The Study of PIK3CA Hotspot Mutations and Co-Occurring with EGFR, KRAS, and TP53 Mutations in Non-Small Cell Lung Cancer.

Objective: PIK3CA-mutant non-small-cell lung cancer (NSCLC) is associated with other genetic mutations and may influence treatment strategies and clinical outcomes. We aimed to characterize PIK3CA mutations co-occurring with several major driver mutations using data from published cohorts and our medical center. Materials and Methods: We analyzed NSCLC patients harboring PIK3CA mutations from The Cancer Genome Atlas (TCGA) and Memorial Sloan Kettering (MSK) databases and retrospectively identified NSCLC patients with PIK3CA-mutants at a single medical center from our electronic records. The Log rank test was used to determine the association between PIK3CA mutations and overall survival (OS) in NSCLC patients. Results: Common hotspot mutations in PIK3CA were found in exon 9 (c.1633G > A, E545K, and c.1624G > A, E542K) and exon 20 (c.3140A > G, H1047R) in all cohorts. Co-occurring mutations of PIK3CA with EGFR, KRAS, and TP53 have been frequently observed in patients with NSCLC, with different percentages in these datasets generated by different background. PIK3CA mutations were observed to be significantly associated with poor OS in lung adenocarcinomas patients in the MSKCC cohort (hazard ratio [HR] = 0.519, 95% confidence interval [CI] = 0.301-0.896; P <0.05). Conclusion: PIK3CA co-occurring mutations in other genes may represent distinct subsets of NSCLC. Further elucidation of the roles of PIK3CA hotspot mutations combined with other driver mutations, including EGFR and KRAS, is needed to guide effective treatment in patients with advanced NSCLC.

High expression of ARHGEF5 predicts unfavorable prognosis in acute myeloid leukemia.

Acute myeloid leukemia (AML) is a highly heterogeneous hematological neoplasm, highlighting the need for new molecular markers to improve prognosis prediction and therapeutic strategies. While Rho guanine nucleotide exchange factor 5 (ARHGEF5) is known to be overexpressed in various cancers, its role in AML is not well understood. This study investigates the correlation between ARHGEF5 expression and AML using data from the Cancer Genome Atlas (TCGA). ARHGEF5 expression levels in AML patients and normal samples were compared using the Wilcoxon rank-sum test. The Kaplan-Meier method and Cox regression analysis (CRA) assessed the association between ARHGEF5 expression and patient survival. A prognostic nomogram was constructed using CRA, incorporating patient age and cytogenetic risk.Our findings indicate significant overexpression of ARHGEF5 in AML compared to normal samples. Elevated ARHGEF5 levels were associated with poor prognosis, particularly in patients ≤ 60 years, those with NPM1 mutations, FLT3 mutation-positive, and wild-type RAS (P < 0.05). CRA confirmed that high ARHGEF5 expression independently predicts poor prognosis. Additionally, 412 differentially expressed genes (DEGs) were identified between high and low ARHGEF5 expression groups, with 216 genes upregulated and 196 downregulated. Pathway enrichment analyses using GO and KEGG, along with protein-protein interaction network and single sample gene set enrichment analyses, revealed key pathways and immune cell associations linked to ARHGEF5. These findings suggest that ARHGEF5 overexpression could serve as a biomarker for unfavorable outcomes in AML, providing insights into the underlying mechanisms of AML onset and progression.

Characterization of tumor suppressors and oncogenes evaluated from TCGA cancers.

Mutations in oncogenes and tumor suppressor genes can significantly impact cellular function during cancer development. A comprehensive analysis of their mutation patterns and significant gene ontology terms can provide insights into cancer emergence and suggest potential targets for drug development. This study analyzes twelve cancer subtypes by focusing on significant genetic and molecular factors. Two common genetic mutations associated with cancer are single nucleotide variants (SNVs) and copy number alterations (CNAs). Oncogenes, derived from mutated proto-oncogenes, disrupt normal cell functions and promote cancer, while tumor suppressor genes, often inactivated by mutations, regulate cell processes like proliferation and DNA damage response. This study analyzed datasets from The Cancer Genome Atlas (TCGA), which provides extensive genomic data across various cancers. In our analysis results, many genes with significant p-values based on Kaplan Meier gene expression data were identified in eight cancers (BRCA, BLCA, HNSC, KIRC, LUAD, KIRP, LUSC, STAD). Moreover, STAD is the only cancer for genes with both significant p-values and functional terms reported. Interestingly, we found that LIHC was the cancer reported with only one CNA mutated gene and its survival plot p-value being significant. Additionally, KICH has no reported significant genes at all. Our study proposed the relationship between tumor suppressor and oncogenes and shed light on cancer tumorigenesis due to genetic mutations.

FN1, a reliable prognostic biomarker for thyroid cancer, is associated with tumor immunity and an unfavorable prognosis.

Thyroid cancer (THCA) is a malignant tumor that affects the endocrine system. At present, an effective treatment for THCA remains elusive, particularly for medullary carcinoma and undifferentiated carcinoma, due to the lack of suitable medications and prognostic markers. Patient RNA-sequencing and clinical data were obtained from The Cancer Genome Atlas and Genotype-Tissue Expression databases. Protein-protein interaction analyses were performed for differentially expressed genes related to THCA. Moreover, the associations between fibronectin 1 (FN1), clinical data, immune checkpoint genes and immune cell infiltration was assessed. The potential functional role of the FN1 gene was evaluated through gene set enrichment analysis. Immunohistochemistry was used to assess FN1 expression in 103 cases of THCA, comprising 32 with papillary carcinoma, 30 with follicular carcinoma, 35 with medullary carcinoma and 6 with undifferentiated carcinoma. Finally, 11 co-expression modules were constructed and the expression of five identified hub genes (FN1, mucin-1, keratin 19, intracellular adhesion molecule 1 and neural cell adhesion molecule) were evaluated. The results demonstrated that higher FN1 gene expression levels were strongly associated with a higher pathologic stage and tumor stage, and were significantly associated with immune cell infiltration in THCA. Significant increases in FN1 protein expression levels were noted among patients diagnosed with four types of THCA, comprising papillary carcinoma, follicular carcinoma, medullary carcinoma and undifferentiated carcinoma. Patients diagnosed with medullary carcinoma and undifferentiated carcinoma, and with low FN1 expression levels, exhibited a significant survival advantage compared with those with high FN1 expression levels. In conclusion, the present study identified five hub genes involved in the onset and progression of THCA. Furthermore, FN1 could serve as a candidate biomarker and a therapeutic target for THCA and may be a key gene mediating THCA immune infiltration.

Characteristics of Oxidative Phosphorylation-Related Subtypes and Construction of a Prognostic Signature in Ovarian Cancer.

BACKGROUND: Ovarian cancer is associated with a high mortality rate. Oxidative Phosphorylation (OXPHOS) is an active metabolic pathway in cancer; nevertheless, its role in ovarian cancer continues to be ambiguous. Therefore, the objective of this study was to identify the prognostic value of OXPHOS-related genes and the immune landscape in ovarian cancer. METHODS: We obtained public ovarian cancer-related datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases and recognized OXPHOS-related genes from the GeneCards database and literature. Cox regression analyses were conducted to identify prognostic OXPHOS-related genes and develop a prognostic nomogram based on the OXPHOS score and clinicopathological features of patients. Functional enrichment analyses were employed to identify related processes. RESULTS: A 12-gene signature was identified to classify the ovarian cancer patients into high- and low-risk groups. The Immunophenoscore (IPS) was higher in the OXPHOS score-high group than in the OXPHOS score-low group, suggesting a better response to immune checkpoint inhibitors. Functional enrichment analyses unveiled that OXPHOS-related genes were considerably abundant in a series of immune processes. The calibration curves of the constructed prognostic nomograms at 1, 2, and 3 years exhibited strong concordance between the anticipated and observed survival probabilities of ovarian cancer patients. CONCLUSION: We have constructed a prognostic model containing 12 OXPHOS-related genes and demonstrated its strong predictive value in ovarian cancer patients. OXPHOS has been found to be closely linked to immune infiltration and the reaction to immunotherapy, which may contribute to improving individualized treatment and prognostic evaluation in ovarian cancer.

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.

Integrating network analysis with differential expression to uncover therapeutic and prognostic biomarkers in esophageal squamous cell carcinoma.

Introduction: Esophageal squamous cell carcinoma (ESCC) accounts for over 90% of all esophageal tumors. However, the molecular mechanism underlying ESCC development and prognosis remains unclear, and there are still no effective molecular biomarkers for diagnosing or predicting the clinical outcome of patients with ESCC. Here, we used bioinformatics analysis to identify potential biomarkers and therapeutic targets for ESCC. Methodology: Differentially expressed genes (DEGs) between ESCC and normal esophageal tissue samples were obtained by comprehensively analyzing publicly available RNA-seq datasets from the TCGA and GTEX. Gene Ontology (GO) annotation and Reactome pathway analysis identified the biological roles of the DEGs. Moreover, the Cytoscape 3.10.1 platform and subsidiary tools such as CytoHubba were used to visualize the DEGs' protein-protein interaction (PPI) network and identify hub genes, Furthermore our results are validated by using Single-cell RNA analysis. Results: Identification of 2524 genes exhibiting altered expression enriched in pathways including keratinization, epidermal cell differentiation, G alpha(s) signaling events, and biological process of cell proliferation and division, extracellular matrix (ECM) disassembly, and muscle function. Moreover, upregulation of hallmarks E2F targets, G2M checkpoints, and TNF signaling. CytoHubba revealed 20 hub genes that had a valuable influence on the progression of ESCC in these patients. Among these, the high expression levels of four genes, CDK1 MAD2L1, PLK1, and TOP2A, were associated with critical dependence for cell survival in ESCC cell lines, as indicated by CRISPR dependency scores, gene expression data, and cell line metadata. We also identify the molecules targeting these essential hub genes, among which GSK461364 is a promising inhibitor of PLK1, BMS265246, and Valrubicin inhibitors of CDK1 and TOP2A, respectively. Moreover, we identified that elevated expression of MMP9 is associated with worse overall survival in ESCC patients, which may serve as potential prognostic biomarker or therapeutic target for ESCC. The single-cell RNA analysis showed MMP9 is highly expressed in myeloid, fibroblast, and epithelial cells, but low in T cells, endothelial cells, and B cells. This suggests MMP9's role in tumor progression and matrix remodeling, highlighting its potential as a prognostic marker and therapeutic target. Discussion: Our study identified key hub genes in ESCC, assessing their potential as therapeutic targets and biomarkers through detailed expression and dependency analyses. Notably, MMP9 emerged as a significant prognostic marker with high expression correlating with poor survival, underscoring its potential for targeted therapy. These findings enhance our understanding of ESCC pathogenesis and highlight promising avenues for treatment.

COA6 promotes the oncogenesis and progression of breast cancer by oxidative phosphorylation pathway.

Mitochondrial oxidative phosphorylation (OXPHOS) has long been considered the primary energy source in breast cancer cells. Cytochrome c oxidase assembly factor 6 (COA6), which functions as a metal chaperone to transport copper to complex Ⅳ during the OXPHOS process, plays a crucial role in the carcinogenesis of lung adenocarcinoma. Nevertheless, its specific function in breast cancer is undefined. The present investigation aimed to clarify COA6's expression profile and regulatory functions in breast cancer, as well as to unveil its underlying mechanisms. Initially, our findings revealed a significant upregulation of COA6 in breast cancer, as evidenced by an analysis of the TCGA database and tissue microarrays. This upregulation correlated with tumor size and histological grade. Additionally, survival analysis revealed that elevated COA6 amounts were correlated with decreased overall survival (OS) in breast cancer. To delve deeper into the functions of COA6, both COA6-overexpressing and COA6-knockdown breast cancer cell models were established. These experiments demonstrated COA6 is pivotal in regulating cell proliferation, apoptosis, migration, and invasion, thereby promoting cancer progression in vitro. Notably, functional enrichment analysis indicated COA6 might be involved in breast cancer progression by modulating oxidative phosphorylation (OXPHOS). Collectively, this study reveals an overt tumorigenic role for COA6 in breast cancer and sheds light on its potential mechanisms, offering valuable therapeutic targets for breast cancer therapy.

ITIH1 suppresses carcinogenesis in renal cell carcinoma through regulation of the NF­κB signaling pathway.

Renal cell carcinoma (RCC) is a common malignancy of the urinary system. Although traditional therapies, such as surgery assisted with chemotherapy have improved the quality of life and survival time of patients with RCC, patients with metastasis or recurrence benefit little from such therapies. At present, little is known about the underlying mechanisms of RCC, rendering treatment selection and implementation challenging. Therefore, investigating the cause and underlying mechanisms of RCC remain of importance to explore potential new avenues for its treatment. Inter-α-trypsin inhibitor heavy chain 1 (ITIH1) is an inflammation-associated gene reported to suppress the progression of liver cancer. However, its role in RCC remains poorly understood. Therefore, the present study aimed to investigate the role and mechanism of ITIH1 in RCC. Based on data obtained from The Cancer Genome Atlas database, ITIH1 expression was demonstrated to be significantly higher in tumor tissues compared with normal tissues, which was in turn negatively associated with the survival of patients with RCC. However, in RCC cells, ITIH1 was shown to be expressed at significantly lower levels compared with those in HK-2 cells. The discrepancy between tissues and cell lines might be due to the different environment of cell growth. ITIH1 knockdown in RCC cells significantly increased cell proliferation and invasion whilst significantly decreasing the apoptosis rate, compared with those in control cells (without ITIH1 knockdown). By contrast, overexpression of ITIH1 significantly inhibited cell proliferation and invasion in RCC cells. In terms of western blotting results, the phosphorylation levels of NF-κB were significantly increased following ITIH1 knockdown. The protein expression level of IκB significantly decreased whereas that of IKK, Cyclin D1, proliferating cell nuclear antigen and α-smooth muscle actin were significantly increased in ITIH1-knockdown cells, compared with those in the control cells (without ITIH1 knockdown). This suggests that the NF-κB pathway may be activated after ITIH1 knockdown. Following treatment with the NF-κB pathway inhibitor JSH-23 in combination with ITIH1 knockdown, RCC cell proliferation and invasion were significantly reduced compared with those after ITIH1 knockdown alone. In summary, results from the present study suggest that ITIH1 can serve an inhibitory role in the progression of RCC, which could potentially be inhibited through the NF-κB signaling pathway.

Immune landscape of hepatocellular carcinoma: The central role of TP53-inducible glycolysis and apoptosis regulator.

Objective: This study aims to address the substantive issue of lacking reliable prognostic biomarkers in hepatocellular carcinoma (HCC) by investigating the relationship between TP53-inducible glycolysis and apoptosis regulator (TIGAR) and HCC prognosis using The Cancer Genome Atlas database. Methods: (1) Integrated statistical analyses, including logistic regression, Wilcoxon signed-rank test, and Kruskal-Wallis test, were conducted to explore the association between TIGAR expression and clinical-pathological features of HCC. (2) The Kaplan-Meier method combined with univariate and multivariate Cox regression models underscored TIGAR as a prognostic factor in HCC. (3) Gene set enrichment analysis (GSEA) revealed key pathways associated with TIGAR, while single-sample gene set enrichment analysis (ssGSEA) determined its relevance to cancer immune infiltration. Results: (1) Elevated TIGAR expression was significantly correlated with decreased survival outcomes in HCC patients. (2) GSEA highlighted the significant link between TIGAR and humoral immunity. (3) ssGSEA revealed a positive correlation between TIGAR expression and infiltration of Th1 and Th2 cells and a negative correlation with Th17 cell infiltration. Conclusion: TIGAR, as a potential prognostic biomarker for HCC, holds significant value in immune infiltration. Understanding the role of TIGAR could contribute to improved prognostic predictions and personalized treatment strategies for HCC patients.

Predictive role of oxidative stress-related genes in colon cancer: a retrospective cohort study based on The Cancer Genome Atlas.

PURPOSE: This study aimed to elucidate the predictive role of an oxidative stress-related genes (OSRGs) model in colon cancer. MATERIALS AND METHODS: First, OSRGs that were differentially expressed between tumor and normal tissues were identified using The Cancer Genome Atlas (TCGA)-(Colorectal Adenocarcinoma) COAD dataset. Then, Lasso COX regression was performed to develop an optimal prognostic model patients were stratified into high- and low-risk groups based on the expression patterns of these genes. The model's validity was confirmed through Kaplan-Meier survival curves and receiver operating characteristic curve (ROC) analysis. Additionally, enrichment analyses were performed using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) to uncover underlying mechanisms. RESULTS: A totally of 115 differentially expressed OSRGs were identified within the TCGA cohort, with 17 significantly linked to overall survival. These 17 genes were used to formulate a prognostic model that differentiated patients into distinct risk groups, with the high-risk group demonstrating a notably inferior overall survival rate. The risk score, when integrated with clinical and pathological data, emerged as an independent prognostic indicator of colon cancer. Further analyses revealed that the disparity in prognostic outcomes between risk groups could be attributed to the reactive oxygen species pathway and the p53 signaling pathway. CONCLUSION: A new prediction model was established based on OSRGs. CYP19A1, NOL3 and UCN were found to be highly expressed in tumor tissues and substantial clinical predictive significance. These findings offer new insights into the role of oxidative stress in colon cancer.

Development of a novel colon adenocarcinoma m6A-related lncRNA pair prognostic model.

Background: Colon adenocarcinoma (COAD) is among the most prevalent malignancies. Changes to N6-methyladenosine (m6A), the most common RNA modification, can affect how COAD develops. Furthermore, the involvement of long noncoding RNA (lncRNA) in COAD is significant, and it exhibits a close association with m6A modification. Nevertheless, the prognostic significance of lncRNAs that are related to m6A modification in COAD remains unclear. This study aims to establish a m6A-related lncRNA pair signature and reveal its prognostic value in COAD. Methods: The current study utilized data from The Cancer Genome Atlas (TCGA) to investigate the predictive significance of m6A-related lncRNA pair signatures in COAD. The identification of m6A-related lncRNAs was conducted through co-expression analysis using the Pearson correlation coefficient. Then, the lncRNA pairs related to prognosis were identified using univariate Cox regression analysis. Receiver operating characteristic (ROC) curves were produced using the least absolute shrinkage and selection operator (LASSO) penalized with Cox analysis to predict overall survival (OS) in order to build a risk score prognostic model. The relationship among the risk scoring model and clinical characteristics, immune-related variables, and medication sensitivity was examined after identifying independent prognostic factors. Results: Thirty-five of the 319 lncRNA pairings associated with m6A were linked to a pattern that predicted risk ratings. It was verified that the risk score model was a reliable predictor that stood alone from clinicopathological features. Differences between high- and low-risk groups were found in clinicopathological traits, immune-related variables, and medication sensitivity analysis according to correlation analyses. Conclusions: Based on paired differentially expressed m6A-related lncRNAs, the proposed COAD prognostic model demonstrated potential clinical predictive value.

Development and Validation of a Carbohydrate Metabolism-Related Model for Predicting Prognosis and Immune Landscape in Hepatocellular Carcinoma Patients.

OBJECTIVE: The activities and products of carbohydrate metabolism are involved in key processes of cancer. However, its relationship with hepatocellular carcinoma (HCC) is unclear. METHODS: The cancer genome atlas (TCGA)-HCC and ICGC-LIRI-JP datasets were acquired via public databases. Differentially expressed genes (DEGs) between HCC and control samples in the TCGA-HCC dataset were identified and overlapped with 355 carbohydrate metabolism-related genes (CRGs) to obtain differentially expressed CRGs (DE-CRGs). Then, univariate Cox and least absolute shrinkage and selection operator (LASSO) analyses were applied to identify risk model genes, and HCC samples were divided into high/low-risk groups according to the median risk score. Next, gene set enrichment analysis (GSEA) was performed on the risk model genes. The sensitivity of the risk model to immunotherapy and chemotherapy was also explored. RESULTS: A total of 8 risk model genes, namely, G6PD, PFKFB4, ACAT1, ALDH2, ACYP1, OGDHL, ACADS, and TKTL1, were identified. Moreover, the risk score, cancer status, age, and pathologic T stage were strongly associated with the prognosis of HCC patients. Both the stromal score and immune score had significant negative/positive correlations with the risk score, reflecting the important role of the risk model in immunotherapy sensitivity. Furthermore, the stromal and immune scores had significant negative/positive correlations with risk scores, reflecting the important role of the risk model in immunotherapy sensitivity. Eventually, we found that high-/low-risk patients were more sensitive to 102 drugs, suggesting that the risk model exhibited sensitivity to chemotherapy drugs. The results of the experiments in HCC tissue samples validated the expression of the risk model genes. CONCLUSION: Through bioinformatic analysis, we constructed a carbohydrate metabolism-related risk model for HCC, contributing to the prognosis prediction and treatment of HCC patients.

TMEM17 Promotes Tumor Progression in Glioblastoma by Activating the PI3K/AKT Pathway.

BACKGROUND: Glioblastoma (GBM) is a highly aggressive and fast-growing brain tumor, characterized by rapid progression, a very poor prognosis, and a high likelihood of recurrence. Thus, effective new therapeutic targets are urgently needed. Transmembrane proteins (TMEMs) have pro-cancer effects on multiple cancer types, but the mechanisms underlying the effects of TMEM17, particularly its role in GBM, remain unclear. METHODS: We conducted bioinformatics analyses and immunohistochemistry to evaluate the role of TMEM17 in a variety of cancer types. Functional assays were conducted included the Cell Counting Kit-8 assay, annexin V-FITC/PI double staining, 5-ethynyl-2'-deoxyuridine (EdU) incorporation assay, wound healing assay, transwell invasion assay, and dual luciferase assay. RESULTS: We found that TMEM17 is associated with a poor prognosis in GBM. Prognostic analyses confirmed that high TMEM17 expression predicted poorer survival, establishing its significance as an independent prognostic factor. Functional assays demonstrated that silencing TMEM17 in GBM cell lines inhibited proliferation and invasion, and induced apoptosis, underscoring its role in tumor aggressiveness. From a mechanistic perspective, we discovered that the Ying Yang 1 (YY1) transcription factor can bind to the promoter of TMEM17, regulating its upregulation. Regarding downstream mechanisms, knocking down TMEM17 inhibited the phosphoinositide 3-kinase/AKT pathway. These findings suggest that TMEM17 plays a significant role in GBM and may be a potential therapeutic target for this cancer. CONCLUSION: These data prove that TMEM17 plays a key role in the regulation of GBM and has great potential as a clinical therapeutic target for GBM.

A comprehensive pan-cancer analysis revealing the role of ITPRIPL1 as a prognostic and immunological biomarker.

Inositol 1,4,5-Trisphosphate Receptor-Interacting Protein-Like 1 (ITPRIPL1), a single-pass type I membrane protein located in the membrane, functions as an inhibitory ligand of CD3ε. Recent studies have shown that its expression suppresses T cells activation and promote tumor immune evasion. Despite increasing evidence suggesting that ITPRIPL1 plays a significant role in tumor growth, no systematic pan-cancer analysis of ITPRIPL1 has been conducted to date. This study utilized datasets curated from The Cancer Genome Atlas, Genotype Tissue-Expression, and Human Protein Atlas to investigate the relationship between ITPRIPL1 expression and clinical outcomes, immune infiltration, and drug sensitivity across 33 cancer types. We employed multiple methods to assess its prognostic value in pan-cancer, such as univariate Cox regression, survival analysis, and ROC curve analysis and explored the relationship between ITPRIPL1 and tumor mutation burden (TMB), tumor microsatellite instability (MSI), CNV, DNA methylation, immune-related genes, immune cell infiltration, and drug sensitivity to reveal its immunological role. The mRNA expression levels of the ITPRIPL1 gene vary significantly across multiple types of cancer and significantly reduced in breast cancer. Conversely, high ITPRIPL1 expression was associated with a better prognosis in BRCA. Furthermore, the expression of ITPRIPL1 highly correlates with the presence of tumor-infiltrating immune cells and immune checkpoint genes across various types of cancers. Additionally, ITPRIPL1 expression was associated with TMB in 6 cancer types and with MSI in 13 cancer types. High expression of ITPRIPL1 serves as a protective factor in certain cancer types, correlating with longer overall survival in BRCA. Our study further confirms that ITPRIPL1 participates in regulating immune infiltration and affecting the prognosis of patients in pan-cancer. These findings underscore the promising potential of ITPRIPL1 as a therapeutic target for human cancer.

CDKL3 is a promising biomarker for diagnosis and prognosis prediction in patients with hepatocellular carcinoma.

Cyclin-dependent kinase-like 3 (CDKL3) has been identified as an oncogene in certain types of tumors. Nonetheless, its function in hepatocellular carcinoma (HCC) is poorly understood. In this study, we conducted a comprehensive analysis of CDKL3 based on data from the HCC cohort of The Cancer Genome Atlas (TCGA). Our analysis included gene expression, diagnosis, prognosis, functional enrichment, tumor microenvironment and metabolic characteristics, tumor burden, mRNA expression-based stemness, alternative splicing, and prediction of therapy response. Additionally, we performed a cell counting kit-8 assay, TdT-mediated dUTP nick-end Labeling staining, migration assay, wound healing assay, colony formation assay, and nude mouse experiments to confirm the functional relevance of CDKL3 in HCC. Our findings showed that CDKL3 was significantly upregulated in HCC patients compared to controls. Various bioinformatic analyses suggested that CDKL3 could serve as a potential marker for HCC diagnosis and prognosis. Furthermore, CDKL3 was found to be involved in various mechanisms linked to the development of HCC, including copy number variation, tumor burden, genomic heterogeneity, cancer stemness, and alternative splicing of CDKL3. Notably, CDKL3 was also closely correlated with tumor immune cell infiltration and the expression of immune checkpoint markers. Additionally, CDKL3 was shown to independently function as a risk predictor for overall survival in HCC patients by multivariate Cox regression analysis. Furthermore, the knockdown of CDKL3 significantly inhibited cell proliferation in vitro and in vivo, indicating its role as an oncogene in HCC. Taken together, our findings suggest that CDKL3 shows promise as a biomarker for the detection and treatment outcome prediction of HCC patients.

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.

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.

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.

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.