PDIA3 correlates with clinical malignant features and immune signature in human gliomas

Comprehensive analysis of CYBB as a prognostic marker and therapeutic target in glioma: A bioinformatics approach

Background

In the central nervous system, glioma is the most common malignant tumor, and patients have a poor prognosis. Identification of novel marker genes and establishment of prognostic models are important for early diagnosis and prognosis determination.

Methods

Download glioma data from the CGGA and TCG databases. Application of bioinformatics to analyze the impact of CYBB on the clinicopathological characteristics, immunological features and prognosis of gliomas. Using single-cell sequencing data from 7 glioblastoma patients in the CGGA database, the role of CYBB in the tumor microenvironment was analyzed. In addition, a prognostic model was constructed based on CYBB high and low differentially expressed genes and mitochondrial genes.

Results

The expression of CYBB is closely related to various clinical features, immune cell infiltration level, immune checkpoint and survival time of patients. A 10-gene prediction model was constructed based on the differentially expressed genes of low and high CYBB and mitochondria-related genes. Glioma patients with higher risk scores had significantly lower survival probabilities. Receiver operating characteristic curves and nomograms were plotted over time to show the predictive accuracy and predictive value of the 10-gene prognostic model.

Conclusions

Our study shows that CYBB is strongly correlated with clinical characteristics features and prognosis of glioma patients, and can be used as a potential therapeutic target. Prognostic models based on CYBB and mitochondrial genes have good performance in predicting prognosis of glioma patients.

Downregulation of PDIA3 inhibits gastric cancer cell growth through cell cycle regulation

OBJECTIVE

Protein disulfide isomerase A3 (PDIA3) promotes the correct folding of newly synthesized glycoproteins in the endoplasmic reticulum. PDIA3 is overexpressed in most tumors, and it may become a biomarker of cancer prognosis and immunotherapy. Our study aims to detect the expression level of PDIA3 in gastric cancer (GC) and its association with GC development as wells as the underlying mechanisms.

METHODS

GC cell lines with PDIA3 knockdown by siRNA, CRISPR-cas9 sgRNAs or a pharmacological inhibitor of LOC14 were prepared and used. PDIA3 knockout GC cells were established by CRISPR-cas9-PDIA3 system. The proliferation, migration, invasion and cell cycle of GC cells were analyzed by cell counting kit-8 assay, wound healing assay, transwell assay and flow cytometry, respectively. Immunodeficient nude mice was used to evaluate the role of PDIA3 in tumor formation. Quantitative PCR and western blot were used for examining gene and protein expressions. RNA sequencing was performed to see the altered gene expression.

RESULTS

The expressions of PDIA3 in GC tissues and cells were increased significantly, and its expression was negatively correlated with the three-year survival rate of GC patients. Down-regulation of PDIA3 by siRNA, LOC14 or CRISPR-cas9 significantly inhibited proliferation, invasion and migration of GC cells TMK1 and AGS, with cell cycle arrested at G2/M phase. Meanwhile, decreased PDIA3 significantly inhibited growth of tumor xenograft in vivo. It was found that cyclin G1 (encoded by CCNG1 gene) expression was decreased by downregulation of PDIA3 in GC cells both in vitro and in vivo. In addition, protein levels of other cell cycle related factors including cyclin D1, CDK2, and CDK6 were also significantly decreased. Further study showed that STAT3 was associated with PDIA3-mediated cyclin G1 regulation.

CONCLUSION

PDIA3 plays an oncogenic role in GC. Our findings unfolded the functional role of PDIA3 in GC development and highlighted a novel target for cancer therapeutic strategy.

The role of PDIA3 in oral squamous cell carcinoma and its value as A diagnostic and prognostic biomarker

Background

This study aimed to investigate the role of protein disulfide isomerase A3 (PDIA3) in oral squamous cell carcinoma (OSCC) and evaluate its significance as a diagnostic and prognostic biomarker.

Methods

Comprehensive bioinformatics analysis of the OSCC dataset from The Cancer Genome Atlas (TCGA) was performed. PDIA3 was depleted in CAL27 and SCC25 OSCC cells by transfection with PDIA3-specific siRNA oligos. The effects of PDIA3 downregulation on cell viability, apoptosis, and cell migration were evaluated using CCK8, ELISA, and wound healing assays, respectively.

Results

The mRNA and protein expression of PDIA3 was significantly up-regulated in OSCC tissues compared to adjacent normal tissues. Knockdown of PDIA3 led to significantly decreased cell viability, increased apoptosis, and suppressed migratory ability in OSCC cells. The Kaplan-Meier survival curve showed that patients with higher PDIA3 expression levels had shorter survival than those with low PDIA3 levels. The receiver operating characteristic (ROC) curve indicated that PDIA3 had high sensitivity and accuracy for detecting OSCC (area under the curve (AUC): 0.917, CI: 0.879-0.955). Univariate and multivariate Cox regression analyses identified PDIA3 as an independent prognostic factor of OSCC. Furthermore, the depletion of PDIA3 inhibited AKT activity in OSCC cells. Gene set enrichment analysis (GSEA) indicated that PDIA3 is involved in various important biological functions and signaling pathways closely related to cancer development.

Conclusion

PDIA3 plays an oncogenic role in OSCC and represents a good candidate as a diagnostic and prognostic biomarker for OSCC.

PDIA3 modulates genomic response to 1,25-dihydroxyvitamin D3 in squamous cell carcinoma of the skin

An active form of vitamin D3 (1,25-dihydroxyvitamin D3) acts through vitamin D receptor (VDR) initiating genomic response, but several studies described also non-genomic actions of 1,25-dihydroxyvitamin D3, implying the role of PDIA3 in the process. PDIA3 is a membrane-associated disulfide isomerase involved in disulfide bond formation, protein folding, and remodeling. Here, we used a transcriptome-based approach to identify changes in expression profiles in PDIA3-deficient squamous cell carcinoma line A431 after 1,25-dihydroxyvitamin D3 treatment. PDIA3 knockout led to changes in the expression of more than 2000 genes and modulated proliferation, cell cycle, and mobility of cells; suggesting an important regulatory role of PDIA3. PDIA3-deficient cells showed increased sensitivity to 1,25-dihydroxyvitamin D3, which led to decrease migration. 1,25-dihydroxyvitamin D3 treatment altered also genes expression profile of A431ΔPDIA3 in comparison to A431WT cells, indicating the existence of PDIA3-dependent genes. Interestingly, classic targets of VDR, including CAMP (Cathelicidin Antimicrobial Peptide), TRPV6 (Transient Receptor Potential Cation Channel Subfamily V Member 6), were regulated differently by 1,25-dihydroxyvitamin D3, in A431ΔPDIA3. Deletion of PDIA3 impaired 1,25-dihydroxyvitamin D3-response of genes, such as PTGS2, MMP12, and FOCAD, which were identified as PDIA3-dependent. Additionally, response to 1,25-dihydroxyvitamin D3 in cancerous A431 cells differed from immortalized HaCaT keratinocytes, used as non-cancerous control. Finally, silencing of PDIA3 and 1,25-dihydroxyvitamin D3, at least partially reverse the expression of cancer-related genes in A431 cells, thus targeting PDIA3 and use of 1,25-dihydroxyvitamin D3 could be considered in a prevention and therapy of the skin cancer. Taken together, PDIA3 has a strong impact on gene expression and physiology, including genomic response to 1,25-dihydroxyvitamin D3.

Protein disulfide isomerase family mediated redox regulation in cancer

Protein disulfide isomerase (PDI) and its superfamilies are mainly endoplasmic reticulum (ER) resident proteins with essential roles in maintaining cellular homeostasis, via thiol oxidation/reduction cycles, chaperoning, and isomerization of client proteins. Since PDIs play an important role in ER homeostasis, their upregulation supports cell survival and they are found in a variety of cancer types. Despite the fact that the importance of PDI to tumorigenesis remains to be understood, it is emerging as a new therapeutic target in cancer. During the past decade, several PDI inhibitors has been developed and commercialized, but none has been approved for clinical use. In this review, we discuss the properties and redox regulation of PDIs within the ER and provide an overview of the last 5 years of advances regarding PDI inhibitors.

The hsa_circ_0000276-ceRNA regulatory network and immune infiltration in cervical cancer

BACKGROUND

Our previous studies have confirmed that miR-154-5p can regulate pRb expression, and thus, play a tumor suppressor role in HPV16 E7-induced cervical cancer. However, its upstream molecules have not been elucidated in the progression of cervical cancer. This study aimed to explore the role of the miR-154-5p upstream molecule, hsa_circ_0000276 in cervical cancer development and its possible mechanisms of action.

METHODS

We detected differences in whole transcriptome expression profiles of cervical squamous carcinoma and tissues adjacent to cervical cancer tissues from patients using microarray technology to predict circular RNAs (circRNAs) with binding sites to miR-154-5p. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) was used to detect the expression of hsa_circ_0000276 (which had the strongest binding capacity to miR-154 and was selected as the target molecule) in cervical cancer tissues, followed by in vitro functional assays. Downstream microRNAs (miRNAs) and mRNAs of hsa_circ_0000276 were identified using transcriptome microarray data and databases, while the protein-protein interaction networks were obtained using STRING. A competing endogenous RNA (ceRNA) network centered on hsa_circ_0000276 was constructed using Cytoscape and GO and KEGG databases. Abnormal expression and prognosis of critical downstream molecules were analyzed using gene databases and molecular experiments. qRT-PCR and western blot analysis was performed to verify the expression of candidate genes.

RESULTS

We identified 4,001 differentially expressed circRNAs between HPV16-positive cervical squamous carcinoma and benign cervical tissues and 760 circRNAs targeting miR-154-5p, including hsa_circ_0000276. hsa_circ_0000276 and miR-154-5p directly bound, and hsa_circ_0000276 was upregulated, in cervical precancerous lesions and cervical cancer tissues and cells. Silencing hsa_circ_0000276 inhibited G1/S transition and cell proliferation and promoted apoptosis in SiHa and CaSki cells. Bioinformatics analysis showed that the hsa_circ_0000276 ceRNA network included 17 miRNAs and seven mRNAs, and downstream molecules of hsa_circ_0000276 were upregulated in cervical cancer tissues. These downstream molecules were associated with a poor prognosis and affected cervical cancer-associated immune infiltration. Of these, expression of CD47, LDHA, PDIA3, and SLC16A1 was downregulated in sh_hsa_circ_0000276 cells.

CONCLUSIONS

Our findings show that hsa_circ_0000276 exerts cancer-promoting effects in cervical cancer and is an underlying biomarker for cervical squamous cell carcinoma.

Bioinformatics-based identification of lipid- and immune-related biomarkers in abdominal aortic aneurysms

Background

Abdominal aortic aneurysm (AAA) manifest as a natural inflammatory process with permanent dilation and terminal rupture. Nevertheless, the pathogenesis of AAA remains a mystery, and treatment is still controversial. Lipid metabolism and immune system are involved in AAA progression, which has been well documented. However, lipid- and immune-related (LIR) biomarkers need to be further elucidated.

Methods

The AAA-related datasets were retrieved from the GEO database, and the datasets were analyzed for differential gene expression by NetworkAnalyst. GO and KEGG pathway enrichment analysis of differentially expressed mRNA (DE-mRNA) was performed using Metscape, and LIR DE-mRNA was further screened. AAA rat model was constructed using porcine pancreatic elastase to verify the differential expression of LIR DE-mRNA.

Results

The GSE47472 and GSE57691 datasets respectively identified 614 (containing 381 down-regulated and 233 up-regulated DE-mRNAs) and 384 (containing 218 down-regulated and 164 up-regulated DE-mRNAs) DE-mRNAs. Intersection and union of DE-mRNAs were 13 and 983, respectively. The main terms involved in the union of DE-mRNAs included "immune system process", "metabolic process", "Chemokine signaling pathway", "hematopoietic cell lineage" and "Cholesterol metabolism". In vivo experiments revealed that LIR DE-mRNAs of PDIA3, TYROBP, and HSPA1A were significantly low expression in AAA abdominal aortic tissues, and HCK and SERPINE1 were significantly high expression, which is consistent with the bioinformatics analysis.

Conclusions

PDIA3, TYROBP, HSPA1A, HCK and SERPINE1 may serve as LIR biomarkers of AAA, which provides new insights and theoretical guidance for the future treatment, early prevention and progression of AAA.

Novel exosome-related risk signature as prognostic biomarkers in glioblastoma

Exosomes are progressively being detected as an indicator for the diagnosis and prognosis of cancer in clinical settings. Many clinical trials have confirmed the impact of exosomes on tumor growth, particularly in anti-tumor immunity and immunosuppression of exosomes. Therefore, we developed a risk score based on genes found in glioblastoma-derived exosomes. In this study, we used the TCGA dataset as the training queue and GSE13041, GSE43378, GSE4412, and CGGA datasets as the external validation queue. Based on machine algorithms and bioinformatics methods, an exosome-generalized risk score was established. We found that the risk score could independently predict the prognosis of patients with glioma, and there were significant differences in the outcomes of patients in the high- and low-risk groups. Univariate and multivariate analyses showed that risk score is a valid predictive biomarker for gliomas. Two immunotherapy datasets, IMvigor210 and GSE78220, were obtained from previous studies. A high-risk score showed a significant association with multiple immunomodulators that could act on cancer immune evasion. The exosome-related risk score could predict the effectiveness of anti-PD-1 immunotherapy. Moreover, we compared the sensitivity of patients with high- and low-risk scores to various anti-cancer drugs and found that patients with high-risk scores had better responses to a variety of anti-cancer drugs. The risk-scoring model established in this study provides a useful tool to predict the total survival time of patients with glioma and guide immunotherapy.

Pan-Cancer Analysis of PDIA3: Identifying It as a Potential Biomarker for Tumor Prognosis and Immunotherapy

Protein disulfide isomerase A3 (PDIA3) is a kind of thiol oxidoreductase with a wide range of functions, and its expression is elevated in a variety of tumors, which is closely related to the invasion and metastasis of tumor cells, and has a significant impact on the immunogenicity of tumor cells. Although more and more studies have shown that PDIA3 plays an important role in the occurrence and development of many tumors, there is no systematic pan-cancer study on PDIA3. Therefore, in this study, the differential expression of PDIA3 in 33 kinds of tumors was analyzed to explore its ability to regulate tumor immunity as a biomarker and evaluate its role in different cancer onset stages or clinical prognosis. In this paper, by analyzing the multilevel data including 33 kinds of cancers in the databases of Cancer Genome Atlas (TCGA), UCSC Xena, Cancer Cell Encyclopedia (CCLE), Genotypic Tissue Expression (GTEx), Human Protein Atlas (HPA), cBioPortal, and GDC; the differential expression level of PDIA3 in different types of malignant tumors and its relationship with prognosis and the potential correlation between PDIA3 expression and microsatellite instability (MSI), tumor mutation load (TMB), mismatch repair gene (MMR), DNA methylation level, and immune infiltration level were analyzed with bioinformatics. The results showed that PDIA3 was highly expressed in 19 types of cancers, but downregulated only in THCA. Next, PDIA3 in different tumors was positively or negatively correlated with patient outcome, Kaplan-Meier survival analysis showed that PDIA3 plays an important role in the prognosis of patients with KIRP, KICH, and CESC and may be used as a prognostic biomarker, and the methylation level of PDIA3 promoter region was closely related to patient outcome in eight tumors. The expression level of PDIA3 was correlated with TMB in 13 tumors and MSI in 9 tumors. Among them, the expression level of PDIA3 in THYM has the strongest correlation with TMB, and the expression level of PDIA3 in READ has the strongest correlation with MSI. In addition, the expression of PDIA3 in eight kinds of tumors, including BRCA, HNSC, THYM, LGG, LUAD, LUSC, PRAD, and THCA, had the highest correlation with the infiltration degree of immune cells, and the expression of PDIA3 had the highest correlation with the infiltration degree of 11 kinds of immune cells, including regulatory T cell and macrophages. And LGG is the tumor most likely to be affected by the tumor microenvironment to affect its development and prognosis. To sum up, this study suggests that PDIA3 plays an important role in the occurrence and development of KIRP, KICH, and CESC and in the immunotherapeutic response of THYM, READ, and LGG and can be used as a prognostic biomarker for these tumors.

CDH6 as a prognostic indicator and marker for chemotherapy in gliomas

Glioma is the most malignant cancer of the central nervous system. There are various therapies for treating gliomas, but their outcomes are not satisfactory. Therefore, new targets for glioma treatment are needed. This study examined the cadherin-6 (CDH6) expression in gliomas using The Cancer Genome Atlas and Chinese Glioma Genome Atlas datasets. CDH6 expression positively correlated with the World Health Organization (WHO) tumor grade and negatively correlated with patient prognosis. A significant decrease in CDH6 promoter methylation was identified with an increase in the WHO grade severity. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses suggested that CDH6 might be involved in cell–cell interactions and immune processes in the glioma microenvironment. Weighted gene co-expression network analysis revealed a correlation between CDH6 and cell adhesion molecules, focal adhesions, phosphatidylinositol 3-kinase-protein kinase B signaling pathways, nuclear division, chromosome segregation, mitotic nuclear division, and immune-related pathways. CDH6 strongly correlated with immunosuppressive cells, including regulatory T cells, monocytes, macrophages, tumor-associated macrophages, and myeloid-derived suppressor cells. It also showed correlations with immune-active cells such as B cells, CD8⁺ T cells, and dendritic cells. Single-cell analysis showed that CDH6 was expressed mainly in astrocyte (AC)-like malignant cells. Differentially expressed genes of AC-like malignant cells were found to be associated with stress response, membranous processes, viral infections, and several types of cancers. Potential drugs associated with high CDH6 expression were also predicted, including AMG-22, rutin, CCT128930, deforolimus, bis(maltolato)oxovanadium, anagrelide, vemurafenib, CHIR-98014, and AZD5582. Thus, this study showed that CDH6 correlates with glioma immune infiltration, it is expressed mainly in AC-like malignant cells, and it may act as a new target for glioma therapy.

A Novel Thrombosis-Related Signature for Predicting Survival and Drug Compounds in Glioblastoma

Glioblastoma is the most common primary tumor in the central nervous system, and thrombosis-associated genes are related to its occurrence and progression. Univariate Cox and LASSO regression analysis were utilized to develop a new prognostic signature based on thrombosis-associated genes. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and HALLMARK were used for functional annotation of risk signature. ESTIMATE, MCP-counter, xCell, and TIMER algorithms were used to quantify immune infiltration in the tumor microenvironment. Genomics of Drug Sensitivity in Cancer (GDSC) was used for selecting potential drug compounds. Risk signature based on thrombosis-associated genes shows moderate performance in prognosis prediction. The functional annotation of the risk signature indicates that the signaling pathways related to the cell cycle, apoptosis, tumorigenesis, and immune suppression are rich in the high-risk group. Somatic mutation analysis shows that tumor-suppressive gene TP53 and oncogene PTEN have higher expression in low-risk and high-risk groups, respectively. Potential drug compounds are explored in risk score groups and show higher AUC values in the low-risk score group. A nomogram with valuable prognostic factors exhibits high sensitivity in predicting the survival outcome of GBM patients. Our research screens out multiple thromboses-associated genes with remarkable clinical significance in GBM and further develops a meaningful prognostic risk signature predicting drug sensitivity and survival outcome.

New insights from the single-cell level: Tumor associated macrophages heterogeneity and personalized therapy

Tumor-associated macrophages (TAMs) are important immune cells in the tumor microenvironment, and their invasion in tumors is closely related to poor prognosis. Although TAMs are recognized as therapeutic targets, their heterogeneity makes studying tumor mechanism and developing drugs targeting TAMs difficult. The study of TAMs heterogeneity can be used to analyze the mechanism of tumor progression and drug resistance, and may provide possible treatment strategies for cancer patients. Single-cell RNA sequencing (scRNA-seq) can reveal the RNA expression profile for each TAM to distinguish heterogeneity, thereby providing a more efficient detection method and more accurate information for TAM-related studies. In this review, by summarizing the research progress in macrophage heterogeneity and other aspects of scRNA-seq over the past five years, we introduced the development of scRNA-seq technology and its application status in solid tumors, analyzed the advantages and selections of scRNA-seq in TAMs, and summarized the detailed specific research fields. To explore the mechanism of tumor progression and drug intervention from single cell level will provide new perspective for personalized treatment strategies targeting macrophages.

Identification of an Inflammatory Response-Related Gene Signature to Predict Survival and Immune Status in Glioma Patients

Background

Glioma is the most common primary brain tumor with high mortality and poor outcomes. As a hallmark of cancers, inflammatory responses are crucial for their progression. The present study is aimed at exploring the prognostic value of inflammatory response-related genes (IRRGs) and constructing a prognostic IRRG signature for gliomas.

Materials and Methods

We investigated the relationship between IRRGs and gliomas by integrating the transcriptomic data for gliomas from public databases. Differentially expressed IRRGs (DE-IRRGs) were identified in the GSE4290 cohort. Further, univariate, least absolute shrinkage and selection operator, and multivariate Cox regression analyses were conducted to construct an IRRG signature using The Cancer Genome Atlas (TCGA) cohort. Gliomas from the Chinese Glioma Genome Atlas (CGGA) cohort were employed for independent validation. The performance of gene signature was assessed by survival and receiver operating characteristic curve analyses. The differences in clinical correlations, immune infiltrate types, immunotherapeutic response predictions, and pathway enrichment among subgroups were investigated via bioinformatic algorithms.

Results

In total, 37 DE-IRRGs were determined, of which 31 were found to be associated with survival. Ultimately, eight genes were retained to construct an IRRG signature that further classified glioma patients into two groups; the high-risk group suffered a poorer outcome as compared to the low-risk group. Furthermore, the high-risk group was significantly correlated with several risk factors, including older age, higher tumor grade, IDH wild type, 1p19q noncodel, and MGMT unmethylation. The nomogram was constructed by integrating the risk scores and other independent clinical characteristics. Moreover, the high-risk group had a greater immune infiltration and was most likely to benefit from immunotherapy. Gene set enrichment analysis suggested that immune and oncogenic pathways were enriched in high-risk glioma patients.

Conclusion

We constructed a signature composed of eight IRRGs for gliomas, which could effectively predict survival and guide decision-making for treatment.

Protein Disulfide-Isomerase A3 Is a Robust Prognostic Biomarker for Cancers and Predicts the Immunotherapy Response Effectively

Background

Protein disulfide isomerase A3 (PDIA3) is a member of the protein disulfide isomerase (PDI) family that participates in protein folding through its protein disulfide isomerase function. It has been reported to regulate the progression of several cancers, but its function in cancer immunotherapy is unknown.

Methods

The RNA-seq data of cancer and normal tissues were downloaded from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) databases. The Cbioportal dataset was used to explore the genomic alteration information of PDIA3 in pan-cancer. Human Protein Atlas (HPA) and ComPPI websites were employed to mine the protein information of PDIA3, and western blot assay was performed to monitor the upregulated PDIA3 expression in clinical GBM samples. The univariate Cox regression and the Kaplan–Meier method were utilized to appraise the prognostic role of PDIA3 in pan-cancer. Gene Set Enrichment Analysis (GSEA) was applied to search the associated cancer hallmarks with PDIA3 expression. TIMER2.0 was the main platform to investigate the immune cell infiltrations related to PDIA3 in pan-cancer. The associations between PDIA3 and immunotherapy biomarkers were performed by Spearman correlation analysis. The immunoblot was used to quantify the PDIA3 expression levels, and the proliferative and invasive ability of glioma cells was determined by colony formation and transwell assays.

Findings

PDIA3 is overexpressed in most cancer types and exhibits prognosis predictive ability in various cancers, and it is especially expressed in the malignant cells and monocytes/macrophages. In addition, PDIA3 is significantly correlated with immune-activated hallmarks, cancer immune cell infiltrations, and immunoregulators, and the most interesting finding is that PDIA3 could significantly predict anti-PDL1 therapy response. Besides, specific inhibitors that correlated with PDIA3 expression in different cancer types were also screened by using Connectivity Map (CMap). Finally, knockdown of PDIA3 significantly weakened the proliferative and invasive ability of glioma cells.

Interpretation

The results revealed that PDIA3 acts as a robust tumor biomarker. Its function in protein disulfide linkage regulation could influence protein synthesis, degradation, and secretion, and then shapes the tumor microenvironment, which might be further applied to develop novel anticancer inhibitors.

Antigen Presentation Machinery Signature-Derived CALR Mediates Migration, Polarization of Macrophages in Glioma and Predicts Immunotherapy Response

Immunogenicity, influenced by tumor antigenicity and antigen presenting efficiency, critically determines the effectiveness of immune checkpoint inhibitors. The role of immunogenicity has not been fully elucidated in gliomas. In this study, a large-scale bioinformatics analysis was performed to analyze the prognostic value and predictive value of antigen presentation machinery (APM) signature in gliomas. ssGSEA algorithm was used for development of APM signature and LASSO regression analysis was used for construction of APM signature-based risk score. APM signature and risk score showed favorable performance in stratifying survival and predicting tumorigenic factors of glioma patients. APM signature and risk score were also associated with different genomic features in both training cohort TCGA and validating cohort CGGA. Furthermore, APM signature-based risk score was independently validated in three external cohorts and managed to predict immunotherapy response. A prognostic nomogram was constructed based on risk score. Risk score-derived CALR was found to mediate the invasion and polarization of macrophages based on the coculture of HMC3 and U251 cells. CALR could significantly predict immunotherapy response. In conclusion, APM signature and APM signature-based risk score could help promote the clinical management of gliomas.

Correlation between low expression of protein disulfide isomerase A3 and lymph node metastasis in papillary thyroid carcinoma and poor prognosis: a clinicopathological study of 1,139 cases with long-term follow-up

The incidence of papillary thyroid carcinoma (PTC) is increasing worldwide. The biomarkers to identify aggressive types of PTC are limited, illustrating the need to establish reliable novel biomarkers. Protein disulfide isomerase A3 (PDIA3) is a chaperone protein that modulates the folding of newly synthesized glycoproteins and stress-responsive proteins in the endoplasmic reticulum. Although the role of PDIA3 in various cancers such as breast, uterine cervix, head and neck, and gastrointestinal tract has been examined, its expression in thyroid cancer has not been reported. We retrospectively reviewed accumulated data with long-term follow-up of 1,139 PTC patients, and investigated the correlation between immunohistochemical expression of PDIA3 in PTC patients and clinicopathological features and prognosis. PDIA3 expression was significantly lower in PTCs compared to normal thyroid tissues (NTT; n = 80, p = 0.002). In PTCs, correlation between low PDIA3 expression and lymph node metastasis (p = 0.018) and the number of positive nodes (p = 0.004) was observed. Patients with low PDIA3 expression exhibited worse cause-specific survival compared to those with high PDIA3 expression (p = 0.013). Our findings indicate that low PDIA3 expression is related to poor clinical outcome in PTC patients, and that PDIA3 may potentially be a novel ancillary biomarker. Further clarification of the biological role of PDIA3 in PTC is warranted for the future clinical application.

Application of Proteomics in the Discovery of Radiosensitive Cancer Biomarkers

Radiation therapy remains an important component of cancer treatment. Gene-encoded proteins were the actual executors of cellular functions. Proteomic was a novel technology that can systematically analysis protein composition and measure their levels of change, this was a high throughput method, and were the import tools in the post genomic era. In recent years, rapid progress of proteomic have been made in the study of cancer mechanism, diagnosis, and treatment. This article elaborates current advances and future directions of proteomics in the discovery of radiosensitive cancer biomarkers.

Functions of RNF Family in the Tumor Microenvironment and Drugs Prediction in Grade II/III Gliomas

Increasing evidence has demonstrated that RING finger (RNF) proteins played a vital role in cellular and physiological processes and various diseases. However, the function of RNF proteins in low-grade glioma (LGG) remains unknown. In this study, 138 RNF family members revealed their role in LGG. The TCGA database was used as the training cohort; two CGGA databases and GSE108474 were selected as external validation cohorts. Patients were grouped into cluster 1 and cluster 2, both in the training and validation cohorts, using consensus clustering analysis. The prognosis of patients in cluster 1 is significantly better than that in cluster 2. Meanwhile, biofunction prediction was further introduced to explore the potential mechanisms that led to differences in survival outcomes. Patients in Cluster 2 showed more complicated immunocytes infiltration and highly immunosuppressive features than cluster 1. Enrichment pathways such as negative regulation of mast cell activation, DNA replication, mismatch repair, Th17 cell differentiation, antigen processing and presentation, dendritic cell antigen processing and presentation, dendritic cell differentiation were also enriched in cluster 2 patients. For the last, the main contributors were distinguished by employing a machine learning algorithm. A lot of targeted and small molecule drugs that are sensitive to patients in cluster 2 were predicted. Importantly, we discovered TRIM8, DTX2, and TRAF5 as the most vital contributors from the RNF family, which were related to immune infiltration in LGG tumor immune landscape. In this study, we demonstrated the predicted role of RNF proteins in LGG. In addition, we found out three markers among RNF proteins that are closely related to the immune aspects of LGG, which might serve as novel therapeutic targets for immunotherapy in the future.

Single-Molecule RNA Sequencing Reveals IFNγ-Induced Differential Expression of Immune Escape Genes in Merkel Cell Polyomavirus-Positive MCC Cell Lines

Merkel cell carcinoma (MCC) is a rare and highly aggressive cancer, which is mainly caused by genomic integration of the Merkel cell polyomavirus and subsequent expression of a truncated form of its large T antigen. The resulting primary tumor is known to be immunogenic and under constant pressure to escape immune surveillance. Because interferon gamma (IFNγ), a key player of immune response, is secreted by many immune effector cells and has been shown to exert both anti-tumoral and pro-tumoral effects, we studied the transcriptomic response of MCC cells to IFNγ. In particular, immune modulatory effects that may help the tumor evade immune surveillance were of high interest to our investigation. The effect of IFNγ treatment on the transcriptomic program of three MCC cell lines (WaGa, MKL-1, and MKL-2) was analyzed using single-molecule sequencing via the Oxford Nanopore platform. A significant differential expression of several genes was detected across all three cell lines. Subsequent pathway analysis and manual annotation showed a clear upregulation of genes involved in the immune escape of tumor due to IFNγ treatment. The analysis of selected genes on protein level underlined our sequencing results. These findings contribute to a better understanding of immune escape of MCC and may help in clinical treatment of MCC patients. Furthermore, we demonstrate that single-molecule sequencing can be used to assess characteristics of large eukaryotic transcriptomes and thus contribute to a broader access to sequencing data in the community due to its low cost of entry.

TNFSF13 Is a Novel Onco-Inflammatory Marker and Correlates With Immune Infiltration in Gliomas

Existing therapeutic strategies for gliomas are restricted; hence, exploration for novel diagnostic indicator and treatment is essential. Here, we performed bioinformatic analyses for TNFSF13 (also known as APRIL), a proliferation-inducing ligand of the tumor necrosis factor (TNF) superfamily, aiming to assess its potential for predicting glioma patient’s prognosis and targeted therapy. TNFSF13 expression was upregulated in the increase of tumor grades based on Xiangya cohort. In high TNFSF13 gliomas, somatic mutation was proved to correlate with amplification of EGFR and deletion of CDKN2A; while mutation of IDH1 was more frequently observed in low TNFSF13 group. We also confirmed the positive correlation between TNFSF13 and infiltrating immune and stromal cells in glioma microenvironment. Further, TNFSF13 was found to be involved in immunosuppression via diverse immunoregulation pathways and was associated with other immune checkpoints and inflammation. Single-cell sequencing revealed an abundant expression of TNFSF13 in neoplastic cells and M2 macrophages, which TNFSF13 might potentially regulate the cell communication via IL-8, C3, and CD44. Lastly, TNFSF13 mediated the activities of transcription factors including FOXO3, MEIS2, and IRF8. Our analyses demonstrated the relevance between TNFSF13 and glioma progress and indicated the potential of TNFSF13 as a novel diagnostic onco-inflammatory biomarker and immunotherapy target of gliomas.

Novel Immune Infiltrating Cell Signature Based on Cell Pair Algorithm Is a Prognostic Marker in Cancer

Tumor-infiltrating immune cells (TIICs) have become an important source of markers for predicting the clinical outcomes of cancer patients. However, measurements of cellular heterogeneity vary due to the frequently updated reference genomes and gene annotations. In this study, we systematically collected and evaluated the infiltration pattern of 65 immune cells. We constructed the Immune Cell Pair (ICP) score based on the cell pair algorithm in 3,715 samples and across 12 independent cancer types, among which, the ICP score from six cancer types was further validated in 2,228 GEO samples. An extensive tumorigenic and immunogenomic analysis was subsequently conducted. As a result, the ICP score showed a robust reliability and efficacy in predicting the survival of patients with gliomas, in pan-cancer samples, and six independent cancer types. Notably, the ICP score was correlated with the genomic alteration features in gliomas. Moreover, the ICP score exhibited a remarkable association with multiple immunomodulators that could potentially mediate immune escape. Finally, the ICP score predicted immunotherapeutic responses with a high sensitivity, allowing a useful tool for predicting the overall survival and guiding immunotherapy for cancer patients.

Immune Infiltrating Cells-Derived Risk Signature Based on Large-scale Analysis Defines Immune Landscape and Predicts Immunotherapy Responses in Glioma Tumor Microenvironment

The glioma tumor microenvironment (TME), composed of several noncancerous cells and biomolecules is known for its complexity of cancer-immune system interaction. Given that, novel risk signature is required for predicting glioma patient responses to immunotherapy. In this study, we systematically evaluated the TME infiltration pattern of 2877 glioma samples. TME phenotypes were determined using the Partitioning Around Medoid method. Machine learning including SVM-RFE and Principal component analysis (PCA) were used to construct a TME scoring system. A total of 857 glioma samples from four datasets were used for external validation of the TME-score. The correlation of TME phenotypes and TME-scores with diverse clinicopathologic characteristics, genomic features, and immunotherapeutic efficacy in glioma patients was determined. Immunohistochemistry staining for the M2 macrophage marker CD68 and CD163, mast cell marker CD117, neutrophil marker CD66b, and RNA sequencing of glioma samples from the XYNS cohort were performed. Two distinct TME phenotypes were identified. High TME-score correlated with a high number of immune infiltrating cells, elevated expression of immune checkpoints, increased mutation rates of oncogenes, and poor survival of glioma patients. Moreover, high TME-score exhibited remarkable association with multiple immunomodulators that could potentially mediate immune escape of cancer. Thus, the TME-score showed the potential to predict the efficacy of anti-PD-1 immunotherapy. Univariate and multivariate analyses demonstrated the TME-score to be a valuable prognostic biomarker for gliomas. Our study demonstrated that TME could potentially influence immunotherapy efficacy in melanoma patients whereas its role in immunotherapy of glioma patients remains unknown. Therefore, a better understanding of the TME landscape in gliomas would promote the development of novel immunotherapy strategies against glioma.

HOXA5 Is Recognized as a Prognostic-Related Biomarker and Promotes Glioma Progression Through Affecting Cell Cycle

Glioma is malignant tumor derives from glial cells in the central nervous system. High-grade glioma shows aggressive growth pattern, and conventional treatments, such as surgical removal and chemo-radiotherapy, archive limitation in the interference of this process. In this work, HOXA5, from the HOX family, was identified as a glioma cell proliferation-associated factor by investigating its feature in the TCGA and CGGA data set. High HOXA5 expression samples contain unfavorable clinical features of glioma, including IDH wild type, un-methylated MGMT status, non-codeletion 1p19q status, malignant molecular subtype. Survival analysis indicates that high HOXA5 expression samples are associated with worse clinical outcome. The CNVs and SNPs profile difference further confirmed the enrichment of glioma aggressive related biomarkers. In the meantime, the activation of DNA damage repair-related pathways and TP53-related pathways is also related to HOXA5 expression. In cell lines, U87MG and U251, by interfering HOXA5 expression significantly inhibit glioma progression and apoptosis, and cell cycle is arrested at the G2/M phase. Collectively, increased HOXA5 expression can promote glioma progression via affecting glioma cell proliferation.

Applications of single-cell sequencing in cancer research: progress and perspectives

Single-cell sequencing, including genomics, transcriptomics, epigenomics, proteomics and metabolomics sequencing, is a powerful tool to decipher the cellular and molecular landscape at a single-cell resolution, unlike bulk sequencing, which provides averaged data. The use of single-cell sequencing in cancer research has revolutionized our understanding of the biological characteristics and dynamics within cancer lesions. In this review, we summarize emerging single-cell sequencing technologies and recent cancer research progress obtained by single-cell sequencing, including information related to the landscapes of malignant cells and immune cells, tumor heterogeneity, circulating tumor cells and the underlying mechanisms of tumor biological behaviors. Overall, the prospects of single-cell sequencing in facilitating diagnosis, targeted therapy and prognostic prediction among a spectrum of tumors are bright. In the near future, advances in single-cell sequencing will undoubtedly improve our understanding of the biological characteristics of tumors and highlight potential precise therapeutic targets for patients.

Ferroptosis-Related Gene Signature Promotes Ovarian Cancer by Influencing Immune Infiltration and Invasion

Ovarian cancer is a kind of gynecological malignancy with high mortality. Ferroptosis is a new type of iron-dependent cell death characterized by the formation of lipid peroxides and excessive accumulation of reactive oxygen species. Studies have shown that ferroptosis modulates tumor genesis, progression, and invasion, including ovarian cancer. Based on the mRNA expression data from TCGA, we construct a scoring system using consensus clustering analysis, univariate Cox regression analysis, and least absolute selection operator. Then, we systematically evaluate the relationship between score and clinical characteristics of ovarian cancer. The result from the prediction of biofunction pathways shows that score serves as an independent prognostic marker for ovarian cancer and affects tumor progression by modulating tumor metastasis. Moreover, immunocytes such as activated CD4 T cell, activated CD8 T cell, regulatory T cells, macrophage, and stromal cells, including adipocytes, epithelial cells, and fibroblast infiltrate more in the tumor microenvironment in a high-score group, indicating ferroptosis can also affect tumor immune landscape. Critically, four potentially sensitive drugs, including staurosporine, epothilone B, DMOG, and HG6-64-1 based on the scores, are predicted, and DMOG is recognized as a novel targeted drug for ovarian cancer. In general, we construct the scoring system based on ferroptosis-related genes that can predict the prognosis of ovarian cancer patients and propose that ferroptosis may affect ovarian cancer progression by mediating tumor metastasis and immune landscape. Novel drugs to target ovarian cancer are also predicted.

Aging-related genes are potential prognostic biomarkers for patients with gliomas

Aging has a significant role in the proliferation and development of cancers. This study explored the expression profiles, prognostic value, and potential roles of aging-related genes in gliomas. We designed risk score and cluster models based on aging-related genes and glioma cases using LASSO Cox regression analysis, consensus clustering analysis and univariate cox regression analyses. High risk score was related to malignant clinical features and poor prognosis based on 10 datasets, 2953 cases altogether. Genetic alterations analysis revealed that high risk scores were associated with genomic aberrations of aging-related oncogenes. GSVA analysis exhibited the potential function of the aging-related genes. More immune cell infiltration was found in high-risk group cases, and glioma patients in high-risk group may be more responsive to immunotherapy. Knock-down of CTSC, an aging-related gene, can inhibit cell cycle progression, colony formation, cell proliferation and increase cell senescence in glioma cell lines in vitro. Indeed, high expression of CTSC was associated with poor prognosis in glioma cases. In conclusion, this study revealed that aging-related genes have prognostic potential for glioma patients and further identified potential mechanisms for aging-related genes in tumorigenesis and progression in gliomas.

The Predictive Value of Monocytes in Immune Microenvironment and Prognosis of Glioma Patients Based on Machine Learning

Gliomas are primary malignant brain tumors. Monocytes have been proved to actively participate in tumor growth. Weighted gene co-expression network analysis was used to identify meaningful monocyte-related genes for clustering. Neural network and SVM were applied for validating clustering results. Somatic mutation and copy number variation were used for defining the features of identified clusters. Differentially expressed genes (DEGs) between the stratified groups after performing elastic regression and principal component analyses were used for the construction of risk scores. Monocytes were associated with glioma patients’ survival and exhibited high predictive value. The prognostic value of risk score in glioma was validated by the abundant expression of immune checkpoint and metabolic profile. Additionally, high risk score was positively associated with the expression of immunogenic and antigen presenting factors, which indicated high immune infiltration. A prognostic model based on risk score demonstrated high accuracy rate of receiver operating characteristic curves. Compared with previous studies, our research dissected functional roles of monocytes from large-scale analysis. Findings of our analyses strongly support an immune modulatory and prognostic role of monocytes in glioma progression. Notably, monocyte could be an effective predictor for therapy responses of glioma patients.

B2M overexpression correlates with malignancy and immune signatures in human gliomas

Because of the limited treatment strategy of gliomas, the key of diagnosis and treatment is finding new molecular biomarkers. Here, we explored the potential of β2-microglobulin (B2M) to serve as a hopeful candidate for immunotherapy or diagnostic biomarker in gliomas. The genomic profiles, clinical characteristics, and immune signatures were analyzed based on TCGA and CGGA databases. We carried out the whole statistical analyses using R project. High B2M expression correlated with worse prognosis. Somatic mutations of gliomas with high B2M expression are associated with PTEN deletion and EGFR amplification. Isocitrate dehydrogenase (IDH) mutations accounted for 82% in gliomas with low B2M expression. In addition, B2M positively correlated with ESTIMATE scores, interacted with infiltrating immune and stromal cell types. B2M also suppressed anti-tumor immunity through immune related processes. Meanwhile, B2M was associated with immune checkpoint molecules and inflammatory activities. Finally, functional annotation of the identified B2M related genes verified that B2M was a potential candidate for immunotherapy. We confirmed that B2M played a critical role in tumor progression, patient prognosis and immunotherapy of gliomas.

Characterization of an endoplasmic reticulum stress-related signature to evaluate immune features and predict prognosis in glioma

Endoplasmic reticulum (ER) stress has considerable impact on cell growth, proliferation, metastasis, invasion, angiogenesis and chemoradiotherapy resistance in various cancers. However, the effect of ER stress on the outcomes of glioma patients remains unclear. In this study, we established an ER stress risk model based on The Cancer Genome Atlas (TCGA) glioma data set to reflect immune characteristics and predict the prognosis of glioma patients. Survival analysis indicated that there were significant differences in the overall survival (OS) of glioma patients with different ER stress-related risk scores. Moreover, the ER stress-related risk signature, which was markedly associated with the clinicopathological properties of glioma patients, could serve as an independent prognostic indicator. Functional enrichment analysis revealed that the risk model correlated with immune and inflammation responses, as well as biosynthesis and degradation. In addition, the ER stress-related risk model indicated an immunosuppressive microenvironment. In conclusion, we present an ER stress risk model that is an independent prognostic factor and indicates the general immune characteristics in the glioma microenvironment.

Identifying Prognostic Significance of RCL1 and Four-Gene Signature as Novel Potential Biomarkers in HCC Patients

BACKGROUND

Hepatocellular carcinoma (HCC) is a highly malignant disease, and it is characterized by rapid progression and low five-year survival rate. At present, there are no effective methods for monitoring the treatment and prognosis of HCC.

METHODS

The transcriptome and gene expression profiles of HCC were obtained from the Cancer Genome Atlas (TCGA) program, International Cancer Genome Consortium (ICGC), and Gene Expression Omnibus (GEO) databases. The random forest method was applied to construct a four-gene prognostic model based on RNA terminal phosphate cyclase like 1 (RCL1) expression. The Kaplan-Meier method was performed to evaluate the prognostic value of RCL1, long noncoding RNAs (AC079061, AL354872, and LINC01093), and four-gene signature (SPP1, MYBL2, TRNP1, and FTCD). We examined the relationship between RCL1 expression and immune cells infiltration, tumor mutation burden (TMB), and microsatellite instability (MSI).

RESULTS

The results of multiple databases indicated that the aberrant expression of RCL1 was associated with clinical outcome, immune cells infiltration, TMB, and MSI in HCC patients. Meanwhile, we found that long noncoding RNAs (AC079061, AL354872, and LINC01093) and RCL1 were significantly coexpressed in HCC patients. We also confirmed that the four-gene signature was an independent prognostic factor for HCC patients. Ferroptosis potential index, immune checkpoint molecules, and clinical feature were found to have obvious correlations with risk score. The area under the receiver operating characteristic curve values for the model were 0.7-0.8 in the training set and the validation set, suggesting high robustness of the four-gene signature. We then built a nomogram for facilitating the use in clinical practice.

CONCLUSION

Our study demonstrated that RCL1 and a novel four-gene signature can be used as prognostic biomarkers for predicting clinical outcome in HCC patients; and this model may assist in individualized treatment monitoring of HCC patients in clinical practice.