Cancer Genome Research Articles

Inverted triplications formed by iterative template switches generate structural variant diversity at genomic disorder loci

The duplication-triplication/inverted-duplication (DUP-TRP/INV-DUP) structure is a complex genomic rearrangement (CGR). Although it has been identified as an important pathogenic DNA mutation signature in genomic disorders and cancer genomes, its architecture remains unresolved. Here, we studied the genomic architecture of DUP-TRP/INV-DUP by investigating the DNA of 24 patients identified by array comparative genomic hybridization (aCGH) on whom we found evidence for the existence of 4 out of 4 predicted structural variant (SV) haplotypes. Using a combination of short-read genome sequencing (GS), long-read GS, optical genome mapping, and single-cell DNA template strand sequencing (strand-seq), the haplotype structure was resolved in 18 samples. The point of template switching in 4 samples was shown to be a segment of ∼2.2-5.5 kb of 100% nucleotide similarity within inverted repeat pairs. These data provide experimental evidence that inverted low-copy repeats act as recombinant substrates. This type of CGR can result in multiple conformers generating diverse SV haplotypes in susceptible dosage-sensitive loci.

Shared genetics between breast cancer and predisposing diseases identifies novel breast cancer treatment candidates.

Current effective breast cancer treatment options have severe side effects, highlighting a need for new therapies. Drug repurposing can accelerate improvements to care, as FDA-approved drugs have known safety and pharmacological profiles. Some drugs for other conditions, such as metformin, an antidiabetic, have been tested in clinical trials for repurposing for breast cancer. Here, we exploit the genetics of breast cancer and linked predisposing diseases to propose novel drug repurposing. We hypothesize that if a predisposing disease contributes to breast cancer pathology, identifying the pleiotropic genes related to the risk of cancer could prioritize drug targets, among all drugs treating a predisposing disease. We aim to develop a method to not only prioritize drug repurposing, but also to highlight shared etiology explaining repurposing. We compile breast cancer's predisposing diseases from literature. For each predisposing disease, we use GWAS summary statistics to identify genes in loci showing genetic correlation with breast cancer. Then, we use a network approach to link these shared genes to canonical pathways, and similarly for all drugs treating the predisposing disease, we link their targets to pathways. In this manner, we are able to prioritize a list of drugs based on each predisposing disease, with each drug linked to a set of implicating pathways. Finally, we evaluate our recommendations against drugs currently under investigation for breast cancer. We identify 84 loci harboring mutations with positively correlated effects between breast cancer and its predisposing diseases; these contain 194 identified shared genes. Out of the 112 drugs indicated for the predisposing diseases, 76 drugs can be linked to shared genes via pathways (candidate drugs for repurposing). Fifteen out of these candidate drugs are already in advanced clinical trial phases or approved for breast cancer (OR = 9.28, p = 7.99e-03, one-sided Fisher's exact test), highlighting the ability of our approach to identify likely successful candidate drugs for repurposing. Our novel approach accelerates drug repurposing for breast cancer by leveraging shared genetics with its known risk factors. The result provides 59 novel candidate drugs alongside biological insights supporting each recommendation.

Comprehensive Analysis of the Function and Prognostic Value of TAS2Rs Family-Related Genes in Colon Cancer.

In the realm of colon carcinoma, significant genetic and epigenetic diversity is observed, underscoring the necessity for tailored prognostic features that can guide personalized therapeutic strategies. In this study, we explored the association between the type 2 bitter taste receptor (TAS2Rs) family-related genes and colon cancer using RNA-sequencing and clinical datasets from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). Our preliminary analysis identified seven TAS2Rs genes associated with survival using univariate Cox regression analysis, all of which were observed to be overexpressed in colon cancer. Subsequently, based on these seven TAS2Rs prognostic genes, two colon cancer molecular subtypes (Cluster A and Cluster B) were defined. These subtypes exhibited distinct prognostic and immune characteristics, with Cluster A characterized by low immune cell infiltration and less favorable outcomes, while Cluster B was associated with high immune cell infiltration and better prognosis. Finally, we developed a robust scoring system using a gradient boosting machine (GBM) approach, integrated with the gene-pairing method, to predict the prognosis of colon cancer patients. This machine learning model could improve our predictive accuracy for colon cancer outcomes, underscoring its value in the precision oncology framework.

3D genomic analysis reveals novel enhancer-hijacking caused by complex structural alterations that drive oncogene overexpression.

Cancer genomes are composed of many complex structural alterations on chromosomes and extrachromosomal DNA (ecDNA), making it difficult to identify non-coding enhancer regions that are hijacked to activate oncogene expression. Here, we describe a 3D genomics-based analysis called HAPI (Highly Active Promoter Interactions) to characterize enhancer hijacking. HAPI analysis of HiChIP data from 34 cancer cell lines identified enhancer hijacking events that activate both known and potentially novel oncogenes such as MYC, CCND1 , ETV1 , CRKL , and ID4 . Furthermore, we found enhancer hijacking among multiple oncogenes from different chromosomes, often including MYC , on the same complex amplicons such as ecDNA. We characterized a MYC - ERBB2 chimeric ecDNA, in which ERBB2 heavily hijacks MYC 's enhancers. Notably, CRISPRi of the MYC promoter led to increased interaction of ERBB2 with MYC enhancers and elevated ERBB2 expression. Our HAPI analysis tool provides a robust strategy to detect enhancer hijacking and reveals novel insights into oncogene activation.

Cancer/testis antigen expression and co-expression patterns in Gastroesophageal Adenocarcinoma.

Gastroesophageal adenocarcinoma (GEAC) poses a significant challenge due to its poor prognosis and limited treatment options. Recently, Cancer/testis antigens (CTAs) have emerged as potential therapy targets due to their high expression in tumor cells and their immunogenic nature. We aimed to explore the expression and co-expression of CTAs in GEAC. We analyzed 63 GEAC patients initially and validated our findings in 329 patients from The Cancer Genome Atlas (TCGA) database. CTA expression was measured after RNA sequencing, while clinical information, including survival outcomes and treatment details, was collected from an institutional database. Co-expression patterns among CTAs were determined using Pearson correlation analysis. The majority of the study cohort were male (87%), Caucasian (94%), and had stage IV disease (64%). CTAs were highly prevalent, ranging from 58-19%. The MAGE gene family showed the highest expression, consistent across both cohorts. The correlation matrix revealed a distinct cluster of significantly co-expressed genes, including MAGEA3, NY-ESO-1, and others (0.27 ≤ r ≤ 0.73). Survival analysis revealed that individual CTAs were associated with poorer survival outcomes in patients not receiving immunotherapy while showing potential for improved survival in those undergoing immunotherapy, although these findings lacked robust reliability. Our study provides a comprehensive characterization of CTA expression and co-expression in GEAC. The strong correlation among CTAs like MAGE, NY-ESO-1, and GAGE suggests a potential for therapies targeting multiple CTAs simultaneously. Further research, including prospective trials, is warranted to assess the prognostic value of CTAs and their suitability as therapeutic targets.

Deep Learning Segmentation of Ascites on Abdominal CT Scans for Automatic Volume Quantification.

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To evaluate the performance of an automated deep learning method in detecting ascites and subsequently quantifying its volume in patients with liver cirrhosis and ovarian cancer. Materials and Methods This retrospective study included contrast-enhanced and noncontrast abdominal-pelvic CT scans of patients with cirrhotic ascites and patients with ovarian cancer from two institutions, National Institutes of Health (NIH) and University of Wisconsin (UofW). The model, trained on The Cancer Genome Atlas Ovarian Cancer dataset (mean age, 60 years ± 11 [SD]; 143 female), was tested on two internal (NIH-LC and NIH-OV) and one external dataset (UofW-LC). Its performance was measured by the Dice coefficient, standard deviations, and 95% confidence intervals, focusing on ascites volume in the peritoneal cavity. Results On NIH-LC (25 patients; mean age, 59 years ± 14; 14 male) and NIH-OV (166 patients; mean age, 65 years ± 9; all female), the model achieved Dice scores of 85.5% ± 6.1% (CI: 83.1%-87.8%) and 82.6% ± 15.3% (CI: 76.4%-88.7%), with median volume estimation errors of 19.6% (IQR: 13.2%-29.0%) and 5.3% (IQR: 2.4%- 9.7%), respectively. On UofW-LC (124 patients; mean age, 46 years ± 12; 73 female), the model had a Dice score of 83.0% ± 10.7% (CI: 79.8%-86.3%) and median volume estimation error of 9.7% (IQR: 4.5%-15.1%). The model showed strong agreement with expert assessments, with r2 values of 0.79, 0.98, and 0.97 across the test sets. Conclusion The proposed deep learning method performed well in segmenting and quantifying the volume of ascites in concordance with expert radiologist assessments. ©RSNA, 2024.

Predicting the Prognosis and Immunotherapeutic Response of Triple-Negative Breast Cancer by Constructing a Prognostic Model Based on CD8+ T Cell-Related Immune Genes.

Triple-negative breast cancer (TNBC) poses a significant challenge for treatment efficacy. CD8+ T cells, which are pivotal immune cells, can be effectively analyzed for differential gene expression across diverse cell populations owing to rapid advancements in sequencing technology. By leveraging these genes, our objective was to develop a prognostic model that accurately predicts the prognosis of patients with TNBC and their responsiveness to immunotherapy. Sample information and clinical data of TNBC were sourced from The Cancer Genome Atlas and METABRIC databases. In the initial stage, we identified 67 differentially expressed genes associated with immune response in CD8+ T cells. Subsequently, we narrowed our focus to three key genes, namely CXCL13, GBP2, and GZMB, which were used to construct a prognostic model. The accuracy of the model was assessed using the validation set data and receiver operating characteristic (ROC) curves. Furthermore, we employed various methods, including Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, immune infiltration, and correlation analyses with CD274 (PD-L1) to explore the model's predictive efficacy in immunotherapeutic responses. Additionally, we investigated the potential underlying biological pathways that contribute to divergent treatment responses. We successfully developed a model capable of predicting the prognosis of patients with TNBC. The areas under the curve (AUC) values for the 1-, 3-, and 5-year survival predictions were 0.618, 0.652, and 0.826, respectively. Employing this risk model, we stratified the samples into high- and low-risk groups. Through KEGG enrichment analysis, we observed that the high-risk group predominantly exhibited enrichment in metabolism-related pathways such as drug and chlorophyll metabolism, whereas the low-risk group demonstrated significant enrichment in cytokine pathways. Furthermore, immune landscape analysis revealed noteworthy variations between (PD-L1) expression and risk scores, indicating that our model effectively predicted the response of patients to immune-based treatments. Our study demonstrates the potential of CXCL13, GBP2, and GZMB as prognostic indicators of clinical outcomes and immunotherapy responses in patients with TNBC. These findings provide valuable insights and novel avenues for developing immunotherapeutic approaches targeting TNBC.

Evaluation of somatic copy number variation detection by NGS technologies and bioinformatics tools on a hyper-diploid cancer genome

BackgroundCopy number variation (CNV) is a key genetic characteristic for cancer diagnostics and can be used as a biomarker for the selection of therapeutic treatments. Using data sets established in our previous study, we benchmark the performance of cancer CNV calling by six most recent and commonly used software tools on their detection accuracy, sensitivity, and reproducibility. In comparison to other orthogonal methods, such as microarray and Bionano, we also explore the consistency of CNV calling across different technologies on a challenging genome.ResultsWhile consistent results are observed for copy gain, loss, and loss of heterozygosity (LOH) calls across sequencing centers, CNV callers, and different technologies, variation of CNV calls are mostly affected by the determination of genome ploidy. Using consensus results from six CNV callers and confirmation from three orthogonal methods, we establish a high confident CNV call set for the reference cancer cell line (HCC1395).ConclusionsNGS technologies and current bioinformatics tools can offer reliable results for detection of copy gain, loss, and LOH. However, when working with a hyper-diploid genome, some software tools can call excessive copy gain or loss due to inaccurate assessment of genome ploidy. With performance matrices on various experimental conditions, this study raises awareness within the cancer research community for the selection of sequencing platforms, sample preparation, sequencing coverage, and the choice of CNV detection tools.

Identification of 8 candidate microsatellite instability loci in colorectal cancer and validation of the ACVR2A mechanism in the tumor progression

This study probes the utility of biomarkers for microsatellite instability (MSI) detection and elucidates the molecular dynamics propelling colorectal cancer (CRC) progression. We synthesized a primer panel targeting 725 MSI loci, informed by The Cancer Genome Atlas (TCGA) and ancillary databases, to construct an amplicon library for next-generation sequencing (NGS). K-means clustering facilitated the distillation of 8 prime MSI loci, including activin A receptor type 2A (ACVR2A). Subsequently, we explored ACVR2A’s influence on CRC advancement through in vivo tumor experiments and hematoxylin–eosin (HE) staining. Transwell assays gauged ACVR2A’s role in CRC cell migration and invasion, while colony formation assays appraised cell proliferation. Western blotting illuminated the impact of ACVR2A suppression on CRC’s PI3K/AKT/mTOR pathway protein expressions under hypoxia. Additionally, ACVR2A’s influence on CRC-induced angiogenesis was quantified via angiogenesis assays. K-means clustering of NGS data pinpointed 32 MSI loci specific to tumor and DNA mismatch repair deficiency (dMMR) tissues. ACVR2A emerged as a pivotal biomarker, discerning MSI-H tissues with 90.97% sensitivity. A curated 8-loci set demonstrated 100% sensitivity and specificity for MSI-H detection in CRC. In vitro analyses corroborated ACVR2A’s critical role, revealing its suppression of CRC proliferation, migration, and invasion. Moreover, ACVR2A inhibition under CRC-induced hypoxia markedly escalated MMP3, CyclinA, CyclinD1, and HIF1α protein expressions, alongside angiogenesis, by triggering the PI3K/AKT/mTOR cascade. The 8-loci ensemble stands as the optimal marker for MSI-H identification in CRC. ACVR2A, a central element within this group, deters CRC progression, while its suppression amplifies PI3K/AKT/mTOR signaling and angiogenesis under hypoxic stress.

Neutrophil in the suppressed immune microenvironment: Critical prognostic factor for lung adenocarcinoma patients with KEAP1 mutation.

It is still unclear whether KEAP1 mutation is detrimental to immunotherapy of lung adenocarcinoma (LUAD) patients, we try to analyse the exact changes in the TME in LUAD patients with KEAP1 mutations and to identify key factors influencing prognosis. A total of 1,029 patients with lung squamous carcinoma (LUSC) or LUAD with data obtained from The Cancer Genome Atlas were included in this study. The TME and OS of patients with LUAD stratified by mutant versus wild-type KEAP1 status were comprehensively measured. Moreover, we classified LUAD patients with KEAP1 mutations into three subtypes, by unsupervised consensus clustering. We further analysed the TME, OS, commutated genes and metabolic pathways of different subgroups. A total of 40 LUAD patients underwent immunotherapy were collected and classified into mutant KEAP1 group and wild-type KEAP1 group. We also conducted immunohistochemical staining in KEAP1-MT groups. Suppressed TME was observed not only in LUAD patients but also in LUSC patients. LUAD patients with mutant KEAP1 underwent immunotherapy had worse PFS than wild-type KEAP1. Unsupervised consensus clustering analysis suggested that the three subtypes of patients exhibited different densities of neutrophil infiltration and had different OS results: cluster 2 patients had significantly higher levels of neutrophils had significantly worse prognoses than those of patients in clusters 1 and 3 and patients with wild-type KEAP1. Univariate and multivariate Cox analyses proved that a high density of neutrophils was significantly associated with worse OS and immunohistochemical staining proved that shorter PFS showed high density of neutrophils. KEAP1 mutation significantly suppresses the tumour immune microenvironment in LUAD patients. LUAD patients with mutant KEAP1 underwent immunotherapy had worse PFS than with wild-type KEAP1. Neutrophils may play an important role in the prognosis of LUAD patients with KEAP1 mutations and may provide a promising therapeutic target.

Comparison of germline and somatic structural variants in cancers reveal systematic differences in variant generating and selection processes.

Although several recent studies have characterized structural variants (SVs) in germline and cancer genomes, the features of SVs in these different contexts have not been directly compared. We examined similarities and differences between 2 million germline and 115 thousand tumor SVs from a cohort of 963 patients from The Cancer Genome Atlas (TCGA). We found significant differences in features related to their genomic sequences and localization that suggest differences between SV-generating processes and selective pressures. For example, we found that transposon-mediated processes shape germline much more than somatic SVs, while somatic SVs more frequently show features characteristic of chromoanagenesis. These differences were extensive enough to enable us to develop a classifier-"the great GaTSV"-that accurately distinguishes between germline and cancer SVs in tumor samples that lack a matched normal sample.

Evergene: an interactive webtool for large-scale gene-centric analysis of primary tumours

Abstract Motivation The data sharing of large comprehensive cancer research projects, such as The Cancer Genome Atlas (TCGA), has improved the availability of high-quality data to research labs around the world. However, due to the volume and inherent complexity of high-throughput omics data, analysis of it is limited by the capacity for performing data processing through programming languages such as R or Python. Existing webtools lack functionality that supports large-scale analysis; typically, users can only input one gene, or a gene list condensed into a gene set, instead of individual gene-level analysis. Furthermore, analysis results are usually displayed without other sample-level molecular or clinical annotations. To address these gaps in the existing webtools, we have developed Evergene using R and Shiny. Results Evergene is a user-friendly webtool that utilises RNA-sequencing data, alongside other sample and clinical annotation, for large-scale gene-centric analysis, including principal component analysis (PCA), survival analysis (SA) and correlation analysis (CA). Moreover, Evergene achieves in-depth analysis of cancer transcriptomic data which can be explored through dimensional reduction methods, relating gene expression with clinical events or other sample information, such as ethnicity, histological classification, and molecular indices. Lastly, users can upload custom data to Evergene for analysis. Availability Evergene webtool is available at https://bshihlab.shinyapps.io/evergene/. The source code and example user input dataset are available at https://github.com/bshihlab/evergene. Supplementary information Supplementary data are available at Bioinformatics Advances online.

Antitumor activity of afatinib in EGFR T790M-negative human oral cancer therapeutically targets mTOR/Mcl-1 signaling axis.

This study investigates the role and effectiveness of the epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) in oral cancer, focusing on the clinical relevance of EGFR and myeloid cell leukemia-1 (Mcl-1) in head and neck cancers (HNCs). It aims to explore the molecular mechanism of afatinib, a TKI, in treating human oral cancer. We conducted an in silico analysis using databases like The Cancer Genome Atlas, Gene Expression Omnibus, and Clinical Proteomic Tumor Analysis Consortium, along with immunohistochemistry staining, to study EGFR and Mcl-1 expression in HNCs. For investigating afatinib's anticancer properties, we performed various in vitro and in vivo analyses, including trypan blue exclusion assay, Western blotting, 4'-6-diamidino-2-phenylindole staining, flow cytometry, quantitative real-time PCR, Mitochondrial membrane potential assay, overexpression vector construction, transient transfection, and a tumor xenograft model. Higher expression levels of EGFR and Mcl-1 were observed in HNC patient tissues compared to normal tissues, with their co-expression significantly linked to poor prognosis. There was a strong correlation between EGFR and Mcl-1 expressions in oral cancer patients. Afatinib treatment induced apoptosis and suppressed Mcl-1 in oral cancer cell lines without the EGFR T790M mutation. The mechanism of afatinib-induced apoptosis involved the EGFR/mTOR/Mcl-1 axis, as shown by the effects of mTOR activator MHY1485 and inhibitor rapamycin. Afatinib also increased Bim expression, mitochondrial membrane permeabilization, and cytochrome c release. It significantly lowered tumor volume without affecting body, liver, and kidney weights. Afatinib, targeting the EGFR/mTOR/Mcl-1 axis, shows promise as a therapeutic strategy for oral cancer, especially in patients with high EGFR and Mcl-1 expressions.

SPDYC serves as a prognostic biomarker related to lipid metabolism and the immune microenvironment in breast cancer.

Breast cancer remains the most common malignant carcinoma among women globally and is resistant to several therapeutic agents. There is a need for novel targets to improve the prognosis of patients with breast cancer. Bioinformatics analyses were conducted to explore potentially relevant prognostic genes in breast cancer using The Cancer Genome Atlas (TCGA) and The Gene Expression Omnibus (GEO) databases. Gene subtypes were categorized by machine learning algorithms. The machine learning-related breast cancer (MLBC) score was evaluated through principal component analysis (PCA) of clinical patients' pathological statuses and subtypes. Immune cell infiltration was analyzed using the xCell and CIBERSORT algorithms. Kyoto Encyclopedia of Genes and Genomes enrichment analysis elucidated regulatory pathways related to speedy/RINGO cell cycle regulator family member C (SPDYC) in breast cancer. The biological functions and lipid metabolic status of breast cancer cell lines were validated via quantitative real-time polymerase chain reaction (RT‒qPCR) assays, western blotting, CCK-8 assays, PI‒Annexin V fluorescence staining, transwell assays, wound healing assays, and Oil Red O staining. Key differentially expressed genes (DEGs) in breast cancer from the TCGA and GEO databases were screened and utilized to establishthe MLBC score. Moreover, the MLBC score we established was negatively correlated with poor prognosis in breast cancer patients. Furthermore, the impacts of SPDYC on the tumor immune microenvironment and lipid metabolism in breast cancer were revealed and validated. SPDYC is closely related to activated dendritic cells and macrophages and is simultaneously correlated with the immune checkpoints CD47, cytotoxic T lymphocyte antigen-4 (CTLA-4), and poliovirus receptor (PVR). SPDYC strongly correlated with C-C motif chemokine ligand 7 (CCL7), a chemokine that influences breast cancer patient prognosis. A significant relationship was discovered between key genes involved in lipid metabolism and SPDYC, such as ELOVL fatty acid elongase 2 (ELOVL2), malic enzyme 1 (ME1), and squalene epoxidase (SQLE). Potent inhibitors targeting SPDYC in breast cancer were also discovered, including JNK inhibitor VIII, AICAR, and JW-7-52-1. Downregulation of SPDYC expression in vitro decreased proliferation, increased the apoptotic rate, decreased migration, and reduced lipid droplets. SPDYC possibly influences the tumor immune microenvironment and regulates lipid metabolism in breast cancer. Hence, this study identified SPDYC as a pivotal biomarker for developing therapeutic strategies for breast cancer.

PLEK2 activates the PI3K/AKT signaling pathway to drive lung adenocarcinoma progression by upregulating SPC25.

Lung adenocarcinoma (LUAD) is the most common subtype of NSCLC, characterized by poor prognosis and frequently diagnosed at advanced. While previous studies have demonstrated pleckstrin-2 (PLEK2) as aberrantly expressed and implicated in tumorigenesis across various tumor types, including LUAD, the molecular mechanisms underlying PLEK2-mediated LUAD progression remain incompletely understood. In this study, we obtained data from The Cancer Genome Atlas (TCGA) database to assess PLEK2 expression in LUAD, a finding further confirmed through analysis of human tissue specimens. PLEK2-silenced LUAD cellular models were subsequently constructed to examine the functional role of PLEK2 both in vitro and in vivo. Our results showed elevated PLEK2 expression in LUAD, correlating with poor patients'prognosis. PLEK2 knockdown led to a significant suppression of LUAD cell proliferation and migration, accompanied by enhanced apoptosis. Moreover, tumor growth in mice injected with PLEK2-silencing LUAD cells was impaired. Gene expression profiling and Co-IP assays suggested direct interaction between PLEK2 and SPC25, with downregulation of SPC25 similarly impairing cell proliferation and migration. Additionally, we revealed phosphoinositide 3-kinase (PI3K)/AKT signaling activation as requisite for PLEK2-induced malignant phenotypes in LUAD. Collectively, our findings underscore PLEK2's oncogenic potential in LUAD, suggesting its utility as a prognostic indicator and therapeutic target for LUAD management.

High Fatty Acid-Binding Protein 4 Expression Associated with Favorable Clinical Characteristics and Prognosis in Papillary Thyroid Carcinoma.

Fatty acid-binding protein 4 (FABP4), a fatty acid transporter that coordinates lipid metabolism, is reported to exert a tumorigenic role in certain cancers. We investigated the effects of FABP4 in the carcinogenesis of thyroid cancer. Bioinformatics data about FABP4 in thyroid cancer were collected from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Sixteen paired papillary thyroid cancer (PTC) tissues from Taipei Medical University (TMU) were gathered, and commercial thyroid cancer complementary (c)DNA and tissue arrays were purchased to measure FABP4 messenger (m)RNA and protein levels. By analyzing data from the GEO and TCGA, we showed that FABP4 mRNA was reduced in PTC and follicular thyroid carcinoma (FTC). In addition, a lower FABP4 mRNA level in PTC was associated with poor clinical parameters and outcomes in the TCGA database. Moreover, FABP4 transcripts and proteins were downregulated in PTC and FTC, and its mRNA expression was associated with PTC staging in clinical specimens. In the TCGA database and TMU cohort, FABP4 mRNA levels were associated with thyroglobulin (r = 0.511 and r = 0.656, respectively), thyroid peroxidase (r = 0.612 and r = 0.909, respectively), and sodium iodide symporter (r = 0.485 and r = 0.637, respectively) transcripts. In conclusion, FABP4 mRNA and protein levels were reduced in PTC and FTC, and may be used as a potential indicator for thyroid cancer evolution in clinical settings. Further, well-designed research to dissect the molecular mechanism of FABP4 in modulating thyroid carcinogenesis is needed.

MPAC: a computational framework for inferring cancer pathway activities from multi-omic data.

Fully capturing cellular state requires examining genomic, epigenomic, transcriptomic, proteomic, and other assays for a biological sample and comprehensive computational modeling to reason with the complex and sometimes conflicting measurements. Modeling these so-called multi-omic data is especially beneficial in disease analysis, where observations across omic data types may reveal unexpected patient groupings and inform clinical outcomes and treatments. We present Multi-omic Pathway Analysis of Cancer (MPAC), a computational framework that interprets multi-omic data through prior knowledge from biological pathways. MPAC uses network relationships encoded in pathways using a factor graph to infer consensus activity levels for proteins and associated pathway entities from multi-omic data, runs permutation testing to eliminate spurious activity predictions, and groups biological samples by pathway activities to prioritize proteins with potential clinical relevance. Using DNA copy number alteration and RNA-seq data from head and neck squamous cell carcinoma patients from The Cancer Genome Atlas as an example, we demonstrate that MPAC predicts a patient subgroup related to immune responses not identified by analysis with either input omic data type alone. Key proteins identified via this subgroup have pathway activities related to clinical outcome as well as immune cell compositions. Our MPAC R package, available at https://bioconductor.org/packages/MPAC, enables similar multi-omic analyses on new datasets.

Colombian consensus for the molecular diagnosis of endometrial cancer

Objective: The Cancer Genome Atlas research program (TCGA) developed the molecular classification for endometrial cancer with prognostic and therapeutic utility, which was replaced by the ProMisE (Proactive Molecular Risk Classifier for Endometrial Cancer) classification by consensus and international guidelines due to its high cost. This article aims to present national recommendations from an expert consensus that allows unification and implementation of the molecular classification for women with endometrial cancer nationwide, with a rational use of resources and technology. Methods: Consensus of 36 experts in clinical oncology, oncological gynecology, pathology, and genetics, with clinical practice in the national territory. The leader group performed a literature review and structuring of questions rated 1 to 9 points. A modified nominal group technique was used. There was a face-to-face meeting with master presentations, deliberative dialogue, and Google Forms (Google LLC, Mountain View, CA, USA) questionnaire voting with analysis and discussion of responses. The non-consensual responses led to a second round of voting. The final manuscript was finally prepared and revised. Results: Seven recommendations were formulated integrating the panelist responses based on evidence, but adjusted to the Colombian context and reality. Recommendation 1. The molecular classification is recommended in all the endometrial cancers using the immunohistochemistry markers as subrogated results from the molecular profile initially proposed in the TCGA classification. Recommendation 2. The sequential test strategy is recommended, starting with the immunohistochemistry markers (p53, MLH1, MSH 2, MSH6, PMS2) simultaneously in all the patients, defining to request POLE (DNA polymerase epsilon) (if available) according to the risk classification based on the surgical piece. Recommendation 3. It is recommended, that the gynecologist oncologist should be the one to request the POLE (if available) according to the final pathology report. This test must be requested for all endometrial cancers stage I-II, except in low risk (stage IA low grade endometrioid histology without linfovascular invasion normal p53) and, stages III-IV without residual disease, without affecting the request of subrogated immunohistochemistry molecular markers upon histology. The consensus proposes that the POLE is requested after the immunohistochemistry and according to the categories in the risk classification established by the 2020 ESGO/ESTRO/ESP guidelines. Recommendation 4. It is recommended to perform immunohistochemistry for hormonal receptors for all women with endometrial cancer and the HER2 in patients with p53abn, simultaneously with the others immunohistochemistry markers. Recommendation 5. It is recommended to perform the immunohistochemistry markers (p53, MLH1, MSH2, MSH6 y PMS2) in an initial endometrial biopsy or curettage when the specimen is adequate and available. In case the initial immunohistochemistry is inconclusive, or there are histological discrepancies between the initial and definitive pathology, it is recommended to repeat the molecular profile in the surgical pathology. The immunohistochemistry markers must be reported in the pathology report according to the CAP (College of American Pathologists) recommendations, independently of the type of sample. Recommendation 6. It is recommended to perform MLH1 promoter methylation testing in patients who exhibit loss of expression of MLH1 in immunohistochemistry whether it is accompanied or not with loss of expression of PMS2. All the patients with deficient MMR (mismatch repair), should be sent for genetic counseling to rule out Lynch syndrome. Recommendation 7. It is recommended to consider the molecular classification in addition to the classical histopathological criteria when making adjuvant judgments, as incorporated by the classification of prognostic groups of the 2020 ESGO/ESTRO/ESP guidelines. Conclusions: It is necessary to implement the molecular classification of endometrial cancer in clinical practice in accordance to the Colombian context, due to its prognostic and probably predictive value. This will enable the characterization of the Colombian population in order to offer individualized guided treatments. This is an academic and nonregulatory document.

A pan-cancer analysis of EphA family gene expression and its association with prognosis, tumor microenvironment, and therapeutic targets

BackgroundErythropoietin-producing human hepatocellular (Eph) receptors stand out as the most expansive group of receptor tyrosine kinases (RTKs). Accumulating evidence suggests that within this expansive family, the EphA subset is implicated in driving cancer cell progression, proliferation, invasion, and metastasis, making it a promising target for anticancer treatment. Nonetheless, the extent of EphA family involvement across diverse cancers, along with its intricate interplay with immunity and the tumor microenvironment (TME), remains to be fully illuminated.MethodsThe relationships between EphA gene expression and patient survival, immunological subtypes, and TME characteristics were investigated based on The Cancer Genome Atlas (TCGA) database. The analyses employed various R packages.ResultsA significant difference in expression was identified for most EphA genes when comparing cancer tissues and non-cancer tissues. These genes independently functioned as prognostic factors spanning multiple cancer types. Moreover, a significant correlation surfaced between EphA gene expression and immune subtypes, except for EphA5, EphA6, and EphA8. EphA3 independently influenced the prognosis of papillary renal cell carcinoma (KIRP). This particular gene exhibited links with immune infiltration subtypes and clinicopathologic parameters, holding promise as a valuable biomarker for predicting prognosis and responsiveness to immunotherapy in patients with KIRP.ConclusionBy meticulously scrutinizing the panorama of EphA genes in a spectrum of cancers, this study supplemented a complete map of the effect of EphA family in Pan-cancer and suggested that EphA family may be a potential target for cancer therapy.

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.