Development and validation of an RNA binding protein-associated prognostic model for hepatocellular carcinoma

Immune gene features and prognosis in colorectal cancer: insights from ssGSEA typing

BACKGROUND

Colorectal cancer (CRC) is a molecularly heterogeneous disease, and its treatment and prognosis vary greatly among subgroups. Therefore, it is necessary to identify prognostic factors associated with the biological heterogeneity of CRC in order to improve patients' survival expectations.

METHODS

We obtained and merged RNA-Seq data along with clinical details for colorectal cancer (CRC) from The Cancer Genome Atlas (TCGA) repository, and then performed immunocluster typing on all CRC specimens. We conducted differential expression gene (DEG) analysis, gene set enrichment analysis (GSEA), and tumor microenvironment (TME) analysis on CRC samples that were divided into high and low Immunity categories. Moreover, we pinpointed prognostic genes from immune-related gene (IRGs) sets, developed a prognostic risk model, and executed survival analysis, receiver operating characteristic (ROC) curve analysis, and independent prognostic analysis. Additionally, we assessed the risk for patients categorized into high- and low-risk groups based on the model. Lastly, we created a Nomogram to customize the prediction of survival outcomes in CRC patients.

RESULTS

CRC samples were divided into high and low Immunity groups based on the median value of the immunity score. Between the two groups, a total of 1550 DEGs were identified and 395 differentially expressed immune-related genes (DE-IRGs) were identified by intersection with 2483 IRGs. The DE-IRGs of the high Immunity group were dominated by Cytokine receptor interactions, chemokine signaling pathways and immune cell-mediated cytotoxicity, and molecule function of immune effector process. TME analysis showed that most of the 27 immune cells and functions were highly enriched in high Immunity group, whose Immune Score, Stromal Score and ESTIMATE Score were significantly higher. Subsequently, a prognostic risk model of CRC was constructed based on 12 prognostic genes, and the accuracy and reliability of the model prediction were verified. Finally, Nomogram enabled accurate individual prediction of the survival prognosis of CRC patients.

CONCLUSIONS

Our study develops an immune-related prognostic model and Nomogram that reliably predicts survival outcomes in CRC patients and enhances understanding of the tumor immunity and molecular mechanisms of CRC.

Identification of alternative lengthening of telomeres-related genes prognosis model in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is a common malignant tumor worldwide, characterized by high mortality. This study aimed to explore the prognostic value and function of alternative lengthening of telomeres (ALT)-related genes in HCC.

METHODS

Differentially expressed genes (DEGs) were identified based on The Cancer Genome Atlas (TCGA) and then intersected with ALT-related genes to obtain ALTDEGs. Risk score model was constructed using the least absolute shrinkage and selection operator (LASSO) algorithm and Cox regression and validated with Gene Expression Omnibus (GEO) datasets. The predictive efficacy of the risk score and ALTs-score was evaluated by Kaplan-Meier curves, time-ROC curves, and the nomogram analyses. The impacts of SMG5 silencing on the HCC cell behaviors were assessed by CCK-8, wound healing, and Transwell assays.

RESULTS

A total of 500 ALTDEGs were screened and 13 genes (CDCA8, SMG5, RAD54B, FOXD2, NOL10, RRP12, CCT5, CCT4, HDAC1, DDX1, HRG, HDAC2, and PPP1CB) were identified for constructing a prognostic model. The overall survival (OS) curves, time-ROC curves, and nomograms based on the risk score or ALTs-score were developed to optimally predict the survival of HCC patients. ALTs-score was correlated with immune infiltration and confirmed its value in predicting immunotherapy outcomes. Furthermore, RT-qPCR demonstrated that eight risk signature genes were up-regulated in HCC cells. SMG5 silencing suppressed the proliferation, migration, and invasion of HCC cells. It was also found that SMG5 silencing reduced C-circle level in SNU-387 cells.

CONCLUSION

We identified new ALT-related prognostic biomarkers for HCC. SMG5 knockdown inhibited the HCC progression, which might be a promising target for HCC therapy.

The regulatory landscape of interacting RNA and protein pools in cellular homeostasis and cancer

Biological systems encompass intricate networks governed by RNA-protein interactions that play pivotal roles in cellular functions. RNA and proteins constituting 1.1% and 18% of the mammalian cell weight, respectively, orchestrate vital processes from genome organization to translation. To date, disentangling the functional fraction of the human genome has presented a major challenge, particularly for noncoding regions, yet recent discoveries have started to unveil a host of regulatory functions for noncoding RNAs (ncRNAs). While ncRNAs exist at different sizes, structures, degrees of evolutionary conservation and abundances within the cell, they partake in diverse roles either alone or in combination. However, certain ncRNA subtypes, including those that have been described or remain to be discovered, are poorly characterized given their heterogeneous nature. RNA activity is in most cases coordinated through interactions with RNA-binding proteins (RBPs). Extensive efforts are being made to accurately reconstruct RNA-RBP regulatory networks, which have provided unprecedented insight into cellular physiology and human disease. In this review, we provide a comprehensive view of RNAs and RBPs, focusing on how their interactions generate functional signals in living cells, particularly in the context of post-transcriptional regulatory processes and cancer.

NR0B1 augments sorafenib resistance in hepatocellular carcinoma through promoting autophagy and inhibiting apoptosis

NR0B1 is frequently activated in hepatocellular carcinoma (HCC). However, the role of NR0B1 is controversial in HCC. In this study, we observed that NR0B1 was an independent poor prognostic factor, negatively correlated with the overall survival of HCC and the relapse-free survival of patients treated with sorafenib. Meanwhile, NR0B1 promoted the proliferation, migration, and invasion of HCC cells, inhibited sorafenib-induced apoptosis, and elevated the IC50 of sorafenib in HCC cells. NR0B1 was further displayed to increase sorafenib-induced autophagic vesicles and activate Beclin1/LC3-II-dependent autophagy pathway. Finally, NR0B1 was revealed to transcriptionally suppress GSK3β that restrains AMPK/mTOR-driven autophagy and increases BAX-mediated apoptosis. Collectively, our study uncovered that the ectopic expression of NR0B1 augmented sorafenib-resistance in HCC cells by activating autophagy and inhibiting apoptosis. Our findings supported that NR0B1 was a detrimental factor for HCC prognosis.

The role of CSTF2 in cancer: from technology to clinical application

A protein called cleavage-stimulating factor subunit 2 (CSTF2, additionally called CSTF-64) binds RNA and is needed for the cleavage and polyadenylation of mRNA. CSTF2 is an important component subunit of the cleavage stimulating factor (CSTF), which is located on the X chromosome and encodes 557 amino acids. There is compelling evidence linking elevated CSTF2 expression to the pathological advancement of cancer and on its impact on the clinical aspects of the disease. The progression of cancers, including hepatocellular carcinoma, melanoma, prostate cancer, breast cancer, and pancreatic cancer, is correlated with the upregulation of CSTF2 expression. This review provides a fresh perspective on the investigation of the associations between CSTF2 and various malignancies and highlights current studies on the regulation of CSTF2. In particular, the mechanism of action and potential clinical applications of CSTF2 in cancer suggest that CSTF2 can serve as a new biomarker and individualized treatment target for a variety of cancer types.

Overexpression of TMEM79 combined with SMG5 is related to prognosis, tumor immune infiltration and drug sensitivity in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is a primary liver malignancy that is now relatively common worldwide. TMEM79 has been reported to play diagnostic and prognostic markers in a variety of cancers and was found to be closely associated with immune infiltration. SMG5 is associated with immune cell infiltration in HCC. Multiple nonsense-mediated mRNA processes require the involvement of SMG5. TMEM79 and SMG5 complexes may be prognostic markers for prostate cancer. However, the relationship between TMEM79 expression in HCC and prognosis, its role and mechanism of action, and its relationship with SMG5 have not been studied. This article focuses on not only the prognostic role of TMEM79 and its biological significance, including immuno-infiltration, tumor mutations and drug sensitivity, but also the interaction with SMG5 in HCC.

METHODS

Differential expression analysis and the multiCox proportional hazards regression analyses of TMEM79 and SMG5 were performed by multiple databases. Then, use IHC to verify our results. Subsequently, we used R software to analyze the clinical phenotype of both: analysis of clinicopathological features, enrichment analysis, analysis of immune infiltration, analysis of immune checkpoints, analysis of drug sensitivity, and immunotherapy.

RESULTS

Both the database studies and the results of our research group showed that TMEM79 and SMG5 were differentially expressed in HCC and normal tissues. Validation of immunohistochemistry showed that differential expression of TMEM79 and SMG5, which influenced the prognosis of patients with HCC, could be an independent prognostic factor. Results of the TCGA database study showed that TMEM79 and SMG5 were correlated with immune infiltration, immune checkpoints, drug sensitivity, and immunotherapy. We typed TMEM79-related molecules in HCC according to R software. Two types of TMEM79 correlated with clinical features, survival of patients with HCC, and immune infiltration.

CONCLUSION

TMEM79 are highly expressed in HCC and play an important role in the prognosis of patients with HCC. TMEM79 and SMG5 are positively correlated and may both associated with immune infiltration, and closely linked to immune checkpoints, drug sensitivity, and immunotherapy in HCC.

PAIP1 regulates expression of immune and inflammatory response associated genes at transcript level in liver cancer cell

Poly(A) binding protein interacting protein 1 (PAIP1) is a translation regulator and also regulate the decay of mRNA. PAIP1 has also been reported to be a marker of increased invasive potential of liver cancer. However, the roles and underlying molecular mechanism of PAIP1 in liver cancer is still unclear. Here, cell viability and the gene expression profile of liver cancer line HepG2 transfected with PAIP1 siRNA was compared with cells transfected with non-targeting control siRNA. The results showed that PAIP1 knockdown inhibited cell viability, and extensively affects expression of 893 genes at transcriptional level in HepG2 cells. Gene function analysis showed that a large number of PAIP1 up-regulated genes were enriched in term of DNA-dependent transcription and the down-regulated genes were enriched in some pathways including immune response and inflammatory response. qPCR confirmed that PAIP1 knockdown positively regulated the expression of selected immune and inflammatory factor genes in HepG2 cells. Expression analysis of TCGA revealed that PAIP1 had positive correlations with two immune associated genes IL1R2 and PTAFR in liver tumor tissue. Taken together, our results demonstrated that PAIP1 was not only a translation regulator, but also a transcription regulator in liver cancer. Moreover, PAIP1 could function as a regulatory factor of immune and inflammatory genes in liver cancer. Thus, our study provides important cues for further study on the regulatory mechanism of PAIP1 in liver cancer.

RNA-Binding Protein MEX3A Interacting with DVL3 Stabilizes Wnt/β-Catenin Signaling in Endometrial Carcinoma

Disease recurrence and metastasis lead to poor prognosis in patients with advanced endometrial carcinoma (EC). RNA-binding proteins (RBPs) are closely associated with tumor initiation and metastasis, but the function and molecular mechanisms of RBPs in EC are unclear. RBPs were screened and identified using the TCGA, GEO, and RBPTD databases. The effect of MEX3A on EC was verified by in vitro and in vivo experiments. Gene set enrichment analysis (GSEA), immunofluorescence (IF), and co-immunoprecipitation (Co-IP) were used to identify potential molecular mechanisms of action. We identified 148 differentially expressed RBPs in EC. MEX3A was upregulated and related to poor prognosis in patients with EC. In vitro and vivo experiments demonstrated that MEX3A promoted the growth, migration, and invasion capacities of EC cells. Mechanistically, DVL3, a positive regulator of the Wnt/β-catenin pathway, also increased the proliferation and metastasis of EC cells. MEX3A enhanced EMT and played a pro-carcinogenic role by interacting with DVL3 to stabilize β-catenin and upregulated the expression of its downstream target genes. MEX3A is upregulated in EC and promotes tumor progression by activating EMT and regulating the Wnt/β-catenin pathway via DVL3. MEX3A may therefore be a novel therapeutic target for EC.

Construction and validation of a prognostic model with RNA binding protein-related mRNAs for the HBV-related hepatocellular carcinoma patients

Hepatocellular carcinoma (HCC) is a common malignancy worldwide with poor clinical outcomes, and the infection of hepatitis B virus (HBV) is the leading cause of this disease. Mounting evidence shows that RNA binding proteins (RBPs) can modulate the progression of cancers. However, the functions and clinical implications of RBP-related mRNAs in HBV-related HCC remain largely unclear. Therefore, we aim to develop a prognostic model based on the RBP-related mRNAs for HBV-related HCC patients. Firstly, we identified 626 differentially expressed RBP-related mRNAs in the HBV-related HCC through the Pearson correlation analysis. Subsequently, the Kaplan-Meier survival, univariate, Least Absolute Shrinkage and Selection Operator (LASSO), and multivariate Cox regression analyses were used to construct a prognostic model comprised of five RBP-related mRNAs. Furthermore, the patients were categorized into the high- and low-risk groups by the prognostic model and the patients in the high-risk group had a poor prognosis. Additionally, the prognostic model was an independent predictor of prognosis, and the accuracy of the prognostic model was proved by the receiver operator characteristic (ROC) analysis. Furthermore, the functional enrichment analysis revealed that various cancer-promoting processes were enriched in the high-risk group. Taken together, our study may provide the HBV-related HCC biomarkers of prognosis to improve the clinical outcomes of patients.

Phosphatidylinositol glycan anchor biosynthesis, class C is a prognostic biomarker and correlates with immune infiltrates in hepatocellular carcinoma

Background and aims: The exact function of Phosphatidylinositol Glycan Anchor Biosynthesis, Class C (PIGC) gene has yet to be elucidated. In the study, we attempted to clarify the correlations of PIGC to prognosis and tumor-infiltrating lymphocytes in hepatocellular carcinoma (HCC).

Methods:PIGC expression was analyzed via the Oncomine database, Gene Expression Profiling Interactive Analysis, Hepatocellular carcinoma data base, Human Protein Atlas database and Tumor Immune Estimation Resource (TIMER). We showed the correlation of PIGC with the clinical characteristics using UALCAN. We evaluated the influence of PIGC on clinical prognosis using Kaplan-Meier plotter databases. And co-expressed genes with PIGC and its regulators were identified using LinkedOmics. The correlations between PIGC and cancer immune infiltrates were investigated via TIMER. We analyzed the drug sensitivity and immunotherapy response via R package.

Results:PIGC was found up-regulated in tumor tissues in multiple HCC cohorts, also increased in HCC patient with different clinical characteristics. High PIGC expression was associated with poorer overall survival. PIGC expression showed a strong positive association with the expression of ACBD6, a strong negative association with AGXT212. The cell components and distribution in treatment and non-treatment of HCC patients were quite distinct, which may reveal the relationship between the immunotherapy with tumor microenvironment. Notably, PIGC expression was positively correlated with infiltrating levels of immune cells.

Conclusion: These findings suggest that PIGC is correlated with prognosis and immune infiltrating in HCC, which can be used as a prognostic biomarker for determining prognosis, laying a foundation for further study of the immune regulatory role of PIGC in HCC.

Cleavage stimulation factor 2 promotes malignant progression of liver hepatocellular carcinoma by activating phosphatidylinositol 3'-kinase/protein kinase B/mammalian target of rapamycin pathway

Liver hepatocellular carcinoma (LIHC) is the most common type, comprising 75-85% of all liver malignancies. We investigated the roles of cleavage stimulation factor 2 (CSTF2) in LIHC and explored the underlying mechanisms. CSTF2 expression and its association with LIHC patient survival probability were analyzed with The Cancer Genome Atlas. CSTF2 expression in LIHC cells was assessed using western blot and quantitative real-time PCR. Alterations in CSTF2 expression were induced by cell transfection. Cell colony formation, apoptosis, proliferation, invasion, and migration were assessed using colony formation, flow cytometry, 5-ethynyl-2'-deoxyuridine, and transwell assays. Pathway enrichment analysis was performed using gene set enrichment analysis (GSEA). The expression of apoptosis-, metastasis-, and pathway-associated factors was determined via western blot. The pathway rescue assay was further performed using 740Y-P or Wortmannin. CSTF2 upregulation was observed in LIHC tissues and cells. Patients with high CSTF2 expression had a lower probability of overall survival. CSTF2 overexpression enhanced colony formation, proliferation, invasion and migration, while repressing apoptosis in LIHC cells. GSEA revealed that CSTF2 was mainly enriched in the phosphatidylinositol 3'-kinase/protein kinase B/mammalian target of rapamycin (PI3K/AKT/mTOR) pathway. Western blot analysis proved that CSTF2 overexpression activated this pathway. CSTF2 knockdown yielded the opposite effects. 740Y-P, a PI3K activator, reversed the CSTF2 knockdown-triggered effects on cell proliferation, apoptosis, invasion, and migration. Moreover, Wortmannin, a PI3K inhibitor, also reversed the CSTF2 overexpression-induced effects on cell proliferation, apoptosis, invasion, and migration. These results indicated that CSTF2 overexpression might exacerbate the malignant phenotypes of LIHC cells via activation of PI3K/AKT/mTOR pathway.

The role of RNA binding proteins in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the leading causes of overall cancer deaths worldwide with limited therapeutic options. Due to the heterogeneity of HCC pathogenesis, the molecular mechanisms underlying HCC development are not fully understood. Emerging evidence indicates that RNA-binding proteins (RBPs) play a vital role throughout hepatocarcinogenesis. Thus, a deeper understanding of how RBPs contribute to HCC progression will provide new tools for early diagnosis and prognosis of this devastating disease. In this review, we summarize the tumor suppressive and oncogenic roles of RBPs and their roles in hepatocarcinogenesis. The diagnostic and therapeutic potential of RBPs in HCC, including their limitations, are also discussed.

A novel machine learning derived RNA-binding protein gene-based score system predicts prognosis of hepatocellular carcinoma patients

Background

Although the expression of RNA-binding protein (RBP) genes in hepatocellular carcinoma (HCC) varies and is associated with tumor progression, there has been no overview study with multiple cohorts and large samples. The HCC-associated RBP genes need to be more accurately identified, and their clinical application value needs to be further explored.

Methods

First, we used the robust rank aggregation (RRA) algorithm to extract HCC-associated RBP genes from nine HCC microarray datasets and verified them in The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) cohort and International Cancer Genome Consortium (ICGC) Japanese liver cancer (ICGC-LIRI-JP) cohort. In addition, the copy number variation (CNV), single-nucleotide variant (SNV), and promoter-region methylation data of HCC-associated RBP genes were analyzed. Using the random forest algorithm, we constructed an RBP gene–based prognostic score system (RBP-score). We then evaluated the ability of RBP-score to predict the prognosis of patients. The relationships between RBP-score and other clinical characteristics of patients were analyzed.

Results

The RRA algorithm identified 30 RBP mRNAs with consistent expression patterns across the nine HCC microarray datasets. These 30 RBP genes were defined as HCC-associated RBP genes. Their mRNA expression patterns were further verified in the TCGA-LIHC and ICGC-LIRI-JP cohorts. Among these 30 RBP genes, some showed significant copy number gain or loss, while others showed differences in the methylation levels of their promoter regions. Some RBP genes were risk factors or protective factors for the prognosis of patients. We extracted 10 key HCC-associated RBP genes using the random forest algorithm and constructed an RBP-score system. RBP-score effectively predicted the overall survival (OS) and disease-free survival (DFS) of HCC patients and was associated with the tumor, node, metastasis (TNM) stage, α-fetoprotein (AFP), and metastasis risk. The clinical value of RBP-score was validated in datasets from different platforms. Cox analysis suggested that a high RBP-score was an independent risk factor for poor prognosis in HCC patients. We also successfully established a combined RBP-score+TNM LASSO-Cox model that more accurately predicted the prognosis.

Conclusion

The RBP-score system constructed based on HCC-associated RBP genes is a simple and highly effective prognostic evaluation tool. It is suitable for different subgroups of HCC patients and has cross-platform characteristics. Combining RBP-score with the TNM staging system or other clinical parameters can lead to an even greater clinical benefit. In addition, the identified HCC-associated RBP genes may serve as novel targets for HCC treatment.

A novel risk signature with 6 RNA binding proteins for prognosis prediction in patients with glioblastoma

Recent studies suggested that RNA binding proteins (RBPs) were related to the tumorigenesis and progression of glioma. This study was conducted to identify prognostic RBPs of glioblastoma (GBM) and construct an RBP signature to predict the prognosis of GBM.Univariate Cox regression analysis was carried out to identify the RBPs associated with overall survival of GBM in the The Cancer Genome Atlas (TCGA), GSE16011, and Repository for Molecular Brain Neoplasia data (Rembrandt) datasets, respectively. Overlapping RBPs from the TCGA, GSE16011, and Rembrandt datasets were selected. The biological role of prognostic RBPs was assessed by Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and protein-protein interaction analyses. Least absolute shrinkage and selection operator regression analysis and multivariate Cox regression analysis were used to construct an RBP-related risk signature. The prognostic value of RBP signature was measured by Kaplan-Meier method and time-dependent receiver operating characteristic curve. A nomogram based on independent prognostic factors was established to predict survival for GBM. The CGGA cohort was used as the validation cohort for external validation.This study identified 27 RBPs associated with the prognosis of GBM and constructed a 6-RPBs signature. Kaplan-Meier curves suggested that high-risk score was associated with a poor prognosis. Area under the curve of 1-, 3-, and 5-year overall survival was 0.618, 0.728, and 0.833 for TCGA cohort, 0.655, 0.909, and 0.911 for GSE16011 cohort, and 0.665, 0.792, and 0.781 for Rembrandt cohort, respectively. A nomogram with 4 parameters (age, chemotherapy, O6-methylguanine-DNA methyltransferase promoter status, and risk score) was constructed. The calibration curve showed that the nomogram prediction was in good agreement with the actual observation.The 6-RBPs signature could effectively predict the prognosis of GBM, and our findings supplemented the prognostic index of GBM to a certain extent.

Prognostic prediction and gene regulation network of EIF2S2 in hepatocellular carcinoma based on data mining

BACKGROUND

Hepatocellular carcinoma (HCC) is a malignant tumor with a high fatality rate, predicting poor prognosis and therapeutic effect. Screening potential prognostic genes in HCC could be a creative way to advance clinical treatment. Eukaryotic translation initiation factor 2 subunit beta (EIF2S2) has reportedly been linked to several tumors, including liver cancer, but the prognostic predictions remain unknown. Therefore, we aimed to clarify the prognostic role and interaction network of EIF2S2 in HCC using bioinformatics data.

METHODS

We screened EIF2S2 using the Oncomine, Ualcan, and TCGA databases. R software was used to analyze the mRNA level and clinicopathological characteristics of hepatocellular carcinoma. Evaluation of the correlations between EIF2S2 and patients' survival was made using the Kaplan-Meier curves and Cox proportional hazards regression model. Then, the influence of EIF2S2 gene mutations on the prognosis of patients was explored by cBioPortal. The protein-protein interaction network of 50 similar genes related to EIF2S2 was implemented by GEPIA2 and Metascape. The LinkedOmics database allowed us to carry out Gene Set Enrichment Analysis. Finally, we constructed the EIF2S2 kinase, miRNA, and transcription factor target networks using GeneMANIA.

RESULTS

EIF2S2 mRNA was overexpressed in HCC and was closely associated with clinicopathological features, including gender, age, race, tumor grade, and stage. There was no correlation between EIF2S2 genetic mutations and prognostic survival. Combining Cox proportional hazards regression model analyses, high-expressed EIF2S2 predicted poor prognosis in HCC patients. Additionally, we screened the top three EIF2S2-related genes (PFDN4, HM13, and SNRPD1), the 50 similar genes, and then constructed a 50-similar-gene protein-protein interaction network identified by the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways using Metascape. EIF2S2 target networks in HCC were identified in kinase, miRNA, and transcription factor networks, including the mitogen-activated protein kinase 1 (MAPK1), miRNAs (Mir-144), and transcription factors (GGAANCGGAANY_UNKNOWN) using GeneMANIA.

CONCLUSIONS

EIF2S2 plays a crucial role in the gene-regulating network of HCC and may be a potential prognostic marker or therapeutic target for HCC patients.

An Epitope Platform for Safe and Effective HTLV-1-Immunization: Potential Applications for mRNA and Peptide-Based Vaccines

Human T-cell lymphotropic virus type 1 (HTLV-1) infection affects millions of individuals worldwide and can lead to severe leukemia, myelopathy/tropical spastic paraparesis, and numerous other disorders. Pursuing a safe and effective immunotherapeutic approach, we compared the viral polyprotein and the human proteome with a sliding window approach in order to identify oligopeptide sequences unique to the virus. The immunological relevance of the viral unique oligopeptides was assessed by searching them in the immune epitope database (IEDB). We found that HTLV-1 has 15 peptide stretches each consisting of uniquely viral non-human pentapeptides which are ideal candidate for a safe and effective anti-HTLV-1 vaccine. Indeed, experimentally validated HTLV-1 epitopes, as retrieved from the IEDB, contain peptide sequences also present in a vast number of human proteins, thus potentially instituting the basis for cross-reactions. We found a potential for cross-reactivity between the virus and the human proteome and described an epitope platform to be used in order to avoid it, thus obtaining effective, specific, and safe immunization. Potential advantages for mRNA and peptide-based vaccine formulations are discussed.

The landscape of tumors-infiltrate immune cells in papillary thyroid carcinoma and its prognostic value

Introduction

Thyroid cancer is a very common malignant tumor in the endocrine system, while the incidence of papillary thyroid carcinoma (PTC) throughout the world also shows a trend of increase year by year. In this study, we constructed two models: ICIscore and Riskscore. Combined with these two models, we can make more accurate and reasonable inferences about the prognosis of PTC patients.

Methods

We selected 481 PTC samples from TCGA and 147 PTC samples from GEO (49 samples in GSE33630, 65 samples in GSE35570 and 33 samples in GSE60542). We performed consistent clustering for them and divided them into three subgroups and screened differentially expressed genes from these three subgroups. Then we divided the differential genes into three subtypes. We also distinguished the up-regulated and down-regulated genes and calculated ICIscore for each PTC sample. ICIscore consists of two parts: (1) the PCAu was calculated from up-regulated genes. (2) the PCAd was calculated from down-regulated genes. The PCAu and PCAd of each sample were the first principal component of the relevant gene. What’s more, we divided the patients into two groups and constructed mRNA prognostic signatures. Additionally we also verified the independent prognostic value of the signature.

Results

Though ICIscore, we were able to observe the relationship between immune infiltration and prognosis. The result suggests that the activation of the immune system may have both positive and negative consequences. Though Riskscore, we could make more accurate predictions about the prognosis of patients with PTC. Meanwhile, we also generated and validated the ICIscore group and Riskscore group respectively.

Conclusion

All the research results show that by combining the two models constructed, ICIscore and Riskscore, we can make a more accurate and reasonable inference about the prognosis of patients with clinical PTC patients. This suggests that we can provide more effective and reasonable treatment plan for clinical PTC patients.