Deubiquitinases as prognostic biomarker and potential drug target for gynecological cancers.

Introduction: Immunotherapy has changed the standard of treatment for many cancers. However, only a small number of gynecologic cancer patients benefit from immunotherapy. The intratumoral immune landscapes are suggested as a predictor of the response to immunotherapies. However, there are no studies that provide a comprehensive immune characterization for gynecologic cancers. Aims: To characterize cellular compositions of the immune infiltrates and investigate if the immune landscape is a predictor for patient prognosis in gynecologic cancers. Methods: Clinical data for ovarian cancer, cervical cancer, and uterine cancer were downloaded via TCGA. RNA data are download from http://gdac.broadinstitute.org. Immune cell infiltration was analyzed using QuanTIseq and EPIC. Immune subtype clusters of patients were identified by Graph-based consensus clustering. Statistical analysis was conducted by GraphPad Prism. Results: Ovarian cancer had the highest percentage of total immune cells (approximately 21%, including CD4 T-Cells, Monocytes, CD8 T-Cells, Dendritic Cells, Macrophages, Neutrophils, NK Cells, B-Cells). Cervical cancer and uterine corpus endometrial carcinoma have lower percentages of immune cells with 17% and 16%, respectively. Furthermore, ovarian cancer had a significantly higher Monocyte and M2-liked Macrophage percentage, but lower percentage for CD8 T cells and Neutrophils compared to cervical cancer and uterine cancer. Cervical cancer had the highest percentage for M1-liked Macrophages and lowest CD4 T cells. Uterine cancer had the highest percentage of dendritic cells. In cervical cancer, higher cell infiltration of CD8 T-Cells and M2-liked Macrophages was associated with a better prognosis. In uterine cancer, patients with higher number of dendritic cells and CD8 T-Cells had significantly better clinical outcomes. However, higher CD4 T-cell infiltration was associated with poor prognosis in uterine cancer. Interestingly, patient’s survival was not affected by the infiltration of any individual immune cells which we analyzed in ovarian cancer. We identified and validated four immune subtypes associated with distinct immune cell infiltration in gynecologic cancers. Cervical and uterine cancer patients from an immune-desert subtype that had the least amount of lymphocyte infiltration and a high level of Monocyte had the worst prognosis. By contrast, Cervical and uterine cancer patients from an immune-warm subtype that had higher infiltration of CD8 T-cell, NK Cell, and dendritic cells had the best prognosis. However, the survival rate of ovarian cancer patients is similar among four different subtypes. Conclusion: Our study provides a conceptual framework to understand the tumor immune microenvironment of different gynecologic cancers. This work also suggests that the immune microenvironment should be considered for the design of combination treatment strategies and guiding the optimal selection of patients for immunotherapy. Citation Format: Wai Chung Chen, Tuo Hu, Chunbo He. The immune cell infiltration and landscape predicts clinical outcomes in gynecologic cancers [abstract]. In: Abstracts: AACR Virtual Special Conference: Tumor Immunology and Immunotherapy; 2020 Oct 19-20. Philadelphia (PA): AACR; Cancer Immunol Res 2021;9(2 Suppl):Abstract nr PO067.

Papillary thyroid cancer (PTC), accounting for more than 80 percent of all cases of thyroid cancer, is a form of a cancerous tumor that has a very favorable prognosis. However, patients diagnosed with PTC who are already in an advanced state have a dismal outlook. This study aimed to establish the diagnostic relevance of PRR15 expression in PTC patients as well as its levels in PTC samples and its connection with immune infiltrates. The TCGA and GEO datasets were combed through to obtain information on PTC patients. The “Limma” program was used to screen for differentially expressed mRNAs (DEMs), and the results were displayed using volcano plots and heat maps. The Wilcoxon test was used to examine the level of PRR15 expression in PTC patients in comparison with that of normal tissues. To study the connection between the immune infiltration level and PRR15 expression in PTC, the single-sample sequence set enrichment analysis (ssGSEA) from the R package was utilized. The expression of PRR15 was analyzed with RT-PCR in PTC cells and normal cells. In order to evaluate the diagnostic significance of PRR15 expression, ROC assays were carried out. Experiments using CCK-8 were carried out to investigate the impact that PRR15 knockdown could have on the proliferation of PTC cells. In this study, 17 overlapped DEMs between PTC specimens and normal specimens were identified, including MPPED2, IPCEF1, SLC4A4, PKHD1L1, DIO1, CRABP1, TPO, TFF3, SPX, TCEAL2, ZCCHC12, SYTL5, PRR15, CHI3L1, SERPINA1, GABRB2, and CITED1. Our attention focused on PRR15 which was highly expressed in PTC specimens as compared with nontumor specimens. PRR15 had an AUC value of 0.926 (95% CI 0.902–0.950) for PTC based on TCGA datasets. Pan-cancer assays suggested PRR15 as an oncogenic gene in many types of tumors. Moreover, we found that PRR15 expression was positively correlated with eosinophils, NK cells, NK CD56bright cells, IDC, macrophages, DC, mast cells, and Th1 cells. Further investigations with CCK-8 demonstrated that inhibiting PRR15 resulted in a decrease in the proliferation of PTC cells. Overall, PRR15 was confirmed to be a biomarker for PTC patients and a predictor of response to immunotherapy.

PurposeColorectal cancer (CRC) is the third most common cancer globally, necessitating novel biomarkers for early diagnosis and treatment. This study proposes an efficient pipeline leveraging an integrated bioinformatics and machine learning framework to enhance the identification of diagnostic and prognostic biomarkers for CRC.MethodsA selection of methylated differentially expressed genes (MeDEGs) and features (genes) was made using both statistical and Machine learning (ML) approaches from publically available datasets. These genes were subjected to STRING network construction and hub genes estimation, separately. Also, essential miRNAs (micro-RNAs) and TFs (Transcription factors) as regulatory elements were revealed and findings were validated through scRNA-seq analysis, promoter methylation, gene expression levels correlated with pathological stage, and interaction with tumor-infiltrating immune cells.ResultsThrough an integrated analysis pipeline, we identified 27 hub genes, among which CTNNB1, GSK3B, IL-1β, MYC, PXDN, TP53, EGFR, SRC, COL1A1, and TGBF1 showed better diagnostic behaviour. Machine learning approach includes the development of K-Nearest Neighbors (KNN), Artificial Neural Networks (ANN), and Random Forest (RF) models using TCGA datasets, achieving an accuracy range between 99 and 100%. The Area Under the Curve (AUC) value for each model is 1.00, signifying good classification performance. The high expression of some diagnostic genes was associated with poor prognosis, concluding IL-1β as both a prognostic and diagnostic biomarker. Additionally, the NF-κB and microRNAs (miR-548d-3p, miR-548-ac) and TFs (NFκB and STAT5A) play a major role in the comprehensive regulatory network for CRC. Furthermore, hub genes such as IL-1β, TGFB1, and COL1A1 were significantly correlated with immune infiltrates, suggesting their potential role in CRC progression.ConclusionOverall, the elevated expression of IL-1β coupled with abnormal DNA methylation, and its consequent effect on the PI3K/Akt signaling pathway are relevant prognostic and therapeutic marker in CRC. Additional molecular candidates reveal insights into the epigenetic regulatory targets of CRC and their association with immune cell infiltration.

Background: Gastric cancer (GC) is the second leading cause of cancer-related mortality and the fifth most common cancer worldwide. However, the underlying mechanisms of competitive endogenous RNAs (ceRNAs) in GC are unclear. This study aimed to construct a ceRNA regulation network in correlation with prognosis and explore a prognostic model associated with GC.Methods: In this study, 1,040 cases of GC were obtained from TCGA and GEO datasets. To identify potential prognostic signature associated with GC, Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) regression were employed. The prognostic value of the signature was validated in the GEO84437 training set, GEO84437 test set, GEO15459 set, and TCGA-STAD. Based on the public databases, TargetScan and starBase, an mRNA-miRNA-lncRNA regulatory network was constructed, and hub genes were identified using the CytoHubba plugin. Furthermore, the clinical outcomes, immune cell infiltration, genetic variants, methylation, and somatic copy number alteration (sCNA) associated with the ceRNA network were derived using bioinformatics methods.Results: A total of 234 prognostic genes were identified. GO and GSEA revealed that the biological pathways and modules related to immune response and fibroblasts were considerably enriched in GC. A nomogram was generated to provide accurate prognostic outcomes and individualized risk estimates, which were validated in the training, test dataset, and two independent validation datasets. Thereafter, an mRNA-miRNA-lncRNA regulatory network containing 4 mRNAs, 22 miRNAs, 201 lncRNAs was constructed. The KCNQ1OT1/hsa-miR-378a-3p/RBMS1 ceRNA network associated with the prognosis was obtained by hub gene analysis and correlation analysis. Importantly, we found that the KCNQ1OT1/miR-378a-3p/RBMS1 axis may play a vital role in the diagnosis and prognosis of GC patients based on Cox regression analyses. Furthermore, our findings demonstrated that mutations and sCNA of the KCNQ1OT1/miR-378a-3p/RBMS1 axis were associated with increased immune infiltration, while the abnormal upregulation of the axis was primarily a result of hypomethylation.Conclusion: Our findings suggest that the KCNQ1OT1/miR-378a-3p/RBMS1 axis may be a potential prognostic biomarker and therapeutic target for GC. Moreover, such findings provide insights into the molecular mechanisms of GC pathogenesis.

IntroductionImmunogenic cell death (ICD) is the phenomenon in which tumor cells undergo the transition from a non-immunogenic state to an immunogenic state upon their demise as a result of external stimuli. While ICD systems have been widely adopted in oncological research, their specific utilization for Uterine Corpus Endometrial Carcinoma (UCEC) investigations has received comparatively little attention.MethodsThe ICD score was assessed using single-sample gene set enrichment analysis (ssGSEA). Differentially expressed genes (DEGs) were identified from transcriptomic data processed with the "DESeq2" R package. A prognostic model was then developed by integrating these DEGs with clinical variables. The immune landscape was characterized through multiple bioinformatics approaches, and immunotherapy response was predicted using the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm. Additionally, drug sensitivity analysis was performed based on the Genomics of Drug Sensitivity in Cancer (GDSC) database.ResultsIn this study, we calculated ICD scores based on 74 ICD-related genes to explore the role of ICD in UCEC progression. We observed that patients with higher ICD scores exhibited a more favorable prognosis, and the score showed a positive correlation with mutation burden (r=0.16, P<0.001). Then we identified 587 upregulated DEGs and 153 downregulated DEGs in high-ICD group compared to low-ICD group. The former was predominantly associated with immune pathways, which was validated in GEO dataset. Using the 64 common DEGs obtained from both TCGA and GEO datasets, we developed a prognostic model specifically tailored for UCEC patients, incorporating five optimal prognostic genes (CD52, SLC30A3, ST8SIA5, STAT1 and TRBC1). Furthermore, the inclusion of clinical factors (stage and ICD score) significantly enhanced the model's predictive ability. The ICD score exhibited positive correlations with immune cell infiltration, as verified by ESTIMATE, xCell, TIMER, MCPcounter, EPIC, and IPS algorithms. Finally, we found that hyper-immunogenicity may be sensitive to immunotherapy and certain drugs (AZD5991, Ibrutinib, Osimertinib, AGI-5198, Savolitinib, Sapitinib, AZ960, AZD3759 and Ruxolitinib), while PCI-34051 and Vorinostat showed sensitivity in patients with hypo-immunogenicity.DiscussionOur results demonstrate that ICD plays an important role in UCEC progression, suggesting that ICD-related markers could serve as potential targets for prognosis and treatment.

Upregulation of B3GNT3 is associated with immune infiltration and activation of NF-κB pathway in gynecologic cancers

Carnitine palmitoyltransferase 2 as a novel prognostic biomarker and immunoregulator in colorectal cancer.

Accumulating evidence demonstrated that FOXD1 dysregulation was correlated with a broad spectrum of malignancies. However, litter is known about the role of FOXD1 in the progression of lung squamous cell carcinoma (LUSC). We conducted the comprehensive bioinformatics analysis to investigate FOXD1 expression in LUSC from TCGA and GEO datasets, and validated the FOXD1 expression pattern in clinical samples using immunohistochemistry method. ESTIMATE and CIBERSORT algorithms were performed to assess the relationship of FOXD1 and tumor microenvironment and immune cell infiltration. Our study showed that FOXD1 expression was significantly upregulated in LUSC tissues in TCGA dataset, validated by GEO datasets and clinical samples. In TCGA dataset, Kaplan-Meier curves showed that high FOXD1 expression was significantly correlated with favorable prognosis in LUSC patients. Moreover, FOXD1 expression has an impact on immune score and the proportions of immune cell infiltration subgroups. Finally, we predicted FOXD1 may be involved in many immune-related biological functions and cancer-related signaling pathways. Taken together, FOXD1 was upregulated in LUSC tissues, and FOXD1 expression could be a potential prognostic marker. FOXD1 might be associated with tumor microenvironment and perhaps a potential target in the tumor immunotherapy.

Increasing evidence supports that immune cell infiltration (ICI) patterns play a key role in the tumor progression of lung squamous cell carcinoma (LUSC). However, to date, the immune infiltration picture of LUSC has not been elucidated. TCGA was used to download multiomics data from LUSC samples. At the same time, we included two datasets on lung squamous cell carcinoma, GSE17710 and GSE157010. To reveal the landscape of tumor immune microenvironment (TIME), the ESTIMATE algorithm, ssGSEA approach, and CIBERSORT analysis are used. To quantify the ICI pattern in a single tumor, consistent clustering is used to determine the LUSC subtype based on the ICI pattern, and principal component analysis (PCA) is used to obtain the ICI score. The prognostic value of the Kaplan-Meier curves is confirmed. GSEA (Gene Set Enrichment Analysis) was used to perform functional annotation. To investigate the immunotherapeutic effects of the ICI score, the immunophenotyping score (IPS) is used. Finally, analyze the mutation data with the "maftools" R package. We identified four different immune infiltration patterns with different prognosis and biological characteristics in 792 LUSC samples. The identification of ICI patterns in individual tumors developed under ICI-related characteristic genes based on the ICI score helps to analyze the biological process, clinical results, immune cell infiltration, immunotherapy effects, and genetic variation. Immune failure is indicated by a high ICI score subtype marked by immunosuppression. Patients with low ICI scores have an abundance of efficient immune cells, which corresponds to the immunological activation phenotype and may have therapeutic benefits. The immunophenotypic score was used as a surrogate indicator of immunotherapy results, and samples with low ICI scores obtained significantly higher immunophenotypic scores. Finally, the relationship between the ICI score and tumor mutation burden (TMB) was proven. This study fully clarified the indispensable role of the ICI model in the complexity and diversity of TIME. The quantitative identification of ICI patterns in a single tumor will help draw the picture of TIME and further optimize precision immunotherapy.

E3 ubiquitin ligase FBXW11 as a novel inflammatory biomarker is associated with immune infiltration and NF-κB pathway activation in pancreatitis and pancreatic cancer

Background: Lung cancer continues to be one of the most fatal malignancies globally. Uncovering differentially expressed genes (DEGs) is crucial for advancing our understanding of tumor mechanisms and discovering new therapeutic targets. This study sought to identify key genes linked to prognosis and immune infiltration in lung cancer through the analysis of public gene expression datasets. Methods: We examined three microarray datasets from the Gene Expression Omnibus (GSE10072, GSE33356, and GSE18842) to detect DEGs between tumor and normal lung tissues. Functional enrichment was performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses to interpret the biological relevance of these genes. Protein-protein interaction (PPI) networks were constructed via STRING and visualized using Cytoscape to screen for central hub genes. The prognostic implications of the hub genes were investigated using Kaplan-Meier Plotter and TIMER2.0 based on data from The Cancer Genome Atlas (TCGA). PECAM1 expression levels and its relationship with immune cell infiltration were further explored using UCSC Xena. Results: A total of 477 DEGs were consistently identified across all three datasets. Among the top 10 down-regulated hub genes, PECAM1 was significantly reduced in tumor tissues. Lower PECAM1 expression was positively associated with better first-progression survival (FPS) in lung cancer patients. This gene was particularly suppressed in lung adenocarcinoma (LUAD) and showed strong correlations with immune cell infiltration. Co-expression analysis revealed that genes linked to PECAM1 are involved in immune-related pathways. Conclusions: Our findings highlight PECAM1 as a potential prognostic biomarker in lung cancer, especially in LUAD. Its association with immune infiltration and patient survival supports its possible utility in early detection and as a candidate for immunotherapy development.

Esophageal squamous cell carcinoma (ESCC) accounts for the main esophageal cancer type, which is related to advanced stage and poor survivals. Therefore, novel diagnostic biomarkers are critically needed. In the current research, we aimed to screen novel diagnostic biomarkers based on machine learning. The expression profiles were obtained from GEO datasets (GSE20347, GSE38129, and GSE75241) and TCGA datasets. Differentially expressed genes (DEGs) were screened between 47 ESCC and 47 nontumor samples. The LASSO regression model and SVM-RFE analysis were carried out for the identification of potential markers. ROC analysis was carried out to assess discriminatory abilities. The expressions and diagnostic values of the candidates in ESCC were demonstrated in the GSE75241 datasets and TCGA datasets. We also explore the correlations between the critical genes and cancer immune infiltrates using CIBERSORT. In this study, we identified 27 DEGs in ESCC: 5 genes were significantly elevated, and 22 genes were significantly decreased. Based on the results of the SVM-RFE and LASSO regression model, we identified five potential diagnostic biomarkers for ESCC, including GPX3, COL11A1, EREG, MMP1, and MMP12. However, the diagnostic values of only GPX3, MMP1, and MMP12 were confirmed in GSE75241 datasets. Moreover, in TCGA datasets, we further confirmed that GPX3 expression was distinctly decreased in ESCC specimens, while the expression of MMP1 and MMP12 was noticeably increased in ESCC specimens. Immune cell infiltration analysis revealed that the expression of GPX3, MMP1, and MMP12 was associated with several immune, such as T cells CD8, macrophages M2, macrophages M0, and dendritic cells activated. Overall, our findings suggested GPX3, MMP1, and MMP12 as novel diagnostic marker and correlated with immune infiltrates in ESCC patients.

BackgroundPancreatic cancer is one of the most commonly diagnosed and lethal malignancies worldwide and has few good biomarkers and therapeutic targets. GABRP is the π subunit of the gamma-aminobutyric acid (GABA) A receptor, which is expressed in a number of non-neuronal tissues. GABRP is significantly upregulated in pancreatic cancer, but its biological and immunological role as well as its clinical diagnostic and prognostic value in pancreatic cancer is still incompletely known.MethodsIn this study, pancreatic adenocarcinoma (PAAD) cohorts from TCGA and GEO datasets were used to compare GABRP mRNA levels in cancerous and normal tissues and protein expression was evaluated using immunohistochemistry. The Kaplan-Meier plotter and GEPIA2 database were used to analyze the correlation between GABRP expression, overall survival, and disease-free survival in pancreatic cancer patients. Gene set enrichment analysis (GSEA) was performed with the Linked Omics database to explore the molecular mechanisms of GABRP in pancreatic cancer. And the correlation between GABRP expression and immune infiltration was explored using the TIMER database, CIBERSORT database and ESTIMATE algorithm.ResultsGABRP mRNA was significantly overexpressed in TCGA-PAAD cohorts (P<0.0001) and enhanced GABRP expression predicted poorer overall survival according to Kaplan-Meier plotter database (P=0.0024) and GEPIA2 (P=0.038). Hypomethylation of promoter (P<0.01) and the regulation of hsa-miR-3655 may contribute to the overexpression of GABRP in pancreatic cancer. GSEA analysis revealed that GABRP played an important role in the immune response. GABRP expression was also correlated with immune infiltration and immune cell markers. Higher GABRP expression was significantly associated with greater infiltration of immune cells and stromal cells into pancreatic cancer microenvironments as well as higher expression of six important immune check point genes including PDCD1 (P<0.05), CD274 (P<0.05), CTLA4 (P<0.01), PDCD1LG2 (P<0.01), TIGHT (P<0.01) and TIM3 (P<0.01).ConclusionsGABRP is a potential prognostic biomarker and is correlated with immune infiltration and tumor microenvironment in pancreatic cancer. This suggests that GABRP may serve as a potential prognostic biomarker and therapeutic target in pancreatic cancer as well as a possible regulator of tumor microenvironment affecting the efficacy of immunotherapy. Further studies are needed to elucidate the molecular mechanism of the immunoregulatory role of GABRP.

Yes-associated protein-1 (YAP1) is an important effector of the Hippo pathway and has crosstalk with other cancer signaling pathways. It induces an immunosuppressive tumor microenvironment by activating pathways in several cellular components. However, the mechanisms by which it drives immune infiltration in pancreatic cancer remain poorly understood. We analyzed the expression of YAP1 as well as its prognostic value and correlations with immune infiltrates in various cancers, with a focus on pancreatic cancer. In particular, using the Oncomine database and Gene Expression Profiling Interactive Analysis (GEPIA) database, we found that YAP1 is differentially expressed between tumor tissues and control tissues in a number of cancers and in particular, is elevated in pancreatic cancer. Using the Kaplan–Meier plotter, GEPIA, and Long-term Outcome and Gene Expression Profiling database of pan-cancers (LOGpc), we further established the prognostic value of YAP1. We found that YAP1 expression was significantly related to outcomes in multiple types of cancer based on data from The Cancer Genome Atlas, particularly in pancreatic cancer. Correlations between YAP1 and immune cell infiltration and immune cell marker expression were examined using Tumor Immune Estimation Resource and GEPIA. High expression levels of YAP1 were significantly associated with a variety of immune markers and immune cell subsets in pancreatic cancer. These results suggest that YAP1 is correlated with patient outcomes and tumor immune cell infiltration in multiple cancer types and is a valuable prognostic biomarker in pancreatic cancer.

This study aimed to investigate the expression levels of the MKNK2 gene in pan-cancer, its prognostic significance, and its relationship with the tumor immune microenvironment, as well as to assess its potential as an immunological and prognostic biomarker. The research utilized data from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Cancer Cell Line Encyclopedia (CCLE), including clinical and mutational information. Bioinformatic tools were employed to analyze the association of MKNK2 with carcinogenesis, including its links to prognosis, immune cell infiltration, tumor immune microenvironment, gene mutation, and the stemness of various tumor cells. A variety of statistical software and analytical tools were applied, including R software, SPSS 27.0, TIMER, CIBERSORT algorithm, and EPIC algorithm. The study found that MKNK2 is abnormally expressed in pan-cancer and is associated with a poor prognosis. The levels of MKNK2 are highly related to immune cell infiltration and tumor stemness. Notably, in liver hepatocellular carcinoma, glioblastoma multiforme, low-grade gliomas, and acute myeloid leukemia, MKNK2 expression shows a strong correlation with clinical outcomes and immune infiltration. Furthermore, the expression of MKNK2 shows significant correlations with immune cell infiltration, immune checkpoints, tumor mutational burden (TMB), microsatellite instability (MSI), and stemness scores across various cancers. The abnormal expression of MKNK2 is associated with tumor progression, immune checkpoint genes, immune cell infiltration, microsatellite instability (MSI), tumor mutational burden (TMB), and stemness in a variety of tumors, especially in glioblastoma multiforme low-grade gliomas (GBMLGG). Therefore, MKNK2 may serve as a potent prognostic physiological marker and provide new avenues for the development of tumor mechanisms and therapeutic strategies targeting MKNK2 to enhance the efficacy of immunotherapy.