Identification of a prognostic DNA repair gene signature in esophageal cancer.

Background: DNA repair plays a crucial role in the development and progression of different types of cancers. Nevertheless, little is known about the role of DNA repair-related genes (DRRGs) in esophageal cancer (EC). The present study aimed to identify a novel DRRGs prognostic signature in EC. Methods: Gene set enrichment analysis (GSEA) was performed to screen 150 genes related to DNA repair, which is the most important enrichment gene set in EC. Univariate and multivariate Cox regression analyses were used to screen DRRGs closely associated with prognosis. The difference in the expression of hub DRRGs between tumor and normal tissues was analyzed. Combined with clinical indicators (including age, gender, and tumor stage), we evaluated whether the 4-DRRGs signature was an independent prognostic factor. In addition, we evaluated the prediction accuracy using a receiver operating characteristic (ROC) curve and visualized the model's performance via a nomogram. Results: Four-DRRGs (NT5C3A, TAF9, BCAP31, and NUDT21) were selected by Cox regression analysis to establish a prognostic signature to effectively classify patients into high- and low-risk groups. The area under the curve (AUC) of the time-dependent ROC of the prognostic signature for 1- and 3-year was 0.769 and 0.720, respectively. Compared with other clinical characteristics, the risk score showed a robust ability to predict the prognosis in EC, especially in the early stage of EC. Furthermore, we constructed a nomogram to interpret the clinical application of the 4-DRRGs signature. Conclusions: In conclusion, we identified a prognostic signature based on the DRRGs for patients with EC, which can contribute independent value in identifying clinical outcomes that complement the TNM system in EC.

Identifying key genes and functionally enriched pathways in acute myeloid leukemia by weighted gene co-expression network analysis.

Acute myeloid leukemia (AML) is characterized by the uncontrolled proliferation of myeloid leukemia cells in the bone marrow and other hematopoietic tissues and is highly heterogeneous. While with the progress of sequencing technology, understanding of the AML-related biomarkers is still incomplete. The purpose of this study is to identify potential biomarkers for prognosis of AML. Based on WGCNA analysis of gene mutation expression, methylation level distribution, mRNA expression, and AML-related genes in public databases were employed for investigating potential biomarkers for the prognosis of AML. This study screened a total of 6153 genes by analyzing various changes in 103 acute myeloid leukemia (AML) samples, including gene mutation expression, methylation level distribution, mRNA expression, and AML-related genes in public databases. Moreover, seven AML-related co-expression modules were mined by WGCNA analysis, and twelve biomarkers associated with the AML prognosis were identified from each top 10 genes of the seven co-expression modules. The AML samples were then classified into two subgroups, the prognosis of which is significantly different, based on the expression of these twelve genes. The differentially expressed 7 genes of two subgroups (HOXB-AS3, HOXB3, SLC9C2, CPNE8, MEG8, S1PR5, MIR196B) are mainly involved in glucose metabolism, glutathione biosynthesis, small G protein-mediated signal transduction, and the Rap1 signaling pathway. With the utilization of WGCNA mining, seven gene co-expression modules were identified from the TCGA database, and there are unreported genes that may be potential driver genes of AML and may be the direction to identify the possible molecular signatures to predict survival of AML patients and help guide experiments for potential clinical drug targets.

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.

Identification and validation of a novel 17 coagulation-related genes signature for predicting prognostic risk in colorectal cancer.

Background: Patients with colorectal cancer commonly experience disturbances in coagulation homeostasis. Activation of the coagulation system contributes to cancer-associated thrombosis as the second risk factor for death in cancer patients. This study intended to discover coagulation-related genes and construct a risk model for colorectal cancer patients' prognosis. Methods: Coagulation-related genes were identified by searching coagulation-related pathways in the Molecular Signatures Database. Transcriptomic data and clinical data were downloaded from the Cancer Genome Atlas and Gene Expression Omnibus datasets. Univariate Cox and backward stepwise regression were utilized to identify prognosis-related genes and construct a predictive risk model for the training cohort. Next, survival analysis determines the risk model's predictive power, correlation with clinicopathological characteristics, and nomogram. Additionally, we characterized the variances in immune cell infiltration, somatic mutations, immune checkpoint molecules, biological functions, and drug sensitivity between the high- and low-score patients. Result: Eight hundred forty-five genes were obtained by searching the theme term "coagulation" after de-duplication. After univariate regression analysis, 69 genes correlated with prognosis were obtained from the Cancer Genome Atlas dataset. A signature consisting of 17 coagulation-related genes was established through backward stepwise regression. The Kaplan-Meier curve indicated a worse prognosis for high-score patients. Time-dependent receiver operating characteristic curve analysis demonstrated high accuracy in predicting overall survival. Further, the results were validated by two independent datasets (GSE39582 and GSE17536). Combined with clinicopathological characteristics, the risk model was proven to be an independent prognostic factor to predict poor pathological status and worse prognosis. Furthermore, high-score patients had significantly higher stromal cell infiltration. Low-score patients were associated with high infiltration of resting memory CD4+ T cells, activated CD4+ T cells, and T follicular helper cells. The low-score patients exhibited increased expression of immune checkpoint genes, and this might be relevant to their better prognosis. High-score patients exhibited lower IC50 values of Paclitaxel, Rapamycin, Temozolomide, Cyclophosphamide, etc. The differential signaling pathways mainly involve the calcium signaling pathway and the neuroactive ligand-receptor interaction. Lastly, a nomogram was constructed and showed a good prediction. Conclusion: The prognostic signature of 17 coagulation-related genes had significant prognostic value for colorectal cancer patients. We expect to improve treatment modalities and benefit more patients through research on molecular features.

Analysis of methylation-driven genes for predicting the prognosis of patients with oral squamous cell carcinoma.

Background: Oral squamous cell carcinoma (OSCC) is a highly aggressive malignancy that is characterized by early distant metastasis and poor prognosis. DNA methylation plays an important role in the etiology and pathogenesis of OSCC. This study aimed to identify methylation-driven genes through bioinformatics analysis as potential biomarkers for early diagnosis and prognostic assessment of OSCC. Methods: Methylation data, RNA sequencing (RNA-seq) data and clinical prognosis information of OSCC patients were retrieved from The Cancer Genome Atlas (TCGA) database. The R packages MethylMix were employed to analyze the correlation between methylation status and corresponding gene expression in tumor and normal tissues to obtain methylation-driven genes. Univariate Cox regression analysis was developed to further screen methylation-driven genes associated with the prognosis of OSCC patients. Subsequently, multivariate Cox regression analysis was utilized to construct a linear prognostic risk prediction model. Furthermore, a combined survival analysis integrating methylation and gene expression was performed to investigate the prognostic value. Results: A total of 374 differentially expressed methylation-driven genes were identified. Seven methylation-driven genes (BST2, KRT15, ZNF134, NT5E, GSTA7P, NAPRT, and GOLPH3L) were found to be significantly associated with patient prognosis. Additionally, four methylation-driven genes (BST2, KRT15, ZNF134 and NAPRT) were used to construct a linear prognostic risk prediction model for OSCC patients. Furthermore, a combined Kaplan-Meier survival analysis revealed that three methylation-driven genes (ZKSCAN7, MFF, ZNF134) alone can be used as independent prognostic markers or drug targets. Conclusions: Our findings facilitate a better understanding of molecular mechanisms of OSCC and provide potential biomarkers of early diagnosis, precision treatment and prognosis evaluation.

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.

Involvement of N4BP2L1, PLEKHA4, and BEGAIN genes in breast cancer and muscle cell development.

Patients with breast cancer show altered expression of genes within the pectoralis major skeletal muscle cells of the breast. Through analyses of The Cancer Genome Atlas (TCGA)-breast cancer (BRCA), we identified three previously uncharacterized putative novel tumor suppressor genes expressed in normal muscle cells, whose expression was downregulated in breast tumors. We found that NEDD4 binding protein 2-like 1 (N4BP2L1), pleckstrin homology domain-containing family A member 4 (PLEKHA4), and brain-enriched guanylate kinase-associated protein (BEGAIN) that are normally highly expressed in breast myoepithelial cells and smooth muscle cells were significantly downregulated in breast tumor tissues of a cohort of 50 patients with this cancer. Our data revealed that the low expression of PLEKHA4 in patients with menopause below 50 years correlated with a higher risk of breast cancer. Moreover, we identified N4BP2L1 and BEGAIN as potential biomarkers of HER2-positive breast cancer. Furthermore, low BEGAIN expression in breast cancer patients with blood fat, heart problems, and diabetes correlated with a higher risk of this cancer. In addition, protein and RNA expression analysis of TCGA-BRCA revealed N4BP2L1 as a promising diagnostic protein biomarker in breast cancer. In addition, the in silico data of scRNA-seq showed high expression of these genes in several cell types of normal breast tissue, including breast myoepithelial cells and smooth muscle cells. Thus, our results suggest their possible tumor-suppressive function in breast cancer and muscle development.

Tumour mutation burden and infiltrating immune cell subtypes influenced the breast cancer prognosis.

Background: Most of its issues are still undecided on the relationship between tumour mutation burden (TMB) and immune-related genes in the breast cancer. This study explores their relationship based on gene mutation and transcription data in The Cancer Genome Atlas (TCGA) database, and the effects of immune cells in TMB and tumour microenvironments on prognosis of breast cancer patients. Methods: Cases were divided into low-TMB and high-TMB subgroups. Differentially expressed immune-related genes were identified in different TMB subgroups, and patient prognosis was predicted by gene function enrichment analysis, invasive immune cells and different clinical pathological features were compared among different TMB subgroups. Results: A total of 986 mutation data from breast cancer patients were obtained. Compared with low-TMB group, the survival period of high-TMB group was relatively longer. A total of 337 differential expression genes were identified in this study. Of these genes, seven differentially expressed immune-related genes were associated with prognosis. In the high-TMB group, activated CD4+ memory T cells and other cells had high expression, the expression ratio of memory B cells and other cells in low-TMB group was high. Conclusions: TMB-related immunological infiltration characteristics showed meaningful value for prognosis prediction for breast cancer patients. Differentially expressed immune-related genes in TMB subgroups provide important information on the survival prediction.

A comparative analysis indicates SLC7A11 expression regulate the prognostic value of KEAP1-NFE2L2-CUL3 mutations in human uterine corpus endometrial carcinoma.

Uterine corpus endometrial cancer (UCEC) is a third most common malignancy in women with a poor prognosis in advanced stages. In this study, we performed an integrated comparative analysis of exome and transcriptome data from The Cancer Genome Atlas (TCGA) of Lung Adenocarcinoma (LUAD), and UCEC patients. Our multi-omics analysis shows that the UCEC patients carrying mutations in the KEAP1-NFE2L2-CUL3 genes were associated with better progression-free survival (PFS), whereas the KEAP1-NFE2L2-CUL3 mutation in LUAD showed poor outcomes. Functional annotations and correlative expression studies show that genes, particularly GCLC and GCLM related to glutathione synthesis are expressed at lower levels in the KEAP1-NFE2L2-CUL3 mutant UCEC compared to LUAD. This events result in glutathione deficiency and it may compromise to combat intracellular reactive oxygen species (ROS). However, the expression of genes involved in the glutathione recycling process was not affected. On the other hand, cellular import of cystine is high due to increased SLC7A11 expression in UCEC. Because glutathione synthesis is impaired, the unconverted cysteine accumulates in cells, leading to di-sulfite stress. Apart from NRF2, ARID1A is one of the positive regulators of SLC7A11. In support, UCEC patients with co-occurrence of KEAP1-NFE2L2-CUL3 and ARID1A mutation shows significantly decreased PFS with decline of SLC7A11 expression as compared to patients carrying only KEAP1-NFE2L2-CUL3 mutation. Thus, we hypothesize that the KEAP1-NFE2L2-CUL3 mutation in UCEC leads to uncontrollable ROS with di-sulfite stress, reflecting a favorable clinical outcome.

Developing a prognostic model using machine learning for disulfidptosis related lncRNA in lung adenocarcinoma.

Disulfidptosis represents a novel cell death mechanism triggered by disulfide stress, with potential implications for advancements in cancer treatments. Although emerging evidence highlights the critical regulatory roles of long non-coding RNAs (lncRNAs) in the pathobiology of lung adenocarcinoma (LUAD), research into lncRNAs specifically associated with disulfidptosis in LUAD, termed disulfidptosis-related lncRNAs (DRLs), remains insufficiently explored. Using The Cancer Genome Atlas (TCGA)-LUAD dataset, we implemented ten machine learning techniques, resulting in 101 distinct model configurations. To assess the predictive accuracy of our model, we employed both the concordance index (C-index) and receiver operating characteristic (ROC) curve analyses. For a deeper understanding of the underlying biological pathways, we referred to the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) for functional enrichment analysis. Moreover, we explored differences in the tumor microenvironment between high-risk and low-risk patient cohorts. Additionally, we thoroughly assessed the prognostic value of the DRLs signatures in predicting treatment outcomes. The Kaplan-Meier (KM) survival analysis demonstrated a significant difference in overall survival (OS) between the high-risk and low-risk cohorts (p < 0.001). The prognostic model showed robust performance, with an area under the ROC curve exceeding 0.75 at one year and maintaining a value above 0.72 in the two and three-year follow-ups. Further research identified variations in tumor mutational burden (TMB) and differential responses to immunotherapies and chemotherapies. Our validation, using three GEO datasets (GSE31210, GSE30219, and GSE50081), revealed that the C-index exceeded 0.67 for GSE31210 and GSE30219. Significant differences in disease-free survival (DFS) and OS were observed across all validation cohorts among different risk groups. The prognostic model offers potential as a molecular biomarker for LUAD prognosis.

Exploring the prognostic analysis of autophagy and tumor microenvironment based on monocyte cells in lung cancer.

A deep understanding of the biological mechanisms of lung cancer offers more precise treatment options for patients. In our study, we integrated data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) to investigate lung adenocarcinoma. Analyzing 538 lung cancer samples and 31 normal samples, we focused on 3076 autophagy-related genes. Using Seurat, dplyr, tidyverse, and ggplot2, we conducted single-cell data analysis, assessing the quality and performing Principal Component Analysis (PCA) and t-SNE analyses. Differential analysis of TCGA data using the "Limma" package, followed by immune infiltration analysis using the CIBERSORT algorithm, led us to identify seven key genes. These genes underwent further scrutiny through consensus clustering and gene set variation analysis (GSVA). We developed a prognostic model using Lasso Cox regression and multivariable Cox analysis, which was then validated with a nomogram, predicting survival rates for lung adenocarcinoma. The model's accuracy and universality were corroborated by ROC curves. Additionally, we explored the relationship between immune checkpoint genes and immune cell infiltration and identified two key genes, HLA-DQB1 and OLR1. This highlighted their potential as therapeutic targets. Our comprehensive approach sheds light on the molecular landscape of lung adenocarcinoma and offers insights into potential treatment strategies, emphasizing the importance of integrating single-cell and genomic data in cancer research.

LARS1 is a Prognostic Biomarker and Exhibits a Correlation with Immune Infiltrates in Hepatocellular Carcinoma.

Purpose: To study the relationship between LARS1 expression and immune infiltration and prognosis in hepatocellular carcinoma (HCC). Patients and Methods: The clinical characteristics together with LARS1 expression levels were obtained from the TCGA database. Immunohistochemistry confirmed LARS1 expression levels in paraneoplastic and tumor tissues. To investigate LARS1-related downstream molecules, a network of protein-protein interactions (PPIs) and the Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG) were built. Furthermore, gene set enrichment analysis (GSEA) was used to analyze the pathways associated with LARS1 expression, whereas Single-sample GSEA (ssGSEA) was applied to perform an association study between immune infiltration and LARS1 gene expression. The TISCH Database and the TISIDB database were used to compare the difference of LARS1 expression in hepatocellular carcinoma and immunomodulators. Results: In comparison to that in normal tissues, the LARS1 expression level was elevated in tumor tissues. LARS1 expression exhibited substantial correlation with AFP, Histologic grade, pathologic stage, Residual tumor, and Vascular invasion in HCC. Higher LARS1 expression in HCC was linked to lower progression-free survival (PFS), disease-specific survival (DSS), and overall survival (OS). According to the GO/KEGG study, the important biological process (neutral lipid metabolic process), cellular component (triglyceride-rich plasma lipoprotein), molecular functions (lipase inhibitor activity), and KEGG pathway (cholesterol metabolism) could be a probable function mechanism in promoting HCC. Various pathways as per GSEA revealed that they were enriched in samples with elevated LARS1 expression. The expression level of LARS1 in malignant tumor cells after immunotherapy was significantly higher than that before immunotherapy. LARS1 was also remarkably linked to the infiltration level and the immunomodulators. Conclusion: LARS1 can be used as a biomarker of HCC, which is associated to immune infiltration of HCC.

RBM39 is a potential prognostic biomarker with functional significance in hepatocellular carcinoma.

Background: RNA-binding motif protein 39 (RBM39) is a well-known RNA-binding protein involved in tumorigenesis; however, its role in hepatocellular carcinoma (HCC) remains unclear. The aim of this study was to investigate the role of RBM39 in HCC. Methods: The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were used to analyze the differential expression of RBM39 in HCC and normal tissues. The prognostic and diagnostic value of RBM39 in HCC was accessed by Kaplan-Meier analysis, Cox regression, and receiver operating characteristic (ROC) curve analyses. Quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry were used to validate the mRNA and protein expression of RBM39 in HCC. Moreover, gene set enrichment analysis (GSEA) was performed to identify key pathways related to RBM39. The correlation between RBM39 expression and immune cell infiltration was evaluated using a single-sample gene set enrichment analysis (ssGSEA). CCK8 and wound healing assays were performed to investigate the proliferation and migration abilities of HCC cells with RBM39 knockdown. Results: RBM39 expression was upregulated in the HCC tissues. High RBM39 expression was significantly associated with advanced T stage, histological grade, and pathological stage and predicted poor overall survival (OS), disease-specific survival (DSS), and progress-free interval (PFI) in HCC patients. The upregulation of RBM39 expression was an independent prognostic factor for OS. Moreover, GSEA enrichment analysis indicated that RBM39 was functionally involved in pathways associated with the cell cycle, DNA replication, the p53 signaling pathway, and primary immunodeficiency. RBM39 expression was associated with infiltration of Th2 cells and dendritic cells (DC). RBM39 knockdown significantly inhibited the proliferation and migration of HCC cells. Conclusions: These findings suggest that high RBM39 expression is associated with poor prognosis and promotes HCC cell proliferation and migration. Based on these results, RBM39 is a promising prognostic biomarker with functional significance for HCC.

Role of ICAM-1 in triple-negative breast cancer.

Intercellular adhesion molecule-1 (ICAM-1) is related to the occurrence and development of a variety of tumors. However, the role of ICAM-1 in the regulation of growth, metastasis, and clinical prognosis of the specific molecular subtypes of breast cancer, triple-negative breast cancer (TNBC), remains to be elucidated. This study explored the role of ICAM-1 in breast cancer and its triple-negative subtypes by systematic bioinformatics methods. The results showed that the expression of ICAM-1 in breast cancer tissues was significantly higher than that in normal tissues, especially in TNBC subtypes. In breast cancer, ICAM-1 mainly activates pathways related to apoptosis and epithelial-mesenchymal transition, while its overexpression in TNBC is associated with inflammatory response, apoptosis, and other processes. TNBC patients displaying higher ICAM-1 expression demonstrate enhanced responses to immunotherapy. High ICAM-1 expression is sensitive to drugs targeting tumor cell proliferation, apoptosis, and angiogenesis. In conclusion, breast cancer is characterized by significantly high expression of ICAM-1, with TNBC subtypes expressing ICAM-1 at much higher levels than other subtypes. The diagnosis, prognosis, development, distant metastases, and immunotherapy of TNBC are correlated with high expression of ICAM-1. This research provides available data for the further study of the diagnosis and treatment of TNBC.

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.

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.

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.

A novel immune-related long noncoding RNA (lncRNA) pair model to predict the prognosis of triple-negative breast cancer.

Background: Breast cancer (BC) is the most prevalent cancer type and is the principal cause of cancer-related death in women. Anti-programmed cell death protein 1/programmed cell death ligand 1 (PD-1/PD-L1) immunotherapy has shown promising effects in metastatic triple-negative breast cancer (TNBC), but the potential factors affecting its efficacy have not been elucidated. Immune-related long noncoding RNAs (irlncRNAs) have been reported to be involved in immune escape to influence the carcinogenic process through the PD-1/PD-L1 signaling pathway. Therefore, exploring the potential regulatory mechanism of irlncRNAs in PD-1/PD-L1 immunotherapy in TNBC is of great importance. Methods: We retrieved transcriptome profiling data from The Cancer Genome Atlas (TCGA) and identified differentially expressed irlncRNA (DEirlncRNA) pairs. Least absolute shrinkage and selection operator (LASSO) regression analysis was performed to construct a risk assessment model. Results: Receiver operating characteristic (ROC) curve analysis indicated that the risk model may serve as a potential prediction tool in TNBC patients. Clinical stage and risk score were proved to be independent prognostic predictors by univariate and multivariate Cox regression analyses. Subsequently, we investigated the correlation between the risk model and tumor-infiltrating immune cells and immune checkpoints. Finally, we identified USP30-AS1 through the StarBase and Multi Experiment Matrix (MEM) databases, predicted the potential target genes of USP30-AS1, and then discovered that these target genes were closely associated with immune responses. Conclusions: Our study constructed a risk assessment model by irlncRNA pairs regardless of expression levels, which contributed to predicting the efficacy of immunotherapy in TNBC. Furthermore, the lncRNA USP30-AS1 in the model was positively correlated with the expression of PD-L1 and provided a potential therapeutic target for TNBC.

DIAPH3 predicts survival of patients with MGMT-methylated glioblastoma.

Background: Glioblastoma is one of the most aggressive primary brain tumors, with a poor outcome despite multimodal treatment. Methylation of the MGMT promoter, which predicts the response to temozolomide, is a well-established prognostic marker for glioblastoma. However, a difference in survival can still be detected within the MGMT methylated group, with some patients exhibiting a shorter survival than others, emphasizing the need for additional predictive factors. Methods: We analyzed DIAPH3 expression in glioblastoma samples from the cancer genome atlas (TCGA). We also retrospectively analyzed one hundred seventeen histological glioblastomas from patients operated on at Saint-Luc University Hospital between May 2013 and August 2019. We analyzed the DIAPH3 expression, explored the relationship between mRNA levels and Patient's survival after the surgical resection. Finally, we assessed the methylation pattern of the DIAPH3 promoter using a targeted deep bisulfite sequencing approach. Results: We found that 36% and 1% of the TCGA glioblastoma samples exhibit copy number alterations and mutations in DIAPH3, respectively. We scrutinized the expression of DIAPH3 at single cell level and detected an overlap with MKI67 expression in glioblastoma proliferating cells, including neural progenitor-like, oligodendrocyte progenitor-like and astrocyte-like states. We quantitatively analyzed DIAPH3 expression in our cohort and uncovered a positive correlation between DIAPH3 mRNA level and patient's survival. The effect of DIAPH3 was prominent in MGMT-methylated glioblastoma. Finally, we report that the expression of DIAPH3 is at least partially regulated by the methylation of three CpG sites in the promoter region. Conclusion: We propose that combining the DIAPH3 expression with MGMT methylation could offer a better prediction of survival and more adapted postsurgical treatment for patients with MGMT-methylated glioblastoma.

Immune landscape in APC and TP53 related tumor microenvironment in colon adenocarcinoma: A bioinformatic analysis.

Introduction: APC and TP53 are the two most regularly mutated genes in colon adenocarcinoma (COAD), especially in progressive malignancies and antitumoral immune response. The current bioinformatics analysis investigates the APC and TP53 gene expression profile in colon adenocarcinoma as a prognostic characteristic for survival, particularly concentrating on the correlated immune microenvironment. Methods: Clinical and genetic data of colon cancer and normal tissue samples were obtained from The Cancer Genome Atlas (TCGA)-COAD and Genotype-Tissue Expression (GTEx) online databases, respectively. The genetic differential expressions were analyzed in both groups via the one-way ANOVA test. Kaplan-Meier survival curves were applied to estimate the overall survival (OS). P < 0.05 was fixed as statistically significant. On Tumor Immune Estimation Resource and Gene Expression Profiling Interactive Analysis databases, the linkage between immune cell recruitment and APC and TP53 status was assessed through Spearman's correlation analysis. Results: APC and TP53 were found mutated in 66.74% and 85.71% of the 454 and 7 TCGA-COAD patients in colon and rectosigmoid junction primary sites, respectively with a higher log2-transcriptome per million reads compared to the GTEx group (318 samples in sigmoid and 368 samples in transverse). Survival curves revealed a worse significant OS for the high-APC and TP53 profile colon. Spearman's analysis of immune cells demonstrated a strong positive correlation between the APC status and infiltration of T cell CD4+, T cell CD8+, NK cell, and macrophages and also a positive correlation between status and infiltration of T cell CD4+, T cell CD8+. Conclusions: APC and TP53 gene mutations prevail in colon cancer and are extremely associated with poor prognosis and shortest survival. The infiltrating T cell CD4+, T cell CD8+, NK cell, and macrophages populate the colon microenvironment and regulate the mechanisms of tumor advancement, immune evasion, and sensitivity to standard chemotherapy. More comprehensive research is needed to demonstrate these results and turn them into new therapeutic outlooks.