Prognostic significance of EIF4G1 in patients with pancreatic ductal adenocarcinoma

Prognostic and functional roles of EIF4G1 in lung squamous cell carcinoma

Eukaryotic translation initiation factor 4 gamma 1 (EIF4G1) is highly expressed in many cancers and affects their occurrence and development. However, the effect of EIF4G1 on the prognosis, biological function and the relevant mechanism in lung squamous cell carcinoma (LSCC) is unclear. Through clinical cases, Cox's proportional hazard model and Kaplan-Meier plotter survival analysis, we find the expression levels of EIF4G1 are dependent on age and clinical stage, high expression of EIF4G1 could be used to predict the overall survival of LSCC patients. LSCC cell line NCI-H1703, NCI-H226 and SK-MES-1infected with EIF4G1 siRNA are used to detect the function of EIF4G1 with cell proliferation and tumorigenesis in vivo and vitro. The data show that EIF4G1 promotes tumor cell proliferation and the G1/S transition of cell cycle in LSCC, then the biological function of LSCC is effected by the AKT/mTOR pathway. Above all, these results have demonstrated that EIF4G1 promotes LSCC cell proliferation and may represent an indicator of prognosis in LSCC.

Translational alterations in pancreatic cancer: a central role for the integrated stress response

mRNA translation is a key mechanism for cancer cell proliferation and stress adaptation. Regulation of this machinery implicates upstream pathways such as PI3K/AKT/mTOR, RAS/MEK/ERK and the integrated stress response (ISR), principally coordinating the translation initiation step. During the last decade, dysregulation of the mRNA translation process in pancreatic cancer has been widely reported, and shown to critically impact on cancer initiation, development and survival. This includes translation dysregulation of mRNAs encoding oncogenes and tumor suppressors. Hence, cancer cells survive a stressful microenvironment through a flexible regulation of translation initiation for rapid adaptation. The ISR pathway has an important role in chemoresistance and shows high potential therapeutic interest. Despite the numerous translational alterations reported in pancreatic cancer, their consequences are greatly underestimated. In this review, we summarize the different translation dysregulations described in pancreatic cancer, which make it invulnerable, as well as the latest drug discoveries bringing a glimmer of hope.

Overexpression of OAS1 Is Correlated With Poor Prognosis in Pancreatic Cancer

Background

OAS1 expression in pancreatic cancer has been confirmed by many studies. However, the prognostic value and mechanism of OAS1 in pancreatic cancer have not been analyzed.

Methods

The RNA-seq in pancreatic cancer were obtained by UCSC XENA and GEO database. In addition, immunohistochemical validation and analysis were performed using samples from the 900th hospital. The prognosis of OAS1 was evaluated by timeROC package, Cox regression analysis, and Kaplan-Meier survival curves. Then, the main functional and biological signaling pathways enrichment and its relationship with the abundance of immune cells were analyzed by bioinformatics.

Results

OAS1 was highly expressed in pancreatic cancer compared with normal pancreatic tissue. High OAS1 expression was associated with poor overall survival (p<0.05). The OAS1 was significantly correlated to TNM staging (p=0.014). The timeROC analysis showed that the AUC of OAS1 was 0.734 for 3-year OS. In addition, the expression of OAS1 was significantly correlated with the abundance of a variety of immune markers. GSEA showed that enhanced signaling pathways associated with OAS1 include Apoptosis, Notch signaling pathway, and P53 signaling pathway.

Conclusions

OAS1 is a valuable prognostic factor in pancreatic cancer. Moreover, it may be a potential immunotherapeutic target.

Construction and validation of a prognostic risk model for breast cancer based on protein expression

Breast cancer (BRCA) is the primary cause of mortality among females globally. The combination of advanced genomic analysis with proteomics characterization to construct a protein prognostic model will help to screen effective biomarkers and find new therapeutic directions. This study obtained proteomics data from The Cancer Proteome Atlas (TCPA) dataset and clinical data from The Cancer Genome Atlas (TCGA) dataset. Kaplan–Meier and Cox regression analyses were used to construct a prognostic risk model, which was consisted of 6 proteins (CASPASE7CLEAVEDD198, NFKBP65-pS536, PCADHERIN, P27, X4EBP1-pT70, and EIF4G). Based on risk curves, survival curves, receiver operating characteristic curves, and independent prognostic analysis, the protein prognostic model could be viewed as an independent factor to accurately predict the survival time of BRCA patients. We further validated that this prognostic model had good predictive performance in the GSE88770 dataset. The expression of 6 proteins was significantly associated with the overall survival of BRCA patients. The 6 proteins and encoding genes were differentially expressed in normal and primary tumor tissues and in different BRCA stages. In addition, we verified the expression of 3 differential proteins by immunohistochemistry and found that CDH3 and EIF4G1 were significantly higher in breast cancer tissues. Functional enrichment analysis indicated that the 6 genes were mainly related to the HIF-1 signaling pathway and the PI3K-AKT signaling pathway. This study suggested that the prognosis-related proteins might serve as new biomarkers for BRCA diagnosis, and that the risk model could be used to predict the prognosis of BRCA patients.

Elevation of EIF4G1 promotes non-small cell lung cancer progression by activating mTOR signalling

Eukaryotic translation initiation factor 4 gamma 1 (EIF4G1), as the key component of the transcription initiation factor complex EIF4F, is significantly upregulated in multiple solid tumours, including lung cancer. However, the function and mechanism of EIF4G1 in the regulation of non‐small‐cell lung cancer (NSCLC) remain unclear. Here, using the clinical samples and the comprehensive survival analysis platforms Kaplan‐Meier plotter, we observed aberrant upregulation of EIF4G1 in NSCLC tissues; furthermore, high expression of EIF4G1 showed association with low differentiation of lung cancer cells and poor overall survival in NSCLC patients. Non‐small‐cell lung cancer cell line A549 and H1703 stably infected with EIF4G1 shRNA were used to determine the function of EIF4G1 in regulating cell proliferation and tumorigenesis in vitro and in vivo. The results demonstrated that EIF4G1 promoted the G1/S transition of the cell cycle and tumour cell proliferation in non‐small cell lung cancer. Mechanistically, EIF4G1 was found to regulate the expression and phosphorylation of mTOR (Ser2448), which mediates the tumorigenesis‐promoting function of EIF4G1. The inhibition of mTOR attenuated the EIF4G1‐induced development and progression of tumours. These findings demonstrated that EIF4G1 is a new potential molecular target for the clinical treatment of non‐small cell lung cancer.

A new 7-gene survival score assay for pancreatic cancer patient prognosis prediction

Gene expression features that are valuable for pancreatic ductal adenocarcinoma (PDAC) prognosis are still largely unknown. We aimed to explore pivotal molecular signatures for PDAC progression and establish an efficient survival score to predict PDAC prognosis. Overall, 163 overlapping genes were identified from three statistical methods, including differentially expressed genes (DEGs), coexpression network analysis (WGCNA), and target genes for miRNAs that were significantly related to PDAC patients' overall survival (OS). Then, according to the optimal value of the cross-validation curve (lambda = 0.031), 7 non-zero coefficients (ARNTL2, DSG3, PTPRR, ANLN, S100A14, ANKRD22, and TSPAN7) were selected to establish a prognostic prediction model of PDAC patients. We further confirmed the expression level of 7 genes using RT-PCR, western blot, and immunohistochemistry staining in PDAC patients' tissues. Our results showed that the ROC curve of the 7-mRNA model indicated good predictive ability for 1- and 2-year OS in three datasets (TCGA: 0.71, 0.69; ICGC: 0.8, 0.74; GEO batch: 0.61, 0.7, respectively). The hazard ratio (HR) of the low-risk group had a similar significant result (TCGA: HR = 0.3723; ICGC: HR = 0.2813; GEO batch: HR = 0.4999; all P < 0.001). Furthermore, Log-rank test results in three cohorts showed that the 7-mRNA assay excellently predicted the prognosis and metastasis, especially in TNM stage I&II subgroups of PDAC. In conclusion, the strong validation of our 7-mRNA signature indicates the promising effectiveness of its clinical application, especially in patients with TNM stages I&II.

Identification of prognostic mRNAs in metastatic cutaneous melanoma

Cutaneous melanoma is the most common cause of skin cancer-related deaths worldwide. There is an urgent need to identify prognostic biomarkers to facilitate decision-making for treatment of metastatic cutaneous melanoma. Gene expression microarrays and RNA-seq technology have recently improved or changed current prognostic and therapeutic strategies for several cancers. However, according to the current melanoma staging system, prognosis is almost entirely dependent on clinicopathological features. To identify novel prognostic biomarkers, we investigated gene expression and clinical data for patients with cutaneous melanoma from three cohorts of The Cancer Genome Atlas and Gene Expression Omnibus. Kaplan-Meier survival analysis using median values of each gene as cutoff value revealed that nine genes (ABCC3, CAPS2, CCR6, CDCA8, CLU, DPF1, PTK2B, SATB1, and SYNE1) were statistically significant prognostic biomarkers of metastatic cutaneous melanoma in all three independent cohorts. Low expression of two genes (CDCA8 and DPF1) and high expression of seven genes (ABCC3, CAPS2, CCR6, CLU, PTK2B, SATB1, and SYNE) were significantly associated with positive metastatic cutaneous melanoma prognoses. In conclusion, we suggest nine novel prognostic biomarkers for cutaneous metastatic melanoma.

FAM213A is linked to prognostic significance in acute myeloid leukemia through regulation of oxidative stress and myelopoiesis

Accurate prediction of malignancies is important in choosing therapeutic strategies. Although there are many genetic and cytogenetic prognostic factors for acute myeloid leukemia (AML), prognosis is difficult to predict because of the heterogeneity of AML. Prognostic factors, including messenger RNA (mRNA) expression, have been determined for other malignancies, but not for AML. A total of 402 patients from The Cancer Genome Atlas, GSE12417 (GPL96, 97), and GSE12417 (GPL570) were included in this study. In Kaplan-Meier curve analyses, high expression of family with sequence similarity 213 member A (FAM213A), which activates antioxidant proteins, was associated with worse prognosis of AML. Similar to the results of the survival curve, C-indices and area under the curve values were high. Current prognostic factors of AML, unlike those of other cancers, do not take mRNA expression into consideration. Thus, the development of mRNA-based prognostic factors would be beneficial for accurate prediction of the survival of AML patients. Additionally, in vivo validation using zebrafish revealed that fam213a is important for myelopoiesis at the developmental stage and is a negative regulator of the p53 tumor suppressor gene. The findings implicate fam213a as a novel prognostic factor for AML patients.

Separated Siamese Twins: Intronic Small Nucleolar RNAs and Matched Host Genes May be Altered in Conjunction or Separately in Multiple Cancer Types

Small nucleolar RNAs (snoRNAs) are non-coding RNAs involved in RNA modification and processing. Approximately half of the so far identified snoRNA genes map within the intronic regions of host genes, and their expression, as well as the expression of their host genes, is dependent on transcript splicing and maturation. Growing evidence indicates that mutations and/or deregulations that affect snoRNAs, as well as host genes, play a significant role in oncogenesis. Among the possible factors underlying snoRNA/host gene expression deregulation is copy number alteration (CNA). We analyzed the data available in The Cancer Genome Atlas database, relative to CNA and expression of 295 snoRNA/host gene couples in 10 cancer types, to understand whether the genetic or expression alteration of snoRNAs and their matched host genes would have overlapping trends. Our results show that, counterintuitively, copy number and expression alterations of snoRNAs and matched host genes are not necessarily coupled. In addition, some snoRNA/host genes are mutated and overexpressed recurrently in multiple cancer types. Our findings suggest that the differential contribution to cancer development of both snoRNAs and host genes should always be considered, and that snoRNAs and their host genes may contribute to cancer development in conjunction or independently.