Solute carrier family 2 members 1 and 2 as prognostic biomarkers in hepatocellular carcinoma associated with immune infiltration

Pan-cancer analysis of SLC2A family genes as prognostic biomarkers and therapeutic targets

Background

The major facilitator superfamily glucose transporters (GLUTs), encoded by solute carrier 2A (SLC2A) genes, mediate the transmembrane movement and uptake of glucose. To satisfy the improved energy demands, glycolysis flux is increased in cancers compared with healthy tissues. Multiple diseases, including cancer, have been associated with GLUTs. Nevertheless, not much research has been done on the functions of SLC2As in pan-cancer prognosis or their clinical treatment potential.

Methods

The SLC2A family genes' level of expression and prognostic values were analyzed in relation to pan-cancer. We then examined the association among SLC2As expression and TME, Stemness score, clinical characteristics, immune subtypes, and drug sensitivity. We merged bioinformatics analysis techniques with up-to-date public databases. Additionally, SLC2As from the KOBAS database were subjected to enrichment analysis.

Results

We discovered that SLC2As' gene expression differed significantly between normal tissues and many malignancies. A number of tumors from various databases demonstrate a relationship between prognosis and SLC2A family gene expression. For instance, SLC2A2 and SLC2A5 were associated with the overall survival (OS) of hepatocellular carcinoma. SLC2A1 was associated with the OS of lung adenocarcinoma and pancreatic adenocarcinoma. Moreover, the SLC2A family gene expression is significantly correlated with the pan-cancer stromal and immune scores, and the RNA and DNA stemness scores. Furthermore, we found that the majority of SLC2As had a strong correlation with the tumor stages in KIRC. The immunological subtypes and all members of the SLC2A gene family exhibited a substantial correlation. Moreover, pathways containing insulin resistance and adipocytokine signaling pathway may influence the progression of some cancers. Finally, there is a significant positive or negative connection between drug sensitivity and SLC2A1 expression.

Conclusion

Our research highlights the significant promise of SLC2As as prognostic indicators and offers insightful approaches for upcoming exploration of SLC2As as putative therapeutic targets in malignancies.

Construction of an M2 macrophage-related prognostic model in hepatocellular carcinoma

Background

M2 macrophages play a crucial role in promoting tumor angiogenesis and proliferation, as well as contributing to chemotherapy resistance and metastasis. However, their specific role in the tumor progression of hepatocellular carcinoma (HCC) and their impact on the clinical prognosis remain to be further elucidated.

Materials and methods

M2 macrophage-related genes were screened using CIBERSORT and weighted gene co-expression network analysis (WGCNA), while subtype identification was performed using unsupervised clustering. Prognostic models were constructed using univariate analysis/least absolute shrinkage selector operator (LASSO) Cox regression. In addition, Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set enrichment analysis (GSEA), gene set variation analysis (GSVA), and mutation analysis were used for further analysis. The relationship between the risk score and tumor mutation burden (TMB), microsatellite instability (MSI), the efficacy of transcatheter arterial chemoembolization (TACE), immunotype, and the molecular subtypes were also investigated. Moreover, the potential role of the risk score was explored using the ESTIMATE and TIDE (tumor immune dysfunction and exclusion) algorithms and stemness indices, such as the mRNA expression-based stemness index (mRNAsi) and the DNA methylation-based index (mDNAsi). In addition, the R package "pRRophetic" was used to examine the correlation between the risk score and the chemotherapeutic response. Finally, the role of TMCC1 in HepG2 cells was investigated using various techniques, including Western blotting, RT-PCR and Transwell and wound healing assays.

Results

This study identified 158 M2 macrophage-related genes enriched in small molecule catabolic processes and fatty acid metabolic processes in HCC. Two M2 macrophage-related subtypes were found and a four-gene prognostic model was developed, revealing a positive correlation between the risk score and advanced stage/grade. The high-risk group exhibited higher proliferation and invasion capacity, MSI, and degree of stemness. The risk score was identified as a promising prognostic marker for TACE response, and the high-risk subgroup showed higher sensitivity to chemotherapeutic drugs (e.g., sorafenib, doxorubicin, cisplatin, and mitomycin) and immune checkpoint inhibitor (ICI) treatments. The expression levels of four genes related to the macrophage-related risk score were investigated, with SLC2A2 and ECM2 showing low expression and SLC16A11 and TMCC1 exhibiting high expression in HCC. In vitro experiments showed that TMCC1 may enhance the migration ability of HepG2 cells by activating the Wnt signaling pathway.

Conclusion

We identified 158 HCC-related M2 macrophage genes and constructed an M2 macrophage-related prognostic model. This study advances the understanding of the role of M2 macrophages in HCC and proposes new prognostic markers and therapeutic targets.

Genomic insights into the postintroduction failure of the Asian icefish Protosalanx chinensis in China

Biological introductions provide a natural ecological experiment unfolding in a recent historical timeframe to elucidate how evolutionary processes (such as founder effects, genetic diversity and adaptation) shape the genomic landscape of populations postintroduction. The Asian icefish, Protosalanx chinensis, is an economically important fishery resource, deliberately introduced into dozens of provinces across China for decades. However, while invading and disturbing the local ecosystem, many introduced populations declined, disappearing mysteriously in a very short time. The way in which various evolutionary forces integrate to result in invasion failure of an introduced population remains unknown. Here, we performed whole-genome sequencing of 10 species from the Salangidae family and 70 Asian icefish (Protosalanx chinensis) individuals from 7 geographic populations in China, aiming to characterize the evolutionary fate of introduced populations. Our results show that compared to other Salangidae species, P. chinensis has low genetic diversity, potentially due to the long-lasting decline in population size. In a recently introducted population, Lugu lake, severe sampling effects and a strong bottleneck further deteriorated the genomic landscape. Although the introduced population showed signs of reduced genetic load, the purging selection efficiency was low. Our selective sweep analysis revealed site frequency changes in candidate genes, including gata1a and hoxd4b, which could be associated with a decrease in dissolved oxygen in the deep-water plateau lake. These findings caution against the widespread introduction of P. chinensis in China and lay the groundwork for future use of this economically species.

Comprehensive analysis of prognostic value and immunotherapy prospect of brain cytoplasmic RNA1 in hepatocellular carcinoma

BACKGROUND

The expression of brain cytoplasmic RNA1 (BCYRN1) is linked to the clinicopathology and prognosis of several types of cancers, among which hepatocellular carcinoma (HCC) is one of the most frequent types of cancer worldwide.

AIM

To explore the prognostic value and immunotherapeutic potential of BCYRN1 in HCC by bioinformatics and meta-analysis.

METHODS

Information was obtained from the Cancer Genome Atlas database. First, the correlation between BCYRN1 expression and prognosis and clinicopathologic characteristics of HCC patients was explored. Univariate and multivariate regression analyses were employed to examine the relationship between BCYRN1 and HCC prognosis. Secondly, potential functions and pathways were explored by means of enrichment analysis of differentially-expressed genes. The relationships between BCYRN1 expression and tumor microenvironment, immune cell infiltration, immune checkpoint, drug sensitivity and immunotherapy effect were also investigated. Finally, three major databases were searched and used to conduct a meta-analysis on the relationship between BCYRN1 expression and patient prognosis.

RESULTS

BCYRN1 expression was significantly higher in HCC compared to normal tissues and was linked to a poor prognosis and clinicopathological characteristics. Enrichment analysis showed that BCYRN1 regulates the extracellular matrix and transmission of signaling molecules, participates in the metabolism of nutrients, such as proteins, and participates in tumor-related pathways. BCYRN1 expression was linked to the tumor microenvironment, immune cell infiltration, drug sensitivity and the efficacy of immunotherapy. Furthermore, the meta-analysis in this study showed that BCYRN1 overexpression was related to a worse outcome in HCC patients.

CONCLUSION

Overexpression of BCYRN1 relates to poor prognosis and may be a potential prognostic factor and immunotherapeutic target in HCC.

A novel hypoxia- and lactate metabolism-related signature to predict prognosis and immunotherapy responses for breast cancer by integrating machine learning and bioinformatic analyses

Background

Breast cancer is the most common cancer worldwide. Hypoxia and lactate metabolism are hallmarks of cancer. This study aimed to construct a novel hypoxia- and lactate metabolism-related gene signature to predict the survival, immune microenvironment, and treatment response of breast cancer patients.

Methods

RNA-seq and clinical data of breast cancer from The Cancer Genome Atlas database and Gene Expression Omnibus were downloaded. Hypoxia- and lactate metabolism-related genes were collected from publicly available data sources. The differentially expressed genes were identified using the “edgeR” R package. Univariate Cox regression, random survival forest (RSF), and stepwise multivariate Cox regression analyses were performed to construct the hypoxia-lactate metabolism-related prognostic model (HLMRPM). Further analyses, including functional enrichment, ESTIMATE, CIBERSORTx, Immune Cell Abundance Identifier (ImmuCellAI), TIDE, immunophenoscore (IPS), pRRophetic, and CellMiner, were performed to analyze immune status and treatment responses.

Results

We identified 181 differentially expressed hypoxia-lactate metabolism-related genes (HLMRGs), 24 of which were valuable prognostic genes. Using RSF and stepwise multivariate Cox regression analysis, five HLMRGs were included to establish the HLMRPM. According to the medium-risk score, patients were divided into high- and low-risk groups. Patients in the high-risk group had a worse prognosis than those in the low-risk group (P < 0.05). A nomogram was further built to predict overall survival (OS). Functional enrichment analyses showed that the low-risk group was enriched with immune-related pathways, such as antigen processing and presentation and cytokine-cytokine receptor interaction, whereas the high-risk group was enriched in mTOR and Wnt signaling pathways. CIBERSORTx and ImmuCellAI showed that the low-risk group had abundant anti-tumor immune cells, whereas in the high-risk group, immunosuppressive cells were dominant. Independent immunotherapy datasets (IMvigor210 and GSE78220), TIDE, IPS and pRRophetic analyses revealed that the low-risk group responded better to common immunotherapy and chemotherapy drugs.

Conclusions

We constructed a novel prognostic signature combining lactate metabolism and hypoxia to predict OS, immune status, and treatment response of patients with breast cancer, providing a viewpoint for individualized treatment.