Cancer Genome Atlas Data Research Articles

Identification and validation of mitophagy-related signatures as a novel prognostic model for colorectal cancer.

Colorectal cancer (CRC) is one of the most commonly diagnosed cancers in the world. Mitophagy is associated with tumorigenesis and development of malignancy. However, the specific role of mitophagy has yet not been systematically explored in CRC. The RNA-sequencing dataset of CRC from The Cancer Genome Atlas (TCGA) and microarray data of gene expression profiles of CRC from Gene Expression Omnibus (GEO) were downloaded. Mitophagy-related gene sets were obtained from the Pathway Unification database. The package "limma" was used for differential gene expression analysis. Kaplan-Meier (KM) survival analyses were utilized to evaluate the prognostic value of the mitophagy regulators. Single-sample gene set enrichment analysis (ssGSEA) was used to estimate the infiltrating immune cells and the activity of immune response. The ConsensusClusterPlus algorithm was used to determine mitophagy-related subtypes. Principal component analysis (PCA) was used to create composite measurement of mitophagy scores. The R packages "survminer" and "ReGlot" were used to plot the nomogram and calibration curves. Integrated analysis of the GEO and TCGA databases revealed some common differentially expressed genes (DEGs) in CRC. MFN2, UBB, PINK1, and PRKN were significantly downregulated in CRC samples as compared to normal samples, and other genes were significantly upregulated in CRC samples. KM survival analyses showed that high expression of ATG12 and MAP1LC3B predicted a poor prognosis, whereas high expression of TOMM22 and TOMM40 predicted a better prognosis. Mitophagy showed significant correlation with immune-related pathways in CRC samples. We identified 2 distinct CRC subtypes with different mitophagy accumulation, of which subtype B had better prognosis and immune activity. The mitophagy score may be employed as a new and efficient clinical predictor in conjunction with other clinical indicators to predict the prognosis of CRC patients. We systematically investigated the CRC heterogeneity with reference to mitophagy based on bioinformatics analyses, and the findings of this study might provide some guidance for future research into potential biomarkers for diagnosis and prognosis prediction of CRC patients.

CCR10-mediated Enhancement of T Cell Trafficking for Improved Tumor Immunotherapy.

The effectiveness of adoptive T cell therapy for solid tumors remains suboptimal, partly attributed to insufficient T cell infiltration into the tumor site. A promising strategy involves directing T cells towards the tumor utilizing tumor-specific chemokine receptors. We analyzed chemokine receptor expression in activated T cells and chemokine expression in breast and lung cancer using The Cancer Genome Atlas (TCGA) data. Subsequently, we generated 1G4 T cell receptor-engineered T (TCR-T) cells with CCR10 and performed in vitro and in vivo efficacy tests. CCR10 exhibited insufficient expression in various human T cells. Analysis of TCGA RNA sequencing data revealed elevated expression of the chemokine CCL28, the corresponding chemokine for CCR10, in breast and lung cancer. Consequently, we generated CCR10-1G4 TCR-T cells. CCR10-1G4 dual expressing TCR-T cells exhibited comparable cellular cytotoxicity but increased mobility compared to 1G4 TCR-T cells in vitro. Furthermore, injecting CCR10-1G4 dual expressing TCR-T cells into a xenograft tumor model demonstrated enhanced in vivo trafficking and a greater reduction of tumor burden. This study highlights the potential of CCR10 for developing efficient adoptive T-cell treatments targeting solid tumors.

The 'Other' subfamily of HECT E3 ubiquitin ligases evaluate the tumour immune microenvironment and prognosis in patients with hepatocellular carcinoma.

Primary liver cancer is the sixth most common cancer and the third leading cause of cancer-related death worldwide. The role of the 'Other' subfamily of HECT E3 ligases (E3s) in hepatocellular carcinoma (HCC) remains unknown. The expression of the 'Other' HECT E3s was performed using The Cancer Genome Atlas (TCGA) data, and the authors found that the 'Other' HECT E3s were differentially expressed in HCC. Prognostic values were assessed using the Kaplan-Meier method and indicated that the high expressions of HECTD2, HECTD3, and HACE1 were associated with a worse clinical prognosis of HCC patients. The expression of HECTD2 was significantly correlated with the infiltration of CD4+ T cells and neutrophils. The levels of HECTD3 and HACE1 were notably related to the dendritic cells and memory B cells infiltrated in HCC. In addition, the three previously mentioned genes have shown to be associated with immune checkpoint genes, such as FOXP3, CCR8, STAT5B, TGFB1 and TIM-3. Moreover, HECTD2 could promote the proliferative activity, cell migration and invasive ability of HCC cells. Collectively, the authors' study demonstrated that HECTD2 was a novel immune-related prognostic biomarker for HCC, providing new insight into the treatment and prognosis of HCC.

Prognostic DNA mutation and mRNA expression analysis of perineural invasion in oral squamous cell carcinoma

This study analyzed oral squamous cell carcinoma (OSCC) genomes and transcriptomes in relation to perineural invasion (PNI) and prognosis using Cancer Genome Atlas data and validated these results with GSE41613 data. Gene set enrichment analysis (GSEA), gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes were conducted. We identified 22 DNA mutations associated with both overall survival (OS) and PNI. Among them, TGFBR1 and RPS6KA4 mRNAs were overexpressed, while TYRO3 and GPR137 mRNAs were underexpressed in PNI patients. Among the 141 mRNA genes associated with both OS and PNI, we found overlap with PNI-related DNA mutations, including ZNF43, TEX10, TPSD1, and PSD3. In GSE41613 data, TGFBR1, RPS6KA4, TYRO3, GPR137, TEX10 and TPSD1 mRNAs were expressed differently according to the prognosis. The 22 DNA-mutated genes clustered into nervous system development, regulation of DNA-templated transcription, and transforming growth factor beta binding. GSEA analysis of mRNAs revealed upregulation of hallmarks epithelial mesenchymal transition (EMT), TNFα signaling via NF-κB, and IL2 STAT5 signaling. EMT upregulation aligned with the TGFBR1 DNA mutation, supporting its significance in PNI. These findings suggest a potential role of PNI genes in the prognosis of OSCC, providing insights for diagnosis and treatment of OSCC.

Deep Learning-Based Identification of Tissue of Origin for Carcinomas of Unknown Primary Using MicroRNA Expression: Algorithm Development and Validation.

Carcinoma of unknown primary (CUP) is a subset of metastatic cancers in which the primary tissue source of the cancer cells remains unidentified. CUP is the eighth most common malignancy worldwide, accounting for up to 5% of all malignancies. Representing an exceptionally aggressive metastatic cancer, the median survival is approximately 3 to 6 months. The tissue in which cancer arises plays a key role in our understanding of sensitivities to various forms of cell death. Thus, the lack of knowledge on the tissue of origin (TOO) makes it difficult to devise tailored and effective treatments for patients with CUP. Developing quick and clinically implementable methods to identify the TOO of the primary site is crucial in treating patients with CUP. Noncoding RNAs may hold potential for origin identification and provide a robust route to clinical implementation due to their resistance against chemical degradation. This study aims to investigate the potential of microRNAs, a subset of noncoding RNAs, as highly accurate biomarkers for detecting the TOO through data-driven, machine learning approaches for metastatic cancers. We used microRNA expression data from The Cancer Genome Atlas data set and assessed various machine learning approaches, from simple classifiers to deep learning approaches. As a test of our classifiers, we evaluated the accuracy on a separate set of 194 primary tumor samples from the Sequence Read Archive. We used permutation feature importance to determine the potential microRNA biomarkers and assessed them with principal component analysis and t-distributed stochastic neighbor embedding visualizations. Our results show that it is possible to design robust classifiers to detect the TOO for metastatic samples on The Cancer Genome Atlas data set, with an accuracy of up to 97% (351/362), which may be used in situations of CUP. Our findings show that deep learning techniques enhance prediction accuracy. We progressed from an initial accuracy prediction of 62.5% (226/362) with decision trees to 93.2% (337/362) with logistic regression, finally achieving 97% (351/362) accuracy using deep learning on metastatic samples. On the Sequence Read Archive validation set, a lower accuracy of 41.2% (77/188) was achieved by the decision tree, while deep learning achieved a higher accuracy of 80.4% (151/188). Notably, our feature importance analysis showed the top 3 most important features for predicting TOO to be microRNA-10b, microRNA-205, and microRNA-196b, which aligns with previous work. Our findings highlight the potential of using machine learning techniques to devise accurate tests for detecting TOO for CUP. Since microRNAs are carried throughout the body via extracellular vesicles secreted from cells, they may serve as key biomarkers for liquid biopsy due to their presence in blood plasma. Our work serves as a foundation toward developing blood-based cancer detection tests based on the presence of microRNA.

Abstract B111: Developing prognostic signatures (ep-Sigs) through epigenetic predictive markers in pancreatic ductal adenocarcinoma (PDA) – A pilot study based on The Cancer Genome Atlas (TCGA) data

Abstract Current genomic testing (mutation profiling) has not proven beneficial for optimal treatment selection or for monitoring drug resistance, which is critical for neoadjuvant therapy (NAT) in borderline resectable/resectable PDA and first-line therapy for advanced PDA. Failure to administer an effective chemotherapy combination results in early progression of the disease, life-threatening complications, and a decline in the patient’s performance status, rendering them ineligible for further anti-cancer treatment. We hypothesized that our panel of epigenetic markers (DNA-methylation changes) is a surrogate for protein/gene expression and will develop an ep-Sigs that also has predictive value. The genes in this panel have been curated from extant literature and belong to one of two broad categories: one with known prognostic value (PvG) and the other with preclinical or clinical evidence of influencing one of the drugs (DrG) used in PDA management (gemcitabine (gem), 5-fluorouracil (5FU), oxaliplatin (Ox), irinotecan (Iri), nab-paclitaxel (NP), paclitaxel (Tax), and cisplatin (Cis)). Some genes/proteins were critical for more than one drug. To accomplish this, we tested the panel in the TCGA pancreatic adenocarcinoma (PAAD) data with 182 patients. For univariate survival analysis (UVA), we used Cox regression models for each marker. For multivariate survival analysis (MVA), we used penalized Cox regression models (i.e., LASSO, Elastic-Net) for identifying marker signatures. The area under the ROC Curve (AUC) was calculated to assess each signature’s predictive performance. We identified 301 cytosines followed by guanine residue (CpG) sites among the 67 genes (gem=35, 5FU=13, 5FU+gem=10, NP=6, Cis=6, and PvG=23) in the panel with significant (p<0.05) association with overall survival (OS) through UVA. We identified five ep-Sigs through MVA that could predict poor OS (p<0.05), which are a combination of markers from PvG and DrG. The number of markers and relevant DrG in them are as follows, 15 & Gem/Cis; 30 & Gem/5FU/Cis/Tax/Iri; 28 & Gem/5FU/NP/Cis/Tax/Iri; 23 & Gem/5FU; 29 & Gem/5FU/NP. We did a similar analysis for RNA-sequencing data from TCGA. We identified 17 markers and 9 signatures on UVA and MVA, respectively. Interestingly, the expression of 100/105 markers in the panel was significantly (p=4.19e-66) high in PDA compared to normal tissues, indicating this panel has diagnostic value too. We believe this project is the first step for this approach, and this concept needs to be evolved with prospective studies and larger samples. The ep-Sigs with a panel of epigenetic markers with a proven influence on resistance to frequently used drugs could help in developing meaningful models that change the approach to managing PDA, especially for NAT. Citation Format: Ashish Manne, Wancai Yang, Davendra Sohal, Ashwini Esnakula, Lianbo Yu. Developing prognostic signatures (ep-Sigs) through epigenetic predictive markers in pancreatic ductal adenocarcinoma (PDA) – A pilot study based on The Cancer Genome Atlas (TCGA) data [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Pancreatic Cancer; 2023 Sep 27-30; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(2 Suppl):Abstract nr B111.

Abstract A102: Secreted frizzled related-protein 2 is prognostic for survival in pancreatic cancer

Abstract INTRODUCTION: Secreted frizzled-related protein 2 (SFRP2) is overexpressed in several tumor types and blocks tumor apoptosis, increases angiogenesis, and promotes T-cell exhaustion. It has also been shown to be associated with fibrosis in non-neoplastic pathologies. We hypothesize SFRP2 is prognostic for survival and associated with PDAC fibrosis and mesenchymal transformation. METHODS: SFRP2 gene expression and protein levels in patients with PDAC were analyzed using NCI’s The Cancer Genome Atlas (TCGA) data and data from resected tumors analyzed for protein levels by western blot. Disease free survival (DFS) and overall survival (OS) were analyzed using Kaplan Meier test. The association of stroma content and SFPR2 RNA expression in human PDAC was studied using Spearman correlation between the ESTIMATE algorithm stromal score and TCGA data. The GEPIA tool was used to compare SFRP2 mRNA levels between PDAC and normal pancreas tissue. Immunohistochemistry (IHC) for SFRP2 in KPC PDAC tumors were correlated with fibrosis stains Picrosirus red and Massone trichome. The role of SFRP2 in mesenchymal transformation was assessed by western blot in fibroblasts. RESULTS: TCGA RNAseq analysis of SFRP2 expression showed highest SFRP2 mRNA levels in pancreatic cancer out of 32 cancer types. GEPIA analysis of PDAC (n=179) and normal pancreas (n=177) mRNA data showed higher levels of SFRP2 mRNA in PDAC compared to normal pancreas (p<.01). High SFRP2 mRNA levels were associated with decreased disease-free survival hazard ratio = 2.6; p=0.0097). PDAC tissue showed higher SFRP2 protein compared to matched normal-adjacent tissue (n=24, p=0.0003). High SFRP2 expression was associated with decreased OS (p=0.0158. The ESTIMATE stromal score correlated with SFRP2 expression in human PDAC (Spearman correlation coefficient 0.81). Fibrosis levels in KPC PDAC tumors correlated with SFRP2 IHC (Spearman correlation coefficient 0.75). Fibroblasts treated with SFRP2 and TGFβ showed higher mesenchymal markers compared to untreated control. CONCLUSIONS: SFRP2 is increased in PDAC, associated with tumor fibrosis, and prognostic for survival. SFRP2 is a novel therapeutic target in PDAC. Citation Format: Lillian Hsu, Julie Siegel, Patrick Nasarre, Rupak Mukherjee, Meghan Gormley, Bailey Richardson, Jordan Morningstar, Imran Khan, Eleanor Hilliard, John O’Bryan, Krist Helke, Laura Spruill, Nathan Dolloff, Nancy Klauber-Demore. Secreted frizzled related-protein 2 is prognostic for survival in pancreatic cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Pancreatic Cancer; 2023 Sep 27-30; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(2 Suppl):Abstract nr A102.

Adaptation of a mutual exclusivity framework to identify driver mutations within oncogenic pathways

Distinguishing genomic alterations in cancer-associated genes that have functional impact on tumor growth and disease progression from the ones that are passengers and confer no fitness advantage have important clinical implications. Evidence-based methods for nominating drivers are limited by existing knowledge on the oncogenic effects and therapeutic benefits of specific variants from clinical trials or experimental settings. As clinical sequencing becomes a mainstay of patient care, applying computational methods to mine the rapidly growing clinical genomic data holds promise in uncovering functional candidates beyond the existing knowledge base and expanding the patient population that could potentially benefit from genetically targeted therapies. We propose a statistical and computational method (MAGPIE) that builds on a likelihood approach leveraging the mutual exclusivity pattern within an oncogenic pathway for identifying probabilistically both the specific genes within a pathway and the individual mutations within such genes that are truly the drivers. Alterations in a cancer-associated gene are assumed to be a mixture of driver and passenger mutations with the passenger rates modeled in relationship to tumor mutational burden. We use simulations to study the operating characteristics of the method and assess false-positive and false-negative rates in driver nomination. When applied to a large study of primary melanomas, the method accurately identifies the known driver genes within the RTK-RAS pathway and nominates several rare variants as prime candidates for functional validation. A comprehensive evaluation of MAGPIE against existing tools has also been conducted leveraging the Cancer Genome Atlas data.

A novel aging-associated lncRNA signature for predicting prognosis in osteosarcoma

Osteosarcoma (OS) is one of the most prevalent bone tumors in adolescents, and the correlation between aging and OS remains unclear. Currently, few accurate and reliable biomarkers have been determined for OS prognosis. To address this issue, we carried out a detailed bioinformatics analysis based on OS with data from the Cancer Genome Atlas data portal and Human Aging Genomic Resources database, as well as in vitro experiments. A total of 88 OS samples with gene expression profiles and corresponding clinical characteristics were obtained. Through univariate Cox regression analysis and survival analysis, 10 aging-associated survival lncRNAs (AASRs) were identified to be associated with the overall survival of OS patients. Based on the expression levels of the 10 AASRs, the OS patients were classified into two clusters (Cluster A and Cluster B). Cluster A had a worse prognosis, while Cluster B had a better prognosis. Then, 5 AASRs were ultimately included in the signature through least absolute shrinkage and selection operator-Cox regression analysis. Kaplan‒Meier survival analysis verified that the high-risk group exhibited a worse prognosis than the low-risk group. Furthermore, univariate and multivariate Cox regression analyses confirmed that the riskScore was an independent prognostic factor for OS patients. Subsequently, we discovered that the risk signature was correlated with the properties of the tumor microenvironment and immune cell infiltration. Specifically, there was a positive association between the risk model and naïve B cells, resting dendritic cells and gamma delta T cells, while it was negatively related to CD8+ T cells. Finally, in vitro experiments, we found that UNC5B-AS1 inhibited OS cells from undergoing cellular senescence and apoptosis, thereby promoting OS cells proliferation. In conclusion, we constructed and verified a 5 AASR-based signature, that exhibited excellent performance in evaluating the overall survival of OS patients. In addition, we found that UNC5B-AS1 might inhibit the senescence process, thus leading to the development and progression of OS. Our findings may provide novel insights into the treatment of OS patients.

TCGAnalyzeR: An Online Pan-Cancer Tool for Integrative Visualization of Molecular and Clinical Data of Cancer Patients for Cohort and Associated Gene Discovery.

For humans, the parallel processing capability of visual recognition allows for faster comprehension of complex scenes and patterns. This is essential, especially for clinicians interpreting big data for whom the visualization tools play an even more vital role in transforming raw big data into clinical decision making by managing the inherent complexity and monitoring patterns interactively in real time. The Cancer Genome Atlas (TCGA) database's size and data variety challenge the effective utilization of this valuable resource by clinicians and biologists. We re-analyzed the five molecular data types, i.e., mutation, transcriptome profile, copy number variation, miRNA, and methylation data, of ~11,000 cancer patients with all 33 cancer types and integrated the existing TCGA patient cohorts from the literature into a free and efficient web application: TCGAnalyzeR. TCGAnalyzeR provides an integrative visualization of pre-analyzed TCGA data with several novel modules: (i) simple nucleotide variations with driver prediction; (ii) recurrent copy number alterations; (iii) differential expression in tumor versus normal, with pathway and the survival analysis; (iv) TCGA clinical data including metastasis and survival analysis; (v) external subcohorts from the literature, curatedTCGAData, and BiocOncoTK R packages; (vi) internal patient clusters determined using an iClusterPlus R package or signature-based expression analysis of five molecular data types. TCGAnalyzeR integrated the multi-omics, pan-cancer TCGA with ~120 subcohorts from the literature along with clipboard panels, thus allowing users to create their own subcohorts, compare against existing external subcohorts (MSI, Immune, PAM50, Triple Negative, IDH1, miRNA, metastasis, etc.) along with our internal patient clusters, and visualize cohort-centric or gene-centric results interactively using TCGAnalyzeR.

Novel quantitative immunohistochemistry method using histone H3, family 3B as the internal reference standard for measuring human epidermal growth factor receptor 2 expression in breast cancer.

Immunohistochemistry (IHC) is an essential technique in surgical and clinical pathology for detecting diagnostic, prognostic, and predictive biomarkers for personalized cancer therapy. However, the lack of standardization and reference controls results in poor reproducibility, and a reliable tool for IHC quantification is urgently required. The objective of this study was to describe a novel approach in which H3F3B (histone H3, family 3B) can be used as an internal reference standard to quantify protein expression levels using IHC. The authors enrolled 89 patients who had human epidermal growth factor receptor 2 (HER2)-positive breast cancer (BC). They used a novel IHC-based assay to measure protein expression using H3F3B as the internal reference standard. H3F3B was uniformly expressed at the protein level in all tumor regions in cancer tissues. HER2 expression levels were measured with the H-score using HALO software. Kaplan-Meier analysis indicated that, among patients who had HER2-positive BC in The Cancer Genome Atlas data set and the authors' data set, the subgroup with low HER2 expression had a significantly better prognosis than the subgroup with high HER2 expression. Furthermore, the authors observed that HER2 expression levels were precisely evaluated using the proposed method, which can classify patients who are at higher risk of HER2-positive BC to receive trastuzumab-based adjuvant therapy. Dual-color IHC with H3F3B is an excellent tool for internal and external quality control of HER2 expression assays. The proposed IHC-based quantification method accurately assesses HER2 expression levels and provides insights for predicting clinical prognosis in patients with HER2-positive BC who receive trastuzumab-based adjuvant therapy.

Histone methyltransferase SUV39H2 regulates apoptosis and chemosensitivity in prostate cancer through AKT/FOXO signaling pathway.

Prostate cancer (PCa) is one of the most common malignant tumors that exhibit both chemoresistance and recurrence. SUV39H2 is highly expressed in many types of human tumors, but its role in the development and progression of PCa has never been clarified. The aim of this study is to elucidate the role of SUV39H2 in the development and progression of PCa, its association with the AKT/FOXO signaling pathway, and its potential implications for PCa diagnosis and treatment. SUV39H2 expression was analyzed in The Cancer Genome Atlas (TCGA) and genotype tissue expression pan-cancer data. The TCGA database was evaluated for SUV39H2 enrichment and its correlation to immune cell infiltration. SUV39H2 levels in PCa tissues and control tissues were determined in 30 patients using qPCR and IHC. Clinical relevance was assessed via The Cancer Genome Atlas (TCGA). In vitro assessments including colony formation assays, Western Blot analysis, CCK-8 assays, and flow cytometry were utilized to establish SUV39H2's contribution to PCa cell growth. The influence of SUV39H2 on PC3 and DU145 cell proliferation was assessed through a cell line-derived xenograft model. Sphere formation assays and qPCR were employed to delineate SUV39H2's role in PCa stemness and chemosensitivity. In vitro macrophage polarization assays provided insights into SUV39H2's association with M2 macrophages, while enrichment analysis shed light on its role in FOXO signaling. PCa tissues expressed higher levels of SUV39H2 than normal tissues. By knocking down SUV39H2, PCa cells were made more chemosensitive to docetaxel and cell proliferation and stemness were inhibited. Additionally, SUV39H2 knockdown significantly inhibited in vivo PCa cell growth and inhibited the polarization of macrophages. Furthermore, SUV39H2 was found to regulate AKT/FOXO signaling by increasing Akt and FOXO3a phosphorylation. Our findings highlight SUV39H2's role in PCa cell apoptosis and chemosensitivity mainly by regulating the AKT/FOXO signaling pathway and suggest that SUV39H2 could be a potential target for PCa diagnosis and treatment.

Copper Chaperone for Superoxide Dismutase Subtypes as a Prognostic Marker in Luminal B Breast Cancer.

Copper chaperone for superoxide dismutase (CCS) is an essential component of the oxidation-reduction system. In breast cancer cells, CCS expression is highly up-regulated, which contributes to cellular proliferation and migration. Breast cancer is a multifaceted disease with different tumor prognoses and responses to clinical treatments, which may be associated with multiple molecular subtypes of CCS. The CCS expression patterns in breast cancer were investigated by TNMplot, cBioPortal, and HPA network database. The correlation of CCS expression with clinicopathological parameters was analyzed using the UALCAN database. The Cancer Genome Atlas (TCGA) data set was used to analyze the Clinical characteristics of CCS in luminal B patients. The bc-GenExMiner database was used to analyze the effects of BReast-CAncer susceptibility gene (BRCA)1/2, TP53 mutation status, estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor (HER) expression on CCS expression. The survival curves and prognostic value of CCS in luminal B breast cancer were performed through Kaplan-Meier curves, univariate and multivariate Cox regression using the PrognoScan, bc-GenExMiner, and Clinical bioinformatics analysis platform. We found that CCS expression was associated with patient age, race, ER, and PR status. We also discovered that BRCA1/2 mutations had an effect on CCS expression. The luminal B subtype had the highest CCS expression, which was linked to poor survival compared with other subtypes. In addition, Kaplan-Meier curve analysis showed that luminal B patients with high CCS mRNA expression showed a poor survival and the CCS gene is an independent predictor of outcome in patients with luminal B breast cancer by univariate and multivariate Cox regression. Our findings emphasize the significant expression of CCS in luminal B breast cancer and its potential as an autonomous prognostic determinant for this specific molecular subtype. These findings suggest that CCS holds promise as a prospective marker for the treatment of luminal B breast cancer.

Exploration of BRCA1 and BRCA2 mutations in gastric cancer patients through next generation sequencing: implications for diagnosis and therapy.

The study aims to characterize BRCA1/2 mutations in Pakistani gastric cancer (GC) patients, identifying unique pathogenic variants and evaluating their potential as diagnostic biomarkers, while also exploring therapeutic avenues for personalized treatment strategies. In this study, we investigated the role of Breast Cancer gene 1 (BRCA1) and Breast Cancer gene 2 (BRCA2) mutations in Pakistani GC patients and their functional implications using Next-Generation Sequencing (NGS). Through NGS, we identified a total of 19 mutations in BRCA1 and 11 mutations in BRCA2, all with high mutation quality scores. In silico analysis revealed one pathogenic mutation in BRCA1 and one in BRCA2, indicating a potential link to disease development. Notably, two pathogenic mutations (BRCA1 p.Ala1823Ser and BRCA2 p.Gln92fs) were exclusively observed in the Pakistani GC population, suggesting unique genetic markers. Further examination utilizing The Cancer Genome Atlas (TCGA) data confirmed the absence of these mutations in non-Pakistani GC samples, emphasizing their specificity. Sanger sequencing validated these findings. Real Time Quantitative PCR (RT-qPCR) analysis indicated significantly decreased BRCA1/2 gene expression in samples harboring pathogenic mutations, suggesting their potential as biomarkers for GC diagnosis. Immunohistochemistry corroborated this by showing reduced protein expression levels in mutated samples. Enrichment analysis highlighted associations of BRCA1/2 genes with DNA damage response pathways and cancer-related processes. DrugBank exploration revealed potential therapeutic agents targeting mutated BRCA1/2, such as Cisplatin and Estradiol, offering new avenues for treatment. Our study identifies novel BRCA1/2 mutations specific to Pakistani GC patients, suggesting a distinct genetic susceptibility profile. These findings not only contribute to understanding GC pathogenesis but also hold implications for personalized treatment strategies in this population.

A Pan-Cancer Analysis of the Immunological and Prognostic Role of BUB1 Mitotic Checkpoint Serine/Threonine Kinase B (BUB1B) in Human Tumors.

BUB1 mitotic checkpoint serine/threonine kinase B (BUB1B) is a member of the spindle assembly checkpoint family and is related to cancer disease progression, invasion, metastasis, and functional promotion of angiogenesis. Several studies have noted that the BUB1B gene is frequently upregulated in various types of cancers. However, the expression patterns of BUB1B across different cancer types and its diagnostic and prognostic potential have not been investigated from a pan-cancer perspective. The Cancer Genome Atlas (TCGA) data were used to explore the diagnostic and prognostic immunological potential of BUB1B in 33 cancer types. BUB1B was almost universally upregulated across all cancers, with increased protein expression in at least six cancer types and an enhanced phosphorylation level of S670 in two cancer types. Furthermore, BUB1B expression was negatively associated with clinical progression and prognosis in most cancers. BUB1B expression was positively associated with tumor mutational burden and microsatellite instability in 17 and 7 cancer types, respectively, and there was a correlation between BUB1B expression and DNA methylation at multiple probes in 30 cancer types. Additionally, a positive relationship existed between BUB1B expression and the infiltration levels of Th2, Tcm, and T helper cells, whereas BUB1B showed a negative correlation with the infiltration levels of other immune cells in multiple cancers. Moreover, functions associated with cell cycle progression and ubiquitin-mediated proteolysis were involved in the functional mechanism of BUB1B. Our pan-cancer study offers a comprehensive understanding of the role of BUB1B in tumorigenesis and tumor immunity across different types of cancer.

Comprehensive analysis identifies long non-coding RNA RNASEH1-AS1 as a potential prognostic biomarker and oncogenic target in hepatocellular carcinoma.

RNASEH1-AS1, a long non-coding RNA (lncRNA) divergently transcribed from the antisense strand of its neighboring protein-coding gene ribonuclease H1 (RNASEH1), has recently been demonstrated to be involved in tumor progression. However, the association between RNASEH1-AS1 and hepatocellular carcinoma (HCC) remains unclear. In the present study, first, the expression of RNASEH1-AS1 in HCC and its correlation with clinicopathological features, prognosis, diagnosis, immune cell infiltration of HCC patients was inspected using relevant R packages based on The Cancer Genome Atlas (TCGA) data. RNASEH1-AS1 was found to be up-regulated in most cancer types, including HCC, and its overexpression was significantly associated with histologic grade and AFP level as well as poor prognosis, and was an independent risk factor affecting overall survival with good diagnostic and prognostic values for HCC. RNASEH1-AS1 was inversely associated with the infiltration of most immune cell types, including plasmacytoid dendritic cells (pDC), B cells and neutrophils. Second, a total of 1109 positively co-expressed genes (PCEGs) of RNASEH1-AS1 were screened out in HCC by correlation analysis in batches (|Spearman's r| >0.4 and adjusted P value <0.01). GO and KEGG enrichment analysis indicated that PCEGs of RNASEH1-AS1 were mainly related to RNA processing, ribosome biogenesis, transcription and histone acetylation. The top 10 hub genes (EIF4A3, WDR43, WDR12, DKC1, NAT10, UTP18, DDX18, BYSL, DDX10, PDCD11) were identified by constructing the protein-protein interaction (PPI) network, and they were all highly expressed in HCC and positively correlated with histological grade. Third, a risk model was constructed based on four RNASEH1-AS1-related hub genes (EIF4A3, WDR12, DKC1, and NAT10) with good prognostic predictive potential via univariate Cox and the least absolute selection operator (LASSO) regression analysis. Fourth, experimental validation revealed that RNASEH1-AS1 was significantly elevated in HCC tissues and several cell lines, and its knockdown could suppress the proliferation, migration, and invasion of HCC cells. Finally, mechanistic studies demonstrated that the stability of RNASEH1-AS1 could be regulated by DKC1 via their direct interaction. Taken together, RNASEH1-AS1 may serve as a potential prognostic and diagnostic biomarker and oncogenic lncRNA for HCC.

NCDN is a Potential Biomarker and Therapeutic Target for Glioblastoma.

Background: Glioblastoma (GBM) is a type of central nervous system malignancy. In our study, we determined the effect of NCDN in GBM patients through The Cancer Genome Atlas (TCGA) data analysis, and studied the effects of NCDN on GBM cell function to estimate its potential as a therapeutic target. Methods: Gene expression profiles of glioblastoma cohort were acquired from TCGA database and analyzed to look for central genes that may serve as GBM therapeutic targets. Then the cell function of NCDN in glioblastoma cell was explored through in vitro cell experiments. Results: Through gene ontology (GO) analysis, weighted gene co-expression network analysis (WGCNA), and survival analysis, we identified three key genes (NCDN, PAK1 and SPRYD3) associated with poor prognosis in glioblastoma. In vitro experiments showed impaired cell migration, apoptosis, and cell cycle arrest in NCDN knockdown cells. Conclusion: NCDN affects the progress and prognosis of glioblastoma by promoting cell migration and inhibiting apoptosis.

Bioinformatic and clinical experimental assay uncovers resistance and susceptibility mechanisms of human glioblastomas to temozolomide and identifies new combined and individual survival biomarkers outperforming MGMT promoter methylation.

Glioblastoma (GBM) is the most aggressive and lethal central nervous system (CNS) tumor. The treatment strategy is mainly surgery and/or radiation therapy, both combined with adjuvant temozolomide (TMZ) chemotherapy. Historically, methylation of MGMT gene promoter is used as the major biomarker predicting individual tumor response to TMZ. This research aimed to analyze genes and molecular pathways of DNA repair as biomarkers for sensitivity to TMZ treatment in GBM using updated The Cancer Genome Atlas (TCGA) data and validate the results on experimental datasets. Survival analysis of GBM patients under TMZ therapy and hazard ratio (HR) calculation were used to assess all putative biomarkers on World Health Organization CNS5 reclassified TCGA project collection of molecular profiles and experimental multicenter GBM patient cohort. Pathway activation levels were calculated for 38 DNA repair pathways. TMZ sensitivity pathway was reconstructed using a human interactome model built using pairwise interactions extracted from 51,672 human molecular pathways. We found that expression/activation levels of seven and six emerging gene/pathway biomarkers served as high-quality positive (HR < 0.61) and negative (HR > 1.63), respectively, patient survival biomarkers performing better than MGMT methylation. Positive survival biomarkers were enriched in the processes of ATM-dependent checkpoint activation and cell cycle arrest whereas negative-in excision DNA repair. We also built and characterized gene pathways which were informative for GBM patient survival following TMZ administration (HR 0.18-0.44, p < 0.0009; area under the curve 0.68-0.9). In this study, a comprehensive analysis of the expression of 361 DNA repair genes and activation levels of 38 DNA repair pathways revealed 13 potential survival biomarkers with increased prognostic potential compared to MGMT methylation. We algorithmically reconstructed the TMZ sensitivity pathway with strong predictive capacity in GBM.

Relevance Analysis of TPM2 and Clinicopathological Characteristics in Breast Cancer.

The function of tropomyosin 2 (TPM2) in breast cancer is still far understudied. In this study, we aim to explore the roles of TPM2 in breast cancer progression. This research included 155 breast cancer tissues. The expression of TPM2 was analyzed by immunohistochemical staining and grading. The mRNA expression of TPM2 in pan-cancer was analyzed with The Cancer Genome Atlas (TCGA) data plate form. The differential expression of TPM2 protein and the differential promoter methylation level of TPM2 between breast cancer tissues and normal breast tissues were analyzed by the UALCAN online database. The relationship between TPM2 and signaling pathways was interpreted by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) pathway enrichment analyses. The survival curve of TPM2 was analyzed across the Kaplan-Meier plotter online database. Furthermore, the relationship between TPM2 expression and infiltrating macrophages was validated through in vitro co-culture experiments. TPM2 expression was significantly down-regulated in breast cancer samples. In addition, TPM2 expression was correlated with lymph node metastasis and high-grade histopathological morphology. The receiver operating characteristic (ROC) curve indicated that TPM2 expression could well distinguish between normal breast tissue and breast cancer tissue. TPM2 may have potential value in breast cancer diagnosis. Bioinformatics analysis illustrated that TPM2 was mainly involved in extracellular matrix organization, collagen fibril organization, cell junction assembly, focal adhesion, cAMP signaling pathway, estrogen signaling pathway, Wnt signaling pathway, and adaptive immune system. TPM2 expression was correlated with immune infiltrating cells and immune checkpoint molecules. Our in vitro co-culture experiments showed that the M2 macrophages could upregulate the expression of TPM2. TPM2 may play key roles in breast cancer occurrence and development, especially in cancer metastasis. TPM2 may be a potential biomarker for breast cancer diagnosis.

Pattern of Expression of MicroRNA in Patients with Radiation-Induced Bladder Injury

Introduction: Bladder cancer (BC) is sensitive to radiation treatment and a subset of patients experience radiation-induced injuries including shrinkage of bladder due to bladder fibrosis. Methods: This study is a retrospective cohort study. Three Japanese BC patients were randomly selected. Using a microRNA (miRNA) array, comparing their samples with or without radiation-induced injuries, we have checked the clustering of miRNA expression. Results: Hsa-miR-130a, hsa-miR-200c, hsa-miR-141, and hsa-miR-96 were found to be highly expressed (>50 times) in patients with fibrotic bladder shrinkage (FBS) compared to those with intact bladder (IB) function. In patients with FBS, hsa-miR-6835, hsa-miR-4675, hsa-miR-371a, and hsa-miR-6885 were detected to have lesser than half expression to IB patients. We have analyzed the significance of these genes in relation to overall survival of 409 BC patients retrieved from the Cancer Genome Atlas data set. All available cutoff values between the lower and upper quartiles of expression are used for the selected genes, and false discovery rate using the Benjamini-Hochberg method is computed to correct for multiple hypothesis testing. We have run combined survival analysis of the mean expression of these four miRNAs highly expressed in FBS patients. 175 patients with high expression had a longer median survival of 98.47 months than 23.73 months in 233 patients with low expression (hazard ratio [HR]: 0.53; 0.39–0.72, log-rank p value: 7.3e−0.5). Combination analysis of all 8 genes including hsa-miR-6835, hsa-miR-4675, hsa-miR-371a, and hsa-miR-6885 showed the same HR for OS. Target scanning for these miRNAs matched specific cytokines known as an early biomarker to develop radiation-induced fibrosis. Conclusions: BC patients with fibrotic radiation injury have specific miRNA expression profile targeting profibrotic cytokines and these miRNAs possibly render to favorable survival.