The Cancer Genome Atlas Program Research Articles

A spectrum of nonsense-mediated mRNA decay efficiency along the degree of mutational constraint

Despite its importance for regulating gene expression, nonsense-mediated mRNA decay (NMD) remains poorly understood. Here, we extend the findings of a previous landmark study that proposed several factors associated with NMD efficiency using matched genome and transcriptome data from The Cancer Genome Atlas Program (TCGA) by incorporating additional data including Genotype-Tissue Expression (GTEx), gnomAD, and metrics for mutational constraints. Factors affecting NMD efficiency are analyzed using an allele-specific expression (ASE)-based measure to reduce noise caused by random variations. Additionally, by combining our data with the updated allele frequency database of gnomAD, we demonstrate the spectrum of NMD efficiency according to the degree of gene-level mutational constraints (degree of a gene-tolerating loss-of-function variants). The NMD prediction model, trained on TCGA data, shows that gene-level mutational constraint is an important predictor of NMD efficiency. Findings of this study suggest the potential role of NMD on shaping the landscape of mutational constraints.

BOLA family genes are the drivers and potential biomarkers of survival in kidney renal clear cell carcinoma patients.

To analyze the diagnostic and prognostic potential of the BOLA gene family in kidney renal clear cell carcinoma (KIRC). This study is an observational study. The whole study was carried out at Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia from January to November 2023. As it involves in silico and in vitro methods, ethical consent was not required. Various in silico and molecular experimental techniques were employed in this study. Significant up-regulation and hypomethylation of BOLA1, BOLA2, and BOLA3 were observed across 12 KIRC cell lines. Retrospective analysis of the cancer genome atlas program (TCGA)-KIRC cohorts confirmed hypomethylation of these genes in KIRC tissues. The cBioPortal analysis showed minimal genetic alterations, with amplification being the most common. Kaplan-Meier plotter data revealed that high BOLA1, BOLA2, and BOLA3 expression correlated with shorter overall survival and relapse-free survival in KIRC. Tumor-immune system interactions database analysis linked BOLA1 expression to immune and molecular subtypes. Gene silencing of BOLA1, BOLA2, and BOLA3 in 786-0 cells reduced cell growth and proliferation, enhancing wound healing capacity. BOLA genes may serve as diagnostic and prognostic markers in KIRC, offering insights into therapeutic targets and disease progression.

Integration of transcriptomics and machine learning for insights into breast cancer: exploring lipid metabolism and immune interactions.

Breast cancer (BRCA) represents a substantial global health challenge marked by inadequate early detection rates. The complex interplay between the tumor immune microenvironment and fatty acid metabolism in BRCA requires further investigation to elucidate the specific role of lipid metabolism in this disease. We systematically integrated nine machine learning algorithms into 184 unique combinations to develop a consensus model for lipid metabolism-related prognostic genes (LMPGS). Additionally, transcriptomics analysis provided a comprehensive understanding of this prognostic signature. Using the ESTIMATE method, we evaluated immune infiltration among different risk subgroups and assessed their responsiveness to immunotherapy. Tailored treatments were screened for specific risk subgroups. Finally, we verified the expression of key genes through in vitro experiments. We identified 259 differentially expressed genes (DEGs) related to lipid metabolism through analysis of the cancer genome atlas program (TCGA) database. Subsequently, via univariate Cox regression analysis and C-index analysis, we developed an optimal machine learning algorithm to construct a 21-gene LMPGS model. We used optimal cutoff values to divide the lipid metabolism prognostic gene scores into two groups according to high and low scores. Our study revealed distinct biological functions and mutation landscapes between high-scoring and low-scoring patients. The low-scoring group presented a greater immune score, whereas the high-scoring group presented enhanced responses to both immunotherapy and chemotherapy drugs. Single-cell analysis highlighted significant upregulation of CPNE3 in epithelial cells. Moreover, by employing molecular docking, we identified niclosamide as a potential targeted therapeutic drug. Finally, our experiments demonstrated high expression of MTMR9 and CPNE3 in BRCA and their significant correlation with prognosis. By employing bioinformatics and diverse machine learning algorithms, we successfully identified genes associated with lipid metabolism in BRCA and uncovered potential therapeutic agents, thereby offering novel insights into the mechanisms and treatment strategies for BRCA.

Evaluation of albumin and lactate dehydrogenase in comparison with cytokeratin 19 fragments and neuron‐specific enolase as diagnostic biomarkers for non‐small cell lung cancer

AbstractBackgroundThe diagnosis of non‐small cell lung cancer (NSCLC) is a clinical issue that requires attention, and more practical and effective biomarkers need to be selected to assist in diagnosis. This study aimed to examine the diagnostic value of serum albumin (ALB), lactate dehydrogenase (LDH), cytokeratin 19 fragments (CYFRA21‐1), and neuron‐specific enolase (NSE) for NSCLC.MethodsThe clinical data of 1048 NSCLC patients and 1125 healthy subjects were extracted from electronic medical records. Receiver operating characteristic (ROC) curve analysis was performed to assess the diagnostic significance of ALB, LDH, CYFRA21‐1, and NSE for NSCLC. The Cancer Genome Atlas (TCGA) data, which included mRNA profiles for ALB and LDH expression, were acquired from TCGA Program. Finally, interactive survival scatter plots and survival analyses for NSCLC patients were evaluated using the Human Protein Atlas and Kaplan–Meier Plotter.ResultsSignificant differences were noted in the levels of ALB and LDH between NSCLC patients and healthy controls. The areas under the ROC curves (AUCs) for ALB and LDH were 0.754 (95% CI: 0.734–0.774) and 0.681 (95% CI: 0.658–0.704), respectively. Moreover, the combination of ALB and LDH raised the AUC to 0.804 (95% CI: 0.785–0.823), and the incorporation of CYFRA21‐1 and NSE further increased the AUC to 0.903 (95% CI: 0.890–0.916). Notably, ALB and LDH might be related to the overall survival of NSCLC patients.ConclusionThis study revealed that ALB and LDH in NSCLC patient serum could improve the diagnostic accuracy of conventional biomarkers for NSCLC.

Prognostic value of a modified pathological staging system for gastric cancer based on the number of retrieved lymph nodes and metastatic lymph node ratio.

The prognosis for gastric cancer (GC) remains grim, underscoring the importance of accurate staging and treatment. Given the potential benefits of using lymph node ratio (LNR) for improved prognostication and treatment planning, it is critical to incorporate examined lymph nodes (ELN) count in an integrated GC staging system. Patients data from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015 was utilized as training set. The Mantel-Cox survival test was used to calculate chi-square values for 40 LNR segments with a 0.025 interval, defining a novel LNR-based N (rN) classification based on the cutoff points. A revised AJCC (rAJCC) staging system was established by replacing the 8th AJCC N staging with a rN classification. The relationship between the ELN count and prognosis or positive lymph node detection was conducted by using multivariable models. The series of the odds ratios and hazard ratios were fitted with a locally weighted scatterplot smoothing (LOWESS) smoother, and the structural break points were determined by Chow test to clarify an optimal minimum ELN count. The integrated GC staging system incorporated both rAJCC system and the ideal ELN count. Discriminatory ability and prognostic homogeneity of the rAJCC and integrated staging system was compared with AJCC staging system in the SEER validation set (2016-2017), the Cancer Genome Atlas Program (TCGA) database, and the Third Affiliated Hospital of Sun Yat-sen University database. The current study found that LNR and ELN count are both significantly associated with the prognosis of GC patients (HR = 0.98, p < 0.001 and HR = 2.51, p < 0.001). Four peaks of the chi-square value were identified as LNR cut-off points at 0.025, 0.175, 0.45 and 0.6 to define a novel rN stage. In comparison to the 8th AJCC staging system, the rAJCC staging system demonstrated significant prognostic advantages and discriminatory ability in the training set (5-Y OS AUC: 71.7 vs. 73.0; AIC: 57,290.7 vs. 57,054.9). The superiority of the rAJCC staging system was confirmed in all validation sets. Using a LOWESS smoother and Chow test, a threshold ELN count of 30 was determined to maximum improvement in the prognosis of node-negative patients without downgrading due to potential metastasis, while also maximizing the detection efficiency of at least one involved lymph node. The integrated staging system, combining the refined rAJCC classification with an optimized ELN count threshold, has demonstrated superior discriminatory performance compared to the standalone rAJCC or the traditional AJCC system. The development of a novel GC staging system, which integrated the LNR-based N classification and the minimum ELN count, has exhibited superior prognostic accuracy, holding promise as a valuable asset in the clinical management of GC. However, it is crucial to recognize the limitations from the retrospective database, which should be addressed in subsequent analyses.

A Graph-Informed Modeling Framework Empowering Gene Pathway Discovery.

This study introduces a novel graph-informed modeling framework for improving the statistical analysis of gene expression data, particularly in the context of identifying differentially expressed gene pathways and gene expression-assisted disease classification in a high-dimensional data setting. By integrating gene regulatory network information into hypothesis testing for the difference between mean vectors and linear discriminant analysis, we aim to effectively capture and utilize previously validated external gene interaction information. Our method leverages a block-coordinate descent approach which enables us to incorporate mixed graph information into linear structural equation modeling, accommodating directed/undirected edges and potential cycles in gene regulatory networks. Extensive simulations under various data scenarios have demonstrated the effectiveness of our approach with improved power for gene pathway tests and disease classification over existing methods. An application to a lung cancer dataset from the Cancer Genome Atlas Program (TCGA) further exemplifies the potential of our graph-informed approach in empowering the detection of differentially expressed gene pathways and gene expression-based classification of different lung cancer stages. Our findings underscore the potential utility of incorporating gene regulatory network information in gene pathway analysis, setting the stage for future advancements in gene pathway discovery, disease diagnosis, and treatment strategies.

LLM-AIx: An open source pipeline for Information Extraction from unstructured medical text based on privacy preserving Large Language Models.

In clinical science and practice, text data, such as clinical letters or procedure reports, is stored in an unstructured way. This type of data is not a quantifiable resource for any kind of quantitative investigations and any manual review or structured information retrieval is time-consuming and costly. The capabilities of Large Language Models (LLMs) mark a paradigm shift in natural language processing and offer new possibilities for structured Information Extraction (IE) from medical free text. This protocol describes a workflow for LLM based information extraction (LLM-AIx), enabling extraction of predefined entities from unstructured text using privacy preserving LLMs. By converting unstructured clinical text into structured data, LLM-AIx addresses a critical barrier in clinical research and practice, where the efficient extraction of information is essential for improving clinical decision-making, enhancing patient outcomes, and facilitating large-scale data analysis. The protocol consists of four main processing steps: 1) Problem definition and data preparation, 2) data preprocessing, 3) LLM-based IE and 4) output evaluation. LLM-AIx allows integration on local hospital hardware without the need of transferring any patient data to external servers. As example tasks, we applied LLM-AIx for the anonymization of fictitious clinical letters from patients with pulmonary embolism. Additionally, we extracted symptoms and laterality of the pulmonary embolism of these fictitious letters. We demonstrate troubleshooting for potential problems within the pipeline with an IE on a real-world dataset, 100 pathology reports from the Cancer Genome Atlas Program (TCGA), for TNM stage extraction. LLM-AIx can be executed without any programming knowledge via an easy-to-use interface and in no more than a few minutes or hours, depending on the LLM model selected.

ExpOmics: a comprehensive web platform empowering biologists with robust multi-omics data analysis capabilities.

High-throughput technologies yield a broad spectrum of multi-omics datasets, which offer unparalleled insights into complex biological systems. However, effectively analyzing this diverse array of data presents challenges, considering factors such as species diversity, data types, costs, and limitations of the available tools. Herein, we present ExpOmics, a comprehensive web platform featuring 7 applications and 4 toolkits, with 28 customizable analysis functions spanning various analyses of differential expression, co-expression, Weighted Gene Co-expression Network Analysis (WGCNA), feature selection, and functional enrichment. ExpOmics allows users to upload and explore multi-omics data without organism restrictions, supporting various expression data, including genes, mRNAs, lncRNAs, miRNAs, circRNAs, piRNAs, and proteins and is compatible with diverse gene nomenclatures and expression values. Moreover, ExpOmics enables users to analyze 22427 transcriptomic datasets of 196 cancer subtypes sourced from 63 projects of The Cancer Genome Atlas Program (TCGA) to identify cancer biomarkers. The analysis results from ExpOmics are presented in high-quality graphical formats suitable for publication and are available for free download. A case study using ExpOmics identified two potential oncogenes, SERPINE1 and SLC43A1, that may regulate colorectal cancer through distinct biological processes. In summary, ExpOmics can serves as a robust platform for global researchers to explore multi-omics data, gain biological insights, and formulate testable hypotheses. ExpOmics is available at http://www.biomedical-web.com/expomics.

The Impact of DAXX, HJURP and CENPA Expression in Uveal Melanoma Carcinogenesis and Associations with Clinicopathological Parameters.

Uveal melanomas (UMs) represent rare malignant tumors associated with grim prognosis for the majority of patients. DAXX (Death Domain-Associated Protein), HJURP (Holliday Junction Recognition Protein) and CENPA (Centromere Protein A) proteins are implicated in epigenetic mechanisms, now in the spotlight of cancer research to better understand the molecular background of tumorigenesis. Herein, we investigated their expression in UM tissues using immunohistochemistry and explored possible correlations with a multitude of clinicopathological and survival parameters. The Cancer Genome Atlas Program (TCGA) was used for the investigation of their mRNA levels in UM cases. Nuclear DAXX expression correlated with an advanced T-stage (p = 0.004), while cytoplasmic expression marginally with decreased disease-free survival (DFS) (p = 0.084). HJURP nuclear positivity also correlated with advanced T-status (p = 0.054), chromosome 3 loss (p = 0.042) and increased tumor size (p = 0.03). More importantly, both nuclear and cytoplasmic HJURP immunopositivity correlated with decreased overall survival (OS) (p = 0.011 and 0.072, respectively) and worse DFS (p = 0.071 and 0.019, respectively). Lastly, nuclear CENPA overexpression was correlated with presence of irido-corneal angle involvement (p = 0.015) and loss of chromosome 3 (p = 0.041). Nuclear and cytoplasmic CENPA immunopositivity associated with decreased OS (p = 0.028) and DFS (p = 0.018), respectively. HJURP and CENPA mRNA overexpression exhibited strong association with tumor epithelioid histology and was linked to worse prognosis. Our results show the compounding role of DAXX, HJURP and CENPA in UM carcinogenesis, designating them as potential biomarkers for assessing prognosis and possible targets for novel therapeutic interventions.

DCTPP1 Expression as a Predictor of Chemotherapy Response in Luminal A Breast Cancer Patients.

Breast cancer (BRCA) remains a significant global health challenge due to its prevalence and lethality, exacerbated by the development of resistance to conventional therapies. Therefore, understanding the molecular mechanisms underpinning chemoresistance is crucial for improving therapeutic outcomes. Human deoxycytidine triphosphate pyrophosphatase 1 (DCTPP1) has emerged as a key player in various cancers, including BRCA. DCTPP1, involved in nucleotide metabolism and maintenance of genomic stability, has been linked to cancer cell proliferation, survival, and drug resistance. This study evaluates the role of DCTPP1 in BRCA prognosis and chemotherapy response. Data from the Cancer Genome Atlas Program (TCGA), Genotype-Tissue Expression (GTEx), and Gene Expression Omnibus (GEO) repositories, analyzed using GEPIA and Kaplan-Meier Plotter, indicate that high DCTPP1 expression correlates with poorer overall survival and increased resistance to chemotherapy in BRCA patients. Further analysis reveals that DCTPP1 gene expression is up-regulated in non-responders to chemotherapy, particularly in estrogen receptor (ER)-positive, luminal A subtype patients, with significant predictive power. Additionally, in vitro studies show that DCTPP1 gene expression increases in response to 5-fluorouracil and doxorubicin treatments in luminal A BRCA cell lines, suggesting a hypothetical role in chemoresistance. These findings highlight DCTPP1 as a potential biomarker for predicting chemotherapy response and as a therapeutic target to enhance chemotherapy efficacy in BRCA patients.

LncRNA ZFAS1 regulates ATIC transcription and promotes the proliferation and migration of hepatocellular carcinoma through the PI3K/AKT signaling pathway

PurposeLong noncoding RNAs (lncRNAs) exert a significant influence on various cancer-related processes through their intricate interactions with RNAs. Among these, lncRNA ZFAS1 has been implicated in oncogenic roles in multiple cancer types. Nevertheless, the intricate biological significance and underlying mechanism of ZFAS1 in the initiation and progression of hepatocellular carcinoma (HCC) remain largely unexplored.MethodsAnalysis of The Cancer Genome Atlas Program (TCGA) database revealed a notable upregulation of lncRNA ZFAS1 in HCC tissues. To explore its function, we investigated colony formation and performed CCK-8 assays to gauge cellular proliferation and wound healing, Transwell assays to assess cellular migration, and an in vivo study employing a nude mouse model to scrutinize tumor growth and metastasis. Luciferase reporter assay was used to confirm the implicated interactions. Rescue experiments were conducted to unravel the plausible mechanism underlying the activation of the PI3K/AKT pathway by lncRNAs ZFAS1 and ATIC.ResultsZFAS1 and ATIC were significantly upregulated in the HCC tissues and cells. ZFAS1 knockdown inhibited cell proliferation and migration. We observed a direct interaction between the lncRNA ZFAS1 and ATIC. ATIC knockdown also suppressed cell proliferation and migration. SC79, an activator of AKT, partially restores the effects of lncRNA ZFAS1/ATIC knockdown on cell proliferation and migration. Knockdown of lncRNA ZFAS1/ATIC inhibited tumor growth and lung metastasis in vivo.ConclusionOverall, lncRNA ZFAS1 regulates ATIC transcription and contributes to the growth and migration of HCC cells through the PI3K/AKT signaling pathway.

A comprehensive meta-analysis of tissue resident memory T cells and their roles in shaping immune microenvironment and patient prognosis in non-small cell lung cancer.

Tissue-resident memory T cells (TRM) are a specialized subset of long-lived memory T cells that reside in peripheral tissues. However, the impact of TRM-related immunosurveillance on the tumor-immune microenvironment (TIME) and tumor progression across various non-small-cell lung cancer (NSCLC) patient populations is yet to be elucidated. Our comprehensive analysis of multiple independent single-cell and bulk RNA-seq datasets of patient NSCLC samples generated reliable, unique TRM signatures, through which we inferred the abundance of TRM in NSCLC. We discovered that TRM abundance is consistently positively correlated with CD4+ T helper 1 cells, M1 macrophages, and resting dendritic cells in the TIME. In addition, TRM signatures are strongly associated with immune checkpoint and stimulatory genes and the prognosis of NSCLC patients. A TRM-based machine learning model to predict patient survival was validated and an 18-gene risk score was further developed to effectively stratify patients into low-risk and high-risk categories, wherein patients with high-risk scores had significantly lower overall survival than patients with low-risk. The prognostic value of the risk score was independently validated by the Cancer Genome Atlas Program (TCGA) dataset and multiple independent NSCLC patient datasets. Notably, low-risk NSCLC patients with higher TRM infiltration exhibited enhanced T-cell immunity, nature killer cell activation, and other TIME immune responses related pathways, indicating a more active immune profile benefitting from immunotherapy. However, the TRM signature revealed low TRM abundance and a lack of prognostic association among lung squamous cell carcinoma patients in contrast to adenocarcinoma, indicating that the two NSCLC subtypes are driven by distinct TIMEs. Altogether, this study provides valuable insights into the complex interactions between TRM and TIME and their impact on NSCLC patient prognosis. The development of a simplified 18-gene risk score provides a practical prognostic marker for risk stratification.

PART1 facilitates tumorigenesis and inhibits ferroptosis by regulating the miR-490-3p/SLC7A11 axis in hepatocellular carcinoma.

Ferroptosis is associated with cancer progression and has a promising application for treating hepatocellular carcinoma (HCC). Long non-coding RNA (lncRNA) participates widely in the regulation of ferroptosis, but the key lncRNA regulators implicated in ferroptosis and their molecular mechanisms remain to be identified. Bioinformatic analysis was performed in R based on The Cancer Genome Atlas Program (TCGA) public database. The relative expression of genes was detected by real-time quantitative PCR. Cell viability was assessed by the CCK8 assay. The cell cycle and apoptosis were detected by flow cytometry. Migration and invasion of HCC cells were detected by Transwell assay and wound healing assay. Expression of relevant proteins was detected by Western blotting. A dual-luciferase reporter assay was used to detect interactions between PART1 (or SLC7A11) and miR-490-3p. The PART1/miR-490-3p/SLC7A11 axis was identified as a potential regulatory pathway of ferroptosis in HCC. PART1 silencing reduced HCC cell proliferation, migration, and metastasis and promoted apoptosis and erastin-reduced ferroptosis. Further investigation revealed that PART1 acted as a competitive endogenous RNA (ceRNA) for miR-490-3p to enhance SLC7A11 expression. Overexpression of miR-490-3p downregulated the expression of SLC7A11, inhibiting the proliferation, invasion, and metastasis of HCC cells while promoting apoptosis and erastin-induced ferroptosis. Knockdown of PART1 in HCC cells significantly improved the sensitivity of HCC cells to sorafenib. Our results revealed that the PART1/miR-490-3p/SLC7A11 axis enhances HCC cell malignancy and suppresses ferroptosis, which provides a new perspective for understanding of the function of long chain non-coding RNAs in HCC. The PART1/miR-490-3p/SLC7A11 axis may be target for improving sorafenib sensitivity in HCC.

Comprehensive bioinformatics analysis and cell line experiments revealed the important role of CDCA3 in sarcoma

BackgroundSarcoma mainly originate from bone and soft tissue and are highly aggressive malignant tumors. Cell division cycle-related protein 3 (CDCA3) is a protein involved in the regulation of the cell cycle, which is highly expressed in a variety of malignant tumors. However, its role in sarcoma remains unclear. This study aims to investigate the function and potential mechanism of CDCA3 in sarcoma and to elucidate its importance in sarcoma. MethodsWe first studied the expression and prognosis of CDCA family members in sarcoma by Oncomine and the Gene Expression Profiling Interactive Analysis (GEPIA). The role of CDCA3 protein in sarcoma was further analyzed by the Cancer Genome Atlas Program (TCGA), the Cancer Cell Lineage Encyclopedia (CCLE), and Linke-dOmics. In addition, immunohistochemistry and Western blot were used to verify the expression of CDCA3 protein in clinical samples as well as sarcoma cell lines (U2OS, SAOS2, MG63, and HOS). Subsequently, in vitro experiments (cloning and scratching experiments) were performed using sh-NC as well as sh-CDCA3 group cells to reveal the biological functions of CDCA3. ResultsWe found that the CDCA family (CDCA3, CDCA4, and CDCA8) is highly expressed in sarcoma, and the expression level of CDCA3, CDCA4, and CDCA8 negatively correlates with the prognosis of sarcoma patients. CDCA3 mRNA was highly expressed in pan-cancer by CCLE and TCGA database analysis. KEGG analysis showed that CDCA3 was mainly enriched in the cell cycle signaling pathway (It promoted the transition of the cell cycle from the G0/G1 phase to the S phase). In the level of immune infiltration, CDCA3 was negatively correlated with pDC cells, CD8+T cells, and cytotoxic cells. Finally, patients with high CDCA3 expression in sarcoma were analyzed for resistance to NU7441 and others, while sensitive to Fulvestrant and Dihydrorotenone. Furthermore, we demonstrated high expression of CDCA3 protein in sarcoma tissues and cell lines by immunohistochemistry and Western blot experiments. Cloning, EDU, scratching, and migration experiments showed that the knockdown of CDCA3 inhibited the Proliferation and progression of sarcoma cells. ConclusionThese results suggest for the first time that knockdown of CDCA3 may inhibit sarcoma progression. CDCA3 may be an effective target for the treatment of sarcoma.

Radiogenomics-Based Risk Prediction of Glioblastoma Multiforme with Clinical Relevance.

Glioblastoma multiforme (GBM)is the most common and aggressive primary brain tumor. Although temozolomide (TMZ)-based radiochemotherapy improves overall GBM patients' survival, it also increases the frequency of false positive post-treatment magnetic resonance imaging (MRI) assessments for tumor progression. Pseudo-progression (PsP) is a treatment-related reaction with an increased contrast-enhancing lesion size at the tumor site or resection margins miming tumor recurrence on MRI. The accurate and reliable prognostication of GBM progression is urgently needed in the clinical management of GBM patients. Clinical data analysis indicates that the patients with PsP had superior overall and progression-free survival rates. In this study, we aimed to develop a prognostic model to evaluate the tumor progression potential of GBM patients following standard therapies. We applied a dictionary learning scheme to obtain imaging features of GBM patients with PsP or true tumor progression (TTP) from the Wake dataset. Based on these radiographic features, we conducted a radiogenomics analysis to identify the significantly associated genes. These significantly associated genes were used as features to construct a 2YS (2-year survival rate) logistic regression model. GBM patients were classified into low- and high-survival risk groups based on the individual 2YS scores derived from this model. We tested our model using an independent The Cancer Genome Atlas Program (TCGA) dataset and found that 2YS scores were significantly associated with the patient's overall survival. We used two cohorts of the TCGA data to train and test our model. Our results show that the 2YS scores-based classification results from the training and testing TCGA datasets were significantly associated with the overall survival of patients. We also analyzed the survival prediction ability of other clinical factors (gender, age, KPS (Karnofsky performance status), normal cell ratio) and found that these factors were unrelated or weakly correlated with patients' survival. Overall, our studies have demonstrated the effectiveness and robustness of the 2YS model in predicting the clinical outcomes of GBM patients after standard therapies.

Investigating the Role of Fat Mass and Obesity-Associated (FTO) Single Nucleotide Polymorphisms and Methylation in Breast Cancer.

Background Fat mass and obesity-associated (FTO) protein is an mRNA demethylase enzyme essential for active genome regulation. The FTO gene codes for a protein that is part of the methylosome complex and has a regulatory role in cancer development. Some studies have shown a relationship between FTO and cancer, where single nucleotide polymorphisms (SNPs) may have some impact on cancer risk. The present study aimed to evaluate the risk of FTO polymorphisms rs9939609, rs1477196, and rs9930506; analyze the methylation status of FTO promoters among Mexican women with breast cancer (BC); and investigate by in silico analysis the methylation status in the region near these polymorphisms. Methods A total of 157 BC patients and 137 healthy controls were genotyped for rs9939609, rs1477196, and rs9930506 FTO polymorphisms by TaqMan SNP Genotyping Assays. Promoter methylation was analyzed by sodium bisulfite and methylation-specific polymerase chain reaction (MSP) for 78 tissue samples. An in silico analysis using The Cancer Genome Atlas Program (TCGA) database was employed to investigate the methylation state in promoter and near polymorphism locations and its relation to survival. Results The AG genotype of FTO rs9930506 was associated with BC protection (P= 0.0025; adjusted OR, 0.27; 95% CI: 0.10-0.70). rs9939609 and rs1477196, according to the results of the present study, had no relation to BC. Promoter methylation status assays by MSP revealed no changes in methylation in BC or healthy tissues. Trying to know more about the methylation in promoters and near polymorphisms' relation to survival, we performed an in silico analysis. Bioinformatics analysis showed a correlation between poor survival and methylation near polymorphisms but not with methylation in the promoter region. Conclusions The AG genotype rs9930506 has a protective function against BC. Whereas high methylation near polymorphisms was related to lower survival, the hypomethylated promoter region does not impact survival.

Silencing of ZNF610 suppresses cell proliferation and migration in lung adenocarcinoma.

Zinc finger proteins (ZNFs) play a significant role in the initiation and progression of tumors. Nevertheless, the specific contribution of ZNF610 to lung adenocarcinoma (LUAD) remains poorly understood. This study sought is to elucidate the role of ZNF610 in LUAD. Transcript data of LUAD were obtained from The Cancer Genome Atlas Program (TCGA) database and processed via R program. The expression of ZNF610 was assessed in various cell lines. To compare the proliferative capacity of cells with or without ZNF610 silencing, CCK8, cell colony formation assay, and Celigo label-free cell counting assay were employed. Furthermore, transwell migration and invasion assays were conducted to evaluate the migratory and invasive abilities of the cells. The expression levels of genes and proteins were assessed using quantitative real-time polymerase chain reaction (qRT-PCR) and western blot techniques. In different LUAD cells, the expression level of ZNF610 was found to be significantly higher in LUAD cells compared to MRC-5 and BASE-2B cells. Moreover, the silencing of ZNF610 resulted in a decrease in cell proliferation and migration abilities. Additionally, the apoptosis rate of cells increased upon silencing ZNF610. Notably, the proportion of cells in the G0/G1 phase increased, while the proportion of cells in the S phase decreased following ZNF610 silencing. Finally, β-catenin and snail were identified as downstream targets of ZNF610 in cells. Our findings suggest that silencing ZNF610 could inhibit LUAD cell proliferation and migration, possibly through the downregulation of β-catenin and snail.

Molecular mechanism of RBM14-mediated promotion of proliferation, migration, and invasion in osteosarcoma.

Osteosarcoma (OS) is an exceptionally aggressive bone neoplasm that predominantly impacts the paediatric and adolescent population, exhibiting unfavourable prognosis. The importance of RNA binding motif protein 14 (RBM14) in the aetiology of OS is not well understood, despite its established involvement in several other types of cancer. In this study, we conducted an analysis of the expression profiles of RBM14 in cancer tissues and cell lines. To achieve this, we will utilised data obtained from various databases including The Cancer Genome Atlas Program (TCGA) project, The Genotype-Tissue Expression (GTEx) Project, Gene Expression Omnibus (GEO) database, and cancer cell line encyclopedia (CCLE) data. Furthermore, this study also aims to examine the effects of RBM14 on the proliferation, migration, and invasive properties of OS cells using cell functional gain and loss studies. In this study, we carried out an in-depth investigation to explore possible molecular pathways that underlie the regulation of the malignant phenotype found in OS by RBM14. This investigation involved integrating data from RBM14 overexpression, RBM14 knockdown RNA-seq experiments, and an array comprising 6,096 perturbed genes obtained from the Genetic Perturbation Similarity Analysis Database (GPSAdb). This research offers an opportunity to build a robust conceptual framework for the potential advancement of novel therapeutic approaches that are especially aimed at attacking OS. RBM14 plays an active role in OS by significantly contributing to the enhancement of cellular proliferation, migration, and invasion. At the molecular level, it is probable that RBM14 exerts control over the malignant characteristics of OS through its modulation of the Hippo signalling system. The above-mentioned findings underscore the significant importance of RBM14 as an intriguing target for therapy for the mitigation and management of OS. This particular protein holds an excellent opportunity for the development of novel and efficacious therapeutic approaches that possess the potential to yield favorable results for patients affected with OS.

THY1 is a prognostic-related biomarker via mediating immune infiltration in lung squamous cell carcinoma (LUSC).

Thymus cell antigen 1 (THY1) has been proven to play pivotal roles in many diseases. However, we do not fully understand its functional mechanism, especially in lung squamous cell carcinoma (LUSC). Here, we aimed to perform a comprehensive analysis to explore the expression and prognostic values of THY1 in LUSC using bioinformatic technology. Some online public databases (e.g., ONCOMINE, PrognoScan, TIMER, Kaplan-Meier plotter, STRING, LinkedOmics, and GEPIA) were used to explore the expression, prognostic significance, and potential molecular mechanism of THY1. The analysis indicated that THY1 was significantly up-regulated and closely correlated with poor prognosis in many malignant tumors, including LUSC. Further analysis revealed that over-expression of THY1 was significantly correlated with clinicopathological parameters (e.g., individual cancer stage, age, smoking habits, nodal metastasis status, and TP53 mutation status) in LUSC. The CpG islands methylation of THY1 was negatively correlated with THY1 mRNA expression in The Cancer Genome Atlas Program (TCGA). Further enrichment analysis of THY1 correlated genes revealed that they were mainly correlated with the formation of extracellular matrix (ECM), and got involved in the pathway of epithelial mesenchymal transition (EMT). Furthermore, differentially expressed THY1 was significantly correlated with immune cell infiltrations and poor prognosis in LUSC. In summary, bioinformatic analysis demonstrated that THY1 was significantly over-expressed and closely correlated with unfavorable prognosis in LUSC, which may apply as a promising diagnostic and therapeutic biomarker for LUSC in the future.

High-throughput sequencing reveals crebanine inhibits colorectal cancer by modulating Tregs immune prognostic target genes

Abstract Objectives Crebanine, an alkaloid exhibiting sedative, anti-inflammatory, and anticancer properties, remains unexplored in terms of its anticancer potential against colorectal cancer (CRC). This study aims to bridge this knowledge gap, specifically investigating whether crebanine can suppress CRC and elucidating its underlying molecular mechanism. Methods We employed the MTT (3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyl tetrazolium bromide) assay, cell scratch assay, and flow cytometry to observe the effects of crebanine on the growth, migration, and apoptosis of CRC SW480 cells, respectively. High-throughput sequencing was employed to detect differentially expressed genes (DEGs) in SW480 cells treated with crebanine. Enriched pathways of these DEGs were identified through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Genes exhibiting the highest correlation in the enriched pathway were further analyzed using clinical data from The Cancer Genome Atlas Program (TCGA) public database, utilizing R software. Results Crebanine effectively inhibited the proliferation, migration, and invasion of SW480 cells, with concentrations of ≥15 μg/mL promoting apoptosis. Analysis revealed that the function of DEGs linked to the most enriched pathways was associated with immune infiltration by regulatory T cells (Tregs). When analyzed in conjunction with clinical data, the genes exhibiting the highest correlation in the enrichment pathway were found to be directly associated with clinical prognostic survival. Conclusions Our study demonstrates that crebanine inhibits colorectal cancer by regulating prognostic target genes related to Tregs. This finding offers a novel approach for pharmacological inhibition and Tregs-targeted therapy in CRC.