Bioinformatics analysis of the role of aldolase A in tumor prognosis and immunity

Fructose 1,6-bisphosphate aldolase: A promising prognostic marker for oral cancer and its role in radiotherapy response

Oral cancer remains a significant global health concern and its early detection plays a crucial role in improving patient outcomes. Identifying reliable prognostic markers is essential to guide treatment decisions and enhance survival rates. Fructose 1,6-bisphosphate aldolase (FBA), a glycolytic enzyme, has emerged as a promising candidate for prognostic assessment of oral cancer. This review highlights the role of FBA in tumorigenesis, its potential utility in predicting disease progression and patient survival, and its influence on response to radiotherapy. Recent studies have suggested that dysregulated metabolic pathways involving FBA may contribute to radiation resistance in oral cancer, emphasizing the need for further exploration of FBA-targeted therapeutic strategies. Understanding the role of FBA in oral cancer pathogenesis could pave the way for the development of personalized treatment strategies, including combined radiotherapy.

Mechanisms of HIF1A-mediated immune evasion in gastric cancer and the impact on therapy resistance

BACKGROUND

The high prevalence and detrimental effects on patient outcomes make gastric cancer (GC) a significant health issue that persists internationally. Existing treatment modalities exhibit limited efficacy, prompting the exploration of immune checkpoint inhibitors as a novel therapeutic approach. However, resistance to immunotherapy poses a significant challenge in GC management, necessitating a profound grasp of the intrinsic molecular pathways.

METHODS

This study focuses on investigating the immunosuppressive mechanisms of quiescent cancer cells (QCCs) in GC, particularly their resistance to T-cell-mediated immune responses. Utilizing mouse models, gene editing techniques, and transcriptome sequencing, we aim to elucidate the interactions between QCCs, immune cells, and key regulatory factors like HIF1A. Functional enrichment analysis will further underscore the role of glycolysis-related genes in mediating immunosuppression by QCCs.

RESULTS

The cancer cells that survived GC treated with T-cell therapy lost their proliferative ability. QCCs, as the main resistance force to immunotherapy, exhibit stronger resistance to CD8+ T-cell attack and possess higher cancer-initiating potential. Single-cell sequencing analysis revealed that the microenvironment in the QCCs region harbors more M2-type tumor-associated macrophages and fewer T cells. This microenvironment in the QCCs region leads to the downregulation of T-cell immune activation and alters macrophage metabolic function. Transcriptome sequencing of QCCs identified upregulated genes related to chemo-resistance, hypoxia, and glycolysis. In vitro cell experiments illustrated that HIF1A promotes the transcription of glycolysis-related genes, and silencing HIF1A in QCCs enhances T-cell proliferation and activation in co-culture systems, induces apoptosis in QCCs, and increases QCCs' sensitivity to immune checkpoint inhibitors. In vivo, animal experiments showed that silencing HIF1A in QCCs can inhibit GC growth and metastasis.

CONCLUSION

Unraveling the molecular mechanisms by which QCCs resist T-cell-mediated immune responses through immunosuppression holds promising implications for refining treatment strategies and enhancing patient outcomes in GC. By delineating these intricate interactions, this study contributes crucial insights into precision medicine and improved therapeutic outcomes in GC management.

Biomarkers Identification in the Microenvironment of Oral Squamous Cell Carcinoma: A Systematic Review of Proteomic Studies

An important determinant for oral squamous cell carcinoma (OSCC) onset and outcome is the composition of the tumor microenvironment (TME). Thus, the study of the interactions occurring among cancer cells, immune cells, and cancer-associated fibroblasts within the TME could facilitate the understanding of the mechanisms underlying OSCC development and progression, as well as of its sensitivity or resistance to the therapy. In this context, it must be highlighted that the characterization of TME proteins is enabled by proteomic methodologies, particularly mass spectrometry (MS). Aiming to identify TME protein markers employable for diagnosing and prognosticating OSCC, we have retrieved a total of 119 articles spanning 2001 to 2023, of which 17 have passed the selection process, satisfying all its criteria. We have found a total of 570 proteins detected by MS-based proteomics in the TME of OSCC; among them, 542 are identified by a single study, while 28 are cited by two or more studies. These 28 proteins participate in extracellular matrix remodeling and/or energy metabolism. Here, we propose them as markers that could be used to characterize the TME of OSCC for diagnostic/prognostic purposes. Noteworthy, most of the 28 individuated proteins share one feature: being modulated by the hypoxia that is present in the proliferating OSCC mass.

Establishment of Chemotherapy Prediction Model Based on Hypoxia-Related Genes for Oral Cancer

Purpose: Identify the hypoxia genes related to chemotherapy resistance of oral cancer, and construct a chemotherapy response model by machine learning algorithm. Methods: 72 oral cancer patients with complete chemotherapy records and chemotherapy reactions were screened from the Cancer Genome Atlas (TCGA) database. According to the chemotherapy reactions, they were divided into chemotherapy sensitive group and chemotherapy resistant group. The differential genes were screened by Limma package. Then the chemotherapy response gene were screened by univariate analysis. Based on the gene expression profile of chemotherapy response, four machine learning algorithms were used to construct the prediction model of chemotherapy response. The core genes were screened by lasso regression analysis. Finally, the prognosis and immune infiltration of the core genes were analyzed. The results were verified by immunohistochemistry (IHC). Results: We obtained 22 hypoxia related differential genes. Univariate analysis found 6 Chemotherapy response genes. Machine learning algorithms show that XGBoost have the best predictive performance for chemotherapy response. ALDOA is the core gene of chemotherapy resistance. Conclusions: Successfully constructed a chemotherapy prediction model for oral cancer by machine learning algorithm. Under hypoxia, the high expression of ALDOA is associated with chemotherapy resistance in oral cancer.

Diverse roles of aldolase enzymes in cancer development, drug resistance and therapeutic approaches as moonlighting enzymes

Aldolase enzymes, particularly ALDOA, ALDOB, and ALDOC, play a crucial role in the development and progression of cancer. While the aldolase family is mainly known for its involvement in the glycolysis pathway, these enzymes also have various pathological and physiological functions through distinct signaling pathways such as Wnt/β-catenin, EGFR/MAPK, Akt, and HIF-1α. This has garnered increased attention in recent years and shed light on other sides of this enzyme. Potential therapeutic strategies targeting aldolases include using siRNA, inhibitors like naphthol AS-E phosphate and TX-2098, and natural compounds such as HDPS-4II and L-carnosine. Additionally, anticancer peptides derived from ALDOA, like P04, can potentially increase cancer cells' sensitivity to chemotherapy. Aldolases also affect cancer drug resistance by different approaches, making them good therapeutic targets. In this review, we extensively explore the role of aldolase enzymes in various types of cancers in proliferation, invasion, migration, and drug resistance; we also significantly explore the possible treatment considering aldolase function.

Recombinant Human Adenovirus Type 5 (H101) Intra-Tumor Therapy in Patients with Persistent, Recurrent, or Metastatic Cervical Cancer: Genomic Profiling Relating to Clinical Efficacy

Objective

Genomic profiles relating to H101 treatment-induced alterations are yet to be achieved. Here, we evaluated the impact of H101 via exome-sequencing approaches aiming to probe for potential biomarkers that are actionable in the treatment of persistent/recurrent/metastatic (P/R/M) cervical cancer.

Methods

Whole exome sequencing (WES) was performd on paired pre- and post-H101 samples from 17 P/R/M cervical cancer patients who received serial intra-tumor injections of H101. Somatic mutations, including high-frequency mutations, microsatellite instability (MSI) status, tumor mutation burden (TMB), clonal evolution, and mutational signature were analyzed.

Results

The median follow-up time after the H101 treatment was 14 months. Complete response was achieved in 9 patients, 3 patients achieved partial response, and 2 patients had stable disease, resulting in an objective response rate (ORR) of 70.6% (95% CI: 46.4%-96.7%). WES analysis showed no difference in treatment-related mutation characteristics, including non-synonymous-SNVs and TMB status. Patients with lower TMB were correlated with improved H101 response rates (P=0.044), whereas the same was not evident in high MSI (MSI-H) versus non-MSI-H patients (P=0.528). We observed a few high-frequency mutation genes (TTN, KMT2D, ALDOA, DNAH7, ADAP1, PTPN23, and THEMIS2) that probably carry functional importance in response to H101 treatment, among which KMT2D and ADAP1 mutations were associated with inferior progression-free survival (PFS) and/or overall survival (OS) (P<0.05). Notably, H101 treatment-induced accumulating subclones or clusters in primary tumors and some (Signature 2) were associated with shorter PFS.

Conclusion

We conducted an unprecedented work via a WES-based approach and provided preliminary insights into H101 treatment-induced genetic aberrations in which some genes (TTN, KMT2D, ALDOA, DNAH7, ADAP1, PTPN23, and THEMIS2) could be considered potential therapeutic targets of H101-containing treatment in cervical carcinoma. Moreover, the therapy-associated characteristics such as clonal evolution and a mutational signature may warrant further evaluation of H101 in clinical settings for treating cervical carcinoma.

Anticancer Peptides Derived from Aldolase A and Induced Tumor-Suppressing Cells Inhibit Pancreatic Ductal Adenocarcinoma Cells

PDAC (pancreatic ductal adenocarcinoma) is a highly aggressive malignant tumor. We have previously developed induced tumor-suppressing cells (iTSCs) that secrete a group of tumor-suppressing proteins. Here, we examined a unique procedure to identify anticancer peptides (ACPs), using trypsin-digested iTSCs-derived protein fragments. Among the 10 ACP candidates, P04 (IGEHTPSALAIMENANVLAR) presented the most efficient anti-PDAC activities. P04 was derived from aldolase A (ALDOA), a glycolytic enzyme. Extracellular ALDOA, as well as P04, was predicted to interact with epidermal growth factor receptor (EGFR), and P04 downregulated oncoproteins such as Snail and Src. Importantly, P04 has no inhibitory effect on mesenchymal stem cells (MSCs). We also generated iTSCs by overexpressing ALDOA in MSCs and peripheral blood mononuclear cells (PBMCs). iTSC-derived conditioned medium (CM) inhibited the progression of PDAC cells as well as PDAC tissue fragments. The inhibitory effect of P04 was additive to that of CM and chemotherapeutic drugs such as 5-Flu and gemcitabine. Notably, applying mechanical vibration to PBMCs elevated ALDOA and converted PBMCs into iTSCs. Collectively, this study presented a unique procedure for selecting anticancer P04 from ALDOA in an iTSCs-derived proteome for the treatment of PDAC.

The HBV web: An insight into molecular interactomes between the hepatitis B virus and its host en route to hepatocellular carcinoma

Hepatitis B virus (HBV) is a major aetiology associated with the development and progression of hepatocellular carcinoma (HCC), the most common primary liver malignancy. Over the past few decades, direct and indirect mechanisms have been identified in the pathogenesis of HBV-associated HCC which include altered signaling pathways, genome integration, mutation-induced genomic instability, chromosomal deletions and rearrangements. Intertwining of the HBV counterparts with the host cellular factors, though well established, needs to be systemized to understand the dynamics of host-HBV crosstalk and its consequences on HCC progression. Existence of a vast array of protein-protein and protein-nucleic acid interaction databases has led to the uncoiling of the compendia of genes/gene products associated with these interactions. This review covers the existing knowledge about the HBV-host interplay and brings it down under one canopy emphasizing on the HBV-host interactomics; and thereby highlights new strategies for therapeutic advancements against HBV-induced HCC.

Single-cell sequencing combined with machine learning reveals the mechanism of interaction between epilepsy and stress cardiomyopathy

Background

Epilepsy is a disorder that can manifest as abnormalities in neurological or physical function. Stress cardiomyopathy is closely associated with neurological stimulation. However, the mechanisms underlying the interrelationship between epilepsy and stress cardiomyopathy are unclear. This paper aims to explore the genetic features and potential molecular mechanisms shared in epilepsy and stress cardiomyopathy.

Methods

By analyzing the epilepsy dataset and stress cardiomyopathy dataset separately, the intersection of the two disease co-expressed differential genes is obtained, the co-expressed differential genes reveal the biological functions, the network is constructed, and the core modules are identified to reveal the interaction mechanism, the co-expressed genes with diagnostic validity are screened by machine learning algorithms, and the co-expressed genes are validated in parallel on the epilepsy single-cell data and the stress cardiomyopathy rat model.

Results

Epilepsy causes stress cardiomyopathy, and its key pathways are Complement and coagulation cascades, HIF-1 signaling pathway, its key co-expressed genes include SPOCK2, CTSZ, HLA-DMB, ALDOA, SFRP1, ERBB3. The key immune cell subpopulations localized by single-cell data are the T_cells subgroup, Microglia subgroup, Macrophage subgroup, Astrocyte subgroup, and Oligodendrocytes subgroup.

Conclusion

We believe epilepsy causing stress cardiomyopathy results from a multi-gene, multi-pathway combination. We identified the core co-expressed genes (SPOCK2, CTSZ, HLA-DMB, ALDOA, SFRP1, ERBB3) and the pathways that function in them (Complement and coagulation cascades, HIF-1 signaling pathway, JAK-STAT signaling pathway), and finally localized their key cellular subgroups (T_cells subgroup, Microglia subgroup, Macrophage subgroup, Astrocyte subgroup, and Oligodendrocytes subgroup). Also, combining cell subpopulations with hypercoagulability as well as sympathetic excitation further narrowed the cell subpopulations of related functions.

Comprehensive analysis to identify GNG7 as a prognostic biomarker in lung adenocarcinoma correlating with immune infiltrates

Background: G Protein Subunit Gamma 7 (GNG7), an important regulator of cell proliferation and cell apoptosis, has been reported to be downregulated in a variety of tumors including lung adenocarcinoma (LUAD). However, the correlation between low expression of GNG7 and prognosis of LUAD as well as the immune infiltrates of LUAD remains unclear.

Methods: The samples were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). R software was performed for statistical analysis. GNG7 expression and its prognostic value in LUAD were assessed through statistically analyzing the data from different databases. A nomogram was constructed to predict the impact of GNG7 on prognosis. Gene set enrichment analysis (GSEA) and single-sample gene set enrichment analyses GSEA (ssGSEA) were employed to determine the potential signal pathways and evaluated the immune cell infiltration regulated by GNG7. The prognostic significance of GNG7 expression associated with immune cell infiltration was investigated using the Tumor Immune Estimation Resource 2.0 (TIMER2.0) and the Kaplan-Meier plotter database. The UALCAN, cBio Cancer Genomics Portal (cBioPortal) and MethSurv database were used to analyze the correlation between the methylation of GNG7 and its mRNA expression as well as prognostic significance.

Results: GNG7 was demonstrated to be down-regulated in LUAD and its low expression was associated with poor prognosis. A clinical reliable prognostic-predictive model was constructed. Pathway enrichment showed that GNG7 was highly related to the B cell receptor signaling pathway. Further analysis showed that GNG7 was positively associated with B cell infiltration and low levels of B cell infiltration tended to associate with worse prognosis in patients with low GNG7 expression. Moreover, methylation analysis suggested hypermethylation may contribute to the low expression of GNG7 in LUAD.

Conclusion: Decreased expression of GNG7 at least partly caused by hypermethylation of the GNG7 promoter is closely associated with poor prognosis and tumor immune cell infiltration (especially B cells) in LUAD. These results suggest that GNG7 may be a promising prognostic biomarker and a potential immunotherapeutic target for LUAD, which provides new insights into immunotherapy for LUAD.