Glycosylphosphatidylinositol anchor biosynthesis pathway-based biomarker identification with machine learning for prognosis and T cell exhaustion status prediction in breast cancer.

As the primary component of anti-tumor immunity, T cells are prone to exhaustion and dysfunction in the tumor microenvironment (TME). A thorough understanding of T cell exhaustion (TEX) in the TME is crucial for effectively addressing TEX in clinical settings and promoting the efficacy of immune checkpoint blockade therapies. In eukaryotes, numerous cell surface proteins are tethered to the plasma membrane via Glycosylphosphatidylinositol (GPI) anchors, which play a crucial role in facilitating the proper translocation of membrane proteins. However, the available evidence is insufficient to support any additional functional involvement of GPI anchors. Here, we investigate the signature of GPI-anchor biosynthesis in the TME of breast cancer (BC)patients, particularly its correlation with TEX. GPI-anchor biosynthesis should be considered as a prognostic risk factor for BC. Patients with high GPI-anchor biosynthesis showed more severe TEX. And the levels of GPI-anchor biosynthesis in exhausted CD8 T cells was higher than normal CD8 T cells, which was not observed between malignant epithelial cells and normal mammary epithelial cells. In addition, we also found that GPI -anchor biosynthesis related genes can be used to diagnose TEX status and predict prognosis in BC patients, both the TEX diagnostic model and the prognostic model showed good AUC values. Finally, we confirmed our findings in cells and clinical samples. Knockdown of PIGU gene expression significantly reduced the proliferation rate of MDA-MB-231 and MCF-7 cell lines. Immunofluorescence results from clinical samples showed reduced aggregation of CD8 T cells in tissues with high expression of GPAA1 and PIGU.

Development of a biomarker signature associated with anoikis to predict prognosis and immunotherapy response in melanoma.

Skin cutaneous melanoma (SKCM) is malignant cancer known for its high aggressiveness and unfavorable prognosis, particularly in advanced tumors. Anoikis is a specific pattern of programmed cell death associated with tumor regeneration, migration, and metastasis. Nevertheless, limited research has been conducted to investigate the function of anoikis in SKCM. Anoikis-related genes (ARGs) were extracted from Genecards to identify SKCM subtypes and to explore the immune microenvironment between the different subtypes. Prognostic models of SKCM were developed by LASSO COX regression analysis. Subsequently, the predictive value of risk scores in SKCM and the association with immunotherapy were further explored. Finally, the expression of 6 ARGs involved in the model construction was detected by immunohistochemistry and PCR. This study identified 20 ARGs significantly associated with SKCM prognosis and performed disease subtype analysis of samples based on these genes, different subtypes exhibited significantly different clinical features and tumor immune microenvironment (TIME) landscapes. The risk score prognostic model was generated by further screening and identification of the six ARGs. The model exhibited a high degree of sensitivity and specificity to predict the prognosis of individuals with SKCM. These high- and low-risk populations showed different immune statuses and drug sensitivity. Further immunohistochemical and PCR experiments identified significant differential expression of the six ARGs in tumor and normal samples. Anoikis-based features may serve as novel prognostic biomarkers for SKCM and may provide important new insights for survival prediction and individualized treatment development.

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.

Pan-cancer analysis of NUP155 and validation of its role in breast cancer cell proliferation, migration, and apoptosis.

NUP155 is reported to be correlated with tumor development. However, the role of NUP155 in tumor physiology and the tumor immune microenvironment (TIME) has not been previously examined. This study comprehensively investigated the expression, immunological function, and prognostic significance of NUP155 in different cancer types. Bioinformatics analysis revealed that NUP155 was upregulated in 26 types of cancer. Additionally, NUP155 upregulation was strongly correlated with advanced pathological or clinical stages and poor prognosis in several cancers. Furthermore, NUP155 was significantly and positively correlated with DNA methylation, tumor mutational burden, microsatellite instability, and stemness score in most cancers. Additionally, NUP155 was also found to be involved in TIME and closely associated with tumor infiltrating immune cells and immunoregulation-related genes. Functional enrichment analysis revealed a strong correlation between NUP155 and immunomodulatory pathways, especially antigen processing and presentation. The role of NUP155 in breast cancer has not been examined. This study, for the first time, demonstrated that NUP155 was upregulated in breast invasive carcinoma (BRCA) cells and revealed its oncogenic role in BRCA using molecular biology experiments. Thus, our study highlights the potential value of NUP155 as a biomarker in the assessment of prognostic prediction, tumor microenvironment and immunotherapeutic response in pan-cancer.

Exploring the interplay of gut microbiota, inflammation, and LDL-cholesterol: a multiomics Mendelian randomization analysis of their causal relationship in acute pancreatitis and non-alcoholic fatty liver disease.

BACKGROUND: Acute pancreatitis and non-alcoholic fatty liver disease are both serious diseases in the digestive system. The pathogenesis of both diseases is extremely complex closely and it related to gut microbiota, inflammation, and blood fat. There is a close relationship between gut microbiota and blood lipids. METHODS: In this study, we used three types of exposure: 412 gut microbiota, 731 inflammatory cells, and 91 inflammatory proteins (pqtls), with LDL-C as an intermediary and acute pancreatitis and non-alcoholic fatty liver disease as outcomes. We mainly used MR-IVW, co-localization analysis, and reverse MR analysis methods for analysis. RESULTS: 7 gut microbiota, 21 inflammatory cells, and 3 inflammatory proteins can affect LDL-C levels. LDL-C is associated with acute pancreatitis and non-alcoholic fatty liver disease. CONCLUSIONS: Three omics were used: 412 gut microbiota, 731 inflammatory cells, and 91 inflammatory proteins (pqtls). It explains the causal relationship between multiomics, LDL- cholesterol, acute pancreatitis, and non-alcoholic fatty liver disease.

Prognostic value of GLIM-defined malnutrition in combination with hand-grip strength or gait speed for the prediction of postoperative outcomes in gastric cancer patients with cachexia.

BACKGROUND: Cancer cachexia is associated with impaired functional and nutritional status and worse clinical outcomes. Global Leadership Initiative in Malnutrition (GLIM) consensus recommended the application of GLIM criteria to diagnose malnutrition in patients with cachexia. However, few previous study has applied the GLIM criteria in patients with cancer cachexia. METHODS: From July 2014 to May 2019, patients who were diagnosed with cancer cachexia and underwent radical gastrectomy for gastric cancer were included in this study. Malnutrition was diagnosed using the GLIM criteria. Skeletal muscle index was measured using abdominal computed tomography (CT) images at the third lumbar vertebra (L3) level. Hand-grip strength and 6-meters gait speed were measured before surgery. RESULTS: A total of 356 patients with cancer cachexia were included in the present study, in which 269 (75.56%) were identified as having malnutrition based on the GLIM criteria. GLIM-defined malnutrition alone did not show significant association with short-term postoperative outcomes, including complications, costs or length of postoperative hospital stays. The combination of low hand-grip strength or low gait speed with GLIM-defined malnutrition led to a significant predictive value for these outcomes. Moreover, low hand-grip strength plus GLIM-defined malnutrition was independently associated with postoperative complications (OR 1.912, 95% CI 1.151-3.178, P = 0.012). GLIM-defined malnutrition was an independent predictive factor for worse OS (HR 2.310, 95% CI 1.421-3.754, P = 0.001) and DFS (HR 1.815, 95% CI 1.186-2.779, P = 0.006) after surgery. The addition of low hand-grip strength or low gait speed to GLIM-defined malnutrition did not increase its predictive value for survival. CONCLUSION: GLIM-defined malnutrition predicted worse long-term survival in gastric cancer patients with cachexia. Gait speed and hand-grip strength added prognostic value to GLIM-defined malnutrition for the prediction of short-term postoperative outcomes, which could be incorporated into preoperative assessment protocols in patients with cancer cachexia.

Prognostic value analysis of cholesterol and cholesterol homeostasis related genes in breast cancer by Mendelian randomization and multi-omics machine learning.

Introduction: The high incidence of breast cancer (BC) prompted us to explore more factors that might affect its occurrence, development, treatment, and also recurrence. Dysregulation of cholesterol metabolism has been widely observed in BC; however, the detailed role of how cholesterol metabolism affects chemo-sensitivity, and immune response, as well as the clinical outcome of BC is unknown. Methods: With Mendelian randomization (MR) analysis, the potential causal relationship between genetic variants of cholesterol and BC risk was assessed first. Then we analyzed 73 cholesterol homeostasis-related genes (CHGs) in BC samples and their expression patterns in the TCGA cohort with consensus clustering analysis, aiming to figure out the relationship between cholesterol homeostasis and BC prognosis. Based on the CHG analysis, we established a CAG_score used for predicting therapeutic response and overall survival (OS) of BC patients. Furthermore, a machine learning method was adopted to accurately predict the prognosis of BC patients by comparing multi-omics differences of different risk groups. Results: We observed that the alterations in plasma cholesterol appear to be correlative with the venture of BC (MR Egger, OR: 0.54, 95% CI: 0.35-0.84, p<0.006). The expression patterns of CHGs were classified into two distinct groups(C1 and C2). Notably, the C1 group exhibited a favorable prognosis characterized by a suppressed immune response and enhanced cholesterol metabolism in comparison to the C2 group. In addition, high CHG score were accompanied by high performance of tumor angiogenesis genes. Interestingly, the expression of vascular genes (CDH5, CLDN5, TIE1, JAM2, TEK) is lower in patients with high expression of CHGs, which means that these patients have poorer vascular stability. The CAG_score exhibits robust predictive capability for the immune microenvironment characteristics and prognosis of patients(AUC=0.79). It can also optimize the administration of various first-line drugs, including AKT inhibitors VIII Imatinib, Crizotinib, Saracatinib, Erlotinib, Dasatinib, Rapamycin, Roscovitine and Shikonin in BC patients. Finally, we employed machine learning techniques to construct a multi-omics prediction model(Risklight),with an area under the feature curve (AUC) of up to 0.89. Conclusion: With the help of CAG_score and Risklight, we reveal the signature of cholesterol homeostasis-related genes for angiogenesis, immune responses, and the therapeutic response in breast cancer, which contributes to precision medicine and improved prognosis of BC.

Novel immune checkpoint-related gene model to predict prognosis and treatment responsiveness in low-grade gliomas.

Recently, studies have shown that immune checkpoint-related genes (ICGs) are instrumental in maintaining immune homeostasis and can be regarded as potential therapeutic targets. However, the prognostic applications of ICGs require further elucidation in low-grade glioma (LGG) cases. In the present study, a unique prognostic gene signature in LGG has been identified and validated as well based on ICGs as a means of facilitating clinical decision-making. The RNA-seq data as well as corresponding clinical data of LGG samples have been retrieved utilizing the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. ICG-defined non-negative matrix factorization (NMF) clustering was performed to categorize patients with LGG into two molecular subtypes with different prognoses, clinical traits, and immune microenvironments. In the TCGA database, a signature integrating 8 genes has been developed utilizing the LASSO Cox method and validated in the GEO database. The signature developed is superior to other well-recognized signatures in terms of predicting the survival probability of patients with LGG. This 8-gene signature was then subsequently applied to categorize patients into high- and low-risk groups, and differences between them in terms of gene alteration frequency were observed. There were remarkable variations in IDH1 (91% and 64%) across low-as well as high-risk groups. Additionally, various analyses like function enrichment, tumor immune microenvironment, and chemotherapy drug sensitivity revealed significant variations across high- and low-risk populations. Overall, this 8-gene signature may function as a useful tool for prognosis and immunotherapy outcome predictions among LGG patients.

The pan-cancer multi-omics landscape of key genes of sialylation combined with RNA-sequencing validation.

BACKGROUND: Sialylation, the process of salivary acid glycan synthesis, plays a pivotal function in tumor growth, immune escape, tumor metastasis, and resistance to drugs. However, the association between sialylation and prognosis, tumor microenvironment (TME), and treatment response in a variety of cancers remains unclear. METHODS: A comprehensive survey of the expression profile, prognostic value, and genetic and epigenetic alterations of sialylation-related genes was performed in pan-cancer. Subsequently, the single-sample gene set enrichment analysis (ssGSEA) algorithm was used to compute sialylation pathway scores in pan-cancer. Correlations of sialylation pathway scores with clinical features, prognosis, and TME were evaluated using multiple algorithms. Finally, the efficacy of the sialylation pathway score in determining the effect of immunotherapy was evaluated. The expression of sialylation-related genes were verified by RNA-sequencing. RESULTS: Significant differences were observed in sialylation-related genes expression between tumors and adjacent normal tissues for most cancer types. Sialylation pathway scores differed according to the type of tumor, where the poor prognosis was correlated with high sialylation pathway scores in uveal melanoma (UVM) and pancreatic adenocarcinoma (PAAD). In addition, sialylation pathway scores were positively associated with the ImmuneScore, StromalScore and immune-related pathways. Moreover, the level of immune cells infiltration was higher in tumors with higher sialylation pathway scores. Finally, patients with high sialylation pathway scores were more sensitive to immunotherapy. CONCLUSION: Sialylation-related genes are essential in pan-cancer. The sialylation pathway score may be used as a biomarker in oncology patients.

ß, ß-Dimethylacrylshikonin potentiates paclitaxel activity, suppresses immune evasion and triple negative breast cancer progression via STAT3Y705 phosphorylation inhibition based on network pharmacology and transcriptomics analysis.

BACKGOUND: Triple negative breast cancer (TNBC) is an extremely aggressive and rapidly progressing cancer, wherein existing therapies provide little benefit to patients. ß, ß-Dimethylacrylshikonin (DMAS), an active naphthoquinone derived from comfrey root, has potent anticancer activity. However, the antitumor function of DMAS against TNBC remains to be proved. PURPOSE: Explore effects of DMAS on TNBC and clarify the mechanism. STUDY DESIGN: Network pharmacology, transcriptomics and various cell functional experiments were applied to TNBC cells to explore the effects of DMAS on TNBC. The conclusions were further validated in xenograft animal models. METHODS: MTT, EdU, transwell, scratch tests, flow cytometry, immunofluorescence, and immunoblot were utilized to assess the activity of DMAS on three TNBC cell lines. The anti-TNBC mechanism of DMAS was clarified by overexpression and knockdown of STAT3 in BT-549 cells. In vivo efficacy of DMAS was analysed using a xenograft mouse model. RESULTS: In vitro analysis revealed that DMAS inhibited the G2/M phase transition and suppressed TNBC proliferation. Additionally, DMAS triggered mitochondrial-dependent apoptosis and reduced cell migration by antagonizing epithelial-mesenchymal transition. Mechanistically, DMAS exerted its antitumour effects by inhibiting STAT3Y705 phosphorylation. STAT3 overexpression abolished the inhibitory effect of DMAS. Further studies showed that treatment with DMAS inhibited TNBC growth in a xenograft model. Notably, DMAS potentiated the sensitivity of TNBC to paclitaxel and inhibited immune evasion by downregulating the immune checkpoint PD-L1. CONCLUSIONS: For the first time, our study revealed that DMAS potentiates paclitaxel activity, suppresses immune evasion and TNBC progression by inhibiting STAT3 pathway. It has the potential as a promising agent for TNBC.