Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation.

Cancer progression involves the gradual loss of a differentiated phenotype and acquisition of progenitor and stem-cell-like features. Here, we provide novel stemness indices for assessing the degree of oncogenic dedifferentiation. We used an innovative one-class logistic regression (OCLR) machine-learning algorithm to extract transcriptomic and epigenetic feature sets derived from non-transformed pluripotent stem cells and their differentiated progeny. Using OCLR, we were able to identify previously undiscovered biological mechanisms associated with the dedifferentiated oncogenic state. Analyses of the tumor microenvironment revealed unanticipated correlation of cancer stemness with immune checkpoint expression and infiltrating immune cells. We found that the dedifferentiated oncogenic phenotype was generally most prominent in metastatic tumors. Application of our stemness indices to single-cell data revealed patterns of intra-tumor molecular heterogeneity. Finally, the indices allowed for the identification of novel targets and possible targeted therapies aimed at tumor differentiation.

A Pan-Cancer Analysis of Enhancer Expression in Nearly 9000 Patient Samples.

The role of enhancers, a key class of non-coding regulatory DNA elements, in cancer development has increasingly been appreciated. Here, we present the detection and characterization of a large number of expressed enhancers in a genome-wide analysis of 8928 tumor samples across 33 cancer types using TCGA RNA-seq data. Compared with matched normal tissues, global enhancer activation was observed in most cancers. Across cancer types, global enhancer activity was positively associated with aneuploidy, but not mutation load, suggesting a hypothesis centered on "chromatin-state" to explain their interplay. Integrating eQTL, mRNA co-expression, and Hi-C data analysis, we developed a computational method to infer causal enhancer-gene interactions, revealing enhancers of clinically actionable genes. Having identified an enhancer ∼140 kb downstream of PD-L1, a major immunotherapy target, we validated it experimentally. This study provides a systematic view of enhancer activity in diverse tumor contexts and suggests the clinical implications of enhancers.

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics.

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale.

Genetic and Functional Drivers of Diffuse Large B Cell Lymphoma.

Diffuse large B cell lymphoma (DLBCL) is the most common form of blood cancer and is characterized by a striking degree of genetic and clinical heterogeneity. This heterogeneity poses a major barrier to understanding the genetic basis of the disease and its response to therapy. Here, we performed an integrative analysis of whole-exome sequencing and transcriptome sequencing in a cohort of 1,001 DLBCL patients to comprehensively define the landscape of 150 genetic drivers of the disease. We characterized the functional impact of these genes using an unbiased CRISPR screen of DLBCL cell lines to define oncogenes that promote cell growth. A prognostic model comprising these genetic alterations outperformed current established methods: cell of origin, the International Prognostic Index comprising clinical variables, and dual MYC and BCL2 expression. These results comprehensively define the genetic drivers and their functional roles in DLBCL to identify new therapeutic opportunities in the disease.

Comprehensive and Integrated Genomic Characterization of Adult Soft Tissue Sarcomas.

Sarcomas are a broad family of mesenchymal malignancies exhibiting remarkable histologic diversity. We describe the multi-platform molecular landscape of 206 adult soft tissue sarcomas representing 6 major types. Along with novel insights into the biology of individual sarcoma types, we report three overarching findings: (1) unlike most epithelial malignancies, these sarcomas (excepting synovial sarcoma) are characterized predominantly by copy-number changes, with low mutational loads and only a few genes (TP53, ATRX, RB1) highly recurrently mutated across sarcoma types; (2) within sarcoma types, genomic and regulomic diversity of driver pathways defines molecular subtypes associated with patient outcome; and (3) the immune microenvironment, inferred from DNA methylation and mRNA profiles, associates with outcome and may inform clinical trials of immune checkpoint inhibitors. Overall, this large-scale analysis reveals previously unappreciated sarcoma-type-specific changes in copy number, methylation, RNA, and protein, providing insights into refining sarcoma therapy and relationships to other cancer types.

GLDADec: marker-gene guided LDA modeling for bulk gene expression deconvolution.

Inferring cell type proportions from bulk transcriptome data is crucial in immunology and oncology. Here, we introduce guided LDA deconvolution (GLDADec), a bulk deconvolution method that guides topics using cell type-specific marker gene names to estimate topic distributions for each sample. Through benchmarking using blood-derived datasets, we demonstrate its high estimation performance and robustness. Moreover, we apply GLDADec to heterogeneous tissue bulk data and perform comprehensive cell type analysis in a data-driven manner. We show that GLDADec outperforms existing methods in estimation performance and evaluate its biological interpretability by examining enrichment of biological processes for topics. Finally, we apply GLDADec to The Cancer Genome Atlas tumor samples, enabling subtype stratification and survival analysis based on estimated cell type proportions, thus proving its practical utility in clinical settings. This approach, utilizing marker gene names as partial prior information, can be applied to various scenarios for bulk data deconvolution. GLDADec is available as an open-source Python package at https://github.com/mizuno-group/GLDADec.

EPHB2 as a key mediator of glioma progression: Insights from microenvironmental receptor ligand-related prognostic gene signature.

Malignant gliomas, characterized by pronounced heterogeneity, a complex microenvironment, and a propensity for relapse and drug resistaniguree, pose significant challenges in oncology. This study aimed to investigate the prognostic value of Ligand and Receptor related genes (LRRGs) within the glioma microenvironment. An intersection of 71 ligand-related genes (LRGs) and 2628 receptor-related genes (RRGs) yielded a total of 69 LRRGs. Utilizing the least absolute shrinkage and selection operator (LASSO) regression analysis, a prognostic RiskScore model comprising 28 LRRGs was constructed. The model demonstrated robust prognostic value, further validated in the TCGA-GBMLGG dataset. Subsequent analyses included differential gene expression, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set enrichment (GSEA), and gene set variation (GSVA) within RiskScore groups. Additionally, evaluations of PPI, mRNA-RBP, mRNA-TF, and mRNA-drug interaction networks were conducted. Four hub genes were identified through differential expression analysis of the 28 LRRGs across various GSE datasets. A multivariate Cox prognostic model was constructed for nomogram analysis, gene mutation analysis, and related expression distribution. This study underscores the role of LRRGs in intercellular communication within the glioma microenvironment and identifies four hub genes crucial for prognostic assessment in clinical glioma patients. These findings offer a potential evaluation framework for glioma patients, enhancing our understanding of the disease and informing future therapeutic strategies.

The systematic identification of survival-related alternative splicing events and splicing factors in glioblastoma.

Glioblastoma multiforme (GBM) is the most common and aggressive primary brain tumor, making it one of the most life-threatening human cancers. Nevertheless, research on the mechanism of action between alternative splicing (AS) and splicing factor (SF) or biomarkers in GBM is limited. AS is a crucial post-transcriptional regulatory mechanism. More than 95% of human genes undergo AS events. AS can diversify the expression patterns of genes, thereby increasing the diversity of proteins and playing a significant role in the occurrence and development of tumors. In this study, we downloaded 599 clinical data and 169 transcriptome analysis data from The Cancer Genome Atlas (TCGA) database. Besides, we collected AS data about GBM from TCGA-SpliceSeq. The overall survival (OS) related AS events in GBM were determined through least absolute shrinkage and selection operator (Lasso) and Cox analysis. Subsequently, the association of these 1825 OS-related AS events with patient survival was validated using the Kaplan-Meier survival analysis, receiver operating characteristic curve, risk curve analysis, and independent prognostic analysis. Finally, we depicted the AS-SF regulatory network, illustrating the interactions between splicing factors and various AS events in GBM. Additionally, we identified three splicing factors (RNU4-1, SEC31B, and CLK1) associated with patient survival. In conclusion, based on AS occurrences, we developed a predictive risk model and constructed an interaction network between GBM-related AS events and SFs, aiming to shed light on the underlying mechanisms of GBM pathogenesis and progression.

MITO END-3: efficacy of avelumab immunotherapy according to molecular profiling in first-line endometrial cancer therapy.

BACKGROUND: Immunotherapy combined with chemotherapy significantly improves progression-free survival (PFS) compared to first-line chemotherapy alone in advanced endometrial cancer (EC), with a much larger effect size in microsatellite instability-high (MSI-H) cases. New biomarkers might help to select patients who may have benefit among those with a microsatellite-stable (MSS) tumor. PATIENTS AND METHODS: In a pre-planned translational analysis of the MITO END-3 trial, we assessed the significance of genomic abnormalities in patients randomized to standard carboplatin/paclitaxel without or with avelumab. RESULTS: Out of 125 randomized patients, 109 had samples eligible for next-generation sequencing analysis, and 102 had MSI tested. According to The Cancer Genome Atlas (TCGA), there were 29 cases with MSI-H, 26 with MSS TP53 wild type (wt), 47 with MSS TP53 mutated (mut), and 1 case with POLE mutation. Four mutated genes were present in >30% of cases: TP53, PIK3CA, ARID1A, and PTEN. Eleven patients (10%) had a BRCA1/2 mutation (five in MSI-H and six in MSS). High tumor mutational burden (≥10 muts/Mb) was observed in all MSI-H patients, in 4 out of 47 MSS/TP53 mut, and no case in the MSS/TP53 wt category. The effect of avelumab on PFS significantly varied according to TCGA categories, being favorable in MSI-H and worst in MSS/TP53 mut (P interaction = 0.003); a similar non-significant trend was seen in survival analysis. ARID1A and PTEN also showed a statistically significant interaction with treatment effect, which was better in the presence of the mutation (ARID1A P interaction = 0.01; PTEN P interaction = 0.002). CONCLUSION: The MITO END-3 trial results suggest that TP53 mutation is associated with a poor effect of avelumab, while mutations of PTEN and ARID1A are related to a positive effect of the drug in patients with advanced EC.

Bioinformatics analysis of the prognosis and biological significance of N6 methyladenine regulators in oral squamous cell carcinoma.

BACKGROUND: Oral squamous cell carcinoma (OSCC) is a common type of cancer. We performed the present study to explore the function and specific regulatory mechanism of m6A in OSCC and to find a new diagnosis and treatment strategy for OSCC. METHODS: Using bioinformatics, we examined the associations between 20 genes associated with methylation and the epidemiological data about OSCC tumor samples. RESULTS: We created two subgroup curves based on the gene expression levels related to m6A methylation. In total, 14 genes were found to be differentially expressed. Significant differences in terms of survival rates, Grade and gender were found among subgroups with different m6A expression levels. Nine genes had areas under the curves greater than 0.7. Therefore, these genes may be utilized for the clinical diagnosis and prognosis of OSCC. Because of their high individual predictive value, HNRNPC and IGF2BP2 were chosen as the two potential predictors. The two regulatory elements were used to create the prognostic signals for OSCC. The developed prognostic signals made it possible to discern between the samples with good and poor prognoses without potential confounding factors. Four genes (HNRNPC, METTL14, YTHDF2 and ALKBH5) combined well with compounds, which had an anti-cancer effect. CONCLUSIONS: Our findings suggested that OSCC-related genes with m6A methylation could be beneficial treatment targets or prognostic indicators.

EMP1 correlated with cancer progression and immune characteristics in pan-cancer and ovarian cancer.

This study examines the prognostic role and immunological relevance of EMP1 (epithelial membrane protein-1) in a pan-cancer analysis, with a focus on ovarian cancer. Utilizing data from TCGA, CCLE, and GTEx databases, we assessed EMP1 mRNA expression and its correlation with tumor progression, prognosis, and immune microenvironment across various cancers. Our results indicate that EMP1 expression is significantly associated with poor prognosis in multiple cancer types, including ovarian, bladder, testicular, pancreatic, breast, brain, and uveal melanoma. Immune-related analyses reveal a positive correlation between EMP1 and immune cell infiltration, particularly neutrophils, macrophages, and dendritic cells, as well as high expression of immune checkpoint such as CD274, HAVCR2, IL10, PDCD1LG2, and TGFB1 in most tumors. In vivo experiments confirm that EMP1 promotes ovarian cancer cell proliferation, metastasis, and invasion. In conclusion, EMP1 emerges as a potential prognostic biomarker and therapeutic target in various cancers, particularly ovarian cancer, due to its influence on tumor progression and immune cell dynamics. Further research is warranted to elucidate the precise mechanisms of EMP1 in cancer biology and to translate these findings into clinical applications.

Exosomal transcript cargo and functional correlation with HNSCC patients' survival.

BACKGROUND: HPV status in a subset of HNSCC is linked with distinct treatment outcomes. Present investigation aims to elucidate the distinct clinicopathological features of HPV-positive and HPV-negative HNSCC and investigate their association with the HNSCC patient survival. MATERIALS AND METHODS: The total RNA of exosomes from HPV-positive (93VU147T) and HPV-negative (OCT-1) HNSCC cells was isolated, and the transcripts were estimated using Illumina HiSeq X. The expression of altered transcripts and their clinical relevance were further analyzed using publicly available cancer transcriptome data from The Cancer Genome Atlas (TCGA). RESULTS: Transcriptomic analyses identified 3785 differentially exported transcripts (DETs) in HPV-positive exosomes compared to HPV-negative exosomes. DETs that regulate the protein machinery, cellular redox potential, and various neurological disorder-related pathways were over-represented in HPV-positive exosomes. TCGA database revealed the clinical relevance of altered transcripts. Among commonly exported abundant transcripts, SGK1 and MAD1L1 showed high expression, which has been correlated with poor survival in HNSCC patients. In the top 20 DETs of HPV-negative exosomes, high expression of FADS3, SGK3, and TESK2 correlated with poor survival of the HNSCC patients in the TCGA database. CONCLUSION: Overall, our study demonstrates that HPV-positive and HPV-negative cells' exosomes carried differential transcripts cargo that may be related to pathways associated with neurological disorders. Additionally, the altered transcripts identified have clinical relevance, correlating with patient survival in HNSCC, thereby highlighting their potential as biomarkers and as therapeutic targets.

Transcriptomics analysis revealed that TAZ regulates the proliferation of KIRC cells through mitophagy.

Transcriptional Co-Activator with PDZ-Binding Motif (TAZ, also known as WWTR1) is a downstream effector of the Hippo pathway, involved in the regulation of organ regeneration and cell differentiation in processes such as development and regeneration. TAZ has been shown to play a tumor-promoting role in various cancers. Currently, many studies focus on the role of TAZ in the process of mitophagy. However, the molecular mechanism and biological function of TAZ in renal clear cell carcinoma (KIRC) are still unclear. Therefore, we systematically analyzed the mRNA expression profile and clinical data of KIRC in The Cancer Genome Atlas (TCGA) dataset. We found that TAZ expression was significantly upregulated in KIRC compared with normal kidney tissue and was closely associated with poor prognosis of patients. Combined with the joint analysis of 36 mitophagy genes, it was found that TAZ was significantly negatively correlated with the positive regulators of mitophagy. Finally, our results confirmed that high expression of TAZ in KIRC inhibits mitophagy and promotes KIRC cell proliferation. In conclusion, our findings reveal the important role of TAZ in KIRC and have the potential to be a new target for KIRC therapy.

The Prospective role of lapatinib as an adjuvant therapy in prevalent cancers: Insights from in silico analysis targeting EGFR and HER2.

INTRODUCTION: Various pieces of evidence suggest an elevation in the levels of EGFR and HER2 in different cancers leading to the proliferation, invasion, and metastasis of cancer cells. In this study, we conducted a comprehensive investigation into the expression alterations of these two receptors in various cancers using in silico data. In addition, we investigated the therapeutic potential of lapatinib as an inhibitor of these receptors in various cancer types. METHODS: RNAseq data for prevalent cancers were downloaded from The Cancer Genome Atlas (TCGA). After initial preprocessing, expression changes of HER2, EGFR, and candidate genes-identified based on their association with EGFR and HER2 signaling pathways-were examined. Human protein atlas data were utilized to assess the protein expression of HER2 and EGFR. GSE129254 was employed to identify molecular pathways and candidate genes associated with lapatinib. The protein-protein interaction network was used to identify lapatinib-influenced hub genes. Clinical data for common cancers were used to investigate the correlation between the expression of candidate genes and patients' mortality rates by Cox regression test. RESULTS: The findings clearly indicated a significant increase in the expression levels of HER2 and EGFR in cancers such as kidney, lung, breast, bladder, pancreas, head and neck, stomach, and endometrial, both at the mRNA and protein levels (p-value <0.01). Additionally, more than 30 % of samples in some cancers showed a twofold increase in HER2 or EGFR expression. The analysis of GSE129254 data revealed that lapatinib reduces the expression of numerous genes associated with cell proliferation. METTL1, LYAR, LTV1, CCND1, NOP2, and DDX21 were identified as hub genes related to the effect of lapatinib. Our results demonstrated that many hub genes exhibited elevated expression in candidate cancers, and the upregulation of some of them was correlated with poor prognosis. CONCLUSION: Our results indicate an upregulation in the expression levels of HER2 and EGFR in certain common cancers, suggesting that lapatinib, in addition to breast cancer, could be considered for the treatment of these cancers. Furthermore, we demonstrated that some genes with increased expression in prevalent cancers and associated with poor prognosis have the potential to be modulated by lapatinib.

Metastasis-free survival of uveal melanoma by tumour size category based on The Cancer Genome Atlas (TCGA) classification in 1001 cases.

BACKGROUND: Uveal melanoma (UM) can be classified by tumour size category and by The Cancer Genome Atlas (TCGA) groups (cytogenetic-based, 4-category prognostic classification into Groups A-D). This study was conducted to assess impact on metastasis-free survival (MFS) in UM by tumour size category based on correlation with TCGA classification. METHODS: Retrospective analysis of 1001 cases categorised as small (0.0-3.0 mm), medium (3.1-8.0 mm) and large (≥8.1 mm), grouped by TCGA classification. RESULTS: Of 1001 cases, TCGA Groups (A/B/C/D) included small (n = 270, 75%/11%/13%/1%), medium (n = 503, 46%/14%/27%/13%) and large (n = 228, 23%/19%/38%/20%) UM. The 5-and 10-year Kaplan-Meier MFS for small UM revealed Group A (98%, 98%), Group B (100%, 100%), Group C (86%, NA) and Group D (100%, NA). For medium UM, the values dropped with Group A (95%, 93%), Group B (90%, 90%), Group C (68%, 38%), and Group D (44%, NA). For large UM, the values dropped further with Group A (94%, 86%), Group B (85%, NA), Group C (40%, 28%), and Group D (23%, NA). Additionally, a comparison (small vs. medium vs. large tumour size category) revealed TCGA low-risk grouping (Groups A or B) in 86% vs. 60% vs. 58% cases with UM. CONCLUSION: By tumour size category, favourable cytogenetics (Groups A or B) is found in 86% of small tumours, 60% of medium tumours, and 58% of large tumours. The MFS at 10 years for favourable cytogenetics was 98% for small tumours, 92% for medium tumours, and 54% for large tumours. Tumour size category can serve as a surrogate for TCGA.

IGL CDR3 Hydropathy and Antigen Chemical Complementarity Associated with Greater Disease-Free Survival in Lung Adenocarcinoma: Implications for Gender Disparities.

With lung cancer remaining a challenging disease, new approaches to biomarker discovery and therapy development are needed. Recent immunogenomics, adaptive immune receptor approaches have indicated that it is very likely that B cells play an important role in mediating better overall outcomes. As such, we assessed physicochemical features of lung adenocarcinoma resident IGL complementarity determining region-3 (CDR3) amino acid (AA) sequences and determined that hydrophobic CDR3 AA sequences were associated with a better disease-free survival (DFS) probability. Further, using a recently developed chemical complementarity scoring algorithm particularly suitable for the evaluation of large patient datasets, we determined that IGL CDR3 chemical complementarity with certain cancer testis antigens was associated with better DFS. Chemical complementarity scores for IGL CDR3-MAGEC1 represented a gender bias, with an overrepresentation of males among the higher IGL-CDR3-CTA complementarity scores that were in turn associated with better DFS (logrank p < 0.065). Overall, this study pointed towards potential biomarkers for prognoses that, in some cases are likely gender-specific; and towards biomarkers for guiding therapy, e.g., IGL-based opportunities for antigen targeting in the lung cancer setting.

A new perspective on Endometrial Carcinoma classification and management strategies in context of molecular subtypes.

Endometrial cancer is the most common gynecological cancer and the second most prevalent female malignancy in the developed world. It is typically diagnosed in postmenopausal women, presenting with the characteristic clinical symptom of uterine abnormal bleeding. In the past, only two histological types were considered. However, it has become increasingly evident that endometrial cancer is a clinically heterogeneous disease, and this heterogeneity is closely associated with the diversity of underlying molecular alterations. The Cancer Genome Atlas classification has significantly advanced the diagnosis, risk stratification, and management of endometrial cancer by categorizing it into four molecular subgroups, each characterized by distinct mutational burdens and copy number alterations.

Prognostic analysis of uveal melanoma based on the characteristic genes of M2-type macrophages in the tumor microenvironment.

Uveal melanoma arises from stromal melanocytes and is the most prevalent primary intraocular tumor in adults. It poses a significant diagnostic and therapeutic challenge due to its high malignancy and early onset of metastases. In recent years, there has been a growing interest in the role of diverse immune cells in tumor cell development and metastasis. Using The Cancer Genome Atlas and the gene expression omnibus databases, and the CIBERSORT method, we investigated the topography of intra-tumor immune infiltration in uveal melanoma in this research. We evaluated the prognosis of uveal melanoma patients using the M2 macrophage immune cell infiltration score in conjunction with clinical tumor patient data. We built a prognostic model based on the distinctive genes of M2 macrophages and combined it with patients' clinical data in the database; we ran a survival prognostic analysis to authenticate the model's accuracy. The functional study revealed the importance of macrophage-associated genes in the development of uveal melanoma. Moreover, the reliability of our prediction model was verified by combining tumor mutational load, immune checkpoint, and drug sensitivity, respectively. Our study provides a reference for the follow-up study of uveal melanoma.

Construction and validation of a prognostic model based on autophagy-related genes for hepatocellular carcinoma in the Asian population.

BACKGROUND AND OBJECTIVE: Hepatocellular carcinoma (HCC), which has a complex pathogenesis and poor prognosis, is one of the most common malignancies worldwide. Hepatitis virus B infection is the most common cause of HCC in Asian patients. Autophagy is the process of digestion and degradation, and studies have shown that autophagy-associated effects are closely related to the development of HCC. In this study, we aimed to construct a prognostic model based on autophagy-related genes (ARGs) for the Asian HCC population to provide new ideas for the clinical management of HCC in the Asian population. METHODS: The clinical information and transcriptome data of Asian patients with HCC were downloaded from The Cancer Genome Atlas (TCGA) database, and 206 ARGs were downloaded from the human autophagy database (HADB). We performed differential and Cox regression analyses to construct a risk score model. The accuracy of the model was validated by using the Kaplan-Meier (K-M) survival curve, receiver operating characteristic (ROC) curve, and univariate and multivariate Cox independent prognostic analyses. The results Thirteen ARGs that were significantly associated with prognosis were finally identified by univariate and multivariate Cox regression analyses. The K-M survival curves showed that the survival rate of the low-risk group was significantly higher than that of the high-risk group (p < 0.001), and the multi-indicator ROC curves further demonstrated the predictive ability of the model (AUC = 0.877). CONCLUSION: The risk score model based on ARGs was effective in predicting the prognosis of Asian patients with HCC.

Construction of a three-component regulatory network of transcribed ultraconserved regions for the identification of prognostic biomarkers in gastric cancer.

Altered expression and functional roles of the transcribed ultraconserved regions (T-UCRs), as genomic sequences with 100% conservation between the genomes of human, mouse, and rat, in the pathophysiology of neoplasms has already been investigated. Nevertheless, the relevance of the functions for T-UCRs in gastric cancer (GC) is still the subject of inquiry. In the current study, we first used a genome-wide profiling approach to analyze the expression of T-UCRs in GC patients. Then, we constructed a three-component regulatory network and investigated potential diagnostic and prognostic values of the T-UCRs. The Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD) dataset was used as a resource for the RNA-sequencing data. FeatureCounts was utilized to quantify the number of reads mapped to each T-UCR. Differential expression analysis was then conducted using DESeq2. In the following, interactions between T-UCRs, microRNAs (miRNAs), and messenger RNAs (mRNAs) were combined into a three-component network. Enrichment analyses were performed and a protein-protein interaction (PPI) network was constructed. The R Survival package was utilized to identify survival-related significantly differentially expressed T-UCRs (DET-UCRs). Using an in-house cohort of GC tissues, expression of two DET-UCRs was furthermore experimentally verified. Our results showed that several T-UCRs were dysregulated in TCGA-STAD tumoral samples compared to nontumoral counterparts. The three-component network was constructed which composed of DET-UCRs, miRNAs, and mRNAs nodes. Functional enrichment and PPI network analyses revealed important enriched signaling pathways and gene ontologies such as "pathway in cancer" and regulation of cell proliferation and apoptosis. Five T-UCRs were significantly correlated with the overall survival of GC patients. While no expression of uc.232 was observed in our in-house cohort of GC tissues, uc.343 showed an increased expression, although not statistically significant, in gastric tumoral tissues. The constructed three-component regulatory network of T-UCRs in GC presents a comprehensive understanding of the underlying gene expression regulation processes involved in tumor development and can serve as a basis to investigate potential prognostic biomarkers and therapeutic targets.