Cancer Genome Research Articles

A Bayesian hierarchical hidden Markov model for clustering and gene selection: Application to kidney cancer gene expression data.

We introduce a Bayesian approach for biclustering that accounts for the prior functional dependence between genes using hidden Markov models (HMMs). We utilize biological knowledge gathered from gene ontologies and the hidden Markov structure to capture the potential coexpression of neighboring genes. Our interpretable model-based clustering characterized each cluster of samples by three groups of features: overexpressed, underexpressed, and irrelevant features. The proposed methods have been implemented in an R package and are used to analyze both the simulated data and The Cancer Genome Atlas kidney cancerdata.

Early prediction of pancreatic cancer through detection of methylated circulating DNA in blood.

e16353 Background: Pancreatic cancer represents a significant oncological challenge, contributing markedly to cancer-associated mortalities. This is attributed to the asymptomatic nature of early-stage disease, its inherently aggressive phenotype, and a notably suboptimal response to existing systemic therapies. Hence, the formulation of efficacious early diagnostic modalities for pancreatic cancer amenable to surgical resection is imperative to augment patient prognoses and enhance survival outcomes. Methods: The Infinium MethylationEPIC v2.0 BeadChip platform facilitated the examination of methylation profiles within plasma samples, while the HumanMethylation450 BeadChip array was employed for analogous analyses in corresponding tissue specimens from Taiwanese pancreatic cancer cohorts, aiding in gene selection. Subsequent methylation status verification was conducted via quantitative Methylation-Specific PCR (qMSP) on both genomic DNA and circulating cell-free DNA samples. For external methylation confirmation within a Western demographic, data from The Cancer Genome Atlas (TCGA) Portal were utilized, with further validation conducted using plasma samples from U.S. pancreatic cancer patients. Total 105 plasma from pancreatic cancer patients and healthy subjects were analyzed to calculate the candidate the accuracy, sensitivity, specificity of biomarkers for predict the pancreatic cancer. Results: The test exhibited an accuracy of 90.4%, sensitivity of 92.3%, and specificity of 90.1% in the detection of pancreatic cancer. Detection levels increased from 0% in healthy individuals to 87.5% in Stage I and 100% in Stages II, III, and IV. The CA19-9 marker displayed limited specificity with a high rate of false positives. Moreover, the test dynamically evaluated tumor burden by monitoring circulating methylated ZFP30 and FBXL7 in the plasma of breast cancer patients post-treatment. Conclusions: The analysis of plasma for methylated ZFP30 and FBXL7 provides an innovative and accurate noninvasive method that outperforms the existing blood biomarker CA19-9 for the prediction and ongoing assessment of tumor burden following treatment in pancreatic cancer.

Insights from C-CAT repository: A novel approach to identifying appropriate patient populations for anticancer drug development using the clinicogenomic nationwide database in Japan.

11159 Background: The integration of Comprehensive Genomic Profiling (CGP) with real-world data (RWD) provides crucial insights for anticancer drug development to achieve precision oncology, therefore, the current global trend to construct as the national projects, yet its clinical impact remains underexplored. The therapeutic efficacy of each line of anti-cancer drug in various cancer types and the relationship between genetic alterations and the effect of anticancer drugs is needed to guide the direction of future drug development promptly and precisely. Methods: A comprehensive analysis was conducted on data from Japan's Center for Cancer Genomics and Advanced Therapeutics (C-CAT) repository between June 2019 and December 2023. Five genomic profiling assays for CGP testing were reimbursed by nation-wide insurance in Japan. Utilizing the clinicogenomic repository of C-CAT, registered data of therapeutic efficacy of anticancer drug including cytotoxic agents, molecular-targeted agents, and immune checkpoint inhibitors were evaluated in association with genetic alterations. Data on anticancer drug of patients (pts) with recurrent or metastatic disease were included in the analysis. Results: The C-CAT registry data encompassed 60,256 pts across 32 tumor organ sites, classified ontology by the OncoTree over 4.5 years. Of these patients, 30,234 were male, and 30,017 were female, with 5 classified as unknown. In terms of age, 31,919 pts were < 65. The most registered origins were colorectal (n=10,110), pancreas (n=8,069), biliary tract (n=5,030), breast (n=3,744), esophagus/stomach (n=3,734), prostate (n=3,701), ovarian/fallopian tube (n=3,521), lung (n=3,427), and soft tissue (n=2,568). TP53mutations (58.3%, n=35,102) were most prevalent, followed by KRAS (25.8%, n=15,521), APC (19.9%, n=11,919), NOTCH3 (14.1%, n=8,502), and PIK3CA (13.0%, n=7,861). The frequency of BRCA1/2mutation was 4.1% (n=2,479). Overall response rate (ORR) for the first-, second-, third-, and fourth-lines treatment were 35.7%, 23.3%, 19.4%, and 19.2%, respectively. Pts treated with cisplatin or carboplatin in the first-line treatment, who had TP53 mutations, showed a better ORR than those without (44.0% vs. 38.9%, p < 0.001). Similarly, pts with BRCA1/2mutation showed a better ORR than those without (53.9% vs. 41.4%, p < 0.001) in the first-line treatment. Conclusions: This study was the first to report the relationship between genetic alterations and the therapeutic effect of anticancer drugs in each line. This approach based on big data analysis potentially accelerates drug development in the appropriate patient population by identifying significant associations between specific genetic mutations and improved outcomes with certain chemotherapy regimens in various cancers.

Retroperitoneal leiomyosarcoma; Demographics, clinicopathological, survival analysis, role of adjuvant radiation and genomic analysis.

e23327 Background: Retroperitoneal leiomyosarcoma (RPL) is an exceedingly rare malignancy and is the second most common subtype of leiomyosarcomas. Most of the information concerning this rare disease is limited to case studies and few literature studies. The study's objectives were to assess the demographics, pathological factors, survival outcomes, genomic landscape and potential for targeted therapy. Methods: Data of retroperitoneal leiomyosarcoma were obtained from the Surveillance, Epidemiology, and End Results (SEER) database, with cases diagnosed between 2000 and 2020 used for this study and the Cancer Genome Atlas containing sequencing information for 27 cases of RPL. Results: A total of 1406 cases were extracted with mean age of diagnosis of 62.1 years. Of these, RPL had a higher incidence in women (71.5%), more cases affected Whites (58.7%). Histologically, in patients with known grading, most patients’ i.e. 62.35% were grade 3, G3 (poorly differentiated). Nearly all (99.9%) patients presented with a tumor size ≥4.1 cm. Local lymph nodes metastasis were positive in 76.0% of cases when lymph node status was reported. In known cases of distant metastases, the most common site was liver (8.9%). The main treatment modality was surgery only (26.7%, hazard ratio, HR 1.17, p-value < 0.001), followed by the standard of care (surgery and radiation) (18.4%, HR 1.03, p-value < 0.001), and radiation only (18.4%). Five-year overall survival was 40% and 5-year cause-specific survival was 64%. The highest 5-year survival was observed in patients undergoing surgery and radiation (57%), and in combination therapy (chemotherapy, surgery, and radiation) the 5-year survival was 39%. Genetic analysis found the most common mutations included TP53 (51.9%), RB1 (25.9%), and ATRX (14.8%). When accounting for deep deletions and rearrangements, total frequency of the alterations was higher such that TP53 (63.0%), RB1 (51.9%), and ATRX (18.5%). Potentially actionable alterations were rare but included activating alterations in FGFR1, PIK3CA and ESR1, as well as deep deletions of BRCA1. Conclusions: Retroperitoneal leiomyosarcoma is a very rare tumor where surgery and radiation has been the conventional therapy. Our study indicates there are genetic alterations in the majority of retroperitoneal leiomyosarcoma. To further understand the pathogenesis and survival factors, patients with RPL should be enrolled into national and international registries.

Bioinformatics and Experimental Study Revealed LINC00982/ miR-183-5p/ABCA8 Axis Suppresses LUAD Progression.

Lung adenocarcinoma (LUAD) is a major health challenge worldwide with an undesirable prognosis. LINC00982 has been implicated as a tumor suppressor in diverse human cancers; however, its role in LUAD has not been fully characterized. Expression level and prognostic value of LINC00982 were investigated in pan-cancer and lung cancer from The Cancer Genome Atlas (TCGA) project. Differential expression analysis based on the LINC00982 expression level was performed in LUAD followed by gene set enrichment analysis (GSEA) and functional enrichment analyses. The association between LINC00982 expression and tumor immune microenvironment characteristics was evaluated. A potential ceRNA regulatory axis was identified and experimentally validated. We found that LINC00982 expression was downregulated and correlated with poor prognosis in LUAD. Enrichment analyses revealed that LINC00982 could inhibit DNA damage repair and cell proliferation, but enhance tumor metabolic reprogramming. We identified a competing endogenous RNA network involving LINC00982, miR-183-5p, and ATP-binding cassette subfamily A member 8 (ABCA8). Luciferase assays confirmed that miR-183-5p can interact with LINC00982 and ABCA8. Forced miR-183-5p expression reduced LINC00982 transcript levels and suppressed ABCA8 expression. Our findings revealed the LINC00982/miR-183-5p/ABCA8 axis as a potential therapeutic target in LUAD.

Tumor origin identification through machine learning and gene expression profiling.

e13597 Background: Accurate identification of tumor origin is crucial for effective diagnosis and treatment, particularly in cases of metastatic tumors. Interpretable machine learning models have displayed significant potential in addressing this problem when imaging and immunohistochemistry (IHC) examinations are ineffective. In this study, we developed a panel using gene expression profiles from various tumors and constructed a robust machine learning model for precise identification of tumor origin. Methods: RNA sequencing (RNA-seq) data of 9462 tumor samples originating from 21 different organs were collected from The Cancer Genome Atlas (TCGA). We conducted feature engineering through unsupervised clustering and differential gene expression analysis, selecting a refined panel of 164 genes from a pool of over 60,000 identifiers. Subsequently, a machine learning classifier grounded in Logistic regression (LR) was trained on 9462 samples with the constructed 164-gene panel. To enhance the adaptability of our model to this task, 10-fold cross-validation was employed in the multi-class mode. Two independent test sets, the Primary tumor set (PT, n=3420, including 19 tumor types) and the Metastatic tumor set (MTP, n=100, all originating from the prostate and spread to bone, liver, etc.), were established with samples from Gene Expression Omnibus (GEO) and other published studies. Notably, all samples underwent FPKM normalization and there was no overlap between training and testing samples. Model performance was assessed using accuracy, specificity, and sensitivity metrics. Results: The 164-gene expression panel achieved a cross-validation accuracy of 96.73%. In the assessment of the PT test set, our model achieved an overall 99.62% specificity, 92.31% accuracy and 89.79% sensitivity, exhibiting performance comparable to similar models in other studies. Additionally, our model accurately traced 91% of metastatic tumors in the MTP test set to the prostate, surpassing previous lines of work by a large margin. Conclusions: Gene expression patterns reveal organ-specific characteristics that could be used to identify tumor origin. The combination of a condensed yet comprehensive gene expression panel with a robust machine learning model serves as a promising tool for tumor diagnosis. Ongoing correlative studies aim to extend predictions from tissue organs to cancer subtypes.

A retrospective machine learning–based analysis of nationwide cancer comprehensive genomic profiling data to identify features associated with recommendation of mutation-based therapy.

e13510 Background: Comprehensive genomic profiling (CGP) has played key roles in cancer precision medicine through optimization of therapeutic interventions based on genomic alterations in cancer cells. However, the probability of discovering mutation-based treatments through CGP remains low. To enhance the effectiveness and efficiency of cancer precision medicine, it is crucial to identify patients who are likely to benefit from CGP tests. This study aims to identify characteristics of patients in which mutation-based treatments are discovered by CGP tests. Methods: We retrospectively analyzed data from 60,655 patients who underwent CGP tests and were registered in the Center for Cancer Genomics and Advanced Therapeutics (C-CAT), the national data center for cancer CGP in Japan. The C-CAT database covers 99.7% of cancer patients who have undergone CGP tests in Japan. Major cancer types included 10,182 cases of bowel cancer, 8,691 pancreas cancer, 5,062 biliary tract cancer, and 3,777 breast cancer. We developed an eXtreme Gradient Boosting (XGBoost) model using machine learning, and used clinical information as input to predict whether one or more Japanese Pharmaceuticals and Medical Devices Agency (PMDA)-approved drugs are discovered through CGP tests or not. Shapley Additive Explanations (SHAP) was employed to extract significant features that contribute to the model prediction. Results: The prediction model achieved an area under the receiver operating characteristic curve of 0.826 for the overall cancer population. Positive SHAP values were observed for patients with breast (mean SHAP in breast cancer patients: 1.38), lung (1.08), bowel (0.75), and pancreas cancers (0.35), while negative SHAP values were associated with head and neck (-1.75), cervical cancers (-1.54), and brain (-1.33). Positive SHAP values were also associated with presence of liver or lymph node metastasis (0.23, 0.08), shorter intervals between diagnosis and CGP testing or specimen collection and CGP testing, and advanced age. Similar trends were observed in cancer-type-specific prediction models, which also identified their own unique features. In the adolescent and young adult (AYA) age group, primary brain tumors were strongly associated with negative SHAP values (-1.37). Conclusions: Our machine learning-based analysis of nationwide CGP data identified features that predict cases in which mutation-based treatments are discovered by CGP tests, both in the overall cancer population and within specific cancer types and the AYA age group. Expedited CGP testing is recommended for patients who match the identified profile to facilitate early targeted therapy interventions.

Lysophosphatidic acid promotes ESCC progression by increasing the level of CCL2 secreted by esophageal epithelial cells.

Lysophosphatidic acid (LPA) is a small bioactive lipid which acts as a potent regulator in various tumor progressions through six G-protein-coupled receptors (LPA1-LPA6). Our previous study demonstrated that the LPA-producing enzyme, autotaxin (ATX), was upregulated in esophageal squamous cell carcinoma (ESCC) and ATX high expression levels indicated a poor prognosis. Esophageal squamous cell carcinoma is a type of malignant tumor which originates from epithelial cells. Its progression can be affected by the interaction between cancer cells and normal cells. However, the impact of LPA on the interaction between esophageal epithelial cells and cancer cells in the development of ESCC remains uncertain. MTS and Edu assays were performed to determine ESCC cell proliferation in culture medium (CM) derived from LPA-stimulated esophageal epithelial cells (Het-1a). A wound healing assay, transwell migration and an invasion assay were performed to assess the metastatic ability of ESCC cells. Cytokine array analysis was conducted to detect the differentially secreted cytokines in CM. The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were utilized to uncover the pathways and cytokines that are influenced by LPA in ESCC. Immunohistochemical staining was employed to measure the expression of ATX and CCL2 in early-stage ESCC. Quantitative real-time PCR, western blot, enzyme-linked immunosorbent assay and an antibody neutralization assay were employed to measure the mechanism of LPA-mediated communication between epithelial cells and cancer cells. Functional experiments showed that exposing ESCC cancer cells to CM from LPA-treated Het-1a results in promoting proliferation, migration, invasion and epithelial-mesenchymal transition processes. Using cytokine array analysis, we discovered that LPA triggers the release of multiple cytokines from epithelial cells. After screening of the TCGA and GEO databases, CCL2 was identified and found to be correlated with ATX expression in ESCC. Furthermore, CCL2 levels in both mRNA expression and secretion were observed to be upregulated in epithelial cells upon stimulation with LPA. Blocking CCL2 effectively reduced the pro-migration influence of CM derived from LPA-treated Het-1a. Mechanism studies have demonstrated that LPA activated the NF-κB signaling pathway through LPA1/3, ultimately causing an increase in CCL2 expression and secretion in Het-1a. Our findings, taken together, demonstrate that CM from LPA-treated esophageal epithelial cells plays a significant role in promoting the progression of ESCC, with CCL2 acting as the primary regulator.

Harnessing cell-free orphan non-coding RNAs as a predictive measure of long-term survival in neoadjuvant breast cancer therapy.

585 Background: Pathologic complete response (pCR) is a valuable metric for predicting survival following neoadjuvant therapy (NAT) in breast cancer; however, blood-based biomarkers may refine the prediction of treatment outcomes and inform follow-up treatment decisions. Orphan non-coding RNAs (oncRNAs) are a novel category of small RNAs that are present in tumors and largely absent in healthy tissue. Here, we explore the feasibility of a tumor-naive oncRNA-based liquid biopsy assay for predicting distant recurrence-free survival (DRFS) in breast cancer patients using a standard blood draw. Methods: Our cohort included 538 high-risk breast cancer patients from the multicenter I-SPY2 trial having serum available after NAT and before surgery (timepoint T3). Of the 538 patients (mean age: 48.8 ± 10.3), 191 (36%) achieved pCR. We isolated small RNA from 0.8 mL of patient serum and sequenced it at an average depth of 50 million 100-bp single-end reads. We used our catalog of tumor-derived oncRNAs, discovered in The Cancer Genome Atlas, to develop an AI model for predicting tumor burden based on tumor size. Our AI model was trained on oncRNA profiles from an independent cohort of 719 serum samples collected at diagnosis from individuals with cancer. We assessed tumor burden by oncRNA in the I-SPY2 cohort at T3 and associated this measure with DRFS using robust cutpoints established for each of the two pCR groups: high- ( n=70) and low-burden ( n=468) cutpoints were verified through cross-validation and almost all folds converged on a single value, which we used in our analysis. Results: The continuous oncRNA-derived tumor burden at T3 had a hazards ratio (HR) of 2.4 (95% CI: 1.1–5.1) for predicting DRFS. This measure remained significant in multivariate Cox models that included pCR (HR = 2.7 [1.3–5.6]) or residual cancer burden (RCB II/III versus 0/I; HR = 3.4 [1.6–7.1]). Patients with pCR had a 3-year DRFS of 94%; patients with pCR having low ( n=180) versus high ( n=11) oncRNA burden resulted in 95% and 73% 3-year DRFS, respectively (HR = 7.4 [2.3–24]). Those without pCR had a 3-year DRFS of 78% and stratification by oncRNA burden resulted in 85% and 71% 3-year DRFS for the low ( n=288) and high ( n=59) burden groups, respectively (HR=2.4 [1.5–3.9]). Importantly, we saw no restricted mean survival time difference (RMST) at 3 years between those with and without pCR among the high oncRNA burden group (0.1-month difference, p=0.97). Conclusions: We demonstrate the utility of a tumor-naive, oncRNA-derived tumor burden AI model as a predictive measure of treatment outcome and as a prognostic biomarker that can be used in addition to pCR to further stratify patients as low or high risk of recurrence. Integration of our oncRNA assay with existing biomarkers and clinicopathologic variables, has the potential to add value for predicting outcomes and prioritizing patients that would benefit from additional care.

BSG Isoform 2 (ENST00000353555) Is a Prognostic Biomarker in Predicting Unfavorable Overall Survival of Patients With Liver Hepatocellular Carcinoma.

CD147, encoded by the BSGgene, has complex transcripts that encode proteins of different lengths. Total BSG transcription is aprognostic biomarker for patients with liver cancer. This study tried to analyze the expression profile and prognostic significance of BSG transcripts in liver cancer. RNAsequencing data from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) project, survival data from TCGA, and protein expression data from the Human Protein Atlas were systematically analyzed. Among the four protein-coding transcripts of BSG, ENST00000353555 encoding basigin-2 is the dominant transcript isoform. It might be an independent prognostic biomarker for unfavorable overall survival in patients with liver cancer (HR: 1.404, 95% CI: 1.1224-1.754, p = 0.003). ENST00000353555 might be a prognostic biomarker linking unfavorable overall survival in liver cancer patients.

A hybrid epithelial/mesenchymal state in head and neck cancer: A biomarker for survival with differential prognosis by self-reported race

Head and neck squamous cell carcinoma (HNSCC) is the 6th leading cause of cancer-related mortality, with racial disparities amplifying the challenges in treatment. Although the relationship between hybrid epithelial/mesenchymal (E/M) states and tumor progression is of interest, no studies have characterized the clinical relevance of hybrid E/M states in head and neck cancer outcomes among self-reported racial cohorts. Given the overlap in gene expression between hybrid E/M malignant cells and cancer-associated fibroblasts, we utilized deconvolution of bulk RNA sequencing data from oral cavity and laryngeal squamous cell carcinoma tumors from The Cancer Genome Atlas. We utilized our previously collected single-cell profiles to generate inferred malignant profiles and then scored these for hybrid E/M. We then conducted a survival analysis on overall and disease-free survival among self-reported Black and White Americans. The hybrid E/M state was differentially associated with head and neck cancer survival by self-reported race and ethnicity, with a stronger association in non-Hispanic Black patients. Black patients with a high hybrid E/M score had a higher risk of death or recurrence (hazard ratio [HR]: 4.18 [95% confidence interval (CI): 2.06, 8.49]) than White patients with a high hybrid E/M score (HR: 1.58 [95% CI: 1.11, 2.26]). Our results suggest a complex interplay of social structure, racism, and genetic diversity. We implore researchers to consider the social and biological context contributing to disparities. A.L.M. received support from the National Institute of Minority Health and Health Disparities (K01MD013897 [principal investigator (PI), A.L.M.]). S.V.P. received support from the National Institute of Dental and Craniofacial Research (R01DE032865 [PI, S.V.P.] and R01DE032371 [PI, S.V.P.]).

SLC7A11 and the glutathione pathway as novel prognostic markers in resectable pancreatic ductal adenocarcinoma: A metabolomics study of clinical specimens

Background/objectivesDespite the poor prognosis associated with pancreatic ductal adenocarcinoma (PDAC), there remains a lack of clarity regarding the metabolic pathways and their significant impact on its phenotype. Therefore, we aimed to utilize metabolomics to capture changes in clinical PDAC tissues and elucidate the significant metabolic pathways close to its phenotypes. MethodsThis basic research was retrospectively validated using database research, immunohistochemistry, and protein analysis based on the findings obtained from metabolomics using clinical tissues collected from prospectively registered patients with PDAC. mRNA expression analysis using a database and protein analysis using archived clinical specimens was performed to validate the candidate pathways identified using metabolomics. Between-group comparisons were analyzed using paired t-tests and log-rank test, and Kaplan–Meier curves illustrated survival times. ResultsPatients subjected to metabolomics revealed a significant increase in glutathione disulfide levels in PDAC tissues when compared to normal pancreatic tissues. The Cancer Genome Atlas database analysis revealed significant changes in glutathione pathway-related mRNAs in PDAC compared to that in the normal pancreas. Protein analysis of previously resected specimens demonstrated a significant increase in SLC7A11 expression in PDAC tissues. The abundance ratio of SLC7A11 isoforms was associated with the post-operative prognosis in resectable PDAC. ConclusionGlutathione disulfide levels were significantly increased in clinical PDAC metabolomics. Additionally, increased mRNA and protein expression in SLC7A11 was observed in PDAC. Furthermore, the SLC7A11 isoform abundance ratio may be a valuable prognostic marker in patients with resectable PDAC.

BSG Isoform 2 (ENST00000353555) Is a Better Component Than Total BSG Expression in Generating Prognostic Signature for Overall Survival of Liver Cancer.

The basigin (BSG) gene, also known as CD147, has been implicated in the progression and prognosis of various cancers, including liver cancer. This study aimed to comprehensively evaluate the prognostic value of total BSG expression and its specific transcript variants, ENST00000353555 and ENST00000545507, in a large cohort of patients with primary liver cancer. The prognostic values of total BSG, ENST00000353555, and ENST00000545507 expression in overall survival (OS) and progression-free interval (PFI) of patients with primary liver cancer were assessed using The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) dataset. Survival analysis, receiver operating characteristic (ROC) analysis, and validation of an extracellular matrix (ECM)-related prognostic signature were performed. In univariate and multivariate analyses, total BSG, ENST00000353555, and ENST00000545507 expression were associated with poor OS in liver cancer patients. ENST00000353555 showed the highest hazard ratio among the three prognostic indicators. ROC analysis revealed that ENST00000353555 had better prognostic performance than total BSG expression. Replacing total BSG with ENST00000353555 in an existing ECM-related prognostic signature marginally increased the area under the curve values for one year from 0.79 to 0.80, and five-year OS from 0.72 to 0.73. ENST00000353555 showed isoform-specific positive correlations with EDNRB, IL10, C10orf54, and VEGFA. ENST00000353555 serves as a better prognostic biomarker than total BSG expression in liver cancer, either as an individual marker or as a component of an ECM-related gene signature. Additionally, ENST00000353555 exhibited isoform-specific positive correlations with several immunosuppressive genes, suggesting a potential role in regulating the tumor microenvironment.

LncRNA PVT1 facilitates the growth and metastasis of colorectal cancer by sponging with miR-3619-5p to regulate TRIM29 expression.

Colorectal cancer (CRC) is the second most common cause of cancer-related death worldwide. Long noncoding RNA (lncRNA) is involved in many malignant tumors. This study aimed to clarify the role of the lncRNA plasmacytoma variant translocation 1 (PVT1) in CRC growth and metastasis. Differentially expressed lncRNAs in CRC were analyzed using the Cancer Genome Atlas. Gene expression profiling interactive analysis and a comprehensive resource for lncRNAs from cancer arrays databases were used to analyze lncRNA PVT1 expression and CRC prognosis, respectively. Cell counting kit-8, wound healing, colony formation, Transwell, and immunofluorescence assays were used to evaluate CRC cell proliferation, migration, invasion, and epithelial-mesenchymal transition (EMT), respectively. Tumor growth and metastasis models were used to explore the PVT1 effect on the growth and metastasis of CRC in vivo. PVT1 was highly expressed in CRC, associated with a poor prognosis of CRC, and showed good diagnostic value. Transfection of sh-PVT1 or pcDNA3.1-PVT1 reduced or increased the proliferation, wound healing rate, colony formation, invasion, and EMT of CRC cells. PVT1 and miR-3619-5p were co-expressed in CRC cytoplasm, and PVT1 acted as a competitive endogenous RNA (ceRNA) by sponging miR-3619-5p to up-regulate tripartite motif containing 29 (TRIM29) expression. MiR-3619-5p overexpression and TRIM29 knockdown reduced proliferation, wound healing rate, invasion, and EMT of CRC cells. However, simultaneous PVT1 and miR-3619-5p overexpression or knockdown of miR-3619-5p and TRIM29 knockdown rescued the malignant phenotype of CRC cells. We first clarified the ceRNA mechanism of PVT1 in CRC, which induced growth and metastasis by sponging with miR-3619-5p to regulate TRIM29.

Remodelin delays non-small cell lung cancer progression by inhibiting NAT10 via the EMT pathway.

Lung cancer remains the foremost reason of cancer-related mortality, with invasion and metastasis profoundly influencing patient prognosis. N-acetyltransferase 10 (NAT10) catalyzes the exclusive N (4)-acetylcytidine (ac4C) modification in eukaryotic RNA. NAT10 dysregulation is linked to various diseases, yet its role in non-small cell lung cancer (NSCLC) invasion and metastasis remains unclear. Our study delves into the clinical significance and functional aspects of NAT10 in NSCLC. We investigated NAT10's clinical relevance using The Cancer Genome Atlas (TCGA) and a group of 98 NSCLC patients. Employing WB, qRT-PCR, and IHC analyses, we assessed NAT10 expression in NSCLC tissues, bronchial epithelial cells (BECs), NSCLC cell lines, and mouse xenografts. Further, knockdown and overexpression techniques (siRNA, shRNA, and plasmid) were employed to evaluate NAT10's effects. A series of assays were carried out, including CCK-8, colony formation, wound healing, and transwell assays, to elucidate NAT10's role in proliferation, invasion, and metastasis. Additionally, we utilized lung cancer patient-derived 3D organoids, mouse xenograft models, and Remodelin (NAT10 inhibitor) to corroborate these findings. Our investigations revealed high NAT10 expression in NSCLC tissues, cell lines and mouse xenograft models. High NAT10 level correlated with advanced T stage, lymph node metastasis and poor overall survive. NAT10 knockdown curtailed proliferation, invasion, and migration, whereas NAT10 overexpression yielded contrary effects. Furthermore, diminished NAT10 levels correlated with increased E-cadherin level whereas decreased N-cadherin and vimentin expressions, while heightened NAT10 expression displayed contrasting results. Notably, Remodelin efficiently attenuated NSCLC proliferation, invasion, and migration by inhibiting NAT10 through the epithelial-mesenchymal transition (EMT) pathway. Our data underscore NAT10 as a potential therapeutic target for NSCLC, presenting avenues for targeted intervention against lung cancer through NAT10 inhibition.

CK2 expression in cancer and associated clinical characteristics.

e15156 Background: The protein casein kinase 2 (CK2) is a ubiquitously expressed, highly pleiotropic kinase. CK2 may have incredibly varied substrate specificity and catalytic activity, as it functions as both a tetrameric and monomeric enzyme. CK2 participates in many cellular processes such as cellular proliferation, growth, and survival, and has significant potential to assist in oncogenic signaling. We and others have demonstrated CK2 gene and protein overexpression, and sometimes underexpression, in a variety of cancers. Here we investigate how the expression of CK2 subunits in cancer is associated with clinical characteristics in several distinct cancer types. Methods: Data analysis was performed on 32 non-overlapping cancer datasets obtained from The Cancer Genome Atlas (TCGA) and Broad GDAC Firehose Legacy projects, one dataset from Cancer Cell Line Encyclopedia (CCLE), and normal tissue datasets from Gene Expression Omnibus. Statistical analysis was performed using RStudio to explore the relationship between the expression levels of the three CK2 alpha transcripts (CSNK2A1, CSNK2A2, and CSNK2A3) and beta transcripts (CSNK2B) with sample clinical characteristics provided by TCGA/Broad. Relationships between CK2 expression and cancer-type specific characteristics were also explored, such as KRAS mutations in colorectal adenocarcinoma and serum AFP at sample procurement in hepatocellular carcinoma. CCLE data was used to explore correlations between cancer-type dependent CK2 gene expression and half maximal inhibitory concentration (IC50) of over 250 anticancer drugs. Tumor Immune Estimation Resource (TIMER) 2.0 was used to assess the relationship between CK2 subunit expression and tumor immune infiltration. Results: As seen in our previous analysis, CK2 transcripts were upregulated in the majority of cancer types analyzed. We also found that CK2 transcript upregulation correlated with worse survival outcomes in several previously unstudied cancers. CK2 expression differed significantly between cancer subtypes such as in ductal carcinoma and lobular carcinoma in breast cancer. CK2 expression significantly correlated with tumor grade, TNM staging, clinical staging in certain cancer types. CK2 expression significantly correlated with altered response to various anticancer drugs in certain cancers. CK2 expression was associated with both increased and decreased immune infiltration in a variety of cancers. Conclusions: Overall, this study demonstrates that CK2 transcript expression correlates with a variety of cancer-type dependent characteristics and clinical outcomes. As a result, CK2 may be useful as a prognostic biomarker and therapeutic target.

Transcriptomic and epigenetic characterization of mucinous adenocarcinoma of the cervix.

e17518 Background: Mucinous adenocarcinoma of the cervix (MAC) is a rare and aggressive histologic subtype of cervical cancer with poorer prognosis than its more common counterparts, usual type endocervical adenocarcinoma (UEA) and squamous cell carcinoma (SCC). MAC is also known to be less responsive to standard chemotherapy and radiation approaches used to treat cervical cancer. In this study, we sought to identify transcriptomic and epigenetic features of MAC that distinguish MAC from UEA and SCC, and to identify features that may contribute to poorer prognosis in MAC compared to UEA and SCC. Methods: Clinical data, gene expression, miRNA expression, and methylation data for 17 MAC, 27 UEA, and 247 SCC from The Cancer Genome Atlas Cervical Cancer Project were obtained and analyzed. Clinical data were compared for the histologic groups using Fisher’s Exact, ANOVA, and Kruskal-Wallis statistics where appropriate. Kaplan-Meier curves were used for survival analyses. Gene expression data was analyzed using DESeq2 for differential expression of mRNA. Pathway enrichment analysis of gene expression data was completed using Qiagen IPA Core Analysis software. Comparative marker selection was used to determine differential expression of miRNA, and differential methylation across histologic subtypes. Analyses were completed both by histology and by disease-progression status. Data across all three platforms were integrated to evaluate for potential explanatory relationships of interest. Results: RNAseq analysis revealed a number of genes differentially expressed in MAC as compared to UEA and SCC. Several of these genes were also found to be differentially expressed in samples from patients with disease progression compared to those without known disease progression including increased expression of PPARGC1A, SLC2A1, and ZNF703 and decreased expression of NR4A3. Analysis also revealed hypomethylation at CpG probe sites mapping to SLC2A1 and decreased expression of miRNAs known to target SLC2A1 and PPARGC1A in MAC tumors . Pathways involving translation regulation, EIF2 signaling, and oxidative phosphorylation were downregulated in MAC whereas pathways involving mitochondrial dysfunction, glucose metabolism, and estrogen receptor signaling were upregulated in MAC. Downregulation of immune and inflammatory response pathways and upregulation of tumor-supporting macrophage activation was seen in MAC tumors with disease progression. Conclusions: We identified novel transcriptomic and epigenetic features and altered pathways in MAC, as well as those features and pathways associated with poorer prognosis in this population. These findings may contribute to further confirmatory research and lead to novel therapeutic strategies for this rare cancer.

Pan-cancer analysis of DCTN2 and its tumour-promoting role in HCC by modulating the AKT pathway.

Dynactin subunit 2 (DCTN2) has been reported to play a role in progression of several tumours; however, the involvement of DCTN2 in potential mechanism or the tumour immune microenvironment among various cancers still remains largely unknown. Therefore, the objective of this study was to comprehensively investigate the expression status and potential function of DCTN2 in various malignancies through different database, such as The Cancer Genome Atlas, the Genotype-Tissue Expression and Gene Expression Omnimus databases. We discovered that DCTN2 expression was high in many type of tumours tissues compared to adjacent non-tumour ones. High DCTN2 signified poor prognosis for patients with tumours. Additionally, Gene Set Enrichment Analysis (GSEA) analysis revealed that DCTN2 was positively correlated with oncogenic pathways, including cell cycle, tumour metastasis-related pathway, while it was negatively with anti-tumour immune signalling pathway, such as INF-γ response. More importantly, we elucidated the functional impact of DCTN2 on hepatocellular carcinoma (HCC) progression and its underlying mechanisms. DCTN2 expression was much higher in HCC tissues than in adjacent non-tumour tissues. Silencing DCTN2 dramatically suppressed the proliferative and metastasis capacities of tumour cell invitro. Mechanistically, DCTN2 exerted tumour-promoting effects by modulating the AKT signalling pathway. DCTN2 knockdown in HCC cells inhibited AKT phosphorylation and its downstream targets as well. Rescue experiments revealed that the anti-tumour effects of DCTN2 knockdown were partially reversed upon AKT pathway activation. Overall, DCTN2 may be a potent biomarker signifying tumour prognosis and a promising therapeutic target for tumour treatment, particularly in HCC.

The effect of resting dendritic cells on overall survival in the hepatocellular carcinoma tumor microenvironment.

e14559 Background: Hepatocellular carcinoma (HCC) continues to have a poor prognosis even in the advent of new immunotherapy regimens. An area of growing interest in HCC is its tumor immune microenvironment. Classifying the immune microenvironment based on available genomics can lead to insights on prognosis and treatment, especially regarding the role of immunotherapy. Methods: RNA sequencing data was extracted from The Cancer Genome Atlas (TCGA), specifically from the hepatocellular carcinoma section (n = 366). CIBERSORT was utilized to organize the immune cell fractions present for each patient sample. Radiant by Shiny was used to perform a linear regression elucidating the relationship between overall survival and several other variables including: age, gender, tumor stage, race, ethnicity, B-cells naïve, B-cells memory, plasma cells, CD8 T-cells, CD4 T-cells, T-cells follicular, T-cells regulatory, natural killer activated and resting cells, monocytes, macrophages (M0, M1, M2), dendritic cells activated and resting, mast cells activated and resting, eosinophils, and neutrophils. Subgroup analyses were also performed via radiant regarding gender, race, and resting dendritic counts. Results: Resting dendritic cells were independently prognostic of overall survival on multivariable analysis (p = 0.041). The coefficient of correlation was +164.53 showing a positive association between the number of resting dendritic cells and the overall survival. Subgroup analysis showed no difference in resting dendritic counts between males and females (p = 0.526). Also, there was no difference seen in resting dendritic counts between populations of Asians and African Americans (p = 0.873), Asians and Whites (p = 0.223), and between Whites and African Americans (p = 0.811). Conclusions: Patients with a higher number of resting dendritic cells in their tumor immune microenvironment had a better overall survival. There was no difference between males and females in the resting dendritic counts. Likewise, there was no difference in the resting dendritic counts between Asian, African American, and white populations. Resting dendritic cells have been shown to be enriched in hepatocellular carcinomas with a low tumor mutational burden. This is a paradigm that should be explored further in the context of immunotherapy. Resting dendritic cells are also in a state of plasticity as they go from resting to active. Future genomic analysis of the tumor microenvironment regarding this plasticity could also lead to enhancing treatment options.

Single-cell signatures identify microenvironment factors in tumors associated with patient outcomes

The cellular components of tumors and their microenvironment play pivotal roles in tumor progression, patient survival, and the response to cancer treatments. Unveiling a comprehensive cellular profile within bulk tumors via single-cell RNA sequencing (scRNA-seq) data is crucial, as it unveils intrinsic tumor cellular traits that elude identification through conventional cancer subtyping methods. Our contribution, scBeacon, is a tool that derives cell-type signatures by integrating and clustering multiple scRNA-seq datasets to extract signatures for deconvolving unrelated tumor datasets on bulk samples. Through the employment of scBeacon on the The Cancer Genome Atlas (TCGA) cohort, we find cellular and molecular attributes within specific tumor categories, many with patient outcome relevance. We developed a tumor cell-type map to visually depict the relationships among TCGA samples based on the cell-type inferences.