heterogeneity in uterine corpus endometrial carcinoma

Key wound healing genes as diagnostic biomarkers and therapeutic targets in uterine corpus endometrial carcinoma: an integrated in silico and in vitro study

BACKGROUND

Uterine Corpus Endometrial Carcinoma (UCEC) is a prevalent gynecologic malignancy with complex molecular underpinnings. This study identifies key woundhealing genes involved in UCEC and elucidates their roles through a comprehensive analysis.

METHODS

In silico and in vitro experiments.

RESULTS

Seventy wound healing-associated genes were extracted from the Gene Ontology (GO) database, and a protein-protein interaction (PPI) network was constructed using the STRING database. CytoHubba analysis in Cytoscape identified six pivotal hub genes: CD44, FGF2, FGF10, KDM6A, FN1, and MMP2. These genes exhibited significantly lower expression in UCEC cell lines compared to normal controls, as confirmed by RT-qPCR. Receiver Operating Characteristic (ROC) analysis demonstrated their potential as diagnostic biomarkers, with Area Under the Curve (AUC) values ranging from 0.94 to 1.00. Validation using TCGA datasets revealed consistent downregulation of these genes in UCEC samples, corroborated by immunohistochemical staining. Promoter methylation analysis showed significantly higher methylation levels in UCEC, correlating with decreased mRNA expression and poor survival outcomes. Genetic alteration analysis indicated frequent mutations in FN1 and KDM6A, although these did not significantly affect survival. Functional analysis using the CancerSEA database highlighted the involvement of these genes in critical cancer-related processes, including angiogenesis, apoptosis, and metastasis. Immune correlation studies revealed significant associations with immune inhibitor genes and distinct expression patterns across immune subtypes. Overexpression studies in UCEC cell lines demonstrated that CD44 and MMP2 reduce proliferative ability while enhancing migration and wound healing.

CONCLUSION

Collectively, these findings underscore the crucial roles of CD44, FGF2, FGF10, KDM6A, FN1, and MMP2 in UCEC pathogenesis, highlighting their potential as biomarkers and therapeutic targets in this malignancy.

Construction of an immune-related gene prognostic model for obese endometrial cancer patients based on bioinformatics analysis

Background

The tumor microenvironment (TME) affected the prognosis of tumors. However, its effect on the outcomes of obese endometrial cancer (EC) patients had not been reported.

Methods

This research performed a retrospective analysis of the transcriptome profiles and medical data of 503 EC patients. Immune scores were assessed by estimation algorithms. Cox and LASSO regression analyses were utilized to pinpoint key genes linked to prognosis, and the RPS was created to forecast the outcomes of obese EC patients. The relationship among genetic mutations and RPS was examined using CNV and somatic mutation information. ssGSEA and GSVA were employed to detect immune infiltration and immune pathway enrichment associated with key genes. The TIDE algorithm and GDSC database were utilized to forecast patients' responses of patients to immunotherapy and chemotherapy, respectively. Finally, we employed the 'rms' R software package to construct the nomogram.

Results

The prognosis of obese EC patients was associated with immune scores. Three key genes (EYA4, MBOAT2 and SCGB2A1) were identified. The risk prognosis score (RPS) for obese EC patients was established by risk stratification and prognostic prediction using prognostic genes. The higher the RPS, the worse the prognosis, and the more malignant the genomic alterations. The high RPS group had a significantly reduced proportion of most immune cells in comparison to the low RPS group. The high RPS group was linked to G2M, MYC and E2F related pathways such as cell proliferation, cell cycle and cell death. Cisplatin, tamoxifen and topotecan had a greater effect on the low RPS group. Notably, the nomogram had a good predictive ability.

Conclusion

Our study designed a reliable RPS for obese EC patients to forecast their prognosis, immune aggressiveness, and responses to immunotherapy and drug treatments.

Upregulation of sperm-associated antigen 5 expression in endometrial carcinoma was associated with poor prognosis and immune dysregulation, and promoted cell migration and invasion

Sperm-associated antigen 5 (SPAG5) regulates cancer cell invasion and is involved in the progression of many cancers. However, the role of SPAG5 in endometrial carcinoma (EC) is still unknown. The purpose of this study was to explore the role of SPAG5 in EC and its potential molecular mechanism. The UALCAN tool and cBioPortal were used to analyze the expression and alterations of SPAG5 in EC, respectively. OncoLnc was used for survival analysis. We analyzed the effects of SPAG5 on immune cell infiltration and the expression levels of immune checkpoints. We also overexpressed and knocked down SPAG5 in EC cells to explore the effect of SPAG5 regulation on migration, invasion, apoptosis, and the cell cycle of EC cells. We found that SPAG5 was overexpressed and the SPAG5 gene was often mutated in EC. High SPAG5 expression was significantly associated with poor overall survival in patients with EC. SPAG5 also affected the level of immune cell infiltration in the TIME and the expression of immune checkpoints lymphocyte activating 3 (LAG3) and T cell immunoreceptor with Ig and ITIM domains (TIGIT) in patients with EC. It may also be involved in the immunotherapy response in these patients. In vitro experiments showed that SPAG5 promotes cancer cell migration and invasion. In conclusion, this study lays the foundation for further understanding the molecular mechanisms of EC involving SPAG5 and contributes to diagnosing and managing this disease.

Pan-Cancer Analysis of ART1 and its Potential Value in Gastric Cancer

Objective: To comprehensively explore the impact of Mono-ADP-ribosyltransferases-1 expression on both prognosis and the intricate landscape of the tumor immune microenvironment across diverse cancer types, our study seeks to delve into the multifaceted interplay between Mono-ADP-ribosyltransferases-1 expression levels and their implications for clinical outcomes and the dynamic milieu of immune responses within tumors. Methods: Genomic, transcriptomic, and clinical datasets spanning diverse cancer types were meticulously curated from The Cancer Genome Atlas and Genotypic Tissue Expression repositories. Initially, our inquiry focused on discerning the prognostic significance and immunological implications of Mono-ADP-ribosyltransferases-1 expression across this heterogeneous spectrum of malignancies. Subsequently, we scrutinized the relationships between Mono-ADP-ribosyltransferases-1 expression levels and a spectrum of factors including RNA modification genes, genetic mutations, and the emergent concept of tumor stemness. Employing functional enrichment analyses, we endeavored to unravel the underlying mechanistic pathways modulated by Mono-ADP-ribosyltransferases-1. Leveraging Bayesian co-localization analysis, we sought to discern the spatial convergence of Mono-ADP-ribosyltransferases-1 expression particularly within the context of digestive tract tumors. Lastly, to corroborate our findings, we conducted in vitro experiments, specifically focusing on Gastric Cancer, thus corroborating the putative oncogenic role attributed to Mono-ADP-ribosyltransferases-1 in this malignancy. Results: Across diverse tumor types, Mono-ADP-ribosyltransferases-1 expression exhibits distinctive patterns compared to normal and adjacent tissues, thereby intertwining with the prognostic outcomes of numerous cancer patients. Noteworthy findings from our immune role identification underscore the pivotal involvement of Mono-ADP-ribosyltransferases-1 in the landscape of tumor immunotherapy. Furthermore, Kyoto Encyclopedia of Genes and Genomes analysis elucidates the enrichment of Mono-ADP-ribosyltransferases-1-associated genes predominantly within the NF-kB, Foxo, and PI3K-Akt signaling cascades, shedding light on potential mechanistic pathways underlying its influence. Bayesian co-localization analysis unveils a compelling genetic correlation between Mono-ADP-ribosyltransferases-1 and digestive tract tumors, accentuating its relevance within this specific oncological domain. Importantly, experimental validation attests to the therapeutic promise of targeting Mono-ADP-ribosyltransferases-1 in the treatment paradigm of gastric cancer, thereby underscoring its potential as a viable therapeutic target deserving of further exploration and clinical translation. Conclusion: This comprehensive pan-cancer analysis unveils crucial insights into the intricate role played by Mono-ADP-ribosyltransferases-1 in the tumorigenesis of diverse malignancies, thereby establishing a robust theoretical framework for subsequent in-depth investigations. Leveraging these insights, targeting Mono-ADP-ribosyltransferases-1-related signaling pathways within the dynamic tumor microenvironment emerges as a promising avenue for novel therapeutic interventions in the realm of tumor immunotherapy. By delineating the interplay between Mono-ADP-ribosyltransferases-1 expression and tumorigenic processes across various cancer types, this study paves the way for innovative therapeutic strategies aimed at disrupting oncogenic signaling cascades and bolstering immune-mediated antitumor responses.

Clinicopathologic Features and Genetic Alterations in Mixed-Type Ampullary Carcinoma

Mixed-type ampullary carcinoma is a subtype that combines intestinal-type (I-type) and pancreatobiliary-type (PB-type) lesions, but few studies have examined its clinicopathologic features and genetic alterations. The differences in genetic alterations between mixed type and other subtypes, as well as the genetic differences between I-type and PB-type lesions in the mixed type, remain unclear. In this study, we compared the clinicopathologic features and prognosis of 110 ampullary carcinomas classified by hematoxylin and eosin and immunohistochemical staining as follows: 63 PB-type, 35 I-type, and 12 mixed-type carcinomas. A comparative analysis of genetic mutations by targeted sequencing of 24 genes was also performed in 3 I-type cases, 9 PB-type cases, and I and PB-type lesions of 6 mixed-type cases. The mixed subtype had a poorer prognosis than the other subtypes, and there was also a similar tendency in the adjuvant group (n = 22). A total of 49 genetic mutations were detected in all 18 lesions for which genetic alteration was analyzed. No genetic mutations specific to the mixed type were found, and it was not possible to determine genetically whether the mixed type had originally been I or PB type. However, 5 of 6 cases had mutations common to both I and PB-type lesions, and additional mutations were found only in either I or PB-type lesions. In support of this, the mixed type more frequently exhibited genetic heterogeneity intratumorally than the other subtypes. Mixed-type tumors are histologically, immunohistochemically, and genetically heterogeneous, and this heterogeneity is associated with poor prognosis and may affect treatment resistance.

MTHFR act as a potential cancer biomarker in immune checkpoints blockades, heterogeneity, tumor microenvironment and immune infiltration

PURPOSE

To evaluate the role and landscape of 5-10-Methylenetetrahydrofolate reductase (MTHFR) to immune infiltration, tumor microenvironment, heterogeneity, immune checkpoints blockades, prognostic significance across cancer types.

METHODS

Data sets of genomic, transcriptomic and clinic features of MTHFR across > 60,000 patients and up to 44 cancer types were comprehensively analyzed using R software.

RESULTS

Expression of MTHFR gene is significantly lower in 17 tumors and correlated with overall survival (OS), disease-specific survival (DSS), progression-free interval (PFI) in specific tumors. Gene alterations of MTHFR are observed significant differences across tumor types. Expression of MTHFR is negatively correlated with the stemness index (mDNAsi, mRNAsi, DMPsi, ENHsi, EREG-mDNAsi and EREG-mRNAsi) in the most cancers. MTHFR showed significantly correlated with 67 types of immune cell infiltration scores in 44 cancer types by XCELL algorithm. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis are conducted to show the core tumor mechanism and biological process. Correlations between MTHFR and biomarkers of heterogeneity (MSI, TMB, MATH, HRD, LOH, Neoantigen, ploidy and purity) are also significant in specific tumors. MTHFR is significantly positively correlated with biomarkers of immune related genes (CD19, CD274, CD80, CD86) and mismatched repair genes (MLH1, PMS2, MSH2, MSH6, EPCAM, MLH3, PMS1, EXO1) in most cancer types. Receiver Operating Characteristics (ROC) analyses show MTHFR could act as a potential biomarker in anti-PD-1 (nivolumab to melanoma) and anti-CTLA4 (ipilimumab to melanoma) group of ontreatment, in anti-PD-1 (pembrolizumab to melanoma) group of pretreatment. Two immunohistochemistry antibodies HPA076180 and HPA077255 are verified in 20 types of tumor and could be used to detect the expression of MTHFR efficiently in clinic.

CONCLUSIONS

MTHFR could predict the response of immune checkpoints blockades, heterogeneity, tumor microenvironment and immune infiltration.

Identification of prognostic immune-related lncRNAs in pancreatic cancer

Long noncoding RNAs (lncRNAs) play a critical role in the immune regulation and tumor microenvironment of pancreatic cancer (PaCa). To construct a novel immune-related prognostic risk model for PaCa and evaluate the prognostic prediction of lncRNAs, essential immune-related lncRNAs (IRlncRNAs) were identified by Pearson correlation analysis of differentially expressed immune-related genes (IRGs) and IRlncRNAs in PaCa from The Cancer Genome Atlas (TCGA) and GTEx databases. Least absolute shrinkage and selection operator (LASSO) regression was also applied to construct a prognostic risk model of IRlncRNAs, and gene set enrichment analysis (GSEA) was further applied for functional annotation for these IRlncRNAs. A total of 148 IRlncRNAs were identified in PaCa to construct a prognostic risk model. Among them, lncRNA LINC02325, FNDC1-AS1, and ZEB2-AS1 were significantly upregulated in 69 pairs of PaCa tissues by qRT-PCR. ROC analyses showed that LINC02325 (AUC = 0.80), FNDC1-AS1 (AUC = 0.76), and ZEB2-AS1 (AUC = 0.75) had a good predictive effect on 5-year survival prognosis. We demonstrated that high expression levels of ZEB2-AS1 and LINC02325 were not only positively associated with tumor size and CA199, but elevated levels of ZEB2-AS1 and FNDC1-AS1 were also positively correlated with tumor stage. GSEA further revealed that immune-related pathways were mainly enriched in the high-risk groups. Several immune-related algorithms demonstrated that four IRlncRNAs were related to immune infiltration, immune checkpoints, and immune-related functions. Thus, the prognostic risk model based on IRlncRNAs in Paca indicates that the four IRlncRNA signatures may serve as predictors of survival and potential predictive biomarkers of the pancreatic tumor immune response.

Novel DNA Damage-Related Subtypes Characterization Identifies Uterine Corpus Endometrial Carcinoma (UCEC) Based on Machine Learning

Objective. Accumulating evidence suggests that DNA damage is associated with numerous gynecological illnesses, particularly advanced uterine corpus endometrial carcinoma (UCEC), illustrating the involvement of the DNA damage pathway in the advancement of UCEC. This research aimed to discover a robust subtype with the potential to contribute to the scientific treatment of UCEC. Methods. In this work, the expression patterns of prognostic DNA damage-related genes were curated, and consensus clustering analyses were undertaken to determine DNA damage subtypes in patients with UCEC in the TCGA cohort. Two DNA damage-related subtypes were identified for further investigation. Differentially expressed genes (DEGs) analysis, gene ontology analysis, mutation analysis, and immune cell infraction analysis were performed to find the molecular mechanism behind it. Finally, the polymerase chain reaction (PCR) was conducted to verify the correlation of the hub genes. Results. In total, 545 patients with UCEC were tested for two distinct DNA damage subtypes. The clinical prognosis was poorer among patients with DNA damage subtype 2 than those in subtype 1. The DEGs analysis and PPI analysis showed that ASMP, BUB1, CENPF, MAD2L1, NCAPG, SGO2, and TOP2A were expressed higher in UCEC tissues than in the normal tissues. Immune cell infraction analysis showed that hub genes were associated with the tumor microenvironment (TME). Conclusion. Altogether, our research identified two distinct DNA damage subtypes that are complicated and heterogeneous. A better knowledge of the characteristics of the TME may be gained by quantitative measurement of DNA damage subtypes in individual patients, which can also lead to the development of more successful treatment regimens.

Comprehensive Analysis Reveals USP45 as a Novel Putative Oncogene in Pan-Cancer

Background: Deubiquitinating enzymes specifically removes ubiquitin molecules from ubiquitin-tagged target proteins, thereby inhibiting the degradation of target proteins and playing an important role in tumor. However, the mechanism of deubiquitinating enzyme USP45 in tumors remains unclear.

Methods: Based on the RNA-seq data of tissues and cell lines in The Cancer Genome Atlas (TCGA) database, GTEx and CCLE database, the pan-cancer analysis of USP45 expression and survival outcome were performed using R software and Kaplan-Meier Plotter. The structural variants, gene mutations and gene copy number alteration of USP45 were analyzed using the TCGA Pan-Cancer Atlas Studies dataset in the cBioPortal database. The relationships between USP45 and mRNA methylation, tumor heterogeneity, tumor stemness, and tumor immunity were performed by Sangerbox platform and TIMER2.0 using Pearson correlation analysis. Through the ENCORI database and string database, we constructed the ceRNA regulatory mechanism and protein-protein interaction network for USP45. Based on the RNA-seq data in TCGA and GTEx databases, we also constructed the downstream regulatory network for USP45 using the Limma and ClusterProfiler packages of R software. At last, the protein expression levels of USP45 were detected by immunohistochemistry in tumor tissue microarrays.

Results: USP45 is upregulated in most types of tumors and negatively correlated with the overall survival and recurrence-free survival of patient. Furthermore, the structural variation, gene mutations and gene copy number variation of USP45 were identified in different types of tumors. The pan-cancer analysis showed that USP45 was closely related to mRNA methylation, tumor heterogeneity and tumor stemness. In most types of tumors, the expression of USP45 was positively correlated with many immune checkpoint molecules and immune regulators such as PD-L1, while negatively correlated with the infiltration levels of NK cells, Th1 cells, macrophages, and dendritic cells in the tumor microenvironment. Finally, we constructed the ceRNA regulatory network, protein-protein interaction network and downstream regulatory network for USP45 in different types of tumors.

Conclusion: Our study firstly explored the putative oncogenic role of USP45 in pan-cancer, and provided insights for further investigation of USP45.

Plasma-Based Measurements of Tumor Heterogeneity Correlate with Clinical Outcomes in Metastatic Colorectal Cancer

Sequencing circulating tumor DNA (ctDNA) from liquid biopsies may better assess tumor heterogeneity than limited sampling of tumor tissue. Here, we explore ctDNA-based heterogeneity and its correlation with treatment outcome in STEAM, which assessed efficacy and safety of concurrent and sequential FOLFOXIRI-bevacizumab (BEV) vs. FOLFOX-BEV for first-line treatment of metastatic colorectal cancer. We sequenced 146 pre-induction and 89 post-induction patient plasmas with a 198-kilobase capture-based assay, and applied Mutant-Allele Tumor Heterogeneity (MATH), a traditionally tissue-based calculation of allele frequency distribution, on somatic mutations detected in plasma. Higher levels of MATH, particularly in the post-induction sample, were associated with shorter progression-free survival (PFS). Patients with high MATH vs. low MATH in post-induction plasma had shorter PFS (7.2 vs. 11.7 months; hazard ratio, 3.23; 95% confidence interval, 1.85−5.63; log-rank p < 0.0001). These results suggest ctDNA-based tumor heterogeneity may have potential prognostic value in metastatic cancers.

Quantification of tumor heterogeneity: from data acquisition to metric generation

Tumors are unique and complex ecosystems, in which heterogeneous cell subpopulations with variable molecular profiles, aggressiveness, and proliferation potential coexist and interact. Understanding how heterogeneity influences tumor progression has important clinical implications for improving diagnosis, prognosis, and treatment response prediction. Several recent innovations in data acquisition methods and computational metrics have enabled the quantification of spatiotemporal heterogeneity across different scales of tumor organization. Here, we summarize the most promising efforts from a common experimental and computational perspective, discussing their advantages, shortcomings, and challenges. With personalized medicine entering a new era of unprecedented opportunities, our vision is that of future workflows integrating across modalities, scales, and dimensions to capture intricate aspects of the tumor ecosystem and to open new avenues for improved patient care.

Intratumor Heterogeneity as a Prognostic Factor in Solid Tumors: A Systematic Review and Meta-Analysis

Background

The landscape of intratumor heterogeneity (ITH) is present from the tumor evolution. ITH is a promising clinical indicator, but the association between ITH and prognosis remains controversial. Therefore, a meta-analysis was performed to explore whether ITH can serve as a valuable prognostic indicator in solid tumors.

Methods

All included studies were from PubMed, Embase, Cochrane, and Web of Science databases up to October 10, 2020. Studies based on ITH with available prognostic information were included. Three researchers independently completed study selection and data extraction following PRISMA guidelines. The random-effect model was used for synthesis. Hazard ratio (HR) and 95% confidence intervals (CI) were used with the endpoint defined by overall survival (OS), disease-specific survival (DFS), and progression-free survival (PFS).

Results

A total of 9,804 solid tumor patients from 21 studies were included. Analysis of specific cancers in the TCGA database showed similar results based on different ITH assessment methods, which provided the logical support for data consolidation. Available evidence revealed a negative relationship between ITH and prognosis for a specific cancer (such as lung cancer). However, the OS results from 14 tumor types showed that high ITH associated with shorter survival time [HR 1.65 (95% CI, 1.42-1.91)]. PFS and DFS analyses showed similar results [HR 1.89 (95% CI, 1.41-2.54) and HR 1.87 (95% CI, 1.15-3.04)] in general. The status of tumor metastasis and sampling models were not the confounding factors.

Conclusions

High ITH is associated with worse prognosis in many solid tumors in general although this association was absent for some cancers. ITH is expected to be a promising clinical prognostic factor for the improvement of assessment, treatment, and surveillance strategy.

Towards multi-omics characterization of tumor heterogeneity: a comprehensive review of statistical and machine learning approaches

The multi-omics molecular characterization of cancer opened a new horizon for our understanding of cancer biology and therapeutic strategies. However, a tumor biopsy comprises diverse types of cells limited not only to cancerous cells but also to tumor microenvironmental cells and adjacent normal cells. This heterogeneity is a major confounding factor that hampers a robust and reproducible bioinformatic analysis for biomarker identification using multi-omics profiles. Besides, the heterogeneity itself has been recognized over the years for its significant prognostic values in some cancer types, thus offering another promising avenue for therapeutic intervention. A number of computational approaches to unravel such heterogeneity from high-throughput molecular profiles of a tumor sample have been proposed, but most of them rely on the data from an individual omics layer. Since the heterogeneity of cells is widely distributed across multi-omics layers, methods based on an individual layer can only partially characterize the heterogeneous admixture of cells. To help facilitate further development of the methodologies that synchronously account for several multi-omics profiles, we wrote a comprehensive review of diverse approaches to characterize tumor heterogeneity based on three different omics layers: genome, epigenome and transcriptome. As a result, this review can be useful for the analysis of multi-omics profiles produced by many large-scale consortia. Contact:sunkim.bioinfo@snu.ac.kr.