ALG3 contributes to the malignant properties of OSCC cells by regulating CDK-Cyclin pathway

Study on the synthesis, characterization, and antitumor mechanism investigation of QZQ-01115 via targeting sphingosine kinase 2

Sphingosine kinase 2 (SphK2) is an oncogenic enzyme that plays an essential role in the development of oral squamous cell carcinoma (OSCC). Therefore, development of SphK2 inhibitors is of great significance for the treatment of OSCC. In this study, we synthesized a series of thiazolidinediones and screened compounds with good inhibitory activity against CAL-27 using cytotoxicity assay. The compounds were further investigated in vitro using a series of in vitro experiments such as Western blot and qPCR were used to investigate the in vivo anti-tumor mechanisms, and in vivo investigation was applied by using a nude mouse ectopic tumor model. The results showed that four new compounds were successfully synthesized, and among which the compound named QZQ-01115 showed the best inhibitory activity against CAL-27 at the concentration of 5.84 ± 0.042 μM. Further mechanistic studies showed that QZQ-01115 could inhibit the proliferation, migration and invasion of CAL-27 cells at a concentration of 4 μM-6 μM. QZQ-01115 affected the PI3K/AKT signaling pathway by influencing the levels of S1P and ceramides in CAL-27, which in turn affected the mTOR/p70S6K, resulting in the blockage of protein synthesis and the blockage of cell cycle at the G0/G0 level. Apoptosis was promoted by down-regulating Bcl-2 and up-regulating Bax. The in vivo results showed that QZQ-01115 reduced the volume and weight of xenograft tumors in nude mice. The induction of apoptosis by QZQ-01115 was further determined by HE staining and immunohistochemical analysis. These results suggest that QZQ-01115 may be a potential candidate for the treatment of OSCC.

Multi-omic and machine learning analysis of mitochondrial RNA modification genes in lung adenocarcinoma for prognostic and therapeutic implications

Lung cancer remains the leading cause of cancer-related deaths, driven by complex pathogenesis and poor prognosis. Recognizing the pivotal role of mitochondrial RNA modifications (MRM) in cancer progression, this study aims to provide a comprehensive analysis of MRM-related genes and their clinical relevance in lung adenocarcinoma (LUAD). Integrating multi-omic datasets, we systematically explored the molecular features of MRM-related genes across various cancers and identified distinct expression patterns and prognostic associations. Single-cell analysis further reveals MRM-driven cell-cell interactions and pathway activation, particularly in cycling and epithelial cells. Using advanced machine learning techniques, we developed a novel prognostic signature-the Mitochondrial RNA Modification-related Signature (MRMS)-comprising nine genes: TXN, LDHA, HMGA1, SFTPB, KRT8, ALG3, S100A16, HSPD1, and ALDOA. The MRMS demonstrates superior predictive performance for LUAD survival compared to previously reported models. Our findings uniquely link MRMS to increased tumor mutational burden, genetic instability, and an immunosuppressive tumor microenvironment characterized by reduced immune cell infiltration and elevated tumor purity. Additionally, MRMS is associated with immunotherapy-related features, suggesting its potential in predicting treatment response. Experimental validation identified ALG3 as an oncogenic driver in LUAD, influencing tumor cell proliferation, migration, and invasion. In conclusion, this study establishes MRMS as a robust prognostic biomarker and highlights its dual role in shaping the tumor immune microenvironment and guiding therapeutic strategies. These findings provide novel insights into mitochondrial RNA modifications and their potential applications in personalized treatment for LUAD.

Integrative multi-omic and machine learning approach for prognostic stratification and therapeutic targeting in lung squamous cell carcinoma

The proliferation, metastasis, and drug resistance of cancer cells pose significant challenges to the treatment of lung squamous cell carcinoma (LUSC). However, there is a lack of optimal predictive models that can accurately forecast patient prognosis and guide the selection of targeted therapies. The extensive multi-omic data obtained from multi-level molecular biology provides a unique perspective for understanding the underlying biological characteristics of cancer, offering potential prognostic indicators and drug sensitivity biomarkers for LUSC patients. We integrated diverse datasets encompassing gene expression, DNA methylation, genomic mutations, and clinical data from LUSC patients to achieve consensus clustering using a suite of 10 multi-omics integration algorithms. Subsequently, we employed 10 commonly used machine learning algorithms, combining them into 101 unique configurations to design an optimal performing model. We then explored the characteristics of high- and low-risk LUSC patient groups in terms of the tumor microenvironment and response to immunotherapy, ultimately validating the functional roles of the model genes through in vitro experiments. Through the application of 10 clustering algorithms, we identified two prognostically relevant subtypes, with CS1 exhibiting a more favorable prognosis. We then constructed a subtype-specific machine learning model, LUSC multi-omics signature (LMS) based on seven key hub genes. Compared to previously published LUSC biomarkers, our LMS score demonstrated superior predictive performance. Patients with lower LMS scores had higher overall survival rates and better responses to immunotherapy. Notably, the high LMS group was more inclined toward "cold" tumors, characterized by immune suppression and exclusion, but drugs like dasatinib may represent promising therapeutic options for these patients. Notably, we also validated the model gene SERPINB13 through cell experiments, confirming its role as a potential oncogene influencing the progression of LUSC and as a promising therapeutic target. Our research provides new insights into refining the molecular classification of LUSC and further optimizing immunotherapy strategies.

ALG3 predicts poor prognosis and increases resistance to anti-PD-1 therapy through modulating PD-L1 N-link glycosylation in TNBC

OBJECTIVE

The aim of this study was to assess the prognostic significance of α-1,3-mannitrotransferase (ALG3) in triple-negative breast cancer (TNBC) and investigate its impact and potential mechanism on the efficacy of anti-PD-1 therapy.

METHODS

Bioinformatics analysis was used to examine the expression of ALG3 in cancer patients using UACLAN and other databases. The associations of the ALG3 gene and the clinicopathological features of breast cancer were examined with bc-GenExMiner database. Correlation between ALG3 expression and survival was further established utilizing the Kaplan-Meier Plotter database. Immunohistochemistry (IHC) was used to analyze the expression of ALG3 in cohort of breast cancer patients from Hubei cancer hospital to confirmed the prognostic value of ALG3 in TNBC. The effect of ALG3 on the levels of infiltrating immune cells was also analyzed. And the mutation module within cBioPortal was utilized to visualize ALG3 mutations in BRCA. The CRISPR/Cas9 technique was used to establish ALG3 low-expression TNBC cell lines. Influence of ALG3 expression on cancer cell proliferation and chemotherapeutic responsiveness was scrutinized in vitro. Animal models were constructed to evaluate the alteration of tumor sensitivity to anti-PD-1 therapy with decreased ALG3 expression. And flow cytometry and IHC were used to investigate the tumor immune microenvironment. Association of PD-L1 Glycosylation and ALG3 expression were also investigated by western blot.

RESULTS

ALG3 expression was elevated in TNBC and was strikingly linked to unfavorable clinical features such as lymphatic node metastasis, high NPI, advanced stage and age, etc. Furthermore, high ALG3 expression was associated with shorter OS in TNBC patients. Mechanistically, ALG3 expression was negatively correlated with the infiltration of CD8+ T cells, CD4+ T cells, and NK cells. ALG3-KO cells had increased sensitivity to chemotherapeutic agents. In animal models, the volume of ALG3-KO tumors was lower than the control group with immunotherapy. ALG3-KO tumors showed an increased proportion of CD8+ T cells, while a decreased proportion of regulatory T cells and M2-type macrophages. The expression level of PD-L1 protein was not affected by ALG3 level, but the glycosylation level was significantly decreased in tumor. Similarly, the glycosylation level of PD-L1 is reduced in ALG3-KO cell in vitro. Additionally, ALG3 knockout lead to reduced tolerance of tumor cells to IFN-γ, thereby enhancing the efficacy of immunotherapy.

CONCLUSION

ALG3 is a potential biomarker for poor prognosis of TNBC and may reduce the efficacy of immunotherapy by modulating the tumor microenvironment and glycosylation of PD-L1.

Deep pan-cancer analysis and multi-omics evidence reveal that ALG3 inhibits CD8+ T cell infiltration by suppressing chemokine secretion and is associated with 5-fluorouracil sensitivity

BACKGROUND

α-1,3-mannosyltransferase (ALG3) holds significance as a key member within the mannosyltransferase family. Nevertheless, the exact function of ALG3 in cancer remains ambiguous. Consequently, the current research aimed to examine the function and potential mechanisms of ALG3 in various types of cancer.

METHODS

Deep pan-cancer analyses were conducted to investigate the expression patterns, prognostic value, genetic variations, single-cell omics, immunology, and drug responses associated with ALG3. Subsequently, in vitro experiments were executed to ascertain the biological role of ALG3 in breast cancer. Moreover, the link between ALG3 and CD8+ T cells was verified using immunofluorescence. Lastly, the association between ALG3 and chemokines was assessed using qRT-PCR and ELISA.

RESULTS

Deep pan-cancer analysis demonstrated a heightened expression of ALG3 in the majority of tumors based on multi-omics evidence. ALG3 emerges as a diagnostic and prognostic biomarker across diverse cancer types. In addition, ALG3 participates in regulating the tumor immune microenvironment. Elevated levels of ALG3 were closely linked to the infiltration of bone marrow-derived suppressor cells (MDSC) and CD8+ T cells. According to in vitro experiments, ALG3 promotes proliferation and migration of breast cancer cells. Moreover, ALG3 inhibited CD8+ T cell infiltration by suppressing chemokine secretion. Finally, the inhibition of ALG3 enhanced the responsiveness of breast cancer cells to 5-fluorouracil treatment.

CONCLUSION

ALG3 shows potential as both a prognostic indicator and immune infiltration biomarker across various types of cancer. Inhibition of ALG3 may represent a promising therapeutic strategy for tumor treatment.

Molecular basis, potential biomarkers, and future prospects of OSCC and PD-1/PD-L1 related immunotherapy methods

Oral squamous cell carcinoma (OSCC) affects a large number of individuals worldwide. Despite advancements in surgery, radiation, and chemotherapy, satisfactory outcomes have not been achieved. In recent years, the success of drugs targeting programmed cell death 1 (PD-1) and programmed cell death ligand 1 (PD-L1) has led to breakthroughs in cancer treatment, but systematic summaries on their effectiveness against OSCC are lacking. This article reviews the latest research on the PD-1/PD-L1 pathway and the potential of combination therapy based on this pathway in OSCC. Further, it explores the mechanisms involved in the interaction of this pathway with exosomes and protein-protein interactions, and concludes with potential future OSCC therapeutic strategies.

Identification of ALG3 as a potential prognostic biomarker in lung adenocarcinoma

Background

The abnormal expression of Alpha-1,3-mannosyltransferase (ALG3) has been implicated in tumor promotion. However, the clinical significance of ALG3 in Lung Adenocarcinoma (LUAD) remains poorly understood. Therefore, we aimed to assess the prognostic value of ALG3 and its association with immune infiltrates in LUAD.

Methods

The transcriptional expression profiles of ALG3 were obtained from the Cancer Genome Atlas (TCGA), comparing lung adenocarcinoma tissue with normal tissues. To determine the prognostic significance of AGL3, Kaplan-Meier plotter, and Cox regression analysis were employed. Logistic regression was utilized to analyze the association between ALG3 expression and clinical characteristics. Additionally, a receiver operating characteristic (ROC) curve and a nomogram were constructed. To explore the underlying mechanisms, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and gene set enrichment analysis (GSEA) was conducted. The relationship between AGL3A mRNA expression and immune infiltrates was investigated using the tumor immune estimation resource (TIMER) and tumor-immune system interaction database (TISIDB). Furthermore, an in vitro experiment was performed to assess the impact of ALG3 mRNA on lung cancer stemness abilities and examine key signaling pathway proteins.

Results

Our results revealed the ALG3 mRNA and protein expression in patients with LUAD was much higher than that in adjacent normal tissues. High expression of ALG3 was significantly associated with N stage (N0, HR = 1.98, P = 0.002), pathological stage (stage I, HR = 2.09, P = 0.003), and the number of pack years (<40, HR = 2.58, P = 0.001). Kaplan-Meier survival analysis showed that high expression of ALG3 was associated with poor overall survival (P < 0.001), disease-free survival (P < 0.001), and progression-free interval (P = 0.007). Through multivariate analysis, it was determined that elevated ALG3 expression independently impacted overall survival (HR = 1.325, P = 0.04). The Tumor Immune Estimation Resource discovered a link between ALG3 expression and tumor-infiltrating immune cells in LUAD. Additionally, ROC analysis proved that ALG3 is a reliable diagnostic marker for LUAD (AUC:0.923). Functional pathways analysis identified that ALG3 is negatively correlated with FAT4. We performed qRT-PCR to assess that knockdown ALG3 expression significantly upregulated FAT4 expression. Spheroid assay and flow cytometry analysis results showed that downregulated of ALG3 inhibited H1975 cell line stemness. Western blot analysis revealed that decreased ALG3 inhibited the YAP/TAZ signal pathway.

Conclusion

High expression of ALG3 is strongly associated with poor prognosis and immune infiltrates in LUAD.

Heat Shock Transcription Factor 2 Is Significantly Involved in Neurodegenerative Diseases, Inflammatory Bowel Disease, Cancer, Male Infertility, and Fetal Alcohol Spectrum Disorder: The Novel Mechanisms of Several Severe Diseases

HSF (heat shock transcription factor or heat shock factor) was discovered as a transcription factor indispensable for heat shock response. Although four classical HSFs were discovered in mammals and two major HSFs, HSF1 and HSF2, were cloned in the same year of 1991, only HSF1 was intensively studied because HSF1 can give rise to heat shock response through the induction of various HSPs' expression. On the other hand, HSF2 was not well studied for some time, which was probably due to an underestimate of HSF2 itself. Since the beginning of the 21st century, HSF2 research has progressed and many biologically significant functions of HSF2 have been revealed. For example, the roles of HSF2 in nervous system protection, inflammation, maintenance of mitosis and meiosis, and cancer cell survival and death have been gradually unveiled. However, we feel that the fact HSF2 has a relationship with various factors is not yet widely recognized; therefore, the biological significance of HSF2 has been underestimated. We strongly hope to widely communicate the significance of HSF2 to researchers and readers in broad research fields through this review. In addition, we also hope that many readers will have great interest in the molecular mechanism in which HSF2 acts as an active transcription factor and gene bookmarking mechanism of HSF2 during cell cycle progression, as is summarized in this review.

Expression of ALG3 in Hepatocellular Carcinoma and Its Clinical Implication

Background: Recent studies have shown that alpha-1,3-mannosyltransferase (ALG3) promoted tumorigenesis and progression in multiple cancer types. Our study planned to explore the clinical implication and potential function of ALG3 in hepatocellular carcinoma.

Materials and Methods: Data from public databases were used to analyze the ALG3 expression and its impact on the clinical significance of patients with HCC. The ALG3 expression was confirmed by qRT-PCR and Western blot. Immunohistochemistry was used to confirm the ALG3 expression and explore its clinical implication in HCC. KEGG, GO, and GSEA enrichment analyses were utilized to explore the biological pathways related to ALG3 in HCC. TIMER2.0 was applied to assess the association between ALG3 and immune infiltration. CCK8, MTT, and transwell assays were used to investigate the role of ALG3 downregulation in HCC cell lines.

Results: qRT-PCR, WB, and IHC proved ALG3 was highly overexpressed in HCC tissues. The Kaplan–Meier analysis verified the overexpression of ALG3 was related to poor overall survival (p < 0.001). Multivariate cox regression analysis showed that the high ALG3 expression was an independent risk prognostic factor. GSEA and TIMER2.0 predicted that ALG3 participates in cell differentiation and cycle and correlates with immune cell infiltration. Transwell assay results showed that ALG3 silencing also impaired the invasion ability of HCC cells.

Conclusion: ALG3 was overexpressed and considered a potential indicator of survival in HCC, and our findings provided a novel therapeutic target for HCC.

Glycosyltransferases in Cancer: Prognostic Biomarkers of Survival in Patient Cohorts and Impact on Malignancy in Experimental Models

Background: Glycosylation changes are a main feature of cancer. Some carbohydrate epitopes and expression levels of glycosyltransferases have been used or proposed as prognostic markers, while many experimental works have investigated the role of glycosyltransferases in malignancy. Using the transcriptomic data of the 21 TCGA cohorts, we correlated the expression level of 114 glycosyltransferases with the overall survival of patients. Methods: Using the Oncolnc website, we determined the Kaplan−Meier survival curves for the patients falling in the 15% upper or lower percentile of mRNA expression of each glycosyltransferase. Results: Seventeen glycosyltransferases involved in initial steps of N- or O-glycosylation and of glycolipid biosynthesis, in chain extension and sialylation were unequivocally associated with bad prognosis in a majority of cohorts. Four glycosyltransferases were associated with good prognosis. Other glycosyltransferases displayed an extremely high predictive value in only one or a few cohorts. The top were GALNT3, ALG6 and B3GNT7, which displayed a p < 1 × 10−9 in the low-grade glioma (LGG) cohort. Comparison with published experimental data points to ALG3, GALNT2, B4GALNT1, POFUT1, B4GALT5, B3GNT5 and ST3GAL2 as the most consistently malignancy-associated enzymes. Conclusions: We identified several cancer-associated glycosyltransferases as potential prognostic markers and therapeutic targets.