HIF1α: A Novel Biomarker with Potential Prognostic and Immunotherapy in Pan-cancer

Exploring the Correlation Between Hypoxia, HIF1A Variants, and Breast Cancer in Different Ethnicities, and Bangladeshi Women: Through ELISA and Integrative Multi-Omics Analysis

Background

Hypoxia, a condition where there is a lack of oxygen, is known to play a role in cancer progression.

Objective

This study investigates the correlation between HIF1A gene-altered expression and hypoxia in Bangladeshi breast cancer (BC) cases and TCGA_BC datasets.

Design

This case-control study compares BC cases to healthy controls to understand the relationship between gene changes and cancer.

Method

This study used advanced analysis methods to examine the transcriptional landscape of BC, and quantitatively assessed its correlation using integrated multi-omics analysis.

Results

In Bangladeshi BC cases, the T allele of HIF1A rs1154946 correlates notably (P-value < .001) with BC incidence. ELISA results confirmed a significant association (P-value < .005) between elevated HIF1A expression and BC-related hypoxia. Bioinformatics eQTL analysis validated the correlation between increased HIF1A expression and rs11549465 T allele (P-value < .01). Structural analyses suggested that rs11549465 (P582S) mutation may decrease protein stability (ΔΔG-value: -1.24 kcal/mole), potentially affecting HIF1A function. HIF1A enrichment analysis in BC underscores strong associations with oxygen levels, hypoxia, metabolic processes, apoptosis, and programed cell death (P-value < .001). Transcriptomic data demonstrated a robust correlation (P-value < .0001) between HIF1A expression and copy-number alterations, mutations, and abnormal methylation. Altered HIF1A expression showed strong negative correlations (P-value < .00001) with methylation and the expression of the ER (ESR1), in Whites. Survival analysis revealed marked differences in overall survival linked to high and low HIF1A expression (P-value < .00001). Furthermore, HIF1A expression significantly correlated (P-value < .000001) with hypoxia, TMB, MSI, and immune infiltration by CD8+ T cells, neutrophils, dendritic, and macrophages, providing deeper insights into the BC microenvironment.

Conclusion

Thus, the HIF1A gene could serve as a promising biomarker for breast cancer progression, control, and survival across ethnicities, emphasizing its role in disease development and regulation.

TPD52 is a Potential Prognostic Biomarker and Correlated with Immune Infiltration: A Pan-cancer Analysis

BACKGROUND

Some tumors have a poor prognosis regarding TPD52 (tumor protein D52). This study aims to explore TPD52's role in the cancer process from a pan-cancer perspective.

METHODS

A pan-cancer analysis was conducted to investigate how TPD52 may be involved in cancer as well as its association with prognosis.

RESULTS

A variety of human cancers express TPD52 abnormally and correlate with clinical stage. There was a significant association between low expression of TPD52 and poor survival in BRCA, KIRP, LAML, LIHC, UCEC, and UVM. TPD52 alterations were most frequently amplified in pan-cancer. The co-occurrence of 10 genes alterations was found in the TPD52 altered group. There was a significant association between TPD52 expression and MSI in four cancer types and TMB in twelve cancer types. There was a significant correlation between TPD52 expression and immunerelated cell infiltration. A significant correlation was found between TPD52 expression in many tumor types and 8 immune checkpoint genes. There were signaling pathways involved in pan-cancer caused by TPD52, including endocytosis, Fc gamma Rmediated phagocytosis, and so on. TPD52 may be involved in chemotherapy and chemoresistance.

CONCLUSION

The TPD52 gene may be important for human cancer treatment.

Comprehensive bioinformatics analysis of FXR1 across pan-cancer: Unraveling its diagnostic, prognostic, and immunological significance

Fragile X-related protein 1 (FXR1) is an RNA-binding protein that belongs to the fragile X-related (FXR) family. Studies have shown that FXR1 plays an important role in cancer cell proliferation, invasion and migration and is differentially expressed in cancers. This study aimed to gain a comprehensive and systematic understanding of the analysis of FXR1's role in cancers. This would lead to a better understanding of how it contributes to the development and progression of various malignancies. this study conducted through The Cancer Genome Atlas (TCGA), GTEx, cBioPortal, TISIDB, GEPIA2 and HPA databases to investigated FXR1's role in cancers. For data analysis, various software platforms and web platforms were used, such as R, Cytoscape, hiplot plateform. A significant difference in FXR1 expression was observed across molecular and immune subtypes and across types of cancer. FXR1 expression correlates with disease-specific survival (DSS), and overall survival (OS) in several cancer pathways, further in progression-free interval (PFI) in most cancers. Additionally, FXR1 showed a correlation with genetic markers of immunomodulators in different cancer types. Our study provides insights into the role of FXR1 in promoting, inhibiting, and treating diverse cancers. FXR1 has the potential to serve as a diagnostic and prognostic biomarker for cancer, with therapeutic value in immune-based, targeted, or cytotoxic treatments. Further clinical validation and exploration of FXR1 in cancer treatment is necessary.

Comprehensive Analysis of DMRT3 as a Potential Biomarker Associated with the Immune Infiltration in a Pan-Cancer Analysis and Validation in Lung Adenocarcinoma

Doublesex and Mab-3 related Transcription Factor 3 (DMRT3) is associated with the prognosis of some tumors. It is possible to explore the role of DMRT3 in the cancer process using bioinformatic approaches and experimental validation. We comprehensively explored the clinical and immunological characteristics of DMRT3. The DMRT3 expression is abnormal in human cancers and correlates with clinical staging. A high DMRT3 expression is significantly associated with poor overall survival (OS) in KIRC, KIRP, LUAD, and UCEC. Amplification was the greatest frequency of the DMRT3 alterations in pan-cancer. The OS was significantly lower in the DMRT3 altered group than in the DMRT3 unaltered group (P = 0.0276). The DMRT3 expression was significantly associated with MSI in three cancer types and TMB in six cancer types. The DMRT3 expression was significantly correlated with the level of the immune cell infiltration and the immune checkpoint genes. The DMRT3 was involved in some pathways in pan-cancer. DMRT3 may play a role in chemotherapy and may be associated with chemoresistance. A ceRNA network of KCNQ1OT1/miR-335-5p/DMRT3 was constructed in LUAD. DMRT3 was significantly upregulated in the LUAD cell lines. DMRT3 was aberrantly expressed in pan-cancer and may promote tumorigenesis and progression via different mechanisms. DMRT3 can be used as a therapeutic target to treat cancer in humans.

Identification of the common differentially expressed genes and pathogenesis between neuropathic pain and aging

Background

Neuropathic pain is a debilitating disease caused by damage or diseases of the somatosensory nervous system. Previous research has indicated potential associations between neuropathic pain and aging. However, the mechanisms by which they are interconnected remain unclear. In this study, we aim to identify the common differentially expressed genes (co-DEGs) between neuropathic pain and aging through integrated bioinformatics methods and further explore the underlying molecular mechanisms.

Methods

The microarray datasets GSE24982, GSE63442, and GSE63651 were downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) and co-DEGs were first identified. Functional enrichment analyses, protein-protein Interaction (PPI) network, module construction and hub genes identification were performed. Immune infiltration analysis was conducted. Targeted transcription factors (TFs), microRNAs (miRNAs) and potential effective drug compounds for hub genes were also predicted.

Results

A total of 563 and 1,250 DEGs of neuropathic pain and aging were screened, respectively. 16 genes were further identified as co-DEGs. The functional analysis emphasizes the vital roles of the humoral immune response and complement and coagulation cascades in these two diseases. Cxcl14, Fblim1, RT1-Da, Serping1, Cfd, and Fcgr2b were identified as hub genes. Activated B cell, mast cell, activated dendritic cell, CD56 bright natural killer cell, effector memory CD8 + T cell, and type 2 T helper cell were significantly up-regulated in the pain and aging condition. Importantly, hub genes were found to correlate with the activated B cell, activated dendritic cell, Gamma delta T cell, central memory CD4 + T cell and mast cell in pain and aging diseases. Finally, Spic, miR-883-5p, and miR-363-5p et al. were predicted as the potential vital regulators for hub genes. Aldesleukin, Valziflocept, MGD-010, Cinryze, and Rhucin were the potential effective drugs in neuropathic pain and aging.

Conclusion

This study identified co-DEGs, revealed molecular mechanisms, demonstrated the immune microenvironment, and predicted the possible TFs, miRNAs regulation networks and new drug targets for neuropathic pain and aging, providing novel insights into further research.

Machine learning identification of cuproptosis and necroptosis-associated molecular subtypes to aid in prognosis assessment and immunotherapy response prediction in low-grade glioma

Both cuproptosis and necroptosis are typical cell death processes that serve essential regulatory roles in the onset and progression of malignancies, including low-grade glioma (LGG). Nonetheless, there remains a paucity of research on cuproptosis and necroptosis-related gene (CNRG) prognostic signature in patients with LGG. We acquired patient data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) and captured CNRGs from the well-recognized literature. Firstly, we comprehensively summarized the pan-cancer landscape of CNRGs from the perspective of expression traits, prognostic values, mutation profiles, and pathway regulation. Then, we devised a technique for predicting the clinical efficacy of immunotherapy for LGG patients. Non-negative matrix factorization (NMF) defined by CNRGs with prognostic values was performed to generate molecular subtypes (i.e., C1 and C2). C1 subtype is characterized by poor prognosis in terms of disease-specific survival (DSS), progression-free survival (PFS), and overall survival (OS), more patients with G3 and tumour recurrence, high abundance of immunocyte infiltration, high expression of immune checkpoints, and poor response to immunotherapy. LASSO-SVM-random Forest analysis was performed to aid in developing a novel and robust CNRG-based prognostic signature. LGG patients in the TCGA and GEO databases were categorized into the training and test cohorts, respectively. A five-gene signature, including SQSTM1, ZBP1, PLK1, CFLAR, and FADD, for predicting OS of LGG patients was constructed and its predictive reliability was confirmed in both training and test cohorts. In both the training and the test datasets (cohorts), higher risk scores were linked to a lower OS rate. The time-dependent ROC curve proved that the risk score had outstanding prediction efficiency for LGG patients in the training and test cohorts. Univariate and multivariate Cox regression analyses showed the CNRG-based prognostic signature independently functioned as a risk factor for OS in LGG patients. Furthermore, we developed a highly reliable nomogram to facilitate the clinical practice of the CNRG-based prognostic signature (AUC > 0.9). Collectively, our results gave a promising understanding of cuproptosis and necroptosis in LGG, as well as a tailored prediction tool for prognosis and immunotherapeutic responses in patients.