Background: Esophageal squamous cell carcinoma (ESCC) is a severe disease with high mortality, and is associated with poor prognosis and frequent lymphatic metastasis. Therefore, prognostic indicators for ESCC are urgently needed. A-kinase anchor-protein 8-like (AKAP8L) is a member of the A kinase anchor-protein (AKAPs) family and is overexpressed in many cancers. However, the role of AKAP8L in ESCC remains unclear. The aim of this study is to investigate the expression patterns and prognostic value of AKAP8L in ESCC. Methods: The mRNA expression of AKAP8L was analyzed from the dataset of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Immunohistochemistry was applied to detect the AKAP8L expression in tissue microarray. Pearson’s chi-square test was carried out for the correlation analysis of clinicopathological features and AKAP8L expression. The prognostic significance of clinicopathological features and AKAP8L expression was determined by univariate and multivariate Cox hazard models. Kaplan–Meier survival curve was used for survival analysis. Results: We found that the mRNA level of AKAP8L was higher in tumor tissues than in adjacent tissues in TCGA and GEO dataset. High AKAP8L expression was associated with poor overall survival (OS) in ESCC patients (p = 0.0039). Besides, AKAP8L expression was highly expressed in patients with lymph node metastasis detected by ESCC tissue microarray (p = 0.0014). The comparison of the different clinicopathological features of ESCC between high and low AKAP8L expression groups revealed that high AKAP8L expression was related to lymph node stage (p = 0.041). Kaplan–Meier survival analysis revealed that high AKAP8L expression indicates an unfavorable progression-free survival (PFS) and OS in ESCC patients (p < 0.0001). Univariate and multivariate analyses confirmed that AKAP8L was an independent prognostic factor for PFS and OS in ESCC (p = 0.003 and p < 0.0001). Conclusions: In conclusion, this study demonstrated that high expression of AKAP8L is associated with poor prognosis of ESCC and can be considered an independent risk factor for ESCC.

Dysregulation of serine/arginine splicing factors (SRSFs) and abnormal alternative splicing (AS) have been widely implicated in various cancers but scarcely investigated in nasopharyngeal carcinoma (NPC). Here we examine the expression of 12 classical SRSFs between 87 NPC and 10 control samples, revealing a significant upregulation of SRSF3 and its association with worse prognosis in NPC. Functional assays demonstrate that SRSF3 exerts an oncogenic function in NPC progression. Transcriptome analysis reveals 1,934 SRSF3-regulated AS events in genes related to cell cycle and mRNA metabolism. Among these events, we verify the generation of a long isoform of AMOTL1 (AMOTL1-L) through a direct bond of the SRSF3 RRM domain with the exon 12 of AMOTL1 to promote exon inclusion. Functional studies also reveal that AMOTL1-L promotes the proliferation and migration of NPC cells, while AMOTL1-S does not. Furthermore, overexpression of AMOTL1-L, but not -S, significantly rescues the inhibitory effects of SRSF3 knockdown. Additionally, compared with AMOTL1-S, AMOTL1-L has a localization preference in the intracellular than the cell membrane, leading to a more robust interaction with YAP1 to promote nucleus translocation. Our findings identify SRSF3/AMOTL1 as a novel alternative splicing axis with pivotal roles in NPC development, which could serve as promising prognostic biomarkers and therapeutic targets for NPC.

The Coatomer protein complex Zeta 1 (COPZ1) has been reported to play an essential role in maintaining the survival of some types of tumors. In this study, we sought to explore the molecular characteristics of COPZ1 and its clinical prognostic value through a pan-cancers bioinformatic analysis. We found that COPZ1 was extremely prevalent in a variety of cancer types, and high expression of COPZ1 was linked to poor overall survival in many cancers, while low expression in LAML and PADC was correlated with tumorigenesis. Besides, the CRISPR Achilles’ knockout analysis revealed that COPZ1 was vital for many tumor cells’ survival. We further demonstrated that the high expression level of COPZ1 in tumors was regulated in multi-aspects, including abnormal CNV, DNA-methylation, transcription factor and microRNAs. As for the functional exploration of COPZ1, we found a positive relationship between COPZ1’s expression and stemness and hypoxia signature, especially the contribution of COPZ1 on EMT ability in SARC. GSEA analysis revealed that COPZ1 was associated with many immune response pathways. Further investigation demonstrated that COPZ expression was negatively correlated with immune score and stromal score, and low expression of COPZ1 has been associated to more antitumor immune cell infiltration and pro-inflammatory cytokines. The further analysis of COPZ1 expression and anti-inflammatory M2 cells showed a consistent result. Finally, we verified the expression of COPZ1 in HCC cells, and proved its ability of sustaining tumor growth and invasion with biological experiments. Our study provides a multi-dimensional pan-cancer analysis of COPZ and demonstrates that COPZ1 can serve as both a prospective target for the treatment of cancer and a prognostic marker for a variety of cancer types.

Background: Precise public health and clinical interventions for the COVID-19 pandemic has spurred a global rush on SARS-CoV-2 variant tracking, but current approaches to variant tracking are challenged by the flood of viral genome sequences leading to a loss of timeliness, accuracy, and reliability. Here, we devised a new co-mutation network framework, aiming to tackle these difficulties in variant surveillance.Methods: To avoid simultaneous input and modeling of the whole large-scale data, we dynamically investigate the nucleotide covarying pattern of weekly sequences. The community detection algorithm is applied to a co-occurring genomic alteration network constructed from mutation corpora of weekly collected data. Co-mutation communities are identified, extracted, and characterized as variant markers. They contribute to the creation and weekly updates of a community-based variant dictionary tree representing SARS-CoV-2 evolution, where highly similar ones between weeks have been merged to represent the same variants. Emerging communities imply the presence of novel viral variants or new branches of existing variants. This process was benchmarked with worldwide GISAID data and validated using national level data from six COVID-19 hotspot countries.Results: A total of 235 co-mutation communities were identified after a 120 weeks' investigation of worldwide sequence data, from March 2020 to mid-June 2022. The dictionary tree progressively developed from these communities perfectly recorded the time course of SARS-CoV-2 branching, coinciding with GISAID clades. The time-varying prevalence of these communities in the viral population showed a good match with the emergence and circulation of the variants they represented. All these benchmark results not only exhibited the methodology features but also demonstrated high efficiency in detection of the pandemic variants. When it was applied to regional variant surveillance, our method displayed significantly earlier identification of feature communities of major WHO-named SARS-CoV-2 variants in contrast with Pangolin's monitoring.Conclusion: An efficient genomic surveillance framework built from weekly co-mutation networks and a dynamic community-based variant dictionary tree enables early detection and continuous investigation of SARS-CoV-2 variants overcoming genomic data flood, aiding in the response to the COVID-19 pandemic.