GSCA: an integrated platform for gene set cancer analysis at genomic, pharmacogenomic and immunogenomic levels

SLC12A9 is an immunological and prognostic biomarker for glioma

BACKGROUND

Glioma is one of the most common malignant brain tumors. It has a high rate of progression and a poor prognosis, and effective biomarkers still need to be identified. The solute carrier family 12 (SLC12) family has been reported to be involved in various physiological and pathological processes, but their functional roles in glioma remain unclear.

METHODS

Using public datasets, we studied the mutation and expression level of SLC12 family genes in glioma and identified the significantly differentially expressed member solute carrier family 12 member 9 (SLC12A9). We further predicted the prognostic role of SLC12A9 in glioma by using the Kaplan-Meier method and Cox regression analysis. Then, we performed biological functional enrichment analysis. We focused on the relationships between SLC12A9 expression and immune infiltration in glioma. Meanwhile, we conducted in vitro experiments to evaluate the effect of SLC12A9 expression on glioma cells.

RESULTS

Among the members of the SLC12 family, SLC12A9 had the highest mutation rate in glioma, with gene amplification as the major mutation type, and its expression was significantly upregulated in glioma. Higher SLC12A9 expression was significantly associated with older age, higher grade, wild-type isocitrate dehydrogenase (IDH), and a worse prognosis. The functional enrichment analysis indicated that SLC12A9 is mainly related to ion channel annotation. Gene set enrichment analysis (GSEA) revealed that SLC12A9 was mainly related to the DNA replication pathway. Furthermore, we found that SLC12A9 correlated with tumor-infiltrating immune cells and immune checkpoints. Thus, SLC12A9 may be involved in regulating the immune response of glioma. Finally, our in vitro experiments revealed that silencing SLC12A9 dramatically inhibited glioma cell growth and migration.

CONCLUSIONS

We showed that SLC12A9 may be a new predictive biomarker for glioma diagnosis, prognosis, and immunotherapy response, offering helpful guidelines to advance glioma treatment.

Expression pattern and prognostic significance of aldehyde dehydrogenase 2 in lung adenocarcinoma as a potential predictor of immunotherapy efficacy

Background

The incidence of alcohol-associated cancers is higher within Asian populations having an increased prevalence of an inactivating mutation in aldehyde dehydrogenase 2 (ALDH2), a mitochondrial enzyme required for the clearance of acetaldehyde, a cytotoxic metabolite of ethanol. The role of alcohol consumption in promoting lung cancer is controversial, and little attention has been paid to the association between alcohol drinking and pulmonary ALDH2 expression.

Methods

We performed a comprehensive bioinformatic analysis of multi-omics data available in public databases to elucidate the role of ALDH2 in lung adenocarcinoma (LUAD).

Results

Transcriptional and proteomic data indicate a substantial pulmonary expression of ALDH2, which is functional for the metabolism of alcohol diffused from the bronchial circulation. ALDH2 expression is higher in healthy lung tissue than in LUAD and inhibits cell cycle, apoptosis, and epithelial-mesenchymal transition pathways. Moreover, low ALDH2 mRNA levels predict poor prognosis and low overall survival in LUAD patients. Interestingly, ALDH2 expression correlates with immune infiltration in LUAD.

Conclusions

A better understanding of the role of ALDH2 in lung tumor progression and immune infiltration might support its potential use as a prognostic marker and therapeutic target for improving immunotherapeutic response.

The regulatory role of KIAA1429 in epithelial-mesenchymal transition in cervical cancer via mediating m6A modification of BTG2

Cervical cancer (CC) represents one of the important cancers affecting global female population worldwide. We sought to elucidate the roles and mechanisms of KIAA1429 in the malignant properties of CC cells and the epithelial-mesenchymal transition (EMT) process. KIAA1429 was predicted to be abnormally expressed in CC and correlate with shortened survival of CC patients by GEPIA2 and GSCA databases. High expression of KIAA1429 in human CC cell lines (SiHa, HT-3) was validated by RT-qPCR and Western blot assays. A series of small interfering (si)RNAs including si-KIAA1429-1, si-KIAA1429-2, si-YTHDF2, si-BTG2, and si-negative control (NC) were utilized to interfere the expression levels of KIAA1429, YTHDF2, and BTG2, respectively. Consequently, KIAA1429 silencing attenuated the proliferation, migratory, and invasive functions of CC cells and repressed EMT while promoting CC cell apoptosis. Mechanistically, KIAA1429 could affect N6-methyladenosine (m6A) modification to attenuate the stability of BTG2 mRNA and down-regulate its expression. Additionally, loss of BTG2 partly counteracted the effects of si-KIAA1429 on repressing the malignant activities of CC cells. The aforementioned results collectively demonstrated that KIAA1429-mediated m6A modification of BTG2 and contributed to malignant progression of CC in vitro.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10616-024-00694-3.

Key wound healing genes as diagnostic biomarkers and therapeutic targets in uterine corpus endometrial carcinoma: an integrated in silico and in vitro study

BACKGROUND

Uterine Corpus Endometrial Carcinoma (UCEC) is a prevalent gynecologic malignancy with complex molecular underpinnings. This study identifies key woundhealing genes involved in UCEC and elucidates their roles through a comprehensive analysis.

METHODS

In silico and in vitro experiments.

RESULTS

Seventy wound healing-associated genes were extracted from the Gene Ontology (GO) database, and a protein-protein interaction (PPI) network was constructed using the STRING database. CytoHubba analysis in Cytoscape identified six pivotal hub genes: CD44, FGF2, FGF10, KDM6A, FN1, and MMP2. These genes exhibited significantly lower expression in UCEC cell lines compared to normal controls, as confirmed by RT-qPCR. Receiver Operating Characteristic (ROC) analysis demonstrated their potential as diagnostic biomarkers, with Area Under the Curve (AUC) values ranging from 0.94 to 1.00. Validation using TCGA datasets revealed consistent downregulation of these genes in UCEC samples, corroborated by immunohistochemical staining. Promoter methylation analysis showed significantly higher methylation levels in UCEC, correlating with decreased mRNA expression and poor survival outcomes. Genetic alteration analysis indicated frequent mutations in FN1 and KDM6A, although these did not significantly affect survival. Functional analysis using the CancerSEA database highlighted the involvement of these genes in critical cancer-related processes, including angiogenesis, apoptosis, and metastasis. Immune correlation studies revealed significant associations with immune inhibitor genes and distinct expression patterns across immune subtypes. Overexpression studies in UCEC cell lines demonstrated that CD44 and MMP2 reduce proliferative ability while enhancing migration and wound healing.

CONCLUSION

Collectively, these findings underscore the crucial roles of CD44, FGF2, FGF10, KDM6A, FN1, and MMP2 in UCEC pathogenesis, highlighting their potential as biomarkers and therapeutic targets in this malignancy.

Comprehensive investigation of matrix metalloproteinases in skin cutaneous melanoma: diagnostic, prognostic, and therapeutic insights

The dysregulation of matrix metalloproteinases (MMPs) in skin cutaneous melanoma (SKCM) represents a critical aspect of tumorigenesis. In this study, we investigated the diagnostic, prognostic, and therapeutic aspects of the MMPs in SKCM. Thirteen SKCM cell lines and seven normal skin cell lines were cultured under standard conditions for experimental analyses. RNA and DNA were extracted, followed by RT-qPCR to assess MMP expression and promoter methylation analysis to determine methylation levels. Functional assays, including cell proliferation, colony formation, and wound healing, were conducted post-MMP7 knockdown using siRNA in A375 cells. Databases like GEPIA2, HPA, MEXPRESS, and miRNet were employed for expression, survival, methylation, and miRNA-mRNA network analyses. We investigated the expression and promoter methylation landscape of MMPs in SKCM cell lines, revealing significant (p-value < 0.05) up-regulation of MMP1, MMP7, MMP9, MMP10, MMP11, MMP12, MMP13, MMP14, and MMP25, alongside down-regulation of MMP2, MMP3, and MMP21. Furthermore, our analysis demonstrated a significant (p-value < 0.05) inverse correlation between MMP expression levels and promoter methylation status, suggesting a potential regulatory role of DNA methylation in MMP dysregulation. Notably, MMP7, MMP11, and MMP14 exhibited significant (p-value < 0.05) associations with the overall survival of SKCM patients, emphasizing their prognostic significance. Additionally, Receiver operating characteristic (ROC) curve analysis highlighted the significant (p-value < 0.05) diagnostic potential of MMP7, MMP11, and MMP14 in distinguishing SKCM from normal individuals. Subsequent validation across multiple cohorts confirmed significant (p-value < 0.05) elevated MMP expression levels in SKCM tissues, particularly in advanced disease stages, further emphasizing their role in tumor progression. Furthermore, we elucidated potential regulatory pathways involving miR-22-3p, which targets MMP7, MMP11, and MMP14 genes in SKCM. Our findings also revealed associations between MMP expression and immune modulation, drug sensitivity, and functional states of SKCM cells. Lastly, MMP7 knockdown in A375 cells significantly significant (p-value < 0.05) impacted several characteristics, including cell proliferation, colony formation, and wound healing. Our findings highlight the diagnostic, prognostic, and therapeutic potential of MMP7, MMP11, and MMP14 in SKCM. These MMPs could serve as biomarkers for early detection and targets for therapy. Future efforts should focus on preclinical and clinical validation to translate these insights into personalized diagnostic and therapeutic strategies.

A multidimensional pan-cancer analysis of NDUFA4L2 and verification of the oncogenic value in colon cancer

NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, 4-like 2 (NDUFA4L2) protein is located in the mitochondria and can regulate cell proliferation. Some studies have shown that the high NDUFA4L2 expression is linked with poor prognosis and cancer progression in various patients with cancers. However, the correlation between NDUFA4L2 and pan-cancer is unknown. In this study, we used the databases and investigated the expression and variation of NDUFA4L2 in many cancers in detail to determine its diagnostic and prognostic values. We also validated the oncogenic capability of NDUFA4L2 in colon cancer by experiments or tissue microarray. In most cancers, NDUFA4L2 expression levels are upregulated, and a high level of NDUFA4L2 expression is associated with poor OS. In addition, the results showed that 10 proteins and 30 genes were correlated to NDUFA4L2. An additional analysis demonstrated that these genes and proteins exhibited either negative or positive correlations with specific pathways. Moreover, we discovered that NDUFA4L2 expression was related to tumor stemness, MSI, TMB, immune cell infiltration and ROS production. Our results show that NDUFA4L2 affects the development and mitochondrial function of colon cancer cell lines. Finally, we identified that NDUFA4L2 was sensitive to 10 anticancer drugs. Our study suggest that NDUFA4L2 could serve as a prognostic and therapeutic biomarker for most cancer, including colon cancer.

Functional Analysis and Experimental Validation of the Prognostic and Immune Effects of the Oncogenic Protein CDC45 in Breast Cancer

Purpose

Cell division cycle protein 45 (CDC45) plays a crucial role in DNA replication. This study investigates its role in breast cancer (BC) and its impact on tumor progression.

Methods

We utilized the GEO database to screen differentially expressed genes (DEGs) and conducted enrichment analysis on these genes. We established a Nomogram model based on CDC45 and other clinical indicators. Additionally, we performed protein-protein interaction (PPI) network construction, drug sensitivity analysis, and immune correlation analysis of CDC45. The function of CDC45 was further verified through cell and animal experiments.

Results

CDC45 is highly expressed in most tumors, including BC. The expression level of CDC45 was significantly associated with age, sex, race, cancer stage, and molecular subtypes (all p < 0.05). CDC45 was incorporated into a Nomogram model, which showed moderate accuracy in predicting patient prognosis. We also analyzed the co-expression genes of CDC45, including TOPBP1, GINS2, MCM5, GINS1, GINS4, POLE2, MCM2, MCM6, MCM4, and MCM7. Furthermore, CDC45 expression was closely linked to immune infiltration levels, immune checkpoint inhibitors, and the therapeutic response to small molecule drugs. Finally, both in vitro and in vivo experiments confirmed the cancer-promoting effect of CDC45 in BC.

Conclusion

The expression level of CDC45 is linked to the prognosis, immune infiltration, and drug sensitivity of BC. In vitro and in vivo experiments have confirmed that CDC45 acts as a cancer-promoting protein in breast cancer.

Bioinformatics analysis of mitochondrial metabolism-related genes demonstrates their importance in renal cell carcinoma

PURPOSE

Clear cell renal cell carcinoma (ccRCC) is resistant to radiotherapy and chemotherapy. Thus, it is necessary to find new diagnostic markers and therapeutic targets to increase the overall outcomes of ccRCC. Recent studies have shown that therapeutic methods that interfere with the energy transfer system can also positively affect the treatment process.

METHODS

The present study is focused on finding markers associated with mitochondrial metabolic pathways that affect the outcome of ccRCC. For this purpose, we investigated various aspects of the relationship between mitochondrial metabolism and ccRCC based on analysis of gene network connections and differentially expressed genes, through assessment of protein-protein interaction, mutations, and promoter methylation on the related genes. We also investigated gene interaction with miRNAs and immune infiltration analysis.

RESULTS

Through these steps, we provided a list of possible diagnostic markers and therapeutic targets for ccRCC.

CONCLUSION

The current study further proved the importance of mitochondrial metabolic pathways in the pathogenesis of ccRCC and provided a list of possible diagnostic markers and therapeutic targets from these pathways that can be used in ccRCC.

Expression of vesiculation-related genes is associated with a tumor-promoting microenvironment: a pan-cancer analysis

BACKGROUND

Small extracellular vesicles (sEV) released by tumor cells (tumor-derived sEV; TEX) mediate intercellular communication between tumor and non-malignant cells and were shown to impact disease progression. This study investigates the relationship between the expression levels of the vesiculation-related genes linked to sEV production and the tumor microenvironment (TME).

METHODS

Two independent gene sets were analyzed, both previously linked to sEV production in various non-malignant or malignant cells. Expression profiles were compared among 28 tumor types listed in the Cancer Genome Atlas (TCGA). Gene expression and survival analysis (GEPIA2), immunogenomic analysis (TISIDB), and genomic analysis (GSCA) were performed.

RESULTS

Vesiculation-related genes were overexpressed in tissues of most tumor types compared to healthy tissues, and high expression levels were associated with worse overall survival in cervical squamous cell carcinoma, kidney chromophobe, lower grade glioma, hepatocellular carcinoma, lung squamous cell carcinoma, and pancreatic adenocarcinoma but with improved overall survival in kidney renal clear cell carcinoma. Expression of these signatures correlated with an increased abundance of infiltrating CD4( +) T cells and dendritic cells, a decreased abundance of B cells and eosinophils, and activation of tumor cell apoptosis and epithelial-mesenchymal transition pathways in all tumor types. 17-AAG was identified as a potential drug candidate to target tumors with elevated expression of vesiculation-related genes.

CONCLUSIONS

Vesiculation-related genes were associated with distinct immunological and genomic landscapes further emphasizing the important role of TEX in cancer progression.

Integrating bioinformatics and machine learning to identify AhR-related gene signatures for prognosis and tumor microenvironment modulation in melanoma

Background

The Aryl Hydrocarbon Receptor (AhR) pathway significantly influences immune cell regulation, impacting the effectiveness of immunotherapy and patient outcomes in melanoma. However, the specific downstream targets and mechanisms by which AhR influences melanoma remain insufficiently understood.

Methods

Melanoma samples from The Cancer Genome Atlas (TCGA) and normal skin tissues from the Genotype-Tissue Expression (GTEx) database were analyzed to identify differentially expressed genes, which were intersected with a curated list of AhR-related pathway genes. Prognostic models were subsequently developed, and feature genes were identified. Advanced methodologies, including Gene Set Enrichment Analysis (GSEA) and immune cell infiltration analysis, were employed to explore the biological significance of these genes. The stability of the machine learning models and the relationship between gene expression and immune infiltrating cells were validated using three independent melanoma datasets. A mouse melanoma model was used to validate the dynamic changes of the feature genes during tumor progression. The relationship between the selected genes and drug sensitivity, as well as non-coding RNA interactions, was thoroughly investigated.

Results

Our analysis identified a robust prognostic model, with four AhR-related genes (MAP2K1, PRKACB, KLF5, and PIK3R2) emerging as key contributors to melanoma progression. GSEA revealed that these genes are involved in primary immunodeficiency. Immune cell infiltration analysis demonstrated enrichment of CD4+ naïve and memory T cells, macrophages (M0 and M2), and CD8+ T cells in melanoma, all of which were associated with the expression of the four feature genes. Importantly, the diagnostic power of the prognostic model and the relevance of the feature genes were validated in three additional independent melanoma datasets. In the mouse melanoma model, Map2k1 and Prkacb mRNA levels exhibited a progressive increase with tumor progression, supporting their role in melanoma advancement.

Conclusions

This study presents a comprehensive analysis of AhR-related genes in melanoma, highlighting MAP2K1, PRKACB, KLF5, and PIK3R2 as key prognostic markers and potential therapeutic targets. The integration of bioinformatics and machine learning provides a robust framework for enhancing prognostic evaluation in melanoma patients and offers new avenues for the development of treatments, particularly for those resistant to current immunotherapies.

Knockdown of CENPF induces cell cycle arrest and inhibits epithelial‑mesenchymal transition progression in glioma

Gliomas are among the most common malignant tumors of the central nervous system. Despite surgical resection followed by postoperative radiotherapy and chemotherapy, their prognosis remains unfavorable. The present study aimed to assess new mechanisms and explore promising prognostic biomarkers for patients with glioma using comprehensive bioinformatics analysis and in vitro and in vivo assays. Overlapping differentially expressed genes were screened from The Cancer Genome Atlas, GSE111260 and GSE16011 samples for protein-protein interaction networks, a risk score model, gene mutation analysis and a nomogram to identify the prognostic hub genes. Subsequently, an immunoassay was performed to determine key genes. Functional and animal assays were then performed to assess the tumorigenesis of the key genes in glioma. Using bioinformatics analysis, centromere protein F (CENPF), kinesin superfamily member 20A, kinesin superfamily protein 4A and marker of proliferation Ki-67 were identified as potential prognostic biomarkers for patients with glioma. Furthermore, CENPF knockdown was demonstrated to suppress the proliferation and metastasis of glioma cells, and induce G2 arrest in the cell cycle. Moreover, CENPF knockdown was revealed to decrease Vimentin and increase E-cadherin levels in glioma cells, and significantly reduce the size and mass of tumors in vivo. Overall, the present study identified new clinical biomarkers and revealed that CENPF may promote glioma progression by regulating the epithelial-mesenchymal transition pathway. By elucidating the complexities of glioma and identifying prognostic biomarkers, the present research enables further improvement of patient outcomes and the advancement of precision medicine for this disease.

Prognostic Significance and Therapeutic Potential of SERPINE1 in Head and Neck Squamous Cell Carcinoma

BACKGROUND

This study aims to elucidate the expression pattern of SERPINE1, assess its prognostic significance, and explore potential therapeutic drugs targeting this molecule.

METHODS AND RESULTS

In this study, we delved into the variations in gene mutation, methylation patterns, and expression levels of SERPINE1 in head and neck squamous cell carcinoma (HNSCC) and normal tissues, leveraging comprehensive analyses of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. The connection between the biological function of the gene and prognosis was scrutinized through immune infiltration and enrichment analyses. Concurrently, we assessed the potential therapeutic value of SERPINE1 through drug sensitivity analysis. It was observed that, particularly in human papillomavirus (HPV) negative HNSCC, SERPINE1 exhibited elevated expression levels, correlating with poorer prognosis. The infiltration levels of eight cell types, such as eosinophils, Tgd, and macrophages, showed a positive correlation with SERPINE1 expression, whereas infiltration levels of four cell types, including cytotoxic cells, B cells, and pDCs, displayed a negative correlation. Furthermore, copy number variations of SERPINE1 were primarily characterized by homologous amplification, positively correlating with its expression, while methylation showed an inverse correlation. The outcomes of drug sensitivity analysis underscored the potential of SERPINE1 as a therapeutic target.

CONCLUSION

Elevated expression of SERPINE1 in HNSCC is intricately linked with adverse prognostic outcomes and has the potential to influence the immune microenvironment. Subsequent investigations are imperative to fully elucidate the prognostic implications of SERPINE1 as a biomarker and to unlock its therapeutic promise as a target for intervention.

Integrated analysis of cell cycle and p53 signaling pathways related genes in breast, colorectal, lung, and pancreatic cancers: implications for prognosis and drug sensitivity for therapeutic potential

Cancer, a leading cause of death worldwide, is projected to increase by 76.6% in new cases and 89.7% in mortality by 2050 (WHO 2022). Among various types, lung cancer is the most prevalent with high morbidity, while breast, colorectal, and pancreatic cancers also show high mortality rates. Cancer progression often involves disruption in cell cycle regulation and signaling pathways, with mutations in genes like TP53, EGFR, and K-RAS playing significant roles. In this study, we analyzed gene expression datasets to identify common molecular signatures across breast, colorectal, lung and pancreatic cancers. Our focus was on genes related to cell cycle regulation and p53 signaling pathway, intending to discover potential biomarkers for improved diagnosis and treatment strategies. The study analyzed GEO datasets; GSE45827, GSE9348, GSE30219, and GSE62165 for breast, colorectal, lung, and pancreatic cancers respectively. Differentially expressed genes (DEGs) were identified using GEO2R, and functional annotation and pathway analysis were performed using WebGestalt. Common cell cycle and p53 signaling genes were acquired from MSigDB using GSEA. A protein-protein interaction network was constructed using STRING and Cytoscape, identifying top hub genes. Validation of hub genes at mRNA and protein levels was done via GEPIA2 and Human Protein Atlas. Survival analysis was conducted using TCGA data by GEPIA2 and LASSO, and drug sensitivity was analyzed with the GSCA drug bank database, highlighting potential therapeutic targets. The study identified 411 common DEGs among these four cancers. Pathway and functional enrichment revealed key biological processes and pathways like p53 signaling, and cell cycle. The intersection of these DEGs with genes involved in cell cycle and p53 signaling, identified 23 common genes that were used for constructing a PPI network. The top 10 hub genes were validated both for mRNA and protein expression, revealing they are significantly overexpressed in all studied cancers. Prognostic relevance showed that MCM4, MCM6, CCNA2, CDC20, and CHEK1 are associated with survival. Additionally, drug sensitivity analysis highlighted key gene-drug interactions, suggesting potential targets for therapeutic intervention.

Pan-cancer analysis reveals the prognostic and immunomodulatory potential of super-enhancer-induced ANGPT2 and experimental validation in colorectal cancer

BACKGROUND

ANGPT2 plays important roles in cancer development. However, there is still no systematic analysis of ANGPT2 in pan-cancer.

METHODS

In this paper, we conducted a pan-cancer analysis to investigate the characteristics of ANGPT2. Furthermore, we investigated the impact of genetic and epigenetic factors on ANGPT2 expression by bioinformatics and assays.

RESULTS

By several TCGA and GEO databases, we identified elevated expression of ANGPT2 in various tumor types. Besides, high expression of ANGPT2 induced poor prognosis of patients in multiple tumors. Through enrichment analysis, we found that ANGPT2 participated in various biological processes, including angiogenesis and immunity. Various immune analyses indicated that high expression of ANGPT2 might suggest a propensity towards a hot tumor microenvironment, but its impact on immunotherapy was negative. Bioinformatics analysis and experiments confirmed that genetic and epigenetic factors explained part of the mechanism behind ANGPT2 abnormal expression. Finally, we screened candidate drugs targeting the ANGPT2 protein by molecular docking and molecular dynamics simulation.

CONCLUSION

ANGPT2 has diagnostic and prognostic values in multiple tumor types. Though with a hot tumor microenvironment, ANGPT2 high expression patients are not suitable for immunotherapy because of its proangiogenic function, contributing to selecting the exact patients for immunotherapy. Both genetic and epigenetic factors influenced ANGPT2 expression, with the influence of super-enhancer being more pronounced. This paper for the first time did the systematic analysis of ANGPT2 and showed its characteristic in pan-cancer. We summarized the biomarker role of ANGPT2 on tumor diagnosis and prognosis, as well as its target role on tumor immunotherapy.

Exploring potential key genes and pathways associatedwith hepatocellular carcinoma prognosis through bioinformatics analysis, followed by experimental validation

BACKGROUND

Liver Hepatocellular Carcinoma (LIHC) is a prevalent and aggressive liver cancer with limited therapeutic options. Identifying key genes involved in LIHC can enhance our understanding of its molecular mechanisms and aid in the development of targeted therapies. This study aims to identify differentially expressed genes (DEGs) and key hub genes in LIHC using bioinformatics approaches and experimental validation.

METHOD

We analyzed two LIHC-related datasets, GSE84598 and GSE19665, from the Gene Expression Omnibus (GEO) database to identify DEGs. Differential expression analysis was performed using the limma package in R to identify DEGs between cancerous and non-cancerous liver tissues. A Protein-Protein Interaction (PPI) network was constructed using STRING to determine key hub genes. Further validation of these hub genes was conducted through UALCAN, OncoDB, and the Human Protein Atlas (HPA) databases for mRNA and protein expression levels. Promoter methylation and mutational analyses were performed using cBioPortal. Kaplan-Meier survival analysis assessed the impact of hub gene expression on patient survival. Correlations with immune cell abundance and drug sensitivity were explored using GSCA. Finally, AURKA was knocked down in HepG2 cells, and cell proliferation, colony formation, and wound healing assays were performed.

RESULTS

Analysis identified 180 DEGs, with four key hub genes, including AURKA, BUB1B, CCNA2, and PTTG1 showing significant overexpression and hypomethylation in LIHC tissues. AURKA knockdown in HepG2 cells led to decreased cell proliferation, reduced colony formation, and impaired wound healing, confirming its role in LIHC progression. These hub genes were also hypomethylated and their elevated expression correlated with poor overall survival.

CONCLUSION

AURKA, BUB1B, CCNA2, and PTTG1 are crucial for LIHC pathogenesis and may serve as potential biomarkers or therapeutic targets. Our findings provide new insights into LIHC mechanisms and suggest promising avenues for future research and therapeutic development.

A Novel Peroxisome-Related Gene Signature Predicts Breast Cancer Prognosis and Correlates with T Cell Suppression

Background

Peroxisomes are increasingly linked to cancer development, yet the prognostic role of peroxisome-related genes (PRGs) in breast cancer remains unclear.

Objective

This study aimed to construct a prognostic model based on PRG expression in breast cancer to clarify their prognostic value and clinical implications.

Methods

Transcriptomic data from TCGA and GEO were used for training and validation cohorts. TME characteristics were analyzed with ESTIMATE, MCP-counter, and CIBERSORT algorithms. qPCR validated mRNA expression levels of risk genes, and data analysis was conducted in R.

Results

Univariate and multivariate Cox regression identified a 7-gene PRG risk signature (ACBD5, ACSL5, DAO, NOS2, PEX3, PEX10, and SLC27A2) predicting breast cancer prognosis in training (n=1069), internal validation (n=327), and external validation (merged from four GEO datasets, n=640) datasets. While basal and Her2 subtypes had higher risk scores than luminal subtypes, a significant prognostic impact of the PRG risk signature was seen only in luminal subtypes. The high-risk subgroup exhibited a higher frequency of focal synonymous copy number alterations (SCNAs), arm-level amplifications and deletions, and single nucleotide variations. These increased genomic aberrations were associated with greater immune suppression and reduced CD8+ T cell infiltration. Bulk RNA sequencing and single-cell analyses revealed distinct expression patterns of peroxisome-related genes (PRGs) in the breast cancer TME: PEX3 was primarily expressed in malignant and stromal cells, while ACSL5 showed high expression in T cells. Additionally, the PRG risk signature demonstrated efficacy comparable to that of well-known biomarkers for predicting immunotherapy responses. Drug sensitivity analysis revealed that the PRG high-risk subgroup was sensitive to inhibitors of BCL-2 family proteins (BCL-2, BCL-XL, and MCL1) and other kinases (PLK1, PLK1, BTK, CHDK1, and EGFR).

Conclusion

The PRG risk signature serves as a promising biomarker for evaluating peroxisomal activity, prognosis, and responsiveness to immunotherapy in breast cancer.

Investigating the clinical significance of OAS family genes in breast cancer: an in vitro and in silico study

BACKGROUND

Breast cancer is the most common malignancy among women worldwide, characterized by complex molecular and cellular heterogeneity. Despite advances in diagnosis and treatment, there is an urgent need to identify reliable biomarkers and therapeutic targets to improve early detection and personalized therapy. The OAS (2'-5'-oligoadenylate synthetase) family genes, known for their roles in antiviral immunity, have emerged as potential regulators in cancer biology. This study aimed to explore the diagnostic and functional relevance of OAS family genes in breast cancer.

METHODOLOGY

Breast cancer cell lines and controls were cultured under specific conditions, and DNA and RNA were extracted for downstream analyses. RT-qPCR, bisulfite sequencing, and Western blotting were employed to assess gene expression, promoter methylation, and knockdown efficiency of OAS family genes. Functional assays, including CCK-8, colony formation, and wound healing, evaluated cellular behaviors, while bioinformatics tools (UALCAN, GEPIA, HPA, OncoDB, cBioPortal, and others) validated findings and explored correlations with clinical data.

RESULTS

The OAS family genes (OAS1, OAS2, OAS3, and OASL) were found to be significantly upregulated in breast cancer cell lines and tissues compared to normal controls. This overexpression was strongly associated with reduced promoter methylation. Receiver operating characteristic (ROC) analysis demonstrated high diagnostic accuracy, with area under the curve (AUC) values exceeding 0.93 for all four genes. Increased OAS expression correlated with advanced cancer stages and poor overall survival in breast cancer patients. Functional analysis revealed their involvement in critical biological processes, including immune modulation and oncogenic pathways. Silencing OAS genes in breast cancer cells significantly inhibited cell proliferation and colony formation, while unexpectedly enhancing migratory capacity. Additionally, correlations with immune cell infiltration, molecular subtypes, and drug sensitivity highlighted their potential roles in the tumor microenvironment and therapeutic response.

CONCLUSION

The findings of this study established OAS family genes as potential biomarkers and key players in breast cancer progression, offering promise as diagnostic biomarkers and therapeutic targets to address unmet clinical needs.

Exploring the function and prognostic value of RPLP0, RPLP1 and RPLP2 expression in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is the most common subtype of non-small cell lung cancer (NSCLC) and is characterized by its heterogeneity and poor prognosis. The role of ribosomal proteins RPLP0, RPLP1 and RPLP2 in multiple cancers has been implicated. However, their function in LUAD and their correlation with the poor prognosis of LUAD remains elusive. In this study, we performed a comprehensive bioinformatic analysis of the impact of these ribosomal proteins on LUAD. Our findings reveal that RPLP0, RPLP1 and RPLP2 are overexpressed in LUAD, which are likely attributed to abnormal copy number variations and decreased methylation levels of their promoters. LUAD patients with high expression of RPLP0, RPLP1 or RPLP2 have worse clinical outcomes in terms of overall survival (OS), first progression (FP) and post-progression survival (PPS), indicating poor prognosis. Moreover, the expression of RPLP0, RPLP1 and RPLP2 affects immune cell infiltration in LUAD tissues. Finally, we identified multiple existing drugs that may inhibit the expression of RPLP1 and RPLP2. Collectively, our data implicate the oncogenic role of RPLP0, RPLP1 and RPLP2 in LUAD and underscore their prognostic value in LUAD patients.

EVA1B facilitates esophageal squamous carcinoma progression and recruitment of immunosuppressive myeloid-derived suppressor cells in the tumor microenvironment

Eva-1 Homolog B (EVA1B) has been preliminarily found to be associated with prognostic outcomes and immune microenvironment in several human cancer types, but the implications of EVA1B in ESCC remain unclear. Human ESCC and paracancerous tissues were gathered in this study, and EVA1B expression was measured via immunoblotting. EC109 and KYSE-180 ESCC cells were stably infected by sh-EVA1B lentivirus, and functional experiments were subsequently implemented. Syngeneic mouse models were built, and the expansion and recruitment of myeloid-derived suppressor cells (MDSCs) were then evaluated. The results showed that EVA1B presented the notable up-regulation in clinical ESCC tissues versus controls, and was connected to more advanced stages and the abundance of MDSCs. Silencing EVA1B notably attenuated proliferation of ESCC cells and tumor growth in syngeneic mouse models. Moreover, EVA1B suppression resulted in apoptosis and cell cycle arrest, and impaired ESCC cell aggressiveness. Among ESCC patients, EVA1B was strongly correlated to EMT pathway activity. Targeted suppression of EVA1B mitigated the expression of Wnt3a, β-catenin and LRP6 in ESCC cells and tumor xenografts. Additionally, inhibition of EVA1B attenuated the expansion and recruitment of MDSCs within the immune microenvironment based upon the reduction in the percentage of CD11b+Gr-1+ immunosuppressive MDSCs as well as the expression of MDSC expansion stimulators (S100A8, S100A9, Arg-1, and VEGF). Collectively, our findings unveiled the contribution of high expression of EVA1B to ESCC progression and MDSCs expansion and recruitment, indicating that targeted suppression of EVA1B may be a potential treatment choice for ESCC patients.

Uncovering the potential of APOD as a biomarker in gastric cancer: A retrospective and multi-center study

Gastric cancer (GC) poses a significant health challenge worldwide, necessitating the identification of predictive biomarkers to improve prognosis. Dysregulated lipid metabolism is a well-recognized hallmark of tumorigenesis, prompting investigation into apolipoproteins (APOs). In this study, we focused on apolipoprotein D (APOD) following comprehensive analyses of APOs in pan-cancer. Utilizing data from the TCGA-STAD and GSE62254 cohorts, we elucidated associations between APOD expression and multiple facets of GC, including prognosis, tumor microenvironment (TME), cancer biomarkers, mutations, and immunotherapy response, and identified potential anti-GC drugs. Single-cell analyses and immunohistochemical staining confirmed APOD expression in fibroblasts within the GC microenvironment. Additionally, we independently validated the prognostic significance of APOD in the ZN-GC cohort. Our comprehensive analyses revealed that high APOD expression in GC patients was notably associated with unfavorable clinical outcomes, reduced microsatellite instability and tumor mutation burden, alterations in the TME, and diminished response to immunotherapy. These findings provide valuable insights into the potential prognostic and therapeutic implications of APOD in GC.

METTL2B m3C RNA transferase: oncogenic role in ovarian cancer progression via regulation of the mTOR/AKT pathway and its link to the tumor immune microenvironment

BACKGROUND

Aberrant expression of N3-methylcytidine methyltransferase 2B (METTL2B) has been observed in various human malignancies, including those of the prostate, liver, breasts, and bladder. However, its role in ovarian cancer (OC) remains largely unexplored. This research preliminarily investigated METTL2B expression in OC and elucidated the associated molecular mechanisms.

METHODS

We utilized three publicly available cancer-related databases (Genotype-Tissue Expression, Gene Expression Omnibus, and The Cancer Genome Atlas) to identify gene signatures in patients with OC and normal individuals with a specific focus on METTL2B. The role of METTL2B in OC was evaluated using patient survival data, and its impact on oncogenic behaviors in both cell and animal models, including growth potential, migration, invasion, and the tumor microenvironment, was examined. This assessment was conducted using bioinformatics tools such as Gene Set Cancer Analysis, GeneMANIA, and Tumor Immune Single-cell Hub 2. Additionally, the association between drug sensitivity and METTL2B expression was analyzed using CellMiner.

RESULTS

METTL2B expression was significantly elevated in OC, highlighting its potential clinical value in the diagnosis and prognosis of OC. Patients with lower METTL2B expression exhibited favorable survival. Furthermore, METTL2B knockdown significantly disrupted oncogenic behaviors in OC cell lines by suppressing the mTOR/AKT signaling pathway. Additionally, bioinformatics-based Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses suggested a close correlation between METTL2B and immune responses.

CONCLUSIONS

Our research confirmed the upregulation of METTL2B in OC, suggesting its oncogenic function. However, METTL2B expression was negatively correlated with the infiltration scores of multiple immune cells, including cytotoxic cells and T cells, indicating its complex role in the tumor immune microenvironment. These findings highlight the significant clinical value of METTL2B in the diagnosis and prognosis of OC.

Clinical relevance and function of HMGB1 gene polymorphism and expression in colorectal cancer

High HMGB1 levels contribute to the development and metastasis of tumors such as colorectal cancer (CRC). The current investigation sought to evaluate the association of a functional InDel polymorphism (rs34000982) on the HMGB1 gene with CRC susceptibility and tumor stage and the clinical relevance of HMGB1 gene expression. A total of 600 CRC patients and 600 healthy control individuals were genotyped by a polymerase chain reaction-polyacrylamide gel electrophoresis assay. The findings demonstrated that the rs34000982 Ins allele or Ins/Ins genotype was associated not only with reduced susceptibility to CRC, especially stage III-IV CRC (Ins vs. Del: OR = 0.65, 95%CI = 0.51-0.82, p < 0.001; Ins/Ins vs. Del/Del: OR = 0.29, 95%CI = 0.14- 0.60, p < 0.001), but also with tumor stage. CRC patients carrying the Ins allele or Ins/Ins genotype had a significantly lower risk of stage III-IV tumors (Ins vs. Del: OR = 0.69, 95%CI = 0.53- 0.91; Ins/Ins vs. Del/Del: OR = 0.41, 95%CI = 0.18-0.94). Functional research revealed that the rs34000982 Ins allele enabled hsa-miR-944 to interact with the 3' untranslated region of HMGB1. In addition, HMGB1 gene expression levels were associated not only with multiple immune cell infiltration, but also with multiple anti-CRC drug sensitivities. The current findings suggest that the HMGB1 rs34000982 polymorphism may serve as a marker of CRC susceptibility and progression in the Chinese population, and HMGB1 levels may serve as an anti-CRC drug sensitivity marker.

CancerHubs: a systematic data mining and elaboration approach for identifying novel cancer-related protein interaction hubs

Conventional approaches to predict protein involvement in cancer often rely on defining either aberrant mutations at the single-gene level or correlating/anti-correlating transcript levels with patient survival. These approaches are typically conducted independently and focus on one protein at a time, overlooking nucleotide substitutions outside of coding regions or mutational co-occurrences in genes within the same interaction network. Here, we present CancerHubs, a method that integrates unbiased mutational data, clinical outcome predictions and interactomics to define novel cancer-related protein hubs. Through this approach, we identified TGOLN2 as a putative novel broad cancer tumour suppressor and EFTUD2 as a putative novel multiple myeloma oncogene.

Towards simplified graph neural networks for identifying cancer driver genes in heterophilic networks

The identification of cancer driver genes is crucial for understanding the complex processes involved in cancer development, progression, and therapeutic strategies. Multi-omics data and biological networks provided by numerous databases enable the application of graph deep learning techniques that incorporate network structures into the deep learning framework. However, most existing methods do not account for the heterophily in the biological networks, which hinders the improvement of model performance. Meanwhile, feature confusion often arises in models based on graph neural networks in such graphs. To address this, we propose a Simplified Graph neural network for identifying Cancer Driver genes in heterophilic networks (SGCD), which comprises primarily two components: a graph convolutional neural network with representation separation and a bimodal feature extractor. The results demonstrate that SGCD not only performs exceptionally well but also exhibits robust discriminative capabilities compared to state-of-the-art methods across all benchmark datasets. Moreover, subsequent interpretability experiments on both the model and biological aspects provide compelling evidence supporting the reliability of SGCD. Additionally, the model can dissect gene modules, revealing clearer connections between driver genes in cancers. We are confident that SGCD holds potential in the field of precision oncology and may be applied to prognosticate biomarkers for a wide range of complex diseases.

GLN2 as a key biomarker and therapeutic target: evidence from a comprehensive pan-cancer study using molecular, functional, and bioinformatic analyses

BACKGROUND

GNL2, a nuclear protein, is involved in ribosome production and cell cycle regulation. However, its expression and function in different types of tumors are not well understood. Comprehensive studies across multiple cancer types are needed to assess the potential of GNL2 as a diagnostic, prognostic, and immunological marker.

METHODS

mRNA expression data, copy number alteration threshold data, masked copy number segmentation data, and DNA methylation 450 K data from The Cancer Genome Atlas (TCGA) pan-cancer cohort were obtained from the Firehose database. Additional data, including miRNA, The Cancer Proteome Atlas (TCPA), mutation data, and clinical information, were sourced from the University of California Santa Cruz (UCSC) Xena database. The cBioPortal database facilitates the examination of GNL2 mutation frequency, location, and 3D structure in the TCGA database. Gene Expression Omnibus (GEO) data verified the transcriptome level expression in the TCGA cohort. Protein expression levels were analyzed via the Human Protein Atlas (HPA) database and the Clinical Proteomic Tumor Analysis Consortium (CPTAC) database. Gene set enrichment analysis (GSEA) was employed to investigate the biological role of GNL2 across cancers. Multiple immune infiltration algorithms from the TIMER2.0 database were utilized to examine the correlation between GNL2 expression and the tumor immune microenvironment. The transcriptome-wide immune infiltration results were validated using 72 single-cell datasets from the Tumor Immune Single-cell Hub (TISCH) database. Pan-cancer survival maps were constructed, and GNL2 expression in different molecular subtypes across cancers was examined. The relationship between GNL2 and drug resistance was investigated using data from CellMiner, GDSC, and CTRP. The Comparative Toxicogenomics Database (CTD) was used to identify chemicals affecting GNL2 expression.

RESULTS

GNL2 is located primarily in the nucleus, and its expression is regulated mainly through somatic copy number alteration (SCNA) and aberrant DNA methylation, according to TCGA data. Database analysis and immunohistochemical results from clinical samples revealed high GNL2 expression in most tumors, which was correlated with diagnostic significance. High GNL2 expression often indicates a poor prognosis with pan-cancer prognostic value. Gene set enrichment analysis (GSEA) suggested that GNL2 is involved in tumor development through cell proliferation-related pathways. GNL2 expression is correlated with the expression of immune-related genes and the infiltration levels of multiple immune cells. The relationships between GNL2 and various drugs and chemicals were examined, revealing its influence on drug sensitivity and identifying five chemicals countering GNL2-mediated pro-cancer effects.

CONCLUSION

Comprehensive bioinformatics analysis of GNL2 in pan-cancer tissues, combined with experimental validation, elucidated the pan-cancer expression pattern of GNL2, determined its diagnostic and prognostic value, and explored the biological functions of GNL2. GNL2 may be involved in the regulation of cell cycle progression and remodeling of the tumor microenvironment and is associated with poor prognosis as a risk factor in most tumors. The potential of GNL2-based cancer therapies is emphasized, assisting in predicting the response to chemotherapy.

Importance of LINC00852/miR-145-5p in breast cancer: a bioinformatics and experimental study

PURPOSE

We aimed to examine the importance of an lncRNA, namely LINC00852, in the pathogenesis of breast cancer.

MATERIALS AND METHODS

In the current study, we used several online tools to examine the importance of LINC00852 in breast cancer. Then, we examined these findings in 50 pairs of breast cancer tissues and adjacent non-cancerous ones. We also re-evaluated the data of miR-145-5p signature from our recent study.

RESULTS

While in silico tools revealed down-regulation of LINC00852 in breast cancer samples, expression assays showed significant up-regulation of this lncRNAs in breast cancer samples compared with matching control samples from Iranian patients. miR-145-5p was under-expressed in breast cancer samples compared with non-cancerous samples. LINC00852 could separate breast cancer tissues from adjacent non-malignant tissues with an AUC value of 0.7218 (P value < 0.001).

CONCLUSION

The current study potentiates LINC00852/miR-145-5p axis as a possible contributor to the pathogenesis of breast cancer.

Ribosomal proteins in hepatocellular carcinoma: mysterious but promising

Ribosomal proteins (RPs) are essential components of ribosomes, playing a role not only in ribosome biosynthesis, but also in various extra-ribosomal functions, some of which are implicated in the development of different types of tumors. As universally acknowledged, hepatocellular carcinoma (HCC) has been garnering global attention due to its complex pathogenesis and challenging treatments. In this review, we analyze the biological characteristics of RPs and emphasize their essential roles in HCC. In addition to regulating related signaling pathways such as the p53 pathway, RPs also act in proliferation and metastasis by influencing cell cycle, apoptosis, angiogenesis, and epithelial-to-mesenchymal transition in HCC. RPs are expected to unfold new possibilities for precise diagnosis and individualized treatment of HCC.

SPP1+ macrophages and FAP+ fibroblasts promote the progression of pMMR gastric cancer

Immunotherapy has become a primary and secondary treatment for gastric cancer (GC) patients with mismatch repair deficiency (dMMR), and is used in both perioperative and advanced stages. The tumor immune microenvironment (TiME) is crucial for immunotherapy efficacy, yet the impact of MMR status on TiME remains understudied. We employed single-cell RNA sequencing (scRNA-seq) to analyze 33 fresh tissue samples from 25 patients, which included 10 normal tissues, 6 dMMR tumor tissues, and 17 pMMR tumor tissues, aiming to characterize the cellular and molecular components of the TiME. The proficient mismatch repair (pMMR) group displayed a significantly higher prevalence of a specific GC cell type, termed GC2, characterized by increased hypoxia, epithelial-mesenchymal transition (EMT), and angiogenic activities compared to the dMMR group. GC2 cells overexpressed BEX3 and GPC3, and they significantly correlated with poorer survival. The pMMR group also showed increased infiltration of SPP1 + macrophages and FAP + fibroblasts, exhibiting strong hypoxic and pro-angiogenic features. Furthermore, a higher proportion of E2 endothelial cells, involved in extracellular matrix (ECM) remodeling and showing heightened VEGF pathway, HIF pathway, and angiogenesis activity, were identified in pMMR patients. Intercellular communication analyses revealed that GC2 cells, SPP1 + macrophages, FAP + fibroblasts, and E2 endothelial cells interact through VEGF, SPP1, and MIF signals, forming a TiME characterized by hypoxia, pro-angiogenesis, and ECM remodeling. This study uncovered TiME heterogeneity among GC patients with different MMR states, highlighting that the pMMR TiME is distinguished by hypoxia, pro-angiogenesis, and ECM remodeling, driven by the presence of GC2 cells, SPP1 + macrophages, FAP + fibroblasts, and E2 endothelial cells. These findings are pivotal for developing targeted immunotherapies for GC patients with pMMR.

Prognostic and therapeutic potential of gene profiles related to tertiary lymphoid structures in colorectal cancer

The role of tertiary lymphoid structures (TLS) in oncology is gaining interest, particularly in colorectal carcinoma, yet a thorough analysis remains elusive. This study pioneered a novel TLS quantification system for prognostic and therapeutic response prediction in colorectal carcinoma, alongside a comprehensive depiction of the TLS landscape. Utilizing single-cell sequencing, we established a TLS score within the Tumor Immune Microenvironment (TIME). Analysis of tertiary lymphoid structure-related genes (TLSRGs) in 1,184 patients with colon adenocarcinoma/rectum adenocarcinoma (COADREAD) from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases led to the identification of two distinct molecular subtypes. Differentially expressed genes (DEGs) further segregated these patients into gene subtypes. A TLS score was formulated using gene set variation analysis (GSVA) and its efficacy in predicting immunotherapy outcomes was validated in two independent cohorts. High-scoring patients exhibited a 'hot' immune phenotype, correlating with enhanced immunotherapy efficacy. Key genes in our model, including C5AR1, APOE, CYR1P1, and SPP1, were implicated in COADREAD cell proliferation, invasion, and PD-L1 expression. These insights offer a novel approach to colorectal carcinoma treatment, emphasizing TLS targeting as a potential anti-tumor strategy.

IGSF1: a biomarker for predicting prognosis, immunotherapy response, and drug candidates in COVID-19 combined hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a highly heterogeneous malignancy with poor prognosis and a common cause of cancer-related death worldwide, and despite ongoing therapeutic breakthroughs, patient survival benefits are limited. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of coronavirus disease 2019 (COVID-19) and poses a major threat to humanity worldwide. As the epidemic continues to develop, more and more people are infected with SARS-CoV-2, including patients with HCC. However, the relationship between COVID-19 and HCC has not yet been fully elucidated. Our study aimed to identify the shared genetic characteristics and molecular mechanisms between COVID-19 and HCC. The data involved in this study come from Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression(GTEx), and Cancer Cell Line Encyclopedia(CCLE) databases. We used differentially expressed genes to perform enrichment analysis to reveal the biological landscape of COVID-19 combined with HCC. In addition, weighted gene co-expression network analysis (WGCNA) was used to study the co-expression network related to COVID-19 and HCC. We then combined the validation datasets to screen out immunoglobulin superfamily member 1 (IGSF1) as the most important core gene. Finally, we extensively studied the functional expression of IGSF1 in tumor samples, normal tissues, and cancer cell lines. The molecular mechanisms related to COVID-19 and HCC are rarely studied. Our study identifies IGSF1 as a potential therapeutic target and immune-related biomarker for patients with COVID-19 and HCC.

Integrative Analysis of Acupuncture Targets and Immune Genes in Diabetes, Diabetic Peripheral Neuropathy, and Adjunct Therapy of Cancer

Introduction

Acupuncture may help treat diabetes mellitus (DM), diabetic peripheral neuropathy (DPN), and adjunct therapy for cancer, but the biological mechanisms and immune-related genes involved are unclear; this study aims to clarify these aspects.

Methods

Comprehensive gene expression analysis revealed differentially expressed genes (DEGs) among DM, DPN, and control samples. Key genes from WGCNA were intersected with DEGs and acupuncture targets. Inflammatory responses, immune processes, signaling pathways, immune cell infiltration, and microRNA-gene interactions were studied. Hub immune-related genes' dysregulation was analyzed for copy number variation and gene methylation. A pan-cancer nomogram model was created to predict survival based on various factors, linking hub genes to cancer properties.

Results

Our analysis found 3,217 and 2,191 DEGs in DM/control and DPN/DM comparisons, respectively, and identified 1,830 potential acupuncture targets. We pinpointed 21 key genes in DM and 43 in DPN, involved in inflammatory responses, immune processes, CAMKK2, and cAMP signaling pathways. Distinct immune cell infiltration patterns, including M0 and M2 macrophages, neutrophils, and follicular helper T cells, were noted. Further analysis revealed microRNAs and TF genes interacting with immune hub genes in both conditions. Dysregulation of eight hub immune-related genes was linked to copy number variation and gene methylation, correlating with cancer prognosis. Co-occurrence of single nucleotide variations and oncogenic mutations was observed in these genes. The pan-cancer nomogram model showed strong prognostic capabilities, and a significant association was found between the eight genes and cancer properties like angiogenesis, EMT, and cell cycle progression.

Discussion

Our findings underscore the pivotal roles of MAPK3, IL1RN, SOD2, CTSD, ESR1, SLC1A1, NPY, and CCR2 in the immune response mediated by acupuncture in the context of DM, DPN, and adjunct therapy for cancer.

Comprehensive pan-cancer analysis reveals ENC1 as a promising prognostic biomarker for tumor microenvironment and therapeutic responses

Accumulating research showed that ENC1 plays a critical role in maintaining the physiological functions. However, little is known about its role in predicting prognosis and immunotherapy response across cancers. In our results, compared to normal tissues, most cancer tissues exhibit increased ENC1 expression. We found that the most common type of genetic variation was gene mutation. In addition, a positive correlation was found between CNV and ENC1 expression. Moreover, the overexpression of ENC1 was positively correlated with poor clinical outcomes. The GSEA results showed that ENC1 is closely correlated with tumor-promoting biological functions in most cancers. ENC1 is also closely negatively associated with the infiltration levels of T cells, activated NK cells, and B cells. Most immunomodulators are positively associated with ENC1. Further, we verified that inhibition of ENC1 expression suppressed the proliferation and migration of breast cancer, pancreatic cancer and glioma cells. In conclusion, our study demonstrated that ENC1 plays a protumorigenic role in most cancers. Additionally, ENC1 is closely correlated with tumor microenvironment features and immune checkpoint inhibitors expression. Overall, ENC1 could serve as a promising potential prognostic biomarker in various tumors.

Multi-omics analysis identifies PTTG1 as a prognostic biomarker associated with immunotherapy and chemotherapy resistance

BACKGROUND

Pituitary tumor-transforming gene 1 (PTTG1) is an important gene in tumour development. However, the relevance of PTTG1 in tumour prognosis, immunotherapy response, and medication sensitivity in human pan-cancer has to be determined.

METHODS

TIMER, GEPIA, the human protein atlas, GEPIA, TISCH2, and cBioportal examined the gene expression, protein expression, prognostic value, and genetic modification landscape of PTTG1 in 33 malignancies based on the TCGA cohort. The association between PTTG1 and tumour immunity, tumour microenvironment, immunotherapy response, and anticancer drug sensitivity was investigated using GSCA, TIDE, and CellMiner CDB. Molecular docking was used to validate the possible chemotherapeutic medicines for PTTG1. Additionally, siRNA-mediated knockdown was employed to confirm the probable role of PTTG1 in paclitaxel-resistant cells.

RESULTS

PTTG1 is overexpressed and associated with poor survival in most tumors. Functional enrichment study revealed that PTTG1 is involved in the cell cycle and DNA replication. A substantial connection between PTTG1 expression and immune cell infiltration points to PTTG1's possible role in the tumour microenvironment. High PTTG1 expression is associated with tumour immunotherapy resistance. The process could be connected to PTTG1, which mediates T cell exhaustion and promotes cytotoxic T lymphocyte malfunction. Furthermore, PTTG1 was found to be substantially linked with sensitivity to several anticancer medications. Suppressing PTTG1 with siRNA reduced clone formation and migration, implying that PTTG1 may play a role in paclitaxel resistance.

CONCLUSION

PTTG1 shows potential as a cancer diagnostic, prognostic, and chemosensitivity marker. Increased PTTG1 expression is linked to resistance to cancer treatment. The mechanism could be linked to PTTG1's role in promoting cytotoxic T lymphocyte dysfunction and mediating T cell exhaustion. It is feasible to consider PTTG1, which is expressed on Treg and Tprolif cells, as a new therapeutic target for overcoming immunotherapy resistance.

Establishment and Verification of a Novel Gene Signature Connecting Hypoxia and Lactylation for Predicting Prognosis and Immunotherapy of Pancreatic Ductal Adenocarcinoma Patients by Integrating Multi-Machine Learning and Single-Cell Analysis

Pancreatic ductal adenocarcinoma (PDAC) has earned a notorious reputation as one of the most formidable and deadliest malignant tumors. Within the tumor microenvironment, cancer cells have acquired the capability to maintain incessant expansion and increased proliferation in response to hypoxia via metabolic reconfiguration, leading to elevated levels of lactate within the tumor surroundings. However, there have been limited studies specifically investigating the association between hypoxia and lactic acid metabolism-related lactylation in PDAC. In this study, multiple machine learning approaches, including LASSO regression analysis, XGBoost, and Random Forest, were employed to identify hub genes and construct a prognostic risk signature. The implementation of the CERES score and single-cell analysis was used to discern a prospective therapeutic target for the management of PDAC. CCK8 assay, colony formation assays, transwell, and wound-healing assays were used to explore both the proliferation and migration of PDAC cells affected by CENPA. In conclusion, we discovered two distinct subtypes characterized by their unique hypoxia and lactylation profiles and developed a risk score to evaluate prognosis, as well as response to immunotherapy and chemotherapy, in PDAC patients. Furthermore, we indicated that CENPA may serve as a promising therapeutic target for PDAC.

The pan-cancer landscape presented ITGA7 as a prognostic determinant, tumor suppressor, and oncogene in multiple tumor types

Integrin α7 (ITGA7) is an extracellular matrix-binding protein. Integrins are the main type of cell adhesive molecules in mammals, playing a role in many biological pathways. Although various studies have shown correlations between ITGA7 and various types of cancer, a comprehensive study at a pan-cancer level has not yet been conducted. In this study, we investigated the function of ITGA7 in distinct tumor types using the multi-omics relevant information, then two CeRNA regulatory network was drawn to identify the ITGA7 hub regulatory RNAs. The results indicated that the expression of ITGA7 varies in different tumors. Overexpression of ITGA7 was correlated with a worse OS in BLCA, LGG, and UVM, and the downregulation of ITGA7 was related to a worse OS in PAAD. In addition, BLCA, and UVM showed poor PFS in association with ITGA7 overexpression, and PAAD, SARC, and THCA indicated poor PFS in correlation with ITGA7 under expression. Further analyses of ITGA7 gene alteration data showed that ITGA7 amplifications may have an impact on Kidney Chromophobe prognosis. In 20 types of tumors, ITGA7 expression was linked to cancer-associated fibroblast infiltration. ITGA7 expression was linked to cancer-associated fibroblast infiltration. ITGA7-Related Gene Enrichment Analysis indicated that ITGA7 expression-correlated and functional binding genes were enriched in homotypic cell-cell adhesion, focal adhesion, and ECM-receptor interaction. This pan-cancer study found that abnormal expression of ITGA7 was correlated with poor prognosis and metastasis in different types of tumors. Thus, the ITGA7 gene may prove to be a promising biomarker for the prognosis and complication prevention of different cancers.

Bioinformatic analysis of molecular characteristics and oncogenic features of CARD14 in human cancer

Aberrant caspase recruitment domain family member 14 (CARD14) signaling has been strongly associated with inflammatory skin conditions. CARD14 acts as a scaffold protein, ultimately activating the transcription factor NF-KB. Although primarily studied in the context of inflammation, recent research has suggested its potential implications in tumorigenesis. In this study, we gathered The Cancer Genome Atlas (TCGA) tumor data to gauge the involvement of CARD14 in cancer, including genetic alterations, expression patterns, survival correlations, immune cell infiltration and functional interactions across diverse cancer types. We found heightened CARD14 expression in most tumors and there was a significant correlation between CARD14 expression and the prognosis of patients for certain tumors. For instance, patients with higher CARD14 expression had a better prognosis in sarcoma, lung, cervix and head and neck cancers. Moreover, CARD14 expression positively correlated with neutrophil infiltration in most of the cancer types analyzed. Finally, enrichment analysis showed that epithelial development and differentiation pathways were involved in the functional mechanism of CARD14. Our results show that CARD14 may have the potential to become a prognostic biomarker in several cancers, hence, further prospective studies will be required for its validation.

LTBR acts as a novel immune checkpoint of tumor-associated macrophages for cancer immunotherapy

Tumor-associated macrophages (TAMs) greatly contribute to immune checkpoint inhibitor (ICI) resistance of cancer. However, its underlying mechanisms and whether TAMs can be promising targets to overcome ICI resistance remain to be unveiled. Through integrative analysis of immune multiomics data and single-cell RNA-seq data (iMOS) in lung adenocarcinoma (LUAD), lymphotoxin β receptor (LTBR) is identified as a potential immune checkpoint of TAMs, whose high expression, duplication, and low methylation are correlated with unfavorable prognosis. Immunofluorescence staining shows that the infiltration of LTBR+ TAMs is associated with LUAD stages, immunotherapy failure, and poor prognosis. Mechanistically, LTΒR maintains immunosuppressive activity and M2 phenotype of TAMs by noncanonical nuclear factor kappa B and Wnt/β-catenin signaling pathways. Macrophage-specific knockout of LTBR hinders tumor growth and prolongs survival time via blocking TAM immunosuppressive activity and M2 phenotype. Moreover, TAM-targeted delivery of LTΒR small interfering RNA improves the therapeutic effect of ICI via reversing TAM-mediated immunosuppression, such as boosting cytotoxic CD8+ T cells and inhibiting granulocytic myeloid-derived suppressor cells infiltration. Taken together, we bring forth an immune checkpoint discovery pipeline iMOS, identify LTBR as a novel immune checkpoint of TAMs, and propose a new immunotherapy strategy by targeting LTBR+ TAMs.

A tumour-associated macrophage-based signature for deciphering prognosis and immunotherapy response in prostate cancer

For the multistage progression of prostate cancer (PCa) and resistance to immunotherapy, tumour-associated macrophage is an essential contributor. Although immunotherapy is an important and promising treatment modality for cancer, most patients with PCa are not responsive towards it. In addition to exploring new therapeutic targets, it is imperative to identify highly immunotherapy-sensitive individuals. This research aimed to establish a signature risk model, which derived from the macrophage, to assess immunotherapeutic responses and predict prognosis. Data from the UCSC-XENA, GEO and TISCH databases were extracted for analysis. Based on both single-cell datasets and bulk transcriptome profiles, a macrophage-related score (MRS) consisting of the 10-gene panel was constructed using the gene set variation analysis. MRS was highly correlated with hypoxia, angiogenesis, and epithelial-mesenchymal transition, suggesting its potential as a risk indicator. Moreover, poor immunotherapy responses and worse prognostic performance were observed in the high-MRS group of various immunotherapy cohorts. Additionally, APOE, one of the constituent genes of the MRS, affected the polarisation of macrophages. In particular, the reduced level of M2 macrophage and tumour progression suppression were observed in PCa xenografts which implanted in Apolipoprotein E-knockout mice. The constructed MRS has the potential as a robust prognostic prediction tool, and can aid in the treatment selection of PCa, especially immunotherapy options.

Construction and validation of a machine learning-based immune-related prognostic model for glioma

BACKGROUND

Glioma stands as the most prevalent primary brain tumor found within the central nervous system, characterized by high invasiveness and treatment resistance. Although immunotherapy has shown potential in various tumors, it still faces challenges in gliomas. This study seeks to develop and validate a prognostic model for glioma based on immune-related genes, to provide new tools for precision medicine.

METHODS

Glioma samples were obtained from a database that includes the ImmPort database. Additionally, we incorporated ten machine learning algorithms to assess the model's performance using evaluation metrics like the Harrell concordance index (C-index). The model genes were further studied using GSCA, TISCH2, and HPA databases to understand their role in glioma pathology at the genomic, molecular, and single-cell levels, and validate the biological function of IKBKE in vitro experiments.

RESULTS

In this study, a total of 199 genes associated with prognosis were identified using univariate Cox analysis. Subsequently, a consensus prognostic model was developed through the application of machine learning algorithms. In which the Lasso + plsRcox algorithm demonstrated the best predictive performance. The model showed a good ability to distinguish two groups in both the training and test sets. Additionally, the model genes were closely related to immunity (oligodendrocytes and macrophages), and mutation burden. The results of in vitro experiments showed that the expression level of the IKBKE gene had a significant effect on the apoptosis and migration of GL261 glioma cells. Western blot analysis showed that down-regulation of IKBKE resulted in increased expression of pro-apoptotic protein Bax and decreased expression of anti-apoptotic protein Bcl-2, which was consistent with increased apoptosis rate. On the contrary, IKBKE overexpression caused a decrease in Bax expression an increase in Bcl-2 expression, and a decrease in apoptosis rate. Tunel results further confirmed that down-regulation of IKBKE promoted apoptosis, while overexpression of IKBKE reduced apoptosis. In addition, cells with down-regulated IKBKE had reduced migration in scratch experiments, while cells with overexpression of IKBKE had increased migration.

CONCLUSION

This study successfully constructed a glioma prognosis model based on immune-related genes. These findings provide new perspectives for glioma prognosis assessment and immunotherapy.

Facilitating integrative and personalized oncology omics analysis with UCSCXenaShiny

The continuous generation of multi-omics and phenotype data is propelling advancements in precision oncology. UCSCXenaShiny was developed as an interactive tool for exploring thousands of cancer datasets available on UCSC Xena. However, its capacity for comprehensive and personalized pan-cancer data analysis is being challenged by the growing demands. Here, we introduce UCSCXenaShiny v2, a milestone update through a variety of improvements. Firstly, by integrating multidimensional data and implementing adaptable sample settings, we create a suite of robust TPC (TCGA, PCAWG, CCLE) analysis pipelines. These pipelines empower users to conduct in-depth analyses of correlation, comparison, and survival in three modes: Individual, Pan-cancer and Batch screen. Additionally, the tool includes download interfaces that enable users to access diverse data and outcomes, several features also facilitate the joint analysis of drug sensitivity and multi-omics of cancer cell lines. UCSCXenaShiny v2 is an open-source R package and a web application, freely accessible at https://github.com/openbiox/UCSCXenaShiny .

SSCI: Self-Supervised Deep Learning Improves Network Structure for Cancer Driver Gene Identification

The pathogenesis of cancer is complex, involving abnormalities in some genes in organisms. Accurately identifying cancer genes is crucial for the early detection of cancer and personalized treatment, among other applications. Recent studies have used graph deep learning methods to identify cancer driver genes based on biological networks. However, incompleteness and the noise of the networks will weaken the performance of models. To address this, we propose a cancer driver gene identification method based on self-supervision for graph convolutional networks, which can efficiently enhance the structure of the network and further improve predictive accuracy. The reliability of SSCI is verified by the area under the receiver operating characteristic curves (AUROC), the area under the precision-recall curves (AUPRC), and the F1 score, with respective values of 0.966, 0.964, and 0.913. The results show that our method can identify cancer driver genes with strong discriminative power and biological interpretability.

Clinical significance of RETN gene expression and rs3219175 G > a polymorphism in cancer

The inflammatory cytokine resistin, which is encoded by the RETN gene, plays a variety of roles in cancer. This study aimed to assess the relationship between RETN gene expression and cancer stage, survival prognosis, immune infiltration, and drug sensitivity, and whether the rs3219175 G > A polymorphism affected the expression of the RETN gene and cancer risk. The clinical significance of RETN gene expression and the rs3219175 polymorphism in cancer was analyzed by the GSCA platform, GTEx database and STATA software. The results showed that RETN gene expression was associated with the stage of thyroid carcinoma, survival prognosis and immune infiltration of certain cancers, and sensitivity to multiple drugs. The rs3219175 polymorphism could influence the expression of the RETN gene in a wide range of tissues. Furthermore, RETN gene rs3219175 polymorphism was significantly associated with cancer risk [GA vs. GG: OR = 2.27, 95%CI = 1.26-4.09; (GA + AA) vs. GG: OR = 2.23, 95%CI = 1.28-3.88; A vs. G: OR = 1.72, 95%CI = 1.15-2.58]. In conclusion, the current study suggested that resistin might serve as a prognostic marker and therapeutic target for certain cancers, and the rs3219175 polymorphism might be used as a marker for predicting cancer risk.

Comprehensive pan-cancer analysis and experiments revealed R3HDM1 as a novel predictive biomarker for prognosis and immune therapy response

Background

R3HDM1, an RNA binding protein with one R3H domain, remains uncharacterized in terms of its association with tumor progression, malignant cell regulation, and the tumor immune microenvironment. This paper aims to fill this gap by analyzing the potential of R3HDM1 in diagnosis, prognosis, chemotherapy, and immune function across various cancers.

Methods

Data was collected from the Firehost database (http://gdac.broadinstitute.org) to obtain the TCGA pan-cancer queue containing tumor and normal samples. Additional data on miRNA, TCPA, mutations, and clinical information were gathered from the UCSC Xena database (https://xenabrowser.net/datapages/). The mutation frequency and locus of R3HDM1 in the TCGA database were examined using the cBioPortal. External validation through GEO data was conducted to assess the differential expression of R3HDM1 in different cancers. Protein expression levels were evaluated using the Clinical Proteomics Tumor Analysis Alliance (CPTAC). The differential expression of R3HDM1 was verified in lung adenocarcinoma cell lines and normal lung glandular epithelial cells via RT-qPCR. Cell migration and proliferation experiments were conducted by knocking down the expression of R3HDM1 in two lung adenocarcinoma cell lines using small interfering RNA. The biological role of R3HDM1 in pan-cancer was explored using the GSEA method. Multiple immune infiltration algorithms from the TIMER2.0 database was employed to investigate the correlation between R3HDM1 expression and the tumor immune microenvironment. Validation of transcriptome immune infiltration was based on 140 single-cell datasets from the TISCH database. The study also characterized a pan-cancer survival profile and analyzed the differential expression of R3HDM1 in different molecular subtypes. The relationship between R3HDM1 and drug resistance was investigated using four chemotherapy data sources: CellMiner, GDSC, CTRP and PRISM. The impact of chemicals on the expression of R3HDM1 was explored through the CTD database.

Result

The study revealed differential expression of R3HDM1 in various tumors, indicating its potential as an early diagnostic marker. Changes in somatic copy number (SCNA) and DNA methylation were identified as factors contributing to abnormal expression levels. Additionally, the study found that R3HDM1 expression is associated with clinical features, metabolic pathways, and important pathways related to metastasis and the immune system. High expression of R3HDM1 was linked to poor prognosis across different tumors and altered drug sensitivity. Furthermore, the expression of R3HDM1 showed significant correlations with immune modulatory molecules and biomarkers of lymphocyte subpopulation infiltration. Finally, the study highlighted four chemicals that could influence the expression of R3HDM1.

Conclusion

Overall, this study proposes that R3HDM1 expression is a promising biomarker for predicting the prognosis of cancer, especially lung adenocarcinoma, and the efficacy of immunotherapy, demonstrating the rationale for further exploration in the development of anti-tumor therapies.

Pan-cancer analysis reveals copper transporters as promising potential targets

Background

Copper transport proteins (SLC31A1, ATP7A, ATP7B) regulate copper levels in the body and may be involved in tumor development. However, their comprehensive expression and function across various cancers remain unclear.

Methods

The expressions of copper transporters in 33 tumors and normal tissues were analyzed using TCGA, GTEx, CCLE, ULCAN, and HPA databases. Cox regression assessed their impact on patient survival. Gene alterations were explored using cBioPortal. Spearman correlation tests were performed to investigate the associations between copper transporters and tumor mutation burden (TMB), microsatellite instability (MSI), and infiltration of immune cells. Gene functions were analyzed using STRING and GeneMANIA databases. Drug sensitivity was assessed using GSCALite database. ATP7B expression in lung squamous cell carcinoma (LUSC) was validated by immunohistochemical staining.

Results

Copper transporters exhibited variable expression patterns across various cancer types, indicating their potential dual role as either oncogenes or tumor suppressor genes, depending on the cancer type. Significant associations were found between these transporters and tumor stage, as well as prognosis in most tumors studied. Pathway analysis identified links between copper transporters and tumor-related pathways like apoptosis and RAS/MAPK. Copy number variation (CNV) analysis revealed varying degrees of gene amplification and deletion of copper transporters in most tumors. Copper transporters exhibited strong correlations with immune features, including TMB, MSI, and immune-infiltrating cells, suggesting their potential role in guiding immunotherapy. They were also associated with sensitivity to various chemotherapeutic and immunotherapeutic drugs. Immunohistochemical tests validated the correlation between elevated ATP7B level and worse progression-free survival (PFS) in LUSC.

Conclusion

Copper transporters may serve as potential tumor markers and therapeutic targets.

Identification of the shared gene MXD3 signatures and biological mechanism in patients with hip pain and prostate cancer

Prostate cancer (PRAD) is recognized as having a significant effect on systemic illnesses. This study examined possible immune cells, metabolic pathways, and genes that may explain the interaction between PRAD and hip pain. We used information retrieved from the Cancer Genome Atlas and the Gene Expression Omnibus databases. To find common genes, we utilized differential expression analysis and weighted gene co-expression network analysis. The genes that were shared were subjected to pathway enrichment studies using Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes. Additionally, hub genes were analyzed using LASSO regression, and a receiver operating characteristic curve was generated based on the screening outcomes. The genes for the nodes were chosen in a protein-protein interaction network that was built. Single-sample gene-set enrichment analysis was performed to identify the differentially expressed genes. Immunohistochemistry staining confirmed hub gene expression, and single-sample gene-set enrichment analysis assessed immune cell infiltration. We concluded by comparing MAX dimerization protein 3 (MXD3) and MAX interactor 1 (MXI1) expression in tumor tissues using Uniform Manifold Approximation and Projection and violin plots in the Tumor lmmune Single-cell Hub database. After analyzing the intersection of the differentially expressed genes and weighted gene co-expression network analysis-significant module genes, we determined that MXD3 was the best shared diagnostic biomarker for PRAD and hip pain. One potential predictor of PRAD development was the MXI1 node gene, which was found in the protein-protein interaction network. The analyses revealed that MXD3 had a relatively positive correlation with neutrophil and T-helper cell infiltration levels, whereas MXI1 had a negative correlation with mast and Tgd cell levels. Tumors had lower levels of MXI1 expression and higher levels of MXD3 expression compared to normal tissues. Endothelial cells, induced pluripotent stem cells, and smooth muscle cells were all found to express MXI1. This is the first study to investigate the close genetic link between hip pain and PRAD using bioinformatics technologies. The 2 most significant genes involved in crosstalk between PRAD and hip pain were MXD3 and MXI1. The immunological responses triggered by T cells, mast cells, and neutrophils may be crucial in the relationship between PRAD and hip pain.

FAM109B plays a tumorigenic role in low-grade gliomas and is associated with tumor-associated macrophages (TAMs)

BACKGROUND

Family with sequence similarity 109, member B (FAM109B) is involved in endocytic transport and affects genetic variation in brain methylation. It is one of the important genes related to immune cell-associated diseases. In the tumor immune system, methylation can regulate tumor immunity and influence the maturation and functional response of immune cells. Whether FAM109B is involved in tumor progression and its correlation with the tumor immune microenvironment has not yet been disclosed.

METHODS

A comprehensive pan-cancer analysis of FAM109B expression, prognosis, immunity, and TMB was conducted. The expression, clinical features, and prognostic value of FAM109B in low-grade gliomas (LGG) were evaluated using TCGA, CGGA, and Gravendeel databases. The expression of FAM109B was validated by qRT-PCR, immunohistochemistry (IHC), and Western blotting (WB). The relationship between FAM109B and methylation, Copy Number Variation (CNV), prognosis, immune checkpoints (ICs), and common chemotherapy drug sensitivity in LGG was explored through Cox regression, Kaplan-Meier curves, and Spearman correlation analysis. FAM109B levels and their distribution were studied using the TIMER database and single-cell analysis. The potential role of FAM109B in gliomas was further investigated through in vitro and in vivo experiments.

RESULTS

FAM109B was significantly elevated in various tumor types and was associated with poor prognosis. Its expression was related to aggressive progression and poor prognosis in low-grade glioma patients, serving as an independent prognostic marker for LGG. Glioma grade was negatively correlated with FAM109B DNA promoter methylation. Immune infiltration and single-cell analysis showed significant expression of FAM109B in tumor-associated macrophages (TAMs). The expression of FAM109B was closely related to gene mutations, immune checkpoints (ICs), and chemotherapy drugs in LGG. In vitro studies showed increased FAM109B expression in LGG, closely related to cell proliferation. In vivo studies showed that mice in the sh-FAM109B group had slower tumor growth, slower weight loss, and longer survival times.

CONCLUSIONS

FAM109B, as a novel prognostic biomarker for low-grade gliomas, exhibits specific overexpression in TAMs and may be a potential therapeutic target for LGG patients.

Bioinformatics tools and resources for cancer and application

ABSTRACT

Tumor bioinformatics plays an important role in cancer research and precision medicine. The primary focus of traditional cancer research has been molecular and clinical studies of a number of fundamental pathways and genes. In recent years, driven by breakthroughs in high-throughput technologies, large-scale cancer omics data have accumulated rapidly. How to effectively utilize and share these data is particularly important. To address this crucial task, many computational tools and databases have been developed over the past few years. To help researchers quickly learn and understand the functions of these tools, in this review, we summarize publicly available bioinformatics tools and resources for pan-cancer multi-omics analysis, regulatory analysis of tumorigenesis, tumor treatment and prognosis, immune infiltration analysis, immune repertoire analysis, cancer driver gene and driver mutation analysis, and cancer single-cell analysis, which may further help researchers find more suitable tools for their research.

Identification of HDAC10 as a candidate oncogene in clear cell renal carcinoma that facilitates tumor proliferation and metastasis

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) remains one of the most lethal urological malignancies even though a great number of improvements in diagnosis and management have achieved over the past few decades. Accumulated evidence revealed that histone deacetylases (HDACs) play vital role in cell proliferation, differentiation and apoptosis. Nevertheless, the biological functions of histone deacetylation modification related genes in ccRCC remains poorly understood.

METHOD

Bulk transcriptomic data and clinical information of ccRCC patients were obtained from the TCGA database and collected from the Chinese PLA General Hospital. A total of 36 histone deacetylation genes were selected and studied in our research. Univariate cox regression analysis, least absolute shrinkage and selection operator (LASSO) regression, random forest (RF) analysis, and protein-protein interaction (PPI) network analysis were applied to identify key genes affecting the prognosis of ccRCC. The 'oncoPredict' algorithm was utilized for drug-sensitive analysis. Gene Set Enrichment Analysis (GSEA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was used to explore the potential biological function. The ssGSEA algorithm was used for tumor immune microenvironment analysis. The expression levels of HDAC10 were validated by RT-PCR and immunohistochemistry (IHC). 5-ethynyl-2'-deoxyuridine (EdU assay), CCK-8 assay, cell transwell migration and invasion assay and colony formation assay were performed to detect the proliferation and invasion ability of ccRCC cells. A nomogram incorporating HDAC10 and clinicopathological characteristics was established to predict the prognosis of ccRCC patients.

RESULT

Two machine learning algorithms and PPI analysis identified four histone deacetylation genes that have a significant association with the prognosis of ccRCC, with HDAC10 being the key gene among them. HDAC10 is highly expressed in ccRCC and its high expression is associated with poor prognosis for ccRCC patients. Pathway enrichment and the experiments of EdU staining, CCK-8 assay, cell transwell migration and invasion assay and colony formation assay demonstrated that HDAC10 mediated the proliferation and metastasis of ccRCC cells and involved in reshaping the tumor microenvironment (TME) of ccRCC. A clinically reliable prognostic predictive model was established by incorporating HDAC10 and other clinicopathological characteristics ( https://nomogramhdac10.shinyapps.io/HDAC10_Nomogram/ ).

CONCLUSION

Our study found the increased expression of HDAC10 was closely associated with poor prognosis of ccRCC patients. HDAC10 showed a pro-tumorigenic effect on ccRCC and promote the proliferation and metastasis of ccRCC, which may provide new light on targeted therapy for ccRCC.

mtPCDI: a machine learning-based prognostic model for prostate cancer recurrence

Background

This research seeks to formulate a prognostic model for forecasting prostate cancer recurrence by examining the interaction between mitochondrial function and programmed cell death (PCD).

Methods

The research involved analyzing four gene expression datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) using univariate Cox regression. These analyses identified genes linked with mitochondrial function and PCD that correlate with recurrence prognosis. Various machine learning algorithms were then employed to construct an optimal predictive model.

Results

A key outcome was the creation of a mitochondrial-related programmed cell death index (mtPCDI), which effectively predicts the prognosis of prostate cancer patients. It was observed that individuals with lower mtPCDI exhibited higher immune activity, correlating with better recurrence outcomes.

Conclusion

The study demonstrates that mtPCDI can be used for personalized risk assessment and therapeutic decision-making, highlighting its clinical significance and providing insights into the biological processes affecting prostate cancer recurrence.