Adjacent Normal Tissue Samples Research Articles

Identification of a novel cuproptosis-related gene signature and integrative analyses in patients with lower-grade gliomas.

BackgroundCuproptosis is a newly discovered unique non-apoptotic programmed cell death distinguished from known death mechanisms like ferroptosis, pyroptosis, and necroptosis. However, the prognostic value of cuproptosis and the correlation between cuproptosis and the tumor microenvironment (TME) in lower-grade gliomas (LGGs) remain unknown.MethodsIn this study, we systematically investigated the genetic and transcriptional variation, prognostic value, and expression patterns of cuproptosis-related genes (CRGs). The CRG score was applied to quantify the cuproptosis subtypes. We then evaluated their values in the TME, prognostic prediction, and therapeutic responses in LGG. Lastly, we collected five paired LGG and matched normal adjacent tissue samples from Sun Yat-sen University Cancer Center (SYSUCC) to verify the expression of signature genes by quantitative real-time PCR (qRT-PCR) and Western blotting (WB).ResultsTwo distinct cuproptosis-related clusters were identified using consensus unsupervised clustering analysis. The correlation between multilayer CRG alterations with clinical characteristics, prognosis, and TME cell infiltration were observed. Then, a well-performed cuproptosis-related risk model (CRG score) was developed to predict LGG patients’ prognosis, which was evaluated and validated in two external cohorts. We classified patients into high- and low-risk groups according to the CRG score and found that patients in the low-risk group showed significantly higher survival possibilities than those in the high-risk group (P<0.001). A high CRG score implies higher TME scores, more significant TME cell infiltration, and increased mutation burden. Meanwhile, the CRG score was significantly correlated with the cancer stem cell index, chemoradiotherapy sensitivity–related genes and immune checkpoint genes, and chemotherapeutic sensitivity, indicating the association with CRGs and treatment responses. Univariate and multivariate Cox regression analyses revealed that the CRG score was an independent prognostic predictor for LGG patients. Subsequently, a highly accurate predictive model was established for facilitating the clinical application of the CRG score, showing good predictive ability and calibration. Additionally, crucial CRGs were further validated by qRT-PCR and WB.ConclusionCollectively, we demonstrated a comprehensive overview of CRG profiles in LGG and established a novel risk model for LGG patients’ therapy status and prognosis. Our findings highlight the potential clinical implications of CRGs, suggesting that cuproptosis may be the potential therapeutic target for patients with LGG.

Proteomic methods identified P75 as marker of poor prognosis in pleuropulmonary blastoma

ObjectivesTo study the causes of the rapid progression of pleuropulmonary blastoma and to identify molecular markers related to its prognosis. Materials and methodsThree pairs of fresh frozen samples of pleuropulmonary blastoma tumors and adjacent normal tissues were analyzed for proteomics, focusing on the protein molecules with significantly increased expression in tumor tissues and related to the cell cycle and DNA replication. The top five protein molecules were selected and verified by immunohistochemistry. To analyze the correlation between the expression of verified protein molecules in pleuropulmonary blastoma and early recurrence/metastasis of pleuropulmonary blastoma. ResultsCompared with the adjacent normal tissues, 1759 proteins were upregulated and 967 proteins were downregulated in pleuropulmonary blastoma. The top five proteins related to the cell cycle and DNA replication were ORC2, P75, Skp2, MCM4 and PCNA. However, only P75, MCM4 and PCNA were upregulated in pleuropulmonary blastoma as determined by immunohistochemistry. Further analysis showed that the expression of P75 in the recurrence/metastasis group was significantly higher than that in the no recurrence/metastasis group, while the expression of MCM4 and PCNA was not significantly different between the recurrence/metastasis group and the no recurrence/metastasis group. ConclusionsMCM4, PCNA and P75 may all play an important role in the progression of pleuropulmonary blastoma. Among them, P75 is related to the prognosis and may be used as a marker to predict the prognosis of pleuropulmonary blastoma.

Ferroptosis-Related lncRNA for the Establishment of Novel Prognostic Signature and Therapeutic Response Prediction to Endometrial Carcinoma.

Background Ferroptosis is a recently described form of intentional cellular damage that is iron-dependent and separate from apoptosis, cellular necrosis, and autophagy. It has been demonstrated to be adequately regulated by long noncoding RNAs (lncRNAs) in various cancers. However, the predictive profile of ferroptosis-related lncRNAs (FRLs) in endometrial carcinoma (EC) is unknown. Herein, FRLs associated with uterine corpus endometrial carcinoma (UCEC) prognosis were screened to predict treatment response in EC. Methods Samples of EC and adjacent normal tissues were obtained from The Cancer Genome Atlas (TCGA) dataset repository. Limma and survival packages in R software were used to screen FRLs associated with the prognosis of EC. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) chord and circle plots of FRLs were also plotted. Next, FRLs screened by the least absolute shrinkage and selection operator (LASSO) method were applied to construct and validate a multivariate Cox proportional risk regression model. Nomogram plots were created to forecast the outcome of UCEC patients, and gene set enrichment analysis (GSEA), principal component analysis (PCA), and immunoassays were performed on the prognostic models. Finally, limma, ggpubr, pRRophetic, and ggplot2 programs were used for drug sensitivity analysis of the prognostic models. Results A signature based on nine FRLs (CFAP58-DT, LINC00443, EMSLR, HYI-AS1, ADIRF-AS1, LINC02474, CDKN2B-AS1, LINC01629, and LINC00942) was constructed. The developed FRL prognostic model effectively discriminated UCEC patients into low-risk and high-risk groups. Immunological checkpoints CD80 and CD40 were strongly expressed in the high-risk group. In addition, the nine FRLs were all more expressed in the high-risk group compared to the low-risk group. Conclusion These findings significantly contribute to the understanding of the function of FRLs in UCEC and provide promising therapeutic strategies for UCEC.

Abstract 754: Pan cancer analysis of germline variants in the CPTAC dataset highlights their significance and functional importance in cancer development

Abstract Germline variants play important roles in cancer susceptibility and development, but identification and interpretation of germline variation impacting cancer risk remains a challenge. There has been little research on the impact of germline variants on somatic global/phosphoproteomics landscapes, and even less so for their impact on other tumor characteristics. Here we use cross-cancer genomic, transcriptomic, and global/phosphoproteomics datasets from CPTAC (Clinical Proteomic Tumor Analysis Consortium) to study the global impact of germline variants (common and rare) and the differential impact of germline vs somatic mutations on oncogenic processes, the tumor microenvironment (TME), and treatment responses. We analyze data from 1,093 cases across 10 cancer types (BR, CO, OV, EC, HNSCC, PDAC, LSCC, LUAD, GBM, and ccRCC). We investigate the impact of rare pathogenic variants across cancer types, discerning cancer-gene associations enriched with predisposing variants. We find novel instances of cancer-gene associations not found using TCGA data alone. Loss of heterozygosity (LOH) analyses of germline variants show that BRCA2 may be involved in predisposition not only in BRCA and OV, but also in LUAD. We also see ATM LOH events in ccRCC, CO, and LSCC. Analyses of the effects of germline vs somatic mutations on gene, protein, and phosphoprotein expression revealed significantly different protein expression levels in carriers of pathogenic germline variants vs somatic mutations, notably within core MMR-genes (MSH2, MSH6, PMS2). We examine trans-effects of germline variants on interacting proteins using proteomic/phosphoproteomic data, finding multiple trans-effects of BRCA1 pathogenic variants on the phosphoprotein expression of genes known to interact with BRCA1. Finally, we explore associations of germline and somatic causal events, noting proteomic and mutational signatures indicative of mutational processes taking place in the cell. We see direct relationships between mutations in core gene members of key DNA-damage response pathways (mismatch repair, homologous recombination repair) and the respective signatures associated with those pathways’ deficiencies. Additional analyses include: quantitative trait locus (QTL) analyses to assess the impact of common germline variants on gene (eQTL), protein (pQTL), and phosphoprotein levels (phQTL); analyses of the impact of germline variants on protein signaling during oncogenesis by mapping germline variants to post-translational modification sites); impact of germline variants on the immune landscape and tumor microenvironment (TME); and analyses of paired tumor and Normal Adjacent Tissue (NAT) samples for the discovery of immunogenic germline variants. This study illustrates the broad influence of germline variants on mutational landscapes and their functional impact on cancer. Citation Format: Fernanda Martins Rodrigues, Nadezhda V. Terekhanova, Oncogenic Drivers, Pathways Group, Clinical Proteomic Tumor Analysis Consortium, Gad Getz, Li Ding. Pan cancer analysis of germline variants in the CPTAC dataset highlights their significance and functional importance in cancer development [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 754.

Abstract 5710: Identification of convergent gene repression mechanisms through integrative genomic and DNA methylation analysis in TRACERx

Abstract Introduction: Lung TRACERx is a prominent study employing multi-region and longitudinal multi-omics sequencing to unravel the evolutionary trajectories of lung cancer. Aberrant DNA methylation patterns have been widely described in nearly all human cancers, yet their interplay with DNA mutations in lung cancer is not well understood. Incorporating the contribution of epigenetic modifications to cancer evolution trajectories within TRACERx could improve our understanding of the intricate relationship between genetic and epigenetic changes in non-small cell lung cancer (NSCLC) evolution. Methods: Multi-region sampling from 38 TRACERx patients including 112 tumor regions and 37 matched normal adjacent tissue samples was performed. Reduced representation bisulfite sequencing (RRBS) was performed to assess DNA methylation and the CAMDAC (Larose-Cadieux et al, 2020) was applied to estimate purified tumor methylation rates and correct for copy number changes. Whole exome sequencing and somatic copy number alterations (SCNAs) were inferred using the ASCAT tool (Van Loo et al, 2010) and Methsig (Pan et al, 2021) was performed to discover new methylation driver genes. Results: Using multi-region sequencing, we identified ubiquitous hypermethylation of 29 known driver genes in both lung adenocarcinoma (LUAD) and squamous cell lung cancer (LUSC), together with an additional 9 and 27 genes exclusive to LUSC and LUAD, respectively. We also identified 13 and 7 driver genes non-ubiquitously hypermethylated exclusively in LUSC and LUAD, respectively. Using a differential methylation based approach, we describe a method to determine the extent of intra-tumor methylation heterogeneity akin to established ITH scores based on genomics data. In addition, we report the identification of novel subtype-specific methylation driver genes enriched in HOX family members which are related to cancer progression. Through integration of DNA methylation and genomic sequencing data, we identify parallel mechanisms contributing towards ubiquitous tumor suppressor gene alterations. At the patient level, multiple driver genes such as NSD1, GATA3 and MGA were subject to repression by both copy number loss and DNA hypermethylation. Finally, we describe dosage-compensation of genes such as the Notch ligands JAG2 and DLK1 that are proximal to amplified oncogenes and hypermethylated during tumor evolution. Conclusion: We describe the contribution of DNA methylation and genomic alterations to altering the landscape of NSCLC. Leveraging DNA methylation, we can determine the extent of convergent repression mechanisms in different regions of the same tumor, assess DNA methylation heterogeneity, and discover DNA methylation-based driver genes in NSCLC. Citation Format: Francisco Gimeno-Valiente, Carla Castignani, Elizabeth Larose-Cadieux, Kezhong Chen, Nana Mensah, Olga Chervova, Thomas Watkins, Pawan Dhami, Heli Vaikkinen, Andrew Feber, TRACERx Consortium, Jonas Demeulemeester, Miljana Tanic, Stephan Beck, Peter Van Loo, Charles Swanton, Nnennaya Kanu. Identification of convergent gene repression mechanisms through integrative genomic and DNA methylation analysis in TRACERx [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5710.

Abstract 3353: Discovery and validation of orphan noncoding RNA profiles across multiple cancers in TCGA and two independent cohorts

Abstract Small non-coding RNAs (sncRNAs) have established roles as post-transcriptional regulators of cancer pathogenesis. We recently reported a novel and previously unannotated class of cancer-specific sncRNAs in breast cancer and demonstrated that breast cancer cells exploit a specific sncRNA to promote cancer metastasis. However, the extent to which these sncRNAs, which we have collectively termed orphan non-coding RNAs (oncRNAs), are present in other cancer types is unknown. To address this question and define a high-confidence set of oncRNAs, we used smRNA-seq data from 6 cancer sites (breast, colorectal, kidney, liver, lung, and stomach) and their corresponding normal tissues from The Cancer Genome Atlas (TCGA; 4,445 cancer, 431 normal) and identified a total of 144,695 oncRNAs that are significantly present in cancer and largely absent in normal tissue (Fisher’s Exact Test and Benjamini-Hochberg correction, FDR &amp;lt; 0.1). To evaluate if this set of TCGA-derived oncRNAs could be validated in independent datasets, we examined smRNA-seq data from two large independent cohorts comprising these same cancer and normal tissue types (Indivumed, Hamburg, Germany). Cohort A consists of 4,024 samples (2,245 cancer, 1,779 normal) and cohort B consists of 2,874 samples (2,063 cancer; 811 normal). oncRNAs in these cohorts were annotated following the same procedure used for TCGA data. TCGA-derived oncRNAs were considered validated in the independent cohorts if they were present in a significantly higher number of cancer samples compared to adjacent normal tissue samples. In cohort A, 140,191 (96.9%) of TCGA-derived oncRNAs were detected in at least one sample, of which 74,634 (51.6%) were validated as oncRNAs. In cohort B, 140,147 (96.9%) oncRNAs were observed and 68,366 (47.2%) were validated. The degree of overlap between the validated oncRNAs in each cohort was significant, with 54,294 (37.5%) overlapping oncRNAs (hypergeometric test, P=0). We also found that oncRNAs are informative of cancer tissue of origin, demonstrating the existence of consistent cancer-specific oncRNA expression profiles in independent studies. Using the TCGA-derived oncRNAs as features, we trained an eXtreme Gradient Boosting (XGB) model on TCGA data to classify cancer samples by the 6 tissues of origin. The TCGA-trained model showed high performance when evaluated on both cohorts A and B, achieving accuracies of 91.5% (95% CI: 90.3%-92.7%) and 96% (94.7%-97%), respectively. For comparison, this model achieved an accuracy of 96% (94.5%-97.2%) on held-out TCGA data (80/20 train/test split). Our results show a robust validation of TCGA-derived oncRNAs in external, independently sourced and processed cancer tissue cohorts across a heterogeneous set of cancer sites. Our machine learning model also demonstrates that oncRNA profiles can be used to predict cancer tissue of origin with high generalizability and accuracy. Citation Format: Jeffrey Wang, Helen Li, Lisa Fish, Kimberly H. Chau, Patrick Arensdorf, Hani Goodarzi, Babak Alipanahi. Discovery and validation of orphan noncoding RNA profiles across multiple cancers in TCGA and two independent cohorts [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3353.

Abstract 1211: Allele-specific copy-number based deconvolution of bulk tumour RNA sequencing data from the TRACERx study

Abstract Introduction: Analyses of bulk RNA sequencing (RNA-seq) data are central to most large-scale tumour sequencing studies. Solid tumors often present a dynamic, heterogeneous environment consisting of both cancer (sub)clones and normal cells. Bulk expression data represent population averages and its interpretation is confounded by both normal cell contamination and somatic copy number alterations. Several computational methods to deconvolve tumour and normal expression profiles from bulk RNA-seq have been developed recently. However, these methods often rely on a set of cell-type-specific reference signatures and ignore the effect of copy number changes. Methods: To address these issues, we have developed a method that formalizes the relationship between allele-specific copy number, expression and sample purity to deconvolve the expression profiles of tumor and normal cells from bulk RNA-seq data in an unbiased manner. Our method was applied to sequencing data produced by the TRACERx consortium, a longitudinal study with multi-region whole-exome and RNA-seq of non-small-cell lung cancers. A total of 414 primary tumor regions and 140 adjacent normal tissue samples from 140 TRACERx patients with matched DNA and RNA sequencing data were processed. Results: Here, we were able to directly deconvolve a median of ~2,000 genes per sample and indirectly infer tumor and normal expression profiles of ~10,000 genes. The accuracy of the deconvolution was validated using in-silico mixtures of patient-derived tumour and normal cells and in regions with loss of heterogeneity (LOH) directly on the bulk sequencing data, where the total fraction of expression attributed to tumor cells can be computed directly using somatic mutations. Our method revealed a strong and constitutive genome-wide overexpression in cancer cells compared to admixed normal cells, this overexpression was more pronounced in lung squamous cell carcinoma than lung adenocarcinoma (p&amp;lt0.001). Multidimensional projection of the purified tumor and normal expression profiles together with the adjacent normal tissue showed clear separation between the purified expression profiles and notably, the deconvolved normal expression was more similar to the normal adjacent profiles than the purified tumor profiles. Conclusion: Overall, these results suggest that our method is able to accurately disentangle the expression of tumour and normal cells from bulk RNA-seq without any previous knowledge. It has potential applications in many studies that include matched RNA-seq and copy number data and can provide new insights functional characterization, the taxonomy of cancer, and tumor evolution. Citation Format: Carla Castignani, Jonas Demeulemeester, Elizabeth Larose Cadieux, Robert E. Hynds, David R. Pearce, Stefan C. Dentro, Peter Van Loo, Charles Swanton, TRACERx Consortium. Allele-specific copy-number based deconvolution of bulk tumour RNA sequencing data from the TRACERx study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1211.

Downregulation of PLK4 expression induces apoptosis and G0/G1-phase cell cycle arrest in keloid fibroblasts.

ObjectivesKeloids are benign fibroproliferative tumors that display many cancer‐like characteristics, such as progressive uncontrolled growth, lack of spontaneous regression, and extremely high rates of recurrence. Polo‐like kinase 4 (PLK4) was recently identified as a master regulator of centriole replication, and its aberrant expression is closely associated with tumorigenesis. This study aimed to investigate the expression and biological role of PLK4 in the pathogenesis of keloids.Materials and MethodsWe evaluated the expression of PLK4 in keloids and adjacent normal skin tissue samples. Then, we established PLK4 knockdown and overexpression cell lines in keloid fibroblasts (KFs) and normal skin fibroblasts (NFs), respectively, to investigate the roles of PLK4 in the regulation of proliferation, migration, invasion, apoptosis, and cell cycle in KFs. Centrinone B (Cen‐B), a highly selective PLK4 inhibitor, was used to inhibit PLK4 activity in KFs to evaluate the therapeutic effect on KFs.ResultsWe discovered that PLK4 was overexpressed in keloid dermal samples and KFs compared with adjacent normal skin samples and NFs derived from the same patients. High PLK4 expression was positively associated with the proliferation, migration, and invasion of KFs. Furthermore, knockdown of PLK4 expression or inhibition of PLK4 activity by Cen‐B suppressed KF growth, induced KF apoptosis via the caspase‐9/3 pathway, and induced cell cycle arrest at the G0/G1 phase in vitro.ConclusionsThese findings demonstrate that PLK4 is a critical regulator of KF proliferation, migration, and invasion, and thus, Cen‐B is a promising candidate drug for keloid treatment.

LncRNA PVT1 Regulates miR-1207-5p to Affect Colon Cancer Proliferation and Migration via the Wnt6/β-catenin2 Pathway.

Background: We aimed to evaluate the effects of lncRNA PTV1 on colon cancer proliferation and migration via the Wnt6/β-catenin2 pathway. Materials and Methods: A total of 117 colon cancer and normal adjacent tissue samples were collected. LncRNA PVT1 and miR-1207-5p expressions in these samples and colon cancer cell lines were detected by Quantitative reverse transcription-polymerase chain reaction (qRT-PCR). LncRNA PVT1-silencing cells and miR-1207-5p-overexpressing Caco-2-siPVT1 cells were constructed, respectively. The effects of lncRNA PVT1 silencing on cell proliferation were assessed by MTT and colony formation assays. The effects on invasion and migration were tested by Transwell and scratch assays respectively. The targeting regulatory relationship between miR-1207-5p and Wnt6 was analyzed by a dual-luciferase reporter assay. The relationship between lncRNA PVT1 and miR-1207-5p was studied by RNA-binding protein immunoprecipitation and RNA pull-down assays. The expressions of proteins in the Wnt6/β-catenin2 pathway were detected by Western blotting. Results: The lncRNA PVT1 mRNA expression in colon cancer tissue was significantly higher than that in normal adjacent tissue (p < 0.05). The expression in lncRNA PVT1-silencing cells was significantly down-regulated (p < 0.05). The colonies of Caco-2-siPVT1 cells decreased, accompanied by a reduced number of cells penetrating Matrigel and migration (p < 0.05). Compared with siPVT1 + NC group, the number of colonies and migration of siPVT1 + miR-1207-5p-overexpressing group increased significantly (p < 0.05). There was a targeting relationship between miR-1207-5p and PVT1. MiR-1207-5p had a targeted binding site with Wnt6. The protein expressions of Wnt6/β-catenin2 in Caco-2-siPVT1 group were significantly lower than those of control and Caco-2-siNC groups (p < 0.05). Conclusion: LncRNA PVT1 was highly expressed in colon cancer. It may enhance the proliferation and migration of colon cancer cells by up-regulating miR-1207-5p level and enhancing the Wnt6/β-catenin2 pathway.

Identification of Angiogenesis-Related Prognostic Biomarkers Associated With Immune Cell Infiltration in Breast Cancer.

Background: This study aimed to explore the prognostic value of angiogenesis-related genes (ARGs) and their association with immune cell infiltration (ICI) in breast cancer (BC). Methods: Transcriptome data of BC were obtained from the TCGA and GEO databases. Differentially expressed ARGs were identified by the limma package. The identification of key genes and construction of the risk score model were performed by univariate and multivariate Cox regression algorithms. The prognostic value of the risk score was assessed by ROC curves and nomogram. GO, KEGG pathway, and GSEA were used to investigate the biological functions of differentially expressed genes (DEGs), and CIBERSORT, ssGSEA, and xCell algorithms were performed to estimate the ICI in high-risk and low-risk groups. The correlations between prognostic biomarkers and differentially distributed immune cells were assessed. Moreover, a ceRNA regulatory network based on prognostic biomarkers was constructed and visualized by Cytoscape software. Results: A total of 18 differentially expressed ARGs were identified between tumor and adjacent normal tissue samples. TNFSF12, SCG2, COL4A3, and TNNI3 were identified as key prognostic genes by univariate and multivariate Cox regression analyses. The risk score model was further constructed based on the four-gene signature and validated in GSE7390 and GSE88770 datasets. ROC curves and nomogram indicated that the risk score had good accuracy for determining BC patient survival. Biological function analysis showed that DEGs in high- and low-risk groups had a high enrichment in immune-related biological processes and signaling pathways. Moreover, significantly different ICIs were found between high- and low-risk groups, such as memory B cells, CD8+ T cells, resting memory CD4+ T cells, follicular helper T cells, regulatory T cells, monocytes, M2 macrophages, and neutrophils, and each prognostic biomarker was significantly correlated with one or more immune cell types. Conclusion: The current study identified novel prognostic ARGs and developed a prognostic model for predicting survival in patients with BC. Furthermore, this study indicated that ICI may act as a bond between angiogenesis and BC. These findings enhance our understanding of angiogenesis in BC and provide novel guidance on developing therapeutic targets for BC patients.

Tumor Infiltration with CD20+CD73+ B Cells Correlates with Better Outcome in Colorectal Cancer.

Immunotherapy has become increasingly important in the treatment of colorectal cancer (CRC). Currently, CD73, also known as ecto-5′-nucleotidase (NT5E), has gained considerable interest as a potential therapeutic target. CD73 is one of the key enzymes catalyzing the conversion of extracellular ATP into adenosine, which in turn exerts potent immune suppressive effects. However, the role of CD73 expression on various cell types within the CRC tumor microenvironment remains unresolved. The expression of CD73 on various cell types has been described recently, but the role of CD73 on B-cells in CRC remains unclear. Therefore, we analyzed CD73 on B-cells, especially on tumor-infiltrating B-cells, in paired tumor and adjacent normal tissue samples from 62 eligible CRC patients. The highest expression of CD73 on tumor-infiltrating B-cells was identified on class-switched memory B-cells, followed by naive B-cells, whereas no CD73 expression was observed on plasmablasts. Clinicopathological correlation analysis revealed that higher CD73+ B-cells infiltration in the CRC tumors was associated with better overall survival. Moreover, metastasized patients showed a significantly decreased number of tumor-infiltrating CD73+ B-cells. Finally, neoadjuvant therapy correlated with reduced CD73+ B-cell numbers and CD73 expression on B-cells in the CRC tumors. As promising new immune therapies are being developed, the role of CD73+ B-cells and their subsets in the development of colorectal cancer should be further explored to find new therapeutic options.

Comparative analysis of tumor control gene and microRNA expression profiles in tumor and adjacent tissues in patients with colorectal cancer

Background. Various tumor control genes and microRNAs (miRNa) play an important role in the development and progression of colorectal cancer (CRC). the expression of these genes can differ significantly in tumor and adjacent healthy tissues. there is no exact data at what distance from the tumor the adjacent healthy tissue is located in terms of gene expression.The aim of the investigation was to study the tumor control genes (E2F3, TGFB, NFKB, KLF-12, EGFR and MMP9), as well as the microRNA genes (microRNA-15, -16, -21 and -210) expression profiles in tumor and adjacent healthy tissues.Material and methods. the study included 19 patients diagnosed with colorectal cancer. the tumor control genes (E2F3, TGFB, NFKB, KLF-12, EGFR and MMP9), as well as the miRNA genes (miRNA-15, -16, -21 and -210) expression levels were investigated in tumor and adjacent normal tissue samples taken during colonoscopy.Results. a decrease in the level of expression of E2F3 (median 3,73, Q1-Q3 2,64 Reu vs. median 6.5, Q1-Q3 6,39 REU, p=0,01) and miRNA-16 (median 2,83, Q1-Q3 4,74 REU vs. median 4,29, Q1-Q3 3,73 REU, p=0,027) and an increase in the expression level of miRNA-21 (median 2,64, Q1-Q3 1,38 REU vs. median 1,41, Q1-Q3 1,21 REU, p&lt;0,001) were found in tumor tissue compared to normal tissue of patients with CRC.Conclusion. significant differences in the E2F3, miRNA-16 and miRNA-21 gene expressions were revealed. an increased level of E2F3 and miRNA-16 expressions at a distance of 1–2 cm from the tumor may be a predictor of tumor recurrence and progression, and an increased miRNA-21 expression in tumor tissue as compared to adjacent tissue may be a negative prognostic factor. this information can be used in further clinical research.

Relative Telomere Length Change in Colorectal Carcinoma and Its Association with Tumor Characteristics, Gene Expression and Microsatellite Instability.

Simple SummaryTelomeres are specialized repeat DNA sequences at the ends of each chromosome. The primary function of telomeres is to prevent genomic instability. Telomeres shorten with every cell division. The role of relative telomere length (RTL) change in colorectal cancer (CRC) is not fully understood. We studied RTL in CRC tissue and adjacent normal tissue from the same patients (n = 165). We found significant shortening of RTL in cancer tissue, irrespective of age group, gender, tumor pathology, location and microsatellite instability (MSI) status. However, shortening was more pronounced in low-grade tumors and in the presence of MSI. Gene expression data suggested an association of telomere shortening with rapid cell division (upregulation of DNA replication and cyclin-dependent kinase genes), as well as downregulation of apoptosis genes. CRC tissue showed upregulation of some telomere maintenance genes.We compared tumor and adjacent normal tissue samples from 165 colorectal carcinoma (CRC) patients to study change in relative telomere length (RTL) and its association with different histological and molecular features. To measure RTL, we used a Luminex-based assay. We observed shorter RTL in the CRC tissue compared to paired normal tissue (RTL 0.722 ± SD 0.277 vs. 0.809 ± SD 0.242, p = 0.00012). This magnitude of RTL shortening (by ~0.08) in tumor tissue is equivalent to RTL shortening seen in human leukocytes over 10 years of aging measured by the same assay. RTL was shorter in cancer tissue, irrespective of age group, gender, tumor pathology, location and microsatellite instability (MSI) status. RTL shortening was more prominent in low-grade CRC and in the presence of microsatellite instability (MSI). In a subset of patients, we also examined differential gene expression of (a) telomere-related genes, (b) genes in selected cancer-related pathways and (c) genes at the genome-wide level in CRC tissues to determine the association between gene expression and RTL changes. RTL shortening in CRC was associated with (a) upregulation of DNA replication genes, cyclin dependent-kinase genes (anti-tumor suppressor) and (b) downregulation of “caspase executor”, reducing apoptosis.

2D-DIGE-Based Proteomic Profiling with Validations Identifies Vimentin as a Secretory Biomarker Useful for Early Detection and Poor Prognosis in Oral Cancers.

Oral tongue squamous cell carcinoma (OTSCC) is an aggressive cancer with high morbidity and mortality rates, despite multimodality management. There are currently no clinically relevant molecular markers that identify patients at higher risk of recurrence and failure. We undertook 2D-DIGE proteomic profiling to study the differentially expressed proteins in OTSCC evaluating their role in prognosis. 2D-DIGE coupled with tandem mass spectrometry was performed on tissues obtained from early staged OTSCC along with its paired apparently adjacent normal tissue samples (n = 10). Top upregulated protein was validated using immunohistochemistry (n = 345), comprising of retrospective early stage OTSCC (n = 150) and prospective series of oral precancers, normal, and oral cancers (n = 195). Saliva samples collected from oral cancer and precancer samples were analyzed by ELISA (n = 146). We found statistically significant differential expression in 151 proteins out of 700 proteins quantified. Top ten differentially regulated proteins were identified using mass spectrometry analysis. We found vimentin, the mesenchymal protein, to be the most upregulated protein in tongue tumor tissues compared to adjacent apparent normal tissues. Vimentin was found to be significantly overexpressed in oral precancers along with cancers compared to normal tissues. The vimentin expression correlated significantly with differentiated states of oral precancers and cancers. Vimentin was also detected at significantly higher levels in saliva collected from oral precancer and cancer patients compared to normal healthy volunteers. Validation of vimentin in an independent series of retrospective early staged OTSCC showed that the vimentin expression is significantly associated with treatment failures and poorer DFS. The vimentin expression is useful as both poor prognostic and early detection marker in oral cancer. Vimentin detection in saliva can be a diagnostic test to detect oral precancers that may have malignant potential, needing closer follow-up, and disease monitoring.

A microarray expression profile and bioinformatic analysis of circular RNA in human esophageal carcinoma

Recent studies indicate that non-coding circular RNAs (circRNAs) are involved in the development of esophageal carcinoma (EC). This study aimed to identify differential expression of circRNAs in EC, which can provide potential biomarkers and therapeutic targets for EC treatment and improve the understanding of tumorigenesis mechanism. First, samples (n=5) of EC tissues and adjacent normal tissue were sent for circRNA microarray detection, Second, further bioinformatic analysis was performed, including circRNA-microRNA (miRNA), co-expression network analysis, Spearman correlation test, and cancer-related circRNA-miRNA axis analysis. Finally, the expression of circRNA that our analysis predicted to be hub genes was verified in samples (n=15) of EC tissues and adjacent normal tissue by real-time polymerase chain reaction (RT-PCR). Microarray identified 102 upregulated and 67 significantly downregulated circRNAs were in EC patients' tumors relative to adjacent normal tissue. One upregulated circRNA (hsa_circRNA_401955) showed the most connection with MREs, therefore was regarded as the hub gene by the Spearman correlation test. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses showed that four primary pathways (mRNA surveillance, cytoskeleton actin regulation, spliceosome, and the NOD-like receptor signaling pathway) were predicted in the hub circRNA's five connected miRNA response elements (MREs). Furthermore, cancer-related circRNA-miRNA axis analyses showed that hsa_circRNA_100375 and its four connected MREs participated in the cancer-related pathway. RT-PCR showed that hsa_circRNA_100375 and hsa_circRNA_401955 were significantly increased in the tumor tissues of EC patients. Abnormal expression of circRNAs was involved in the tumorigenesis of EC. Key circRNAs, namely hsa_circRNA_401955 and hsa_circRNA_100375, may be as potential biomarkers and therapeutic targets for the treatment of EC.

Identification of Prognostic Genes in Hepatocellular Carcinoma.

BackgroundPrevious studies have demonstrated the important role of tumor stem cells (TSCs) in the development of hepatocellular carcinoma (HCC); however, TSC-related genetic markers have not been investigated.AimThe aim of the present study was to identify stem cell-related signature genes to predict the prognosis of HCC, using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases.MethodsIn total, 423 liver HCC tissue samples, including 373 tumor and 50 adjacent normal tissue samples from TCGA, and 115 primary tumor and 52 adjacent non-tumor tissue samples from the GEO GSE76427 database, were used in the present study. The non-negative matrix factorization (NMF) algorithm, t-distributed stochastic neighbor embedding (t-SNE) algorithm and Cox regression analysis were combined for model construction and validation.ResultsOverall, six clusters were identified using the NMF and t-SNE algorithms with 470 stem cell-related genes. The results demonstrated that patients in cluster 5 had the worst prognosis. For multivariate Cox survival analysis, 15 genes with optimal lambda values were chosen and eight genes were incorporated into the final regression model using the optimal Akaike information criterion value. Validation of the risk model using the aforementioned eight signature genes demonstrated the models strong reliability and stable predictive performance.ConclusionThe results of the present study indicated that the eight-gene (Hes family BHLH transcription factor 5, KIT ligand, methyltransferase-like 3, proteasome 26S subunit non-ATPase 1, Ras-related protein Rab-10, treacle ribosome biogenesis factor 1, YTH N6-methyladenosine RNA binding protein 2 and Zinc Finger CCCH-Type Containing 13) signature constructed by the model may be reliable in predicting the prognosis of patients with HCC.

LncRNA HOTAIR promotes proliferation, invasion and migration in NSCLC cells via the CCL22 signaling pathway.

Long noncoding RNA (LncRNA) is a new type of regulatory RNA. LncRNA HOX antisense intergenic RNA (HOTAIR), as an oncogene in non-small cell lung cancer (NSCLC), is one of the key determinants of tumor progression. However, its possible molecular mechanism and the immunomodulatory pathway involved in NSCLC are still unclear. This study aims to explore whether HOTAIR promotes proliferation, migration and invasion of the NSCLC cells by inhibiting the expression of C-C Motif Chemokine Ligand 22 (CCL22). We collected 30 clinical samples of cancer and adjacent normal tissues from the patients with NSCLC, using real-time quantitative polymerase chain reaction (RT-qPCR) to detect the LncRNA HOTAIR and CCL22 mRNA expression in tissues. Immunohistochemistry was used to detect the protein expression of CCL22 in cancer and adjacent normal tissues. Cell experiments were conducted to verify that LncRNA HOTAIR regulates the expression of CCL22 and participates in the progress of NSCLC. The antisense oligonucleotide (ASO) probe interfering with LncRNA HOTAIR and the interference fragment of CCL22 (si-CCL22) were constructed. A549 cells were co-transfected with ASO-HOTAIR and si-CCL22. We used RT-qPCR to detect the expression of LncRNA HOTAIR and CCL22 mRNA in the cells, enzyme-linked immunosorbent assay (ELISA) used to detect the CCL22 protein level in the cell supernatant. 3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium (MTS) assay was applied to detect cell proliferation, the Flow cytometry to detect cell apoptosis. Finally, the Transwell test was utilized to detect cell migration and invasion. In conclusion, this study suggests that HOTAIR may promote proliferation, migration and invasion of the NSCLC cells by inhibiting CCL22 expression, which may play a key role in NSCLC cell immunity.

DLC1 Is a Prognosis-Related Biomarker Correlated With Tumor Microenvironment Remodeling in Endometrial Carcinoma.

Background and AimDeleted in liver cancer 1 (DLC1) is confirmed as a metastasis suppressor gene in endometrial carcinoma (EC). However, its functional mechanisms remain unclear. This study aimed to explore the relationship between DLC1 expression and EC.MethodsThe Cancer Genome Atlas database was used for evaluating the expression of DLC1 in pan-cancer. CIBERSORT was used to assess the relationship between DLC1 and tumor immune infiltration. We applied real-time quantitative polymerase chain reaction to determine the expression of DLC1 in EC and adjacent normal tissue samples. The targeting endogenous protein levels were assessed using the dataset from the cBioPortal database.ResultsDLC1 expression negatively correlated with the clinical characteristics (clinical stage, histologic grade) and positively correlated with the survival of patients with uterine corpus EC (UCEC). The gene set enrichment analysis displayed that the low-expression DLC1 group was enriched in metabolic pathways. Concomitantly, the high-expression DLC1 group was enriched in tumor immune-related activities. The CIBERSORT analysis showed that the number of resting memory CD4 T cells and resting mast cells positively correlated with DLC1 expression, while the number of macrophages M2 had a negative correlation, indicating that DLC1 played a key role in mediating immune cell infiltration. The target gene validation confirmed that DLC1 expression was downregulated in tumor samples. The target protein level was consistently downregulated in tumor samples.ConclusionsDLC1 levels might be useful in predicting the prognosis of patients with UCEC and especially governing the status of tumor microenvironment transition from immune-dominant to metabolic-dominant. The findings shed a different light on the immune therapeutics of UCEC.

Development of a prognostic pyroptosis-related gene signature for head and neck squamous cell carcinoma patient

ObjectiveHead and neck squamous cell carcinoma (HNSCC) is a major threat to public health. Pyroptosis is a form of inflammatory programmed cell death that is still incompletely understood. The role of pyroptotic cell death in HNSCC remains to be fully defined. As such, the present study was developed to explore the potential prognostic utility of a pyroptosis-related gene (PRG) signature in HNSCC.MethodsPRG expression patterns and the associated mutational landscape in HNSCC were analyzed, after which a 6-gene prognostic model was constructed through least absolute shrinkage and selection operator (LASSO) and Cox regression analyses using the TCGA dataset, followed by validation with two GEO datasets (GSE41643 and GSE65858). The relative expression of the genes in the prognostic model was assessed via RT-qPCR in tumor and paired adjacent normal tissue samples from a 32-patient cohort. Potential predictors of patient outcomes associated with this 6-gene model were identified through topological degree analyses of a protein–protein interaction network. Moreover, the prognostic value of NLRP3 as a predictor of HNSCC patient prognosis was established through immunohistochemical (IHC) analyses of samples from 176 HNSCC patients. Lastly, in vitro studies were performed to further demonstrate the relevance of NLRP3 in the context of HNSCC development.ResultsDifferentially expressed PRGs were able to readily differentiate between HNSCC tumors and normal tissues. Risk scores derived from the 6-gene PRG model were independent predictors of HNSCC patient prognosis, and genes that were differentially expressed between low- and high-risk groups were associated with tumor immunity. RT-qPCR assays also showed the potential protective role of NLRP3 in HNSCC patients. IHC analyses further supported the value of NLRP3 as a predictor of HNSCC patient outcomes. Invasion and migration assays demonstrated the potential role of NLRP3 in the inhibition of HNSCC development.ConclusionsOverall, these results highlight a novel prognostic gene signature that offers value in the context of HNSCC patient evaluation, although additional research will be essential to elucidate the mechanisms linking these PRGs to HNSCC outcomes.

MetaNetwork Enhances Biological Insights from Quantitative Proteomics Differences by Combining Clustering and Enrichment Analyses.

Interpreting proteomics data remains challenging due to the large number of proteins that are quantified by modern mass spectrometry methods. Weighted gene correlation network analysis (WGCNA) can identify groups of biologically related proteins using only protein intensity values by constructing protein correlation networks. However, WGCNA is not widespread in proteomic analyses due to challenges in implementing workflows. To facilitate the adoption of WGCNA by the proteomics field, we created MetaNetwork, an open-source, R-based application to perform sophisticated WGCNA workflows with no coding skill requirements for the end user. We demonstrate MetaNetwork's utility by employing it to identify groups of proteins associated with prostate cancer from a proteomic analysis of tumor and adjacent normal tissue samples. We found a decrease in cytoskeleton-related protein expression, a known hallmark of prostate tumors. We further identified changes in module eigenproteins indicative of dysregulation in protein translation and trafficking pathways. These results demonstrate the value of using MetaNetwork to improve the biological interpretation of quantitative proteomics experiments with 15 or more samples.