Background: The pathogenesis of nasopharyngeal carcinoma (NPC) is a complicated process involving genetic predisposition, Epstein-Bar Virus infection, and genetic alterations. Although some oncogenes and tumor suppressor genes have been previously reported in NPC, a complete understanding of the pathogenesis of NPC in the context of global gene expression, transcriptional pathways and biomarker assessment remains to be elucidated. Methods: Total RNA from 32 pathologically-confirmed cases of poorly-differentiated NPC was divided into pools inclusive of four consecutive specimens and each pool (T1 to T8) was co-hybridized with pooled RNA from 24 normal non-cancerous nasopharyngeal tissues (NP) to a human 8K cDNA array platform. The reliability of microarray data was validated for selected genes by semi-quantitative RT-PCR and immunohistochemistry. Results: Stringent statistical filtering parameters identified 435 genes to be up-regulated and 257 genes to be down-regulated in NPC compared to NP. Seven up-regulated genes including CYC1, MIF, LAMB3, TUBB2, UBE2C and TRAP1 had been previously proposed as candidate common cancer biomarkers based on a previous extensive comparison among various cancers and normal tissues which did not, however, include NPC or NP. In addition, nine known oncogenes and tumor suppressor genes, MIF, BIRC5, PTTG1, ATM, FOXO1A, TGFBR2, PRKAR1A, KLF5 and PDCD4 were identified through the microarray literature-based annotation search engine MILANO, suggesting these genes may be specifically involved in the promotion of the malignant conversion of nasopharyngeal epithelium. Finally, we found that these differentially expressed genes were involved in apoptosis, MAPK, VEGF and B cell receptor signaling pathways and other functions associated with cell growth, signal transduction and immune system activation. Conclusion: This study identified potential candidate biomarkers, oncogenes/tumor suppressor genes involved in several pathways relevant to the oncogenesis of NPC. This information may facilitate the determination of diagnostic and therapeutic targets for NPC as well as provide insights about the molecular pathogenesis of NPC.

Background: The aim of the present study was to analyze the expression of eukaryotic translation initiation factor 4 gamma 1 (EIF4G1) in nasopharyngeal carcinoma (NPC) and its correlation with clinicopathologic features, including patients' survival time. Methods: Using real-time PCR, we detected the expression of EIF4G1 in normal nasopharyngeal tissues, immortalized nasopharyngeal epithelial cell lines NP69, NPC tissues and cell lines. EIF4G1 protein expression in NPC tissues was examined using immunohistochemistry. Survival analysis was performed using Kaplan-Meier method. The effect of EIF4G1 on cell invasion and tumorigenesis were investigated. Results: The expression levels of EIF4G1 mRNA were significantly greater in NPC tissues and cell lines than those in the normal nasopharyngeal tissues and NP69 cells (P < 0.001). Immunohistochemical analysis revealed that the expression of EIF4G1 protein was higher in NPC tissues than that in the nasopharyngeal tissues (P < 0.001). In addition, the levels of EIF4G1 protein in tumors were positively correlated with tumor T classification (P = 0.039), lymph node involvement (N classification, P = 0.008), and the clinical stages (P = 0.003) of NPC patients. Patients with higher EIF4G 1 expression had shorter overall survival time (P = 0.019). Multivariate analysis showed that EIF4G1 expression was an independent prognostic indicator for the overall survival of NPC patients. Using shRNA to knock down the expression of EIF4G1 not only markedly inhibited cell cycle progression, proliferation, migration, invasion, and colony formation, but also dramatically suppressed in vivo xenograft tumor growth. Conclusion: Our data suggest that EIF4G1 can serve as a biomarker for the prognosis of NPC patients.

Prevalent copy number alteration is the most prominent genetic characteristic associated with ovarian cancer (OV) development, but its role in immune evasion has not been fully elucidated. In this study, we identified RAD21, a key component of the cohesin complex, as a frequently amplified oncogene that could modulate immune response in OV. Through interrogating the RAD21-regulated transcriptional program, we found that RAD21 directly interacts with YAP/TEAD4 transcriptional corepressors and recruits the NuRD complex to suppress interferon (IFN) signaling. In multiple clinical cohorts, RAD21 overexpression is inversely correlated with IFN signature gene expression in OV. We further demonstrated in murine syngeneic tumor models that RAD21 ablation potentiated anti–PD-1 efficacy with increased intratumoral CD8⁺ T cell effector activity. Our study identifies a RAD21–YAP/TEAD4–NuRD corepressor complex in immune modulation, and thus provides a potential target and biomarker for precision immunotherapy in OV.

Endometrioid endometrial carcinoma (EEC) is the most dominant subtype of endometrial cancer. Aberrant transcriptional regulation has been implicated in EEC. Herein, we characterized mRNA and miRNA transcriptomes by RNA sequencing in EEC to investigate potential molecular mechanisms underlying the pathogenesis. Total mRNA and small RNA were simultaneously sequenced by next generation sequencing technology for 3 pairs of stage I EEC and adjacent non-tumorous tissues. On average, 52,716,765 pair-end 100 bp mRNA reads and 1,669,602 single-end 50 bp miRNA reads were generated. Further analysis indicated that 7 miRNAs and 320 corresponding target genes were differentially expressed in the three stage I EEC patients. Six of all the seven differentially expressed miRNAs were targeting on eleven differentially expressed genes in the cell cycle pathway. Real-time quantitative PCR in sequencing samples and other independent 21 pairs of samples validated the miRNA-mRNA differential co-expression, which were involved in cell cycle pathway, in the stage I EEC. Thus, we confirmed the involvement of hsa-let-7c-5p and hsa-miR-99a-3p in EEC and firstly found dysregulation of hsa-miR-196a-5p, hsa-miR-328-3p, hsa-miR-337-3p, and hsa-miR-181c-3p in EEC. Moreover, synergistic regulations among these miRNAs were detected. Transcript sequence variants such as single nucleotide variant (SNV) and short insertions and deletions (Indels) were also characterized. Our results provide insights on dysregulated miRNA-mRNA co-expression and valuable resources on transcript variation in stage I EEC, which implies the new molecular mechanisms that underlying pathogenesis of stage I EEC and supplies opportunity for further in depth investigations.

With the development of novel anti-HER2 targeted drugs, such as ADCs, it has become increasingly important to accurately interpret HER2 expression in breast cancer. Previous studies have demonstrated high intra-observer and inter-observer variabilities in evaluating HER2 staining by human eyes. There exists a strong requirement to develop artificial intelligence (AI) systems to achieve high-precision HER2 expression scoring for better clinical therapy. In the present study, we collected breast cancer tissue samples and stained consecutive sections with anti-Calponin and anti-HER2 antibodies. High-quality digital images were selected from immunohistochemical slides and interpreted as HER2 3+, 2+, 1+, and 0. AI models were trained and assessed using annotated training and testing sets. The AI model was trained to automatically identify ductal carcinoma in situ (DCIS) by Calponin staining and myoepithelial annotation and filter out DCIS components in HER2-stained slides using image-overlapping techniques. Furthermore, we organized two-phase validation studies. In phase one, pathologists interpreted 112 HER2 whole-slide images (WSIs) without AI assistance, whereas in phase two, pathologists read the same slides using the AI system after a washing period of 2 weeks. Our AI model greatly improved the accuracy of reading (0.902 vs. 0.710). The number of HER2 1 + patients misdiagnosed as HER2 0 was significantly reduced (32/279 vs. 65/279), and they benefitted from ADC drugs. In addition, the AI algorithm improved the intra-group consistency of HER2 readings by pathologists with different years of experience (intra-class correlation coefficient [ICC]: 0.872–0.926 vs. 0.818–0.908), with the improvement most pronounced among junior pathologists (0.885 vs. 0.818). We proposed a high-precision AI system to identify and filter out DCIS components and automatically evaluate HER2 expression in invasive breast cancer.