BAIAP2L2 facilitates the malignancy of prostate cancer (PCa) via VEGF and apoptosis signaling pathways

BAIAP2L2 facilitates hepatocellular carcinoma progression and immune evasion of via targeting JAK1-mediated pathway and PD-L1 expression

Hepatocellular carcinoma (HCC) poses a serious threat to human health due to its high mortality rate. Recently, breakthrough progress has been made in immunotherapy field. However, the mechanisms underlying HCC progression and immune escape are still unclear. This study aimed to investigate the impact of brain-specific angiogenesis inhibitor 1-associated protein 2-like 2 (BAIAP2L2) in HCC and elucidate its potential mechanisms in this context. Clinical data revealed that the overexpression of BAIAP2L2 correlated with tumor progression and poor prognosis in HCC patients. Functional assays demonstrated that BAIAP2L2 facilitates HCC proliferation, metastasis, invasion, and PD-L1-mediated immune evasion both in vitro and in vivo. Mechanistically, we observed co-localization and interaction between BAIAP2L2 and JAK1 within HCC cells, in turn enhancing the activation of the JAK1/STAT3 signaling pathway. Utilizing the JAK1 inhibitor Ruxolitinib effectively reversed BAIAP2L2-induced cellular processes such as proliferation, migration, invasion, and PD-L1 upregulation. Overall, our results emphasize that BAIAP2L2 plays a crucial role in driving tumor progression and immune evasion in HCC through the JAK1-mediated signaling pathway, thus proposing BAIAP2L2 as a promising therapeutic target for HCC treatment.

Identification of diagnostic biomarkers in prostate cancer-related fatigue by construction of predictive models and experimental validation

BACKGROUND

Cancer-related fatigue (CRF) is a prominent cancer-related complication occurring in Prostate cancer (PCa) patients, profoundly affecting prognosis. The lack of diagnostic criteria and biomarkers hampers the management of CRF.

METHODS

The CRF-related data and PCa single-cell data were retrieved from the GEO database and clinical data was downloaded from the TCGA database. The univariate logistic/Cox regression analysis were used to construct the prediction models. The predictive value of models was analyzed using the ROC curve and Kaplan-Meier survival. The hub genes were screened by an intersection analysis of DEGs. The mice model of PCa and PCa-related fatigue were established, and fatigue-like behaviors of mice were detected. The expression of selected hub genes was validated by RT-PCR and IHC analysis.

RESULTS

The diagnosis and risk models showed great predictive value both in the training and validation dataset. Five genes (Baiap2l2, Cacng4, Sytl2, Sec31b and Ms4a1) that enriched the CXCL signaling were identified as hub genes. Among all hub genes, the MS4A1 expression is the most significant in PCa-related fatigue mice.

CONCLUSIONS

We identified MS4A1 as a promising biomarker for the diagnosis of PCa-related fatigue. Our findings would lay a foundation for revealing the pathogenesis and developing therapies for PCa-related fatigue.

BAIAP2L2 promotes the malignancy of hepatocellular carcinoma via GABPB1-mediated reactive oxygen species imbalance

Hepatocellular carcinoma (HCC) is a common type of cancer worldwide and ranks as the fourth leading cause of cancer-related deaths. This research investigation identified an upregulation of BAI1-associated protein 2-like 2 (BAIAP2L2) in HCC tissues, which was found to be an independent prognostic factor for overall survival in HCC patients. BAIAP2L2 was observed to enhance cell proliferation, metastasis, stemness, cell cycle progression, and inhibit apoptosis in HCC. Mechanistically, NFκB1 was found to stimulate BAIAP2L2 transcription by directly binding to its promoter region. BAIAP2L2 interacts with GABPB1 to inhibit its ubiquitin-mediated degradation and promote its nuclear translocation. BAIAP2L2 inhibits the levels of reactive oxygen species (ROS) by regulating GABPB1, thereby promoting cancer properties in HCC and reducing the sensitivity of HCC to lenvatinib. In summary, this study elucidates the role and underlying mechanism of BAIAP2L2 in HCC, providing a potential biomarker and therapeutic target for this disease.

Transforming growth factor-β1 and vascular endothelial growth factor levels in senile acute myeloid leukemia and correlation with prognosis

BACKGROUND

Acute myeloid leukemia (AML) is a disease in which immature hematopoietic cells accumulate in the bone marrow and continuously expand, inhibiting hematopoiesis. The treatment and prognosis of this disease have always been unsatisfactory.

AIM

To investigate the correlation between vascular endothelial growth factor (VEGF) and transforming growth factor-β1 (TGFβ1) expression and prognosis in older adults with AML.

METHODS

This study enrolled 80 patients with AML (AML group), including 36 with complete response (AML-CR), 23 with partial response (AML-PR), and 21 with no response (AML-NR). The expression levels of VEGF and TGFβ1 were detected by reverse transcription polymerase chain reaction in bone marrow mononuclear cells isolated from 56 healthy controls. Kaplan-Meier analysis was performed to assess overall survival (OS) and progression- or disease-free survival (DFS). Prognostic risk factors were analyzed using a Cox proportional hazards model.

RESULTS

The AML group showed a VEGF level of 2.68 ± 0.16. VEGF expression was lower in patients with AML-CR than those with AML-PR or AML-NR (P < 0.05). TGFβ1 expression in the AML group was 0.33 ± 0.05. Patients with AML-CR showed a higher TGFβ1 expression than those with AML-PR or AML-NR (P < 0.05). VEGF and TGFβ1 expression in patients with AML was significantly correlated with the counts of leukocytes, platelets, hemoglobin, and peripheral blood immature cells (P < 0.05); Kaplan-Meier survival analysis revealed that patients with high TGFβ1 expression had better OS and DFS than those with low TGFβ1 expression (P < 0.05), whereas patients with low VEGF levels showed better OS and DFS than those with high VEGF levels (P < 0.05). VEGF, TGFβ1, and platelet count were identified by the Cox proportional hazards model as independent risk factors for OS (P < 0.05), while VEGF, TGFβ1, and white blood cell count were independent risk factors for DFS (P < 0.05).

CONCLUSION

Decreased VEGF expression and increased TGFβ1 expression in patients with AML provide valuable references for determining and individualizing clinical treatment strategies.

Recent advances and future directions on small molecule VEGFR inhibitors in oncological conditions

"A journey of mixed emotions" is a quote that best describes the progress chart of vascular endothelial growth factor receptor (VEGFR) inhibitors as cancer therapeutics in the last decade. Exhilarated with the Food and Drug Administration (FDA) approvals of numerous VEGFR inhibitors coupled with the annoyance of encountering the complications associated with their use, drug discovery enthusiasts are on their toes with an unswerving determination to enhance the rate of translation of VEGFR inhibitors from preclinical to clinical stage. The recently crafted armory of VEGFR inhibitors is a testament to their growing dominance over other antiangiogenic therapies for cancer treatment. This review perspicuously underscores the earnest attempts of the researchers to extract the antiproliferative potential of VEGFR inhibitors through the design of mechanistically diverse structural assemblages. Moreover, this review encompasses sections on structural/molecular properties and physiological functions of VEGFR, FDA-approved VEGFR inhibitors, and hurdles restricting the activity range/clinical applicability of VEGFR targeting antitumor agents. In addition, tactics to overcome the limitations of VEGFR inhibitors are discussed. A clear-cut viewpoint transmitted through this compilation can provide practical directions to push the cart of VEGFR inhibitors to advanced-stage clinical investigations in diverse malignancies.

Genome-Wide Extrachromosomal Circular DNA Profiling of Paired Hepatocellular Carcinoma and Adjacent Liver Tissues

Hepatocellular carcinoma (HCC) develops through multiple mechanisms. While recent studies have shown the presence of extrachromosomal circular DNA (eccDNA) in most cancer types, the eccDNA expression pattern and its association with HCC remain obscure. We aimed to investigate this problem. The genome-wide eccDNA profiles of eight paired HCC and adjacent non-tumor tissue samples were comprehensively elucidated based on Circle-seq, and they were further cross-analyzed with the RNA sequencing data to determine the association between eccDNA expression and transcriptome dysregulation. A total of 60,423 unique eccDNA types were identified. Most of the detected eccDNAs were smaller than 1 kb, with a length up to 182,363 bp and a mean sizes of 674 bp (non-tumor) and 813 bp (tumor), showing a greater association with gene-rich rather than with gene-poor regions. Although there was no statistical difference in length and chromosome distribution, the eccDNA patterns between HCC and adjacent non-tumor tissues showed significant differences at both the chromosomal and single gene levels. Five of the eight HCC tissues showed significantly higher amounts of chromosome 22-derived eccDNA expression compared to the non-tumor tissue. Furthermore, two genes, SLC16A3 and BAIAP2L2, with a higher transcription level in tumor tissues, were related to eccDNAs exclusively detected in three HCC samples and were negatively associated with survival rates in HCC cohorts from public databases. These results indicate the existence and massive heterogeneity of eccDNAs in HCC and adjacent liver tissues, and suggest their potential association with dysregulated gene expression.

BAIAP2L2 is a novel prognostic biomarker related to migration and invasion of HCC and associated with cuprotosis

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death worldwide, and its pathophysiological mechanisms remain unknown. IRSp53 family members, such as BAIAP2L1, participate in the progression of multiple tumors. However, the role of BAIAP2L2 in HCC remains unclear. This study comprehensively analyzed the potential role of BAIAP2L2 in HCC using bioinformatic techniques. The expression of BAIAP2L2 in HCC was analyzed using The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), International Cancer Genome Consortium (ICGC), and Human Protein Atlas (HPA) databases and in vitro experiments. In addition, the prognostic value of BAIAP2L2 in HCC was analyzed using the TCGA database. TCGA and GEO database were used to analyze the role of BAIAP2L2 in immune features. We also explored the function of BAIAP2L2 in methylation and cuprotosis. The CellMiner database was used to analyze the relationship between BAIAP2L2 expression and drug sensitivity. Our study revealed that BAIAP2L2 is overexpressed in HCC and promotes the migration and invasion of HCC cells. BAIAP2L2 may affect the prognosis of HCC by regulating immunity, methylation, and cuprotosis. BAIAP2L2 is a novel HCC prognostic gene involved in immune infiltration associated with cuprotosis and may be a potential prognosis and therapeutic target for HCC.

Safety and Efficacy of Treatment with/without Ramucirumab in Advanced or Metastatic Cancer: A Meta-Analysis of 11 Global, Double-Blind, Phase 3 Randomized Controlled Trials

Ramucirumab, as a vascular endothelial growth factor receptor-2 inhibitor, was first approved in 2014 for treated advanced or metastatic gastric/gastroesophageal junction (GEJ) adenocarcinoma. This study deeply analyzed the efficacy and safety of advanced or metastatic cancer treated with ramucirumab, which included 11 global, double-blind, phase 3 randomized controlled trials with a total of 7410 patients. Subgroup analysis based on different cancer types showed that standard regimens plus ramucirumab significantly increased progression-free survival and overall survival compared with placebo groups in patients with advanced non-small-cell lung cancer (NSCLC), hepatocellular carcinoma, gastric cancer, or GEJ adenocarcinoma. Although a higher proportion of patients achieved overall response and disease control than those treated with placebo, the overall response was not statistically significant between the two groups in advanced NSCLC. Grade 3 or worse treatment-emergent adverse events (TEAEs) that occurred in at least 5% of patients were neutropenia (30.5% in the ramucirumab group vs. 23.5% in the placebo group), leucopenia (14.8% vs. 9.2%), weight decreased (14.2% vs. 8.0%), myalgia (11.7% vs. 7.7%), fatigue (10.9% vs. 7.7%), hypertension (9.2% vs. 2.3%), and anaemia (6.2% vs. 7.7%). In the TEAEs of special interest, the ramucirumab group had a significantly higher incidence of bleeding (mainly grade 1-2 epistaxis and gastrointestinal bleeding), hypertension, proteinuria, liver injury/failure (grade 1-2), venous thromboembolism (grade 1-2), and gastrointestinal perforation (grade ≧3) than the control group.

Prognostic value and immunological role of BAIAP2L2 in liver hepatocellular carcinoma: A pan-cancer analysis

BACKGROUND

In recent years, the role of BAI1-associated protein 2-like 2 (BAIAP2L2) in the prognosis and immune microenvironment of various cancers has attracted increasing attention. However, its clinical value and immune infiltration in liver hepatocellular carcinoma (LIHC) remain unclear.

OBJECTIVE

To investigate the prognostic value of BAIAP2L2 and its correlation with immune infiltration in LIHC, we conducted corresponding data mining.

METHODS

In this study, The Cancer Genome Atlas, GTEx, StarBase, UALCAN, TIMER, GEPIA, Human Protein Atlas, Kaplan-Meier Plotter, cBioPortal, LinkedOmics, STRING and BioGPS databases were used to analyze BAIAP2L2 in cancers. Logistic regression and Cox regression were performed to analyze the correlation between clinical features and BAIAP2L2 expression in LIHC. In addition, the diagnostic and prognostic values of BAIAP2L2 in LIHC were determined by receiver operating characteristic (ROC) curves and nomograms. Single-sample gene set enrichment analysis (ssGSEA), BioGPS and TIMER were used to analyze the correlation between BAIAP2L2 and immune infiltration. More importantly, quantitative real-time polymerase chain reaction was used to verify BAIAP2L2 expression in a liver cancer cell line and a normal cell line. Visualization of data was mostly achieved using R language, version 3.6.3.

RESULTS

High BAIAP2L2 levels indicated poor overall survival (OS) and disease-free survival (DFS) of patients with LIHC. Abnormally increased expression of BAIAP2L2 in LIHC may be the result of both genetic alterations and lower DNA methylation levels. Furthermore, Cox regression analysis showed that high BAIAP2L2 expression was an independent risk factor for OS and DFS in patients with liver cancer. ROC curves and nomograms also confirmed the diagnostic and prognostic values of BAIAP2L2 in LIHC. Additionally, a PPI network of BAIAP2L2 was established and results implyed that BAIAP2L2 interacts with MTSS1, AMPH, FCHO1, SYT9, PDK2, MTSS1L, PM20D1, CHST4 and PALM3. ssGSEA showed that BAIAP2L2 was associated with T cells and natural killer cells. Simultaneously, the TIMER database showed that the expression of BAIAP2L2 in LIHC was positively correlated with tumor infiltrating cells, including B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils and dendritic cells.

CONCLUSIONS

Through pan-cancer analysis, prognostic and immunological value of BAIAP2L2 in LIHC was identified. This is the first report on the potential of BAIAP2L2 as a prognostic biomarker and its correlation with immune infiltration in LIHC.

Prediction of hepatocellular carcinoma prognosis and immunotherapeutic effects based on tryptophan metabolism-related genes

Background

L-tryptophan (Trp) metabolism involved in mediating tumour development and immune suppression. However, comprehensive analysis of the role of the Trp metabolism pathway is still a challenge.

Methods

We downloaded Trp metabolism-related genes’ expression data from different public databases, including TCGA, Gene Expression Omnibus (GEO) and Hepatocellular Carcinoma Database (HCCDB). And we identified two metabolic phenotypes using the ConsensusClusterPlus package. Univariate regression analysis and lasso Cox regression analysis were used to establish a risk model. CIBERSORT and Tracking of Indels by DEcomposition (TIDE) analyses were adopted to assess the infiltration abundance of immune cells and tumour immune escape.

Results

We identified two metabolic phenotypes, and patients in Cluster 2 (C2) had a better prognosis than those in Cluster 1 (C1). The distribution of clinical features between the metabolic phenotypes showed that patients in C1 tended to have higher T stage, stage, grade, and death probability than those of patients in C2. Additionally, we screened 739 differentially expressed genes (DEGs) between the C1 and C2. We generated a ten-gene risk model based on the DEGs, and the area under the curve (AUC) values of the risk model for predicting overall survival. Patients in the low-risk subgroup tended to have a significantly longer overall survival than that of those in the high-risk group. Moreover, univariate analysis indicated that the risk model was significantly correlated with overall survival. Multivariate analysis showed that the risk model remained an independent risk factor in hepatocellular carcinoma (p < 0.0001).

Conclusions

We identified two metabolic phenotypes based on genes of the Trp metabolism pathway, and we established a risk model that could be used for predicting prognosis and guiding immunotherapy in patients with hepatocellular carcinoma.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-022-02730-8.

A Personalized Genomics Approach of the Prostate Cancer

Decades of research identified genomic similarities among prostate cancer patients and proposed general solutions for diagnostic and treatments. However, each human is a dynamic unique with never repeatable transcriptomic topology and no gene therapy is good for everybody. Therefore, we propose the Genomic Fabric Paradigm (GFP) as a personalized alternative to the biomarkers approach. Here, GFP is applied to three (one primary—“A”, and two secondary—“B” & “C”) cancer nodules and the surrounding normal tissue (“N”) from a surgically removed prostate tumor. GFP proved for the first time that, in addition to the expression levels, cancer alters also the cellular control of the gene expression fluctuations and remodels their networking. Substantial differences among the profiled regions were found in the pathways of P53-signaling, apoptosis, prostate cancer, block of differentiation, evading apoptosis, immortality, insensitivity to anti-growth signals, proliferation, resistance to chemotherapy, and sustained angiogenesis. ENTPD2, AP5M1 BAIAP2L1, and TOR1A were identified as the master regulators of the “A”, “B”, “C”, and “N” regions, and potential consequences of ENTPD2 manipulation were analyzed. The study shows that GFP can fully characterize the transcriptomic complexity of a heterogeneous prostate tumor and identify the most influential genes in each cancer nodule.