Pan-cancer analysis of the prognosis and immunological role of AKAP12: A potential biomarker for resistance to anti-VEGF inhibitors

Predicting the Prognosis of Bladder Cancer Patients Through Integrated Multi-omics Exploration of Chemotherapy-Related Hypoxia Genes

Bladder cancer is a prevalent malignancy with high mortality rates worldwide. Hypoxia is a critical factor in the development and progression of cancers. However, whether and how hypoxia-related genes (HRGs) could affect the development and the chemotherapy response of bladder cancer is still largely unexplored. This study comprehensively explored the complex molecular landscape associated with hypoxia in bladder cancer by analyzing 260 hypoxia genes based on transcriptomic and genomic data in 411 samples. Employing the 109 dysregulated hypoxia genes for consensus clustering, we delineated two distinct bladder cancer clusters characterized by disparate survival outcomes and distinct oncogenic roles. We defined a HPscore that was correlated with a variety of clinical features, including TNM stages and pathologic grades. Tumor immune landscape analysis identified three immune clusters and close interactions between hypoxia genes and the various immune cells. Utilizing a network-based method, we defined 129 HRGs exerting influence on apoptotic processes and critical signaling pathways in cancer. Further analysis of chemotherapy drug sensitivity identified potential drug-target HRGs. We developed a Risk Score model that was related to the overall survival of bladder cancer patients based on doxorubicin-target HRGs: ACTG2, MYC, PDGFRB, DHRS2, and KLRD1. This study not only enhanced our understanding of bladder cancer at the molecular level but also provided promising avenues for the development of targeted therapies, representing a significant step toward the identification of effective treatments and addressing the urgent need for advancements in bladder cancer management.

Single-cell multi-omics reveals insights into differentiation of rare cell types in mucinous colorectal cancer

Neuroendocrine cells have been implicated in therapeutic resistance and worse overall survival in many cancer types. Mucinous colorectal cancer (mCRC) is uniquely enriched for enteroendocrine cells (EECs), the neuroendocrine cell of the normal colon epithelium, as compared to non-mucinous CRC. Therefore, targeting EEC differentiation may have clinical value in mCRC. Here, single cell multi-omics was used to uncover epigenetic alterations that accompany EEC differentiation, identify STAT3 as a novel regulator of EEC specification, and discover a rare cancer-specific cell type with enteric neuron-like characteristics. Further experiments demonstrated that lysine-specific demethylase 1 (LSD1) and CoREST2 mediate STAT3 demethylation and regulate STAT3 chromatin binding. Knockdown of CoREST2 in an orthotopic xenograft mouse model resulted in decreased primary tumor growth and lung metastases. In culmination, these results provide rationale for new LSD1 inhibitors that target the interaction between LSD1 with STAT3 or CoREST2, which may improve clinical outcomes for patients with mCRC.

High-accuracy prediction of colorectal cancer chemotherapy efficacy using machine learning applied to gene expression data

Introduction: FOLFOX and FOLFIRI chemotherapy are considered standard first-line treatment options for colorectal cancer (CRC). However, the criteria for selecting the appropriate treatments have not been thoroughly analyzed. Methods: A newly developed machine learning model was applied on several gene expression data from the public repository GEO database to identify molecular signatures predictive of efficacy of 5-FU based combination chemotherapy (FOLFOX and FOLFIRI) in patients with CRC. The model was trained using 5-fold cross validation and multiple feature selection methods including LASSO and VarSelRF methods. Random Forest and support vector machine classifiers were applied to evaluate the performance of the models. Results and Discussion: For the CRC GEO dataset samples from patients who received either FOLFOX or FOLFIRI, validation and test sets were >90% correctly classified (accuracy), with specificity and sensitivity ranging between 85%-95%. In the datasets used from the GEO database, 28.6% of patients who failed the treatment therapy they received are predicted to benefit from the alternative treatment. Analysis of the gene signature suggests the mechanistic difference between colorectal cancers that respond and those that do not respond to FOLFOX and FOLFIRI. Application of this machine learning approach could lead to improvements in treatment outcomes for patients with CRC and other cancers after additional appropriate clinical validation.

Metastasis suppressor genes in clinical practice: are they druggable?

Since the identification of NM23 (now called NME1) as the first metastasis suppressor gene (MSG), a small number of other gene products and non-coding RNAs have been identified that suppress specific parameters of the metastatic cascade, yet which have little or no ability to regulate primary tumor initiation or maintenance. MSG can regulate various pathways or cell biological functions such as those controlling mitogen-activated protein kinase pathway mediators, cell-cell and cell-extracellular matrix protein adhesion, cytoskeletal architecture, G-protein-coupled receptors, apoptosis, and transcriptional complexes. One defining facet of this gene class is that their expression is typically downregulated, not mutated, in metastasis, such that any effective therapeutic intervention would involve their re-expression. This review will address the therapeutic targeting of MSG, once thought to be a daunting task only facilitated by ectopically re-expressing MSG in metastatic cells in vivo. Examples will be cited of attempts to identify actionable oncogenic pathways that might suppress the formation or progression of metastases through the re-expression of specific metastasis suppressors.

AKAP12 inhibits esophageal squamous carcinoma cell proliferation, migration, and cell cycle via the PI3K/AKT signaling pathway

Esophageal squamous cell carcinoma (ESCC) consistently ranks as one of the most challenging variants of squamous cell carcinomas, primarily due to the lack of effective early detection strategies. We herein aimed to elucidate the underlying mechanisms and biological role associated with A-kinase anchoring protein 12 (AKAP12) in the context of ESCC. Bioinformatic analysis had revealed significantly lower expression level of AKAP12 in ESCC tissue samples than in their non-cancerous counterparts. To gain deeper insights into the potential role of AKAP12 in the progression of ESCC, we conducted a single-gene set enrichment analysis of AKAP12 on ESCC datasets. Our findings suggested that AKAP12 exhibits functions inhibiting cell cycle progression, tumor proliferation, and epithelial-mesenchymal transition. To further validate our findings, we subjected ESCC cell lines to AKAP12 overexpression using CRISPR/Cas9-SAM. In vitro analyses demonstrated that increased expression of AKAP12 significantly reduced cell proliferation, migration, and cell cycle progression. Simultaneously, genes associated with this biological role undergo corresponding regulatory shifts. These observations provided valuable insights into the biological role played by AKAP12 in ESCC progression. In summary, AKAP12 shows promise as a new potential biomarker for early ESCC diagnosis, offering potential advantages for subsequent therapeutic intervention and disease management.

Identification of hub genes in bladder transitional cell carcinoma through ceRNA network construction integrated with gene network analysis

Bladder transitional cell carcinoma (BTCC) forms more than 90% of bladder cancer cases. It brings challenges to the early diagnosis and therapy of BTCC, due to lack of efficient screening biomarkers. We used weighted gene co-expression network analysis (WGCNA) combined competing endogenous RNA (ceRNA) network construction depending on TCGA datasets to investigate potential hub genes and regulatory pathways associated with occurrence and progression of BTCC. We further used real-time polymerase chain reaction (RT-PCR) to validate the relative expression genes correlated with BTCC. By WGCNA, the gene co-expression module with 11 genes was found corelated with BTCC tumour stage and prognosis after survival analyses. Ultimately, we put 100 highly stage-related genes into the above constructed ceRNA network and then constructed another new network. Among them, all elements in AC112721.1/LINC00473/AC128709.1-hsa-mir-195-RECK and LINC00460-hsa-mir-429-ZFPM2 axes were simultaneously corelated with overall survival. RT-PCR showed that AKAP12 was downregulated in tumour tissues. The hub genes screened out in the present study may provide ideals for further treatment on BTCC.

Integrative analysis identifies AKAP8L as an immunological and prognostic biomarker of pan-cancer

A-kinase anchoring protein 8L (AKAP8L) belong to the A-kinase anchoring protein (AKAP) family. Recent studies have proved that AKAP8L is associated with the progression of various tumors. To establish a more complete understanding of the significance of AKAP8L across various types of cancers, we conducted a detailed analysis of multiple histological datasets, including the level of gene expression in pancancer, biological function, molecular characteristics, as well as the diagnostic and prognostic value of AKAP8L in pancancer. Furthermore, we focused on renal clear cell carcinoma (KIRC), and of explored the correlation of AKAP8L with clinical characteristics, prognosis of distinct patient subsets, co-expression genes and differentially expressed genes (DEG). We also performed the immunohistochemical staining and semi-quantitative verification of the monoclonal antibody established by AKAP8L. Our findings indicate that AKAP8L expression varied significantly not only across most cancer types, but also across different cancer molecules and immune subtypes. In addition, the robust ability to accurately predict cancer and its strong correlation with the prognosis of cancer strongly suggest that AKAP8L may be a potential biomarker for cancer diagnosis and prognosis. Furthermore, the high expression levels of AKAP8L were related to the worse overall survival (OS), disease-specific survival (DSS) as well as progression-free interval (PFI) of KIRC with statistical significance, especially among distinct clinical subgroups of KIRC. To sum up, AKAP8L has the potential to serve as a critical molecular biomarker for the diagnosis and prognosis of pancancer, an independent prognostic risk factor of KIRC, and a novel molecular target for cancer therapies.