Screening and Identification of Neutrophil Extracellular Trap-related Diagnostic Biomarkers for Pediatric Sepsis by Machine Learning.

Neutrophil extracellular trap (NET) is released by neutrophils to trap invading pathogens and can lead to dysregulation of immune responses and disease pathogenesis. However, systematic evaluation of NET-related genes (NETRGs) for the diagnosis of pediatric sepsis is still lacking. Three datasets were taken from the Gene Expression Omnibus (GEO) database: GSE13904, GSE26378, and GSE26440. After NETRGs and differentially expressed genes (DEGs) were identified in the GSE26378 dataset, crucial genes were identified by using LASSO regression analysis and random forest analysis on the genes that overlapped in both DEGs and NETRGs. These crucial genes were then employed to build a diagnostic model. The diagnostic model's effectiveness in identifying pediatric sepsis across the three datasets was confirmed through receiver operating characteristic curve (ROC) analysis. In addition, clinical pediatric sepsis samples were collected to measure the expression levels of important genes and evaluate the diagnostic model's performance using qRT-PCR in identifying pediatric sepsis in actual clinical samples. Next, using the CIBERSORT database, the relationship between invading immune cells and diagnostic markers was investigated in more detail. Lastly, to evaluate NET formation, we measured myeloperoxidase (MPO)-DNA complex levels using ELISA. A group of five important genes (MME, BST1, S100A12, FCAR, and ALPL) were found among the 13 DEGs associated with NET formation and used to create a diagnostic model for pediatric sepsis. Across all three cohorts, the sepsis group had consistently elevated expression levels of these five critical genes as compared to the normal group. Area under the curve (AUC) values of 1, 0.932, and 0.966 indicate that the diagnostic model performed exceptionally well in terms of diagnosis. Notably, when applied to the clinical samples, the diagnostic model also showed good diagnostic capacity with an AUC of 0.898, outperforming the effectiveness of traditional inflammatory markers such as PCT, CRP, WBC, and NEU%. Lastly, we discovered that children with high ratings for sepsis also had higher MPO-DNA complex levels. In conclusion, the creation and verification of a five-NETRGs diagnostic model for pediatric sepsis performs better than established markers of inflammation.

Predictive Value of a Diagnostic Five-Gene Biomarker for Pediatric Sepsis.

Background: Pediatric sepsis has a very high morbidity and mortality rate. The purpose of this study was to evaluate diagnostic biomarkers and immune cell infiltration in pediatric sepsis. Methods: Three datasets (GSE13904, GSE26378, and GSE26440) were downloaded from the gene expression omnibus (GEO) database. After identifying overlapping genes in differentially expressed genes (DEGs) and modular sepsis genes selected via a weighted gene co-expression network (WGCNA) in the GSE26378 dataset, pivotal genes were further identified by using LASSO regression and random forest analysis to construct a diagnostic model. Receiver operating characteristic curve (ROC) analysis was used to validate the efficacy of the diagnostic model for pediatric sepsis. Furthermore, we used qRT-PCR to detect the expression levels of pivotal genes and validate the diagnostic model's ability to diagnose pediatric sepsis in 65 actual clinical samples. Results: Among 294 overlapping genes of DEGs and modular sepsis genes, five pivotal genes (STOM, MS4A4A, CD177, MMP8, and MCEMP1) were screened to construct a diagnostic model of pediatric sepsis. The expression of the five pivotal genes was higher in the sepsis group than in the normal group. The diagnostic model showed good diagnostic ability with AUCs of 1, 0.986, and 0.968. More importantly, the diagnostic model showed good diagnostic ability with AUCs of 0.937 in the 65 clinical samples and showed better efficacy compared to conventional inflammatory indicators such as procalcitonin (PCT), white blood cell (WBC) count, C-reactive protein (CRP), and neutrophil percentage (NEU%). Conclusion: We developed and tested a five-gene diagnostic model that can reliably identify pediatric sepsis and also suggest prospective candidate genes for peripheral blood diagnostic testing in pediatric sepsis patients.

Increased PAFAH1B3 was associated with poor prognosis and T-cell exhaustion microenvironment in hepatocellular carcinoma.

The link between T-cell exhaustion (TEX) and PAFAH1B3 in hepatocellular carcinoma (HCC) remains unknown, even though both of them are related to overall survival. PAFAH1B3 expression was investigated in TCGA and ICGC data, and 50 paired clinical tissue section samples were used for qRT-PCR and immunohistochemistry (IHC) confirmation. The Immunocell Abundance Identifier (ImmuCellAI) was used to precisely calculate the abundance of TEX, and its accuracy was verified by single-cell RNA-seq and labeling of CD8 + T cells in clinical tissue sections. The IMVigor 210 cohort was used to demonstrate the predictive value of PAFAH1B3 for immunotherapy efficacy. Increased PAFAH1B3 is a standalone effector of poor prognosis in HCC patients. Patients who had greater PAFAH1B3 levels had more TEX infiltration. PAFAH1B3 expression was increased in TEX in the single-cell RNA-seq data. Patients with high PAFAH1B3 expression were more likely to respond favorably to PD1/PD-L1 treatment. In conclusion, PAFAH1B3 is closely related to TEX in the tumor microenvironment (TME) and can be a useful indicator for PD1/PD-L1 therapy.

Lysosome-Related Diagnostic Biomarkers for Pediatric Sepsis Integrated by Machine Learning.

Background: There is currently no biomarker that can reliably identify sepsis, despite recent scientific advancements. We systematically evaluated the value of lysosomal genes for the diagnosis of pediatric sepsis. Methods: Three datasets (GSE13904, GSE26378, and GSE26440) were obtained from the gene expression omnibus (GEO) database. LASSO regression analysis and random forest analysis were employed for screening pivotal genes to construct a diagnostic model between the differentially expressed genes (DEGs) and lysosomal genes. The efficacy of the diagnostic model for pediatric sepsis identification in the three datasets was validated through receiver operating characteristic curve (ROC) analysis. Furthermore, a total of 30 normal samples and 35 pediatric sepsis samples were gathered to detect the expression levels of crucial genes and assess the diagnostic model's efficacy in diagnosing pediatric sepsis in real clinical samples through real-time quantitative PCR (qRT-PCR). Results: Among the 83 differentially expressed genes (DEGs) related to lysosomes, four key genes (STOM, VNN1, SORT1, and RETN) were identified to develop a diagnostic model for pediatric sepsis. The expression levels of these four key genes were consistently higher in the sepsis group compared to the normal group across all three cohorts. The diagnostic model exhibited excellent diagnostic performance, as evidenced by area under the curve (AUC) values of 1, 0.971, and 0.989. Notably, the diagnostic model also demonstrated strong diagnostic ability with an AUC of 0.917 when applied to the 65 clinical samples, surpassing the efficacy of conventional inflammatory indicators such as procalcitonin (PCT), white blood cell (WBC) count, C-reactive protein (CRP), and neutrophil percentage (NEU%). Conclusion: A four-gene diagnostic model of lysosomal function was devised and validated, aiming to accurately detect pediatric sepsis cases and propose potential target genes for lysosomal intervention in affected children.

Prognostic clinical phenotypes associated with tumor stemness in the immune microenvironment of T-cell exhaustion for hepatocellular carcinoma.

T-cell exhaustion (TEX) and high heterogeneity of cancer stem cells (CSCs) are associated with progression, metastasis, and treatment resistance in hepatocellular carcinoma (HCC). Here, we aim to characterize TEX-stemness-related genes (TEXSRGs) and screen for HCC patients who are more sensitive to immunotherapy. The immune cell abundance identifier (ImmuCellAI) was utilized to precisely evaluate the abundance of TEX and screen TEX-related genes. The stemness index (mRNAsi) of samples was analyzed through the one-class logistic regression (OCLR) algorithm. Application of the non-negative matrix decomposition algorithm (NMF) for subtype identification of HCC samples. The different subtypes were assessed for differences in prognosis, tumor microenvironment (TME) landscape, and immunotherapy treatment response. Then, the TEXSRGS-score, which can accurately forecast the survival outcome of HCC patients, was built by LASSO-Cox and multivariate Cox regression, and experimentally validated for the most important TEXSRGs. We also analyzed the expression of TEXSRGs and the infiltration of CD8+ T cells in clinical samples using qRT-PCR and immunohistochemistry (IHC). Based on 146 TEXSRGs, we found two distinct clinical phenotypes with different TEX infiltration abundance, tumor stemness index, enrichment pathways, mutational landscape, and immune cell infiltration through the non-negative matrix decomposition algorithm (NMF), which were confirmed in the ICGC dataset. Utilizing eight TEXSRGs linked to clinical outcome, we created a TEXSRGs-score model to further improve the clinical applicability. Patients can be divided into two groups with substantial differences in the characteristics of immune cell infiltration, TEX infiltration abundance, and survival outcomes. The results of qRT-PCR and IHC analysis showed that PAFAH1B3, ZIC2, and ESR1 were differentially expressed in HCC and normal tissues and that patients with high TEXSRGs-scores had higher TEX infiltration abundance and tumor stemness gene expression. Regarding immunotherapy reaction and immune cell infiltration, patients with various TEXSRGs-score levels had various clinical traits. The outcome and immunotherapy efficacy of patients with low TEXSRGs-score was favorable. In conclusion, we identified two clinical subtypes with different prognoses, TEX infiltration abundance, tumor cell stemness index, and immunotherapy response based on TEXSRGs, and developed and validated a TEXSRGs-score capable of accurately predicting survival outcomes in HCC patients by comprehensive bioinformatics analysis. We believe that the TEXSRGs-score has prospective clinical relevance for prognostic assessment and may help physicians select prospective responders in preference to current immune checkpoint inhibitors (ICIs).

Telomere-related prognostic biomarkers for survival assessments in pancreatic cancer.

Human telomeres are linked to genetic instability and a higher risk of developing cancer. Therefore, to improve the dismal prognosis of pancreatic cancer patients, a thorough investigation of the association between telomere-related genes and pancreatic cancer is required. Combat from the R package "SVA" was performed to correct the batch effects between the TCGA-PAAD and GTEx datasets. After differentially expressed genes (DEGs) were assessed, we constructed a prognostic risk model through univariate Cox regression, LASSO-Cox regression, and multivariate Cox regression analysis. Data from the ICGC, GSE62452, GSE71729, and GSE78229 cohorts were used as test cohorts for validating the prognostic signature. The major impact of the signature on the tumor microenvironment and its response to immune checkpoint drugs was also evaluated. Finally, PAAD tissue microarrays were fabricated and immunohistochemistry was performed to explore the expression of this signature in clinical samples. After calculating 502 telomere-associated DEGs, we constructed a three-gene prognostic signature (DSG2, LDHA, and RACGAP1) that can be effectively applied to the prognostic classification of pancreatic cancer patients in multiple datasets, including TCGA, ICGC, GSE62452, GSE71729, and GSE78229 cohorts. In addition, we have screened a variety of tumor-sensitive drugs targeting this signature. Finally, we also found that protein levels of DSG2, LDHA, and RACGAP1 were upregulated in pancreatic cancer tissues compared to normal tissues by immunohistochemistry analysis. We established and validated a telomere gene-related prognostic signature for pancreatic cancer and confirmed the upregulation of DSG2, LDHA, and RACGAP1 expression in clinical samples, which may provide new ideas for individualized immunotherapy.

Prognostic Lysosome-Related Biomarkers for Predicting Drug Candidates in Hepatocellular Carcinoma: An Insilco Analysis.

Background: Lysosomes play an important role in enhancing tumorigenesis and chemoresistance in hepatocellular carcinoma (HCC). Therefore, a detailed analysis of the role of lysosome-related genes could improve the poor prognosis of HCC patients. Methods: Lysosome-associated genes were downloaded from the GO and Genome Enrichment Analysis (GSEA) databases. After analyzing lysosome-associated differentially expressed genes (DEGs) between the TCGA and GTEx cohorts, we used univariate Cox regression, LASSO-Cox regression, stepwise Cox regression, and multivariate Cox regression analyses to build a predictive risk model. The ICGC cohort was used as a test cohort for the prognostic signature's validation. It was also assessed how significantly the signature affected the tumor microenvironment (TME) and sensitivity to immune checkpoint inhibitors. To investigate the expression of this signature in clinical samples, qRT-PCR and immunohistochemistry (IHC) were carried out in 50 normal tissues and 59 HCC tissues. Results: A total of 894 lysosome-associated genes were obtained. After identifying 113 lysosome-associated DEGs, we constructed a five-gene prognostic signature (RRAGD, AP1M2, CRHBP, NCSTN, and SLCO4C1) that can be effectively applied to the prognostic classification of HCC patients in TCGA and ICGC cohorts. Additionally, we discovered that this signature can affect the proportion of macrophage infiltration in TME. We also evaluated several tumor-sensitive medicines that affect this signature. Finally, we discovered that HCC tissues had lower amounts of CRHBP compared to normal tissues by the qRT-PCR and IHC assay. Conclusion: We developed and validated a predictive signature of five lysosome-related genes for HCC patients and verified the downregulation of CRHBP expression in clinical samples, which may provide fresh perspectives for customized immunotherapy.

Identification and validation of a novel four-gene diagnostic model for neonatal early-onset sepsis with bacterial infection.

Neonatal early-onset sepsis (EOS) has unfortunately been the third leading cause of neonatal death worldwide. The current study is aimed at discovering reliable biomarkers for the diagnosis of neonatal EOS through transcriptomic analysis of publicly available datasets. Whole blood mRNA expression profiling of neonatal EOS patients in the GSE25504 dataset was downloaded and analyzed. The binomial LASSO model was constructed to select genes that most accurately predicted neonatal EOS. Then, ROC curves were generated to assess the performance of the predictive features in differentiating between neonatal EOS and normal infants. Finally, the miRNA-mRNA network was established to explore the potential biological mechanisms of genes within the model. Four genes (CST7, CD3G, CD247, and ANKRD22) were identified that most accurately predicted neonatal EOS and were subsequently used to construct a diagnostic model. ROC analysis revealed that this diagnostic model performed well in differentiating between neonatal EOS and normal infants in both the GSE25504 dataset and our clinical cohort. Finally, the miRNA-mRNA network consisting of the four genes and potential target miRNAs was constructed. Through bioinformatics analysis, a diagnostic four-gene model that can accurately distinguish neonatal EOS in newborns with bacterial infection was constructed, which can be used as an auxiliary test for diagnosing neonatal EOS with bacterial infection in the future. CONCLUSION: In the current study, we analyzed gene expression profiles of neonatal EOS patients from public databases to develop a genetic model for predicting sepsis, which could provide insight into early molecular changes and biological mechanisms of neonatal EOS. WHAT IS KNOWN: • Infants with suspected EOS usually receive empiric antibiotic therapy directly after birth. • When blood cultures are negative after 48 to 72 hours, empirical antibiotic treatment is often halted. Needless to say, this is not a short time. Additionally, because of the concern for inadequate clinical sepsis production and the limited sensitivity of blood cultures, the duration of antibiotic therapy for the kid is typically extended. WHAT IS NEW: • We established a 4-gene diagnostic model of neonatal EOS with bacterial infection by bioinformatics analysis method. The model has better diagnostic performance compared with conventional inflammatory indicators such as CRP, Hb, NEU%, and PCT.

Regulatory T-cells-related signature for identifying a prognostic subtype of hepatocellular carcinoma with an exhausted tumor microenvironment.

Regulatory T-Cells (Tregs) are important in the progression of hepatocellular cancer (HCC). The goal of this work was to look into Tregs-related genes and develop a Tregs-related prognostic model. We used the weighted gene co-expression network analysis (WGCNA) to look for Tregs-related genes in the TCGA, ICGC, and GSE14520 cohorts and then used the non-negative matrix factorization (NMF) algorithm to find Tregs-related subpopulations. The LASSO-Cox regression approach was used to determine Tregs-related genes, which were then condensed into a risk score. A total of 153 overlapping genes among the three cohorts were considered Tregs-related genes. Based on these genes, two Tregs-associated clusters that varied in both prognostic and biological characteristics were identified. When compared with Cluster 1, Cluster 2 was a TME-exhausted HCC subpopulation with substantial immune cell infiltration but a poor prognosis. Five Tregs-related genes including HMOX1, MMP9, CTSC, SDC3, and TNFRSF11B were finally used to construct a prognostic model, which could accurately predict the prognosis of HCC patients in the three datasets. Patients in the high-risk scores group with bad survival outcomes were replete with immune/inflammatory responses, but exhausted T cells and elevated PD-1 and PD-L1 expression. The results of qRT-PCR and immunohistochemical staining (IHC) analysis in clinical tissue samples confirmed the above findings. Moreover, the signature also accurately predicted anti-PD-L1 antibody responses in the IMvigor210 dataset. Finally, HMOX1, MMP9, and TNFRSF11B were expressed differently in Hep3B and Huh7 cells after being treated with a PD1/PD-L1 inhibitor. In conclusion, our study uncovered a Tregs-related prognostic model that could identify TME- exhausted subpopulations and revealed that PD1/PD-L1 inhibitors could alter the expression levels of HMOX1, MMP9, and TNFRSF11B in Hep3B and Huh7 cells, which might help us better understand Tregs infiltration and develop personalized immunotherapy treatments for HCC patients.

A novel stemness-hypoxia-related signature for prognostic stratification and immunotherapy response in hepatocellular carcinoma.

BACKGROUND: The specific differentiation potential, unlimited proliferation, and self-renewal capacity of cancer stem cells (CSCs) are closely related to the occurrence, recurrence, and drug resistance of hepatocellular carcinoma (HCC), as well as hypoxia. Therefore, an in-depth analysis of the relationship between HCC stemness, oxygenation status, and the effectiveness of immunotherapy is necessary to improve the poor prognosis of HCC patients. METHODS: The weighted gene co-expression network analysis (WGCNA) was utilized to find hypoxia-related genes, and the stemness index (mRNAsi) was evaluated using the one-class logistic regression (OCLR) technique. Based on stemness-hypoxia-related genes (SHRGs), population subgroup categorization using NMF cluster analysis was carried out. The relationship between SHRGs and survival outcomes was determined using univariate Cox regression. The LASSO-Cox regression strategy was performed to investigate the quality and establish the classifier associated with prognosis. The main effect of risk scores on the tumor microenvironment (TME) and its response to immune checkpoint drugs was also examined. Finally, qRT-PCR was performed to explore the expression and prognostic value of the signature in clinical samples. RESULTS: After identifying tumor stemness- and hypoxia-related genes through a series of bioinformatics analyses, we constructed a prognostic stratification model based on these SHRGs, which can be effectively applied to the prognostic classification of HCC patients and the prediction of immune checkpoint inhibitors (ICIs) efficacy. Independent validation of the model in the ICGC cohort yielded good results. In addition, we also constructed hypoxic cell models in Herp3B and Huh7 cells to verify the expression of genes in the prognostic model and found that C7, CLEC1B, and CXCL6 were not only related to the tumor stemness but also related to hypoxia. Finally, we found that the constructed signature had a good prognostic value in the clinical sample. CONCLUSIONS: We constructed and validated a stemness-hypoxia-related prognostic signature that can be used to predict the efficacy of ICIs therapy. We also verified that C7, CLEC1B, and CXCL6 are indeed associated with stemness and hypoxia through a hypoxic cell model, which may provide new ideas for individualized immunotherapy.

Identification of HM13 as a prognostic indicator and a predictive biomarker for immunotherapy in hepatocellular carcinoma.

BACKGROUND: Histocompatibility minor 13 (HM13) is a signal sequence stubbed intramembrane cleavage catalytic protein that is essential for cell signaling, intracellular communication, and cancer. However, the expression of HM13 and its prognostic value, association with tumor-infiltrating immune cells (TIICs) in the microenvironment, and potential to predict immunotherapeutic response in HCC are unknown. METHODS: The HM13 expression, clinicopathology analysis, and its influence on survival were analyzed in multiple public databases and further verified in collected HCC and normal tissues by qRT-PCR and immunohistochemistry staining assay (IHC). Furthermore, the lentivirus vector encoding HM13-shRNA to manipulate HM13 expression was selected to investigate whether HM13 could influence the malignant growth and metastasis potential of HCC cells. Finally, significant impacts of HM13 on the HCC tumor microenvironment (TME) and reaction to immune checkpoint inhibitors were analyzed. RESULTS: Upregulated HM13 was substantially correlated with poor prognosis in patients with HCC, and could facilitate the proliferation and migratory potential of HCC cells. Additionally, patients with high HM13 expression might be more sensitive to immunotherapy. CONCLUSIONS: HM13 might be a prognostic biomarker and potential molecular therapeutic target for HCC.

Endoplasmic Reticulum Stress-Related Signature for Predicting Prognosis and Immune Features in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) with cancer cells under endoplasmic reticulum (ER) stress has a poor prognosis. This study is aimed at discovering credible biomarkers for predicting the prognosis of HCC based on ER stress-related genes (ERSRGs). We constructed a novel four-ERSRG prognostic risk model, including PON1, AGR2, SSR2, and TMCC1, through a series of bioinformatic approaches, which can accurately predict survival outcomes in HCC patients. Higher risk scores were linked to later grade, recurrence, advanced TNM stage, later T stage, and HBV infection. In addition, 20 fresh frozen tumors and normal tissues from HCC patients were collected and used to validate the genes expressed in the signature by qRT-PCR and immunohistochemical (IHC) assays. Moreover, we found the ER stress-related signature could reflect the infiltration levels of different immune cells in the tumor microenvironment (TME) and forecast the efficacy of immune checkpoint inhibitor (ICI) treatment. Finally, we created a nomogram incorporating this ER stress-related signature. In conclusion, our constructed four-gene risk model associated with ER stress can accurately predict survival outcomes in HCC patients, and the model's risk score is associated with the poor clinical classification.

A novel Cuproptosis-related LncRNA signature to predict prognosis in hepatocellular carcinoma.

Increased intracellular toxicity due to an imbalance in copper homeostasis caused by copper ion accumulation could regulate the rate of cancer cell growth and proliferation. The goal of this study was to create a novel Cuproptosis-related lncRNA signature that may be utilized to predict survival and immunotherapy in HCC patients. Cuproptosis-associated lncRNAs and differentially expressed lncRNAs between HCC tumor tissue and normal tissue were discovered first. By LASSO-Cox analysis, the overlapping lncRNAs were then utilized to build a Cuproptosis-associated lncRNA signature, which might be used to predict patient prognosis and responsiveness to immune checkpoint blockade (ICB) therapy. Differences in the infiltration of immune cell subpopulations between high and low-risk score subgroups were also analyzed. Moreover, a nomogram based on the Cuproptosis-associated lncRNA signature and clinical features was developed and demonstrated to have good predictive potential. Finally, qRT-PCR was performed in HerpG2 and MHCC-97H cell lines to explore whether these lncRNAs were indeed involved in the process of Cuproptosis. In summary, we created a prognostic lncRNA profile linked to Cuproptosis to forecast response to immunotherapy, which may provide a new potential non-apoptotic therapeutic perspective for HCC patients.

Platelet-Related Molecular Subtype to Predict Prognosis in Hepatocellular Carcinoma.

Purpose: Complex crosstalk between tumor cells and platelets is closely related to the development, relapse, and drug resistance of hepatocellular carcinoma (HCC). Therefore, an intensive analysis of the relationship between platelet-related genes and the effectiveness of immunotherapy is necessary for improving the poor prognosis of HCC patients. Methods: Genes associated with platelets in the GeneCards database were collected and were used to identify molecular subtypes using a non-negative matrix decomposition algorithm (NMF) and constructed a platelet-related genes-based prognostic stratification model by the LASSO-Cox regression and stepwise Cox regression analysis. The effect of this feature on the immune microenvironment of HCC and the response to immune checkpoint inhibitors was also explored. Results: After identifying two molecular subtypes, we constructed a platelet-related genes-based prognostic stratification model that can be effectively used for immune checkpoint inhibitor (PD1, PD-L1, PD-L2, and CTLA4) efficacy and prognosis prediction in HCC patients, which was subsequently validated using patient samples from ICGC, GSE14520 and a small sample size clinical cohort. We also found downregulation of PAFAH1B3 remarkably inhibited the proliferation and migration ability of Hep3B cells by cytological experiments. Conclusion: We constructed a prognostic classifier based on platelet-related genes that could effectively classify HCC patients for prognostic prediction and provide new light on the selection of optimal individualized antiplatelet therapy for HCC patients in future clinical practice.

A novel tumor doubling time-related immune gene signature for prognosis prediction in hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) has become a global health issue of wide concern due to its high prevalence and poor therapeutic efficacy. Both tumor doubling time (TDT) and immune status are closely related to the prognosis of HCC patients. However, the association between TDT-related genes (TDTRGs) and immune-related genes (IRGs) and the value of their combination in predicting the prognosis of HCC patients remains unclear. The current study aimed to discover reliable biomarkers for anticipating the future prognosis of HCC patients based on the relationship between TDTRGs and IRGs. METHODS: Tumor doubling time-related genes (TDTRGs) were acquired from GSE54236 by using Pearson correlation test and immune-related genes (IRGs) were available from ImmPort. Prognostic TDTRGs and IRGs in TCGA-LIHC dataset were determined to create a prognostic model by the LASSO-Cox regression and stepwise Cox regression analysis. International Cancer Genome Consortium (ICGC) and another cohort of individual clinical samples acted as external validations. Additionally, significant impacts of the signature on HCC immune microenvironment and reaction to immune checkpoint inhibitors were observed. RESULTS: Among the 68 overlapping genes identified as TDTRG and IRG, a total of 29 genes had significant prognostic relevance and were further selected by performing a LASSO-Cox regression model based on the minimum value of λ. Subsequently, a prognostic three-gene signature including HECT domain and ankyrin repeat containing E3 ubiquitin protein ligase 1 (HACE1), C-type lectin domain family 1 member B (CLEC1B), and Collectin sub-family member 12 (COLEC12) was finally identified by stepwise Cox proportional modeling. The signature exhibited superior accuracy in forecasting the survival outcomes of HCC patients in TCGA, ICGC and the independent clinical cohorts. Patients in high-risk subgroup had significantly increased levels of immune checkpoint molecules including PD-L1, CD276, CTLA4, CXCR4, IL1A, PD-L2, TGFB1, OX40 and CD137, and are therefore more sensitive to immune checkpoint inhibitors (ICIs) treatment. Finally, we first found that overexpression of CLEC1B inhibited the proliferation and migration ability of HuH7 cells. CONCLUSIONS: In summary, the prognostic signature based on TDTRGs and IRGs could effectively help clinicians classify HCC patients for prognosis prediction and individualized immunotherapies.

A Novel Five-Gene Signature for Prognosis Prediction in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) has been a global health issue and attracted wide attention due to its high incidence and poor outcomes. In this study, our purpose was to explore an effective prognostic marker for HCC. Five cohort profile datasets from GEO (GSE25097, GSE36376, GSE62232, GSE76427 and GSE101685) were integrated with TCGA-LIHC and GTEx dataset to identify differentially expressed genes (DEGs) between normal and cancer tissues in HCC patients, then 5 upregulated differentially expressed genes and 32 downregulated DEGs were identified as common DEGs in total. Next, we systematically explored the relationship between the expression of 37 common DEGs in tumor tissues and overall survival (OS) rate of HCC patients in TCGA and constructed a novel prognostic model composed of five genes (AURKA, PZP, RACGAP1, ACOT12 and LCAT). Furthermore, the predicted performance of the five-gene signature was verified in ICGC and another independent clinical samples cohort, and the results demonstrated that the signature performed well in predicting the OS rate of patients with HCC. What is more, the signature was an independent hazard factor for HCC patients when considering other clinical factors in the three cohorts. Finally, we found the signature was significantly associated with HCC immune microenvironment. In conclusion, the prognostic five-gene signature identified in our present study could efficiently classify patients with HCC into subgroups with low and high risk of longer overall survival time and help clinicians make decisions for individualized treatment.

Prognostic Significance of Pregnancy Zone Protein and Its Correlation with Immune Infiltrates in Hepatocellular Carcinoma.

AIM: Human pregnancy zone protein (PZP) is a pregnancy-related protein which is increased dramatically during pregnancy. However, the expression of PZP and its prognostic value, association with tumor-infiltrating immune cells (TIICs) in microenvironment and potential biological process in HCC were unclear. METHODS: The PZP expression, clinicopathology analysis and its influence on survival were analyzed by GEPIA and HPA. Fifty-nine HCC samples and 30 corresponding noncancerous tissues were collected and retrospectively analyzed to verify the results of bioinformatics analysis. Further, TIMER and CIBERSORT were performed to identify the significantly alerted biological process and affections of PZP expression on the immune system in patients with HCC. Finally, IHC assay of CD4+ T cells and Treg cells was performed to confirm the results of immune infiltrates analysis by TIMER and CIBERSORT. RESULTS: PZP expression was downregulated in HCC tissues and its low level was substantially correlated with poor prognosis in patients with HCC. TIMER analysis showed that PZP expression had a positive correlation with the levels of macrophage and neutrophil. Furthermore, CIBERSORT analysis showed that resting memory CD4 T cells were increased in high PZP expression group, while the results of Tregs were the opposite. Finally, the IHC results of CD4+ T cells and Treg cells showed that only Tregs were negatively associated with PZP expression. CONCLUSION: PZP was identified as a novel prognosis biomarker of HCC and might play a vital role in the regulation and recruitment of TIICs in HCC immune microenvironment.

Expression and Prognostic Significance of BANF1 in Triple-Negative Breast Cancer.

AIM: To investigate the expression of barrier-to-autointegration factor 1 (BANF1) and its prognostic significance in triple-negative breast cancer (TNBC). METHODS: BANF1 immunohistochemical detection was performed in 60 TNBC specimens and 30 normal control tissues. Real-time PCR was performed to assess the expression of BANF1 gene in TNBC tissues and their correlations with proliferation and metastasis. Kaplan-Meier survival analysis was used to assess the effect of BANF1 expression on the relapse-free survival (RFS) of TNBC patients. Univariable and multivariable Cox proportional hazards regression model analysis was used to confirm independent prognostic factors. RESULTS: Expression of BANF1 in TNBC was significantly higher than that of the normal control group (p<0.001), and it was related to the status of lymph node metastasis and TNM staging (p<0.05), and not related to age and tumor size (p>0.05). BANF1 expression has a positive correlation with MKI67 and MTA1 expression (p<0.01). Univariable analysis showed that expression of BANF1, the status of lymph node metastasis and TNM stage were related to the relapse-free survival (RSF) of TNBC patients (p<0.001, p=0.001, p=0.013, respectively). Multivariable Cox regression indicated that the status of lymph node metastasis was an independent prognostic factor for TNBC patients (p<0.001). The survival curve suggested that the survival times for TNBC patients with high BANF1 expression have no difference compared with that for the low-expression patients (p>0.05). CONCLUSION: Expression of BANF1 may play a role in the occurrence and development of TNBC. Lymph node metastasis was the only independent prognostic factor predicts a poor prognosis.

TPX2 siRNA regulates growth and invasion of esophageal cancer cells.

PURPOSE: Observe how specific small RNA interference (siRNA) aimed at TPX2 gene suppresses TPX2 gene expression in esophageal cancer EC9706 cells and the effect on esophageal cancer cell growth and invasion ability. METHODS: Transfect TPX2 siRNA into EC9706 cells via lipofectamin 2000. The experiments were divided into three groups, a negative control, a blank control and an siRNA interference group (24h, 48h, 72h, 96h). We examined RNA and protein level alteration of the TPX2 gene after TPX2 siRNA transfection by RT-PCR and Western blot analysis. Detection of how TPX2 siRNA influences EC9706 cell proliferation was done by MTT, cell apoptosis monitored through Tunel assay, in vitro invasion ability via Boyden chamber and cell cycle change by flow cytometry. RESULTS: After effective siRNA transfection, TPX2 mRNA and protein expression level in siRNA interference group were (0.31±0.08, 0.39±0.12),72h after transfection, significantly lower than blank control group (1.00±0.01) and negative control group (0.98±0.11), (F=71.182, t1=8.17, t2=7.90, P<0.05); MTT results demonstrated that cell growth and proliferation were inhibited and the inhibition rate was up to 35.4% (P<0.05) compared with the control group. TUNEL results indicated that cell apoptosis index in siRNA interference group was 18.28±0.35, higher than that in blank control group (4.07±0.26)and negative control group (4.13±0.22), (F=244.5, t1=60.61, t2=53.32, P<0.01). Boyden chamber results showed that the transmembrane cell number was 45.30±8.08 in siRNA interference group, less than blank control group (121.90±7.83), (F=122.46, t1=11.81, t2=10.47, P<0.01); besides, in siRNA interference group cell invasion inhibition rate was 71.42±9.12, higher than negative control group (5.65±3.55), (t=14.256, P<0.01). Flow cytometry results illustrated that more EC9706 cells went into apoptosis and cell cycle arrested in S phase. Similar results were obtained by in vivo transplantation, as TPX2 siRNA transfection significantly reduced tumor growth of the xenograft in nude mice. CONCLUSION: siRNA could effectively inhibit the invasion and metastasis of EC9706 cells, promote the apoptosis of tumor cells and may become a new approach for treatment of esophageal carcinoma.