Expression of LOX Suggests Poor Prognosis in Gastric Cancer

Identification of differentially expressed mRNAs as novel predictive biomarkers for gastric cancer diagnosis and prognosis

BACKGROUND

Gastric cancer (GC) has a high mortality rate worldwide. Despite significant progress in GC diagnosis and treatment, the prognosis for affected patients still remains unfavorable.

AIM

To identify important candidate genes related to the development of GC and identify potential pathogenic mechanisms through comprehensive bioinformatics analysis.

METHODS

The Gene Expression Omnibus database was used to obtain the GSE183136 dataset, which includes a total of 135 GC samples. The limma package in R software was employed to identify differentially expressed genes (DEGs). Thereafter, enrichment analyses of Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were performed for the gene modules using the clusterProfile package in R software. The protein-protein interaction (PPI) networks of target genes were constructed using STRING and visualized by Cytoscape software. The common hub genes that emerged in the cohort of DEGs that was retrieved from the GEPIA database were then screened using a Venn Diagram. The expression levels of these overlapping genes in stomach adenocarcinoma samples and non-tumor samples and their association with prognosis in GC patients were also obtained from the GEPIA database and Kaplan-Meier curves. Moreover, real-time quantitative polymerase chain reaction (RT-qPCR) and western blotting were performed to determine the mRNA and protein levels of glutamic-pyruvic transaminase (GPT) in GC and normal immortalized cell lines. In addition, cell viability, cell cycle distribution, migration and invasion were evaluated by cell counting kit-8, flow cytometry and transwell assays. Furthermore, we also conducted a retrospective analysis on 70 GC patients diagnosed and surgically treated in Wenzhou Central Hospital, Dingli Clinical College of Wenzhou Medical University, The Second Affiliated Hospital of Shanghai University between January 2017 to December 2020. The tumor and adjacent normal samples were collected from the patients to determine the potential association between the expression level of GPT and the clinical as well as pathological features of GC patients.

RESULTS

We selected 19214 genes from the GSE183136 dataset, among which there were 250 downregulated genes and 401 upregulated genes in the tumor samples of stage III-IV in comparison to those in tumor samples of stage I-II with a P-value < 0.05. In addition, GO and KEGG results revealed that the various upregulated DEGs were mainly enriched in plasma membrane and neuroactive ligand-receptor interaction, whereas the downregulated DEGs were primarily enriched in cytosol and pancreatic secretion, vascular smooth muscle contraction and biosynthesis of the different cofactors. Furthermore, PPI networks were constructed based on the various upregulated and downregulated genes, and there were a total 15 upregulated and 10 downregulated hub genes. After a comprehensive analysis, several hub genes, including runt-related transcription factor 2 (RUNX2), salmonella pathogenicity island 1 (SPI1), lysyl oxidase (LOX), fibrillin 1 (FBN1) and GPT, displayed prognostic values. Interestingly, it was observed that GPT was downregulated in GC cells and its upregulation could suppress the malignant phenotypes of GC cells. Furthermore, the expression level of GPT was found to be associated with age, lymph node metastasis, pathological staging and distant metastasis (P < 0.05).

CONCLUSION

RUNX2, SPI1, LOX, FBN1 and GPT were identified key hub genes in GC by bioinformatics analysis. GPT was significantly associated with the prognosis of GC, and its upregulation can effectively inhibit the proliferative, migrative and invasive capabilities of GC cells.

Lysine Deacetylation Is a Key Function of the Lysyl Oxidase Family of Proteins in Cancer

Mammalian members of the lysyl oxidase (LOX) family of proteins carry a copper-dependent monoamine oxidase domain exclusively within the C-terminal region, which catalyzes ε-amine oxidation of lysine residues of various proteins. However, recent studies have demonstrated that in LOX-like (LOXL) 2-4 the C-terminal canonical catalytic domain and N-terminal scavenger receptor cysteine-rich (SRCR) repeats domain exhibit lysine deacetylation and deacetylimination catalytic activities. Moreover, the N-terminal SRCR repeats domain is more catalytically active than the C-terminal oxidase domain. Thus, LOX is the third family of lysine deacetylases in addition to histone deacetylase and sirtuin families. In this review, we discuss how the LOX family targets different cellular proteins for deacetylation and deacetylimination to control the development and metastasis of cancer.

System biology analysis to develop diagnostic biomarkers, monitoring pathological indexes, and novel therapeutic approaches for immune targeting based on maggot bioactive compounds and polyphenolic cocktails in mice with gastric cancer

Early diagnosis and prognosis are prerequisites for mitigating mortality in gastric cancer (GaCa). Identifying some causative or sensitive elements (coding RNA (cRNA)-non-cRNAs (ncRNAs)) can be very helpful in the early diagnosis of GaCa. Notably, despite significant development in the GaCa treatment, the outcome of patients does not remain satisfactory due to limitations such as multi-drug resistance and tumor relapse. Therefore, more attention has been drawn to complementary therapies and the use of supplements. In this regard, Polyphenol natural compounds (PNC) and maggot larvae (MaLa) alone or in combination were administered along with chemotherapy (paclitaxel) to N-methyl-N-nitrosourea (MNU)- induced murine tumor model. In addition, in order to identify potential diagnostic or prognostic biomarkers, transcriptomics analysis was performed through a bioinformatics approach. Then transcription profile of ncRNAs with their target hub genes was assessed through qPCR Real-Time, Western blot, and ELISA. According to the bioinformatics results, 17 hub genes (e.g., IL-6, CXCL8, MKI67, IL-2, IL-4, IL-10, IL-1β, SPP1, LOX, COL1A1, and IFN-γ) were explored that contribute towards inflammation and oxidative stress and ultimately GaCa development. Upstream of the mentioned hub genes, regulatory factors (lncRNA XIST and NEAT1) were also identified and introduced as prognosis and diagnosis biomarkers for GaCa. Our results showed that PNC alone and in combination with MaLa was able to reduce the size and number of tumors, which is related to the reduction of genes expression levels (including IL-6, CXCL8, MKI67, IL-2, IL-4, IL-10, IL-1β, SPP1, LOX, COL1A1, IFN-γ, NEAT1, and XIST). In conclusion, PNC and MaLa have the potential to be considered as complementary and improving chemotherapy due to their effective compounds. Also, the introduced hub gene and lncRNA in addition to diagnostic and prognostic biomarkers can be used as druggable proteins for novel therapeutic targeting of GaCa.

Global trends in research related to functional dyspepsia and anxiety or depression over the past two decades: a bibliometric analysis

Background and purpose

Functional dyspepsia (FD) is a prevalent global disorder of the upper digestive tract characterized by functional impairment. It often coexists with anxiety/depression, significantly impairing occupational productivity and overall quality of life. This study aimed to identify emerging patterns and prominent themes within FD and anxiety/depression research through bibliometric analysis to help explore new innovative avenues for investigating this type of FD.

Methods

A comprehensive review of literature encompassing FD and anxiety/depression was conducted using the Science Citation Index Extension of the Web of Science Core Collection from 2003 to 2023. Information extracted comprised "Full Record and Cited References." Bibliometric analysis of relevant publications, including country, institution, author, journal, citations, and keywords, was conducted using CiteSpace, VOSviewer, and Bibliometrix package in R and Excel.

Results

Studies related to FD and anxiety/depression have demonstrated an ascending trajectory since 2003. Our bibliometric analysis identified 338 studies published by 2023. NEUROGASTROENTEROLOGY AND MOTILITY emerged as the most prolific journal, while GASTROENTEROLOGY retained pre-eminence within the top 10 published journals. China emerged as the most prolific country, with two institutions within the top 10 in terms of volume of publications. The Mayo Clinic stood as the foremost institution in terms of publication volume, with the Chengdu University of Traditional Chinese Medicine exhibiting robust collaborative engagement. Eminent author influence was attributed to Talley NJ of Newcastle University, Australia. Clusters of extensively cited papers and prevalent keywords delineate the status and trend of FD and anxiety/depression research. This encompasses FD, anxiety, depression, sleep disorders, and functional gastrointestinal disorders. Furthermore, the timeline view map or trend-term analysis suggested that duodenal low-grade inflammation ("duodenal eosinophilia" and "mast cells") might be a new concern associated with FD and anxiety/depression.

Conclusion

Employing bibliometric analysis, this study revealed prevalent focal areas and new trends within FD and anxiety/depression research. These insights serve as valuable guidance for scholars seeking to delve into new research avenues.

Screening of potential biomarkers for polycystic ovary syndrome and identification of expression and immune characteristics

BACKGROUND

Polycystic ovary syndrome (PCOS) seriously affects the fertility and health of women of childbearing age. We look forward to finding potential biomarkers for PCOS that can aid clinical diagnosis.

METHODS

We acquired PCOS and normal granulosa cell (GC) expression profiles from the Gene Expression Omnibus (GEO) database. After data preprocessing, differentially expressed genes (DEGs) were screened by limma package, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and Gene Set Enrichment Analysis (GSEA) were performed. Recursive feature elimination (RFE) algorithm and the least absolute shrinkage and selection operator (LASSO) Cox regression analysis were used to acquire feature genes as potential biomarkers. Time-dependent receiver operator characteristic curve (ROC curve) and Confusion matrix were used to verify the classification performance of biomarkers. Then, the expression characteristics of biomarkers in PCOS and normal cells were analyzed, and the insulin resistance (IR) score of samples was computed by ssGSEA. Immune characterization of biomarkers was evaluated using MCP counter and single sample gene set enrichment analysis (ssGSEA). Finally, the correlation between biomarkers and the scores of each pathway was assessed.

RESULTS

We acquired 93 DEGs, and the enrichment results indicated that most of DEGs in PCOS group were significantly enriched in immune-related biological pathways. Further screening results indicated that JDP2 and HMOX1 were potential biomarkers. The area under ROC curve (AUC) value and Confusion matrix of the two biomarkers were ideal when separated and combined. In the combination, the training set AUC = 0.929 and the test set AUC = 0.917 indicated good diagnostic performance of the two biomarkers. Both biomarkers were highly expressed in the PCOS group, and both biomarkers, which should be suppressed in the preovulation phase, were elevated in PCOS tissues. The IR score of PCOS group was higher, and the expression of JDP2 and HMOX1 showed a significant positive correlation with IR score. Most immune cell scores and immune infiltration results were significantly higher in PCOS. Comprehensive analysis indicated that the two biomarkers had strong correlation with immune-related pathways.

CONCLUSION

We acquired two potential biomarkers, JDP2 and HMOX1. We found that they were highly expressed in the PCOS and had a strong positive correlation with immune-related pathways.

Multi-omics analysis reveals the involvement of origin recognition complex subunit 6 in tumor immune regulation and malignant progression

Background

Origin recognition complex 6 (ORC6) is one of the six highly conserved subunit proteins required for DNA replication and is essential for maintaining genome stability during cell division. Recent research shows that ORC6 regulates the advancement of multiple cancers; however, it remains unclear what regulatory impact it has on the tumor immune microenvironment.

Methods

Unpaired Wilcoxon rank sum and signed rank tests were used to analyze the differences in the expression of ORC6 in normal tissues and corresponding tumor tissues. Multiple online databases have evaluated the genetic alterations, protein expression and localization, and clinical relevance of ORC6. To evaluate the potential prognostic impact and diagnostic significance of ORC6 expression, we carried out log-rank, univariate Cox regression, and receiver operating characteristic curve analysis. The ICGC-LIRI-JP cohort, CGGA-301 cohort, CGGA-325 cohort, CGGA-693 cohort, and GSE13041 cohort were used for external validation of the study findings. The associations between ORC6 expression and immune cell infiltration, immune checkpoint expression, and immunotherapy cohorts was further analyzed. To explore the functional and signaling pathways related to ORC6 expression, gene set enrichment analysis was performed. To clarify the expression and function of ORC6 in hepatocellular carcinoma (LIHC) and glioma, we conducted in vitro experiments.

Results

Expression of ORC6 is upregulated in the majority of cancer types and is associated with poor patient prognosis, notably in cases of LIHC and gliomas. In addition, ORC6 may be involved in multiple signaling pathways related to cancer progression and immune regulation. High expression of ORC6 correlates with an immunosuppressive state in the tumor microenvironment. The results of further immunotherapy cohort analysis suggested that patients in the ORC6 high-expression group benefited from immunotherapy. Inhibiting ORC6 expression suppressed the proliferative and migratory abilities of LIHC and glioma cells.

Conclusion

High expression of ORC6 may be used as a biomarker to predict the poor prognosis of most tumor patients. The high expression of ORC6 may be involved in the regulation of the tumor immunosuppressive environment, and it is expected to become a molecular target for inhibiting tumor progression.

Identification of molecular subtypes based on PANoptosis-related genes and construction of a signature for predicting the prognosis and response to immunotherapy response in hepatocellular carcinoma

Background

Previous studies have demonstrated that PANoptosis is strongly correlated with cancer immunity and progression. This study aimed to develop a PANoptosis-related signature (PANRS) to explore its potential value in predicting the prognosis and immunotherapy response of hepatocellular carcinoma (HCC).

Methods

Based on the expression of PANoptosis-related genes, three molecular subtypes were identified. To construct a signature, the differentially expressed genes between different molecular subtypes were subjected to multivariate least absolute shrinkage and selection operator Cox regression analyses. The risk scores of patients in the training set were calculated using the signature. The patients were classified into high-risk and low-risk groups based on the median risk scores. The predictive performance of the signature was evaluated using Kaplan-Meier plotter, receiving operating characteristic curves, nomogram, and calibration curve. The results were validated using external datasets. Additionally, the correlation of the signature with the immune landscape and drug sensitivity was examined. Furthermore, the effect of LPCAT1 knockdown on HCC cell behavior was verified using in vitro experiments.

Results

This study developed a PANRS. The risk score obtained by using the PANRS was an independent risk factor for the prognosis of patients with HCC and exhibited good prognostic predictive performance. The nomogram constructed based on the risk score and clinical information can accurately predicted the survival probability of patients with HCC. Patients with HCC in the high-risk groups have high immune scores and tend to generate an immunosuppressive microenvironment. They also exhibited a favorable response to immunotherapy, as evidenced by high tumor mutational burden, high immune checkpoint gene expression, high human leukocyte antigen gene expression, low tumor immune dysfunction and low exclusion scores. Additionally, the PANRS enabled the identification of 15 chemotherapeutic agents, including sorafenib, for patients with HCC with different risk levels, guiding clinical treatment. The signature gene LPCAT1 was upregulated in HCC cell lines. LPCAT1 knockdown markedly decreased HCC cell proliferation and migration.

Conclusion

PANRS can accurately predict the prognosis and immunotherapy response of patients with HCC and consequently guide individualized treatment.

Forces exerted and transduced by cancer-associated fibroblasts during cancer progression

Although it is well-known that cancer-associated fibroblasts (CAFs) play a key role in regulating tumor progression, the effects of mechanical tissue changes on CAFs are understudied. Myofibroblastic CAFs (myCAFs), in particular, are known to alter tumor matrix architecture and composition, heavily influencing the mechanical forces in the tumor microenvironment (TME), but much less is known about how these mechanical changes initiate and maintain the myCAF phenotype. Additionally, recent studies have pointed to the existence of CAFs in circulating tumor cell clusters, indicating that CAFs may be subject to mechanical forces beyond the primary TME. Due to their pivotal role in cancer progression, targeting CAF mechanical regulation may provide therapeutic benefit. Here, we will discuss current knowledge and summarize existing gaps in how CAFs regulate and are regulated by matrix mechanics, including through stiffness, solid and fluid stresses, and fluid shear stress.

Identification and characterization of putative biomarkers and therapeutic axis in Glioblastoma multiforme microenvironment

Non-cellular secretory components, including chemokines, cytokines, and growth factors in the tumor microenvironment, are often dysregulated, impacting tumorigenesis in Glioblastoma multiforme (GBM) microenvironment, where the prognostic significance of the current treatment remains unsatisfactory. Recent studies have demonstrated the potential of post-translational modifications (PTM) and their respective enzymes, such as acetylation and ubiquitination in GBM etiology through modulating signaling events. However, the relationship between non-cellular secretory components and post-translational modifications will create a research void in GBM therapeutics. Therefore, we aim to bridge the gap between non-cellular secretory components and PTM modifications through machine learning and computational biology approaches. Herein, we highlighted the importance of BMP1, CTSB, LOX, LOXL1, PLOD1, MMP9, SERPINE1, and SERPING1 in GBM etiology. Further, we demonstrated the positive relationship between the E2 conjugating enzymes (Ube2E1, Ube2H, Ube2J2, Ube2C, Ube2J2, and Ube2S), E3 ligases (VHL and GNB2L1) and substrate (HIF1A). Additionally, we reported the novel HAT1-induced acetylation sites of Ube2S (K211) and Ube2H (K8, K52). Structural and functional characterization of Ube2S (8) and Ube2H (1) have identified their association with protein kinases. Lastly, our results found a putative therapeutic axis HAT1-Ube2S(K211)-GNB2L1-HIF1A and potential predictive biomarkers (CTSB, HAT1, Ube2H, VHL, and GNB2L1) that play a critical role in GBM pathogenesis.

Integrated bioinformatics analysis for conducting a prognostic model and identifying immunotherapeutic targets in gastric cancer

BACKGROUND

Gastric cancer is the third leading cause of death from cancer worldwide and has a poor prognosis. Practical risk scores and prognostic models for gastric cancer are lacking. While immunotherapy has succeeded in some cancers, few gastric cancer patients benefit from immunotherapy. Immune genes and the tumor microenvironment (TME) are essential for cancer progression and immunotherapy response. However, the roles of immune genes and the tumor microenvironment in immunotherapy remain unclear. The study aimed to construct a prognostic prediction model and identify immunotherapeutic targets for gastric cancer (GC) patients by exploring immune genes and the tumor microenvironment.

RESULTS

An immune-related risk score (IRRS) model, including APOH, RNASE2, F2R, DEFB126, CXCL6, and CXCL3 genes, was constructed for risk stratification. Patients in the low-risk group, which was characterized by elevated tumor mutation burden (TMB) have higher survival rate. The risk level was remarkably correlated with tumor-infiltrating immune cells (TIICs), the immune checkpoint molecule expression, and immunophenoscore (IPS). CXCL3 and CXCL6 were significantly upregulated in gastric cancer tissues compared with normal tissues using the UALCAN database and RT-qPCR. The nomogram showed good calibration and moderate discrimination in predicting overall survival (OS) at 1-, 3-, and 5- year for gastric cancer patients using risk-level and clinical characteristics.

CONCLUSION

Our findings provided a risk stratification and prognosis prediction tool for gastric cancer patients and further the research into immunotherapy in gastric cancer.

Clinicopathological and prognostic value of lysyl oxidase expression in gastric cancer: a systematic review, meta-analysis and bioinformatic analysis

The association between the expression of Lysyl oxidase (LOX) and its clinicopathological parameters and prognosis in patients with gastric cancer (GC) is still disputed. We performed this meta-analysis and bioinformatics analysis to clarify the relationship between the expression and methylation level of LOX with its clinicopathological parameters and prognostic value. We applied odds ratios with a 95% confidence interval to study the associations between LOX expression and clinicopathological parameters and overall survival (OS) in GC patients. In addition, association analysis of promoter methylation levels and expression of LOX with its prognostic value was performed using the Cancer Genome Atlas (TCGA) and four Gene Expression Omnibus (GEO) datasets. The PRISMA 2020 checklist was used to guide the data extraction and analysis. This meta-analysis includes seven clinical studies with a total of 1435 GC patients. LOX expression was related to lymph node metastasis and tumor distant metastasis in GC patients, but not to gender, tumor differentiation, Lauren classification, or tumor depth of invasion. Patients with GC grouped in high-expression of LOX had a much worse OS than those in low-expression. In addition, TCGA and four GEO datasets with 1279 samples were included in the bioinformatics analysis. The bioinformatics analysis showed that patients with high LOX levels had poor OS; low levels of methylation at some cg sites in the LOX gene were strongly related to poor OS and PFS; and methylation levels of LOX are negatively correlated with advanced tumor stage. The conclusion from comprehensive DNA methylation and gene expression analysis supports LOX as a specific diagnostic and prognosis biomarker in GC. LOX expression was related to lymph node metastasis, tumor distant metastasis and poor prognosis in GC. Low methylation levels were related to advanced tumor stage and poor prognosis in GC. Integrative analysis supports LOX as a specific diagnostic and prognosis biomarker in GC.

Extracellular matrix mechanobiology in cancer cell migration

The extracellular matrix (ECM) is pivotal in modulating tumor progression. Besides chemically stimulating tumor cells, it also offers physical support that orchestrates the sequence of events in the metastatic cascade upon dynamically modulating cell mechanosensation. Understanding this translation between matrix biophysical cues and intracellular signaling has led to rapid growth in the interdisciplinary field of cancer mechanobiology in the last decade. Substantial efforts have been made to develop novel in vitro tumor mimicking platforms to visualize and quantify the mechanical forces within the tissue that dictate tumor cell invasion and metastatic growth. This review highlights recent findings on tumor matrix biophysical cues such as fibrillar arrangement, crosslinking density, confinement, rigidity, topography, and non-linear mechanics and their implications on tumor cell behavior. We also emphasize how perturbations in these cues alter cellular mechanisms of mechanotransduction, consequently enhancing malignancy. Finally, we elucidate engineering techniques to individually emulate the mechanical properties of tumors that could help serve as toolkits for developing and testing ECM-targeted therapeutics on novel bioengineered tumor platforms. STATEMENT OF SIGNIFICANCE: Disrupted ECM mechanics is a driving force for transitioning incipient cells to life-threatening malignant variants. Understanding these ECM changes can be crucial as they may aid in developing several efficacious drugs that not only focus on inducing cytotoxic effects but also target specific matrix mechanical cues that support and enhance tumor invasiveness. Designing and implementing an optimal tumor mimic can allow us to predictively map biophysical cue-modulated cell behaviors and facilitate the design of improved lab-grown tumor models with accurately controlled structural features. This review focuses on the abnormal changes within the ECM during tumorigenesis and its implications on tumor cell-matrix mechanoreciprocity. Additionally, it accentuates engineering approaches to produce ECM features of varying levels of complexity which is critical for improving the efficiency of current engineered tumor tissue models.

Identification and Validation of an m6A Modification of JAK-STAT Signaling Pathway–Related Prognostic Prediction Model in Gastric Cancer

Background: Gastric cancer (GC) is one of the malignant tumors worldwide. Janus (JAK)–signal transduction and activator of transcription (STAT) signaling pathway is involved in cellular biological process and immune function. However, the association between them is still not systematically described. Therefore, in this study, we aimed to identify key genes involved in JAK-STAT signaling pathway and GC, as well as the potential mechanism.

Methods: The Cancer Genome Atlas (TCGA) database was the source of RNA-sequencing data of GC patients. Gene Expression Omnibus (GEO) database was used as the validation set. The predictive value of the JAK-STAT signaling pathway-related prognostic prediction model was examined using least absolute shrinkage and selection operator (LASSO); survival, univariate, and multivariate Cox regression analyses; and receiver operating characteristic curve (ROC) analyses to examine the predictive value of the model. Quantitative real-time polymerase chain reaction (qRT-PCR) and chi-square test were used to verify the expression of genes in the model and assess the association between the genes and clinicopathological parameters of GC patients, respectively. Then, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set enrichment analysis, version 3.0 (GSEA), sequence-based RNA adenosine methylation site predictor (SRAMP) online websites, and RNA immunoprecipitation (RIP) experiments were used to predict the model-related potential pathways, m6A modifications, and the association between model genes and m6A.

Results: A four-gene prognostic model (GHR, PIM1, IFNA8, and IFNB1) was constructed, namely, riskScore. The Kaplan–Meier curves suggested that patients with high riskScore expression had a poorer prognosis than those with low riskScore expression (p = 0.006). Multivariate Cox regression analyses showed that the model could be an independent predictor (p < 0.001; HR = 3.342, 95%, CI = 1.834–6.088). The 5-year area under time-dependent ROC curve (AUC) reached 0.655. The training test set verified these results. Further analyses unveiled an enrichment of cancer-related pathways, m6A modifications, and the direct interaction between m6A and the four genes.

Conclusion: This four-gene prognostic model could be applied to predict the prognosis of GC patients and might be a promising therapeutic target in GC.

EXTL3 could serve as a potential biomarker of prognosis and immunotherapy for prostate cancer and its potential mechanisms

Background

Exostosin like glycosyltransferase 3 (EXTL3) had been reported to be associated with immune deficiency and play prognostic roles in various cancers. However, little is known about the associations between EXTL3 and prostate cancer (PCa). Hence, this article was designed to clarify their associations.

Methods

All original data were downloaded from The Cancer Genome Atlas (TCGA) database. Gene set enrichment analysis (GSEA) and CellMiner database was utilized, respectively, to identify EXTL3-related signaling pathways and drugs. We explored the relationships between EXTL3 expression and immunity to further evaluate the involvement of EXTL3 in response to immunotherapies. LncRNA/RBP/EXTL3 mRNA networks were also identified for its potential mechanism.

Results

Compared with normal prostate samples, EXTL3 was poorly expressed in PCa samples not only in mRNA expression levels, but also in protein expression levels, with worse overall survival (P < 0.05) and this gene could be an independent prognostic biomarker for PCa (both P < 0.05). EXTL3 was revealed to be markedly linked with seven signaling pathways in PCa by GSEA, including calcium, chemokine, ERBB, JAK STAT, MAPK, WNT, oxidative phosphorylation pathways. EXTL3 expression was also revealed to be significantly associated with MSI, immune cells, immune checkpoint molecules, tumor microenvironment and immune cells infiltration. We further predicted immune responses of EXTL3 gene to immunotherapies by TIDE database and the IMvigor210 cohort. A total of six LncRNA/RBP/EXTL3 mRNA networks were eventually identified for its potential mechanisms.

Conclusions

EXTL3 could serve as a potential biomarker of prognosis and immunotherapy for PCa and six LncRNA/RBP/EXTL3 mRNA networks were also identified for its potential mechanisms.

A novel epithelial-mesenchymal transition gene signature for the immune status and prognosis of hepatocellular carcinoma

Background

This study clarified whether EMT-related genes can predict immunotherapy efficacy and overall survival in patients with HCC.

Methods

The RNA-sequencing profiles and patient information of 370 samples were derived from the Cancer Genome Atlas (TCGA) dataset, and EMT-related genes were obtained from the Molecular Signatures database. The signature model was constructed using the least absolute shrinkage and selection operator Cox regression analysis in TCGA cohort. Validation data were obtained from the International Cancer Genome Consortium (ICGC) dataset of patients with HCC. Kaplan–Meier analysis and multivariate Cox analyses were employed to estimate the prognostic value. Immune status and tumor microenvironment were estimated using a single-sample gene set enrichment analysis (ssGSEA). The expression of prognostic genes was verified using qRT-PCR analysis of HCC cell lines.

Results

A signature model was constructed using EMT-related genes to determine HCC prognosis, based on which patients were divided into high-risk and low-risk groups. The risk score, as an independent factor, was related to tumor stage, grade, and immune cells infiltration. The results indicated that the most prognostic genes were highly expressed in the HCC cell lines, but GADD45B was down-regulated. Enrichment analysis suggested that immunoglobulin receptor binding and material metabolism were essential in the prognostic signature.

Conclusion

Our novel prognostic signature model has a vital impact on immune status and prognosis, significantly helping the decision-making related to the diagnosis and treatment of patients with HCC.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12072-022-10354-3.

Construction of a Novel LncRNA Signature Related to Genomic Instability to Predict the Prognosis and Immune Activity of Patients With Hepatocellular Carcinoma

Background

Genomic instability (GI) plays a crucial role in the development of various cancers including hepatocellular carcinoma. Hence, it is meaningful for us to use long non-coding RNAs related to genomic instability to construct a prognostic signature for patients with HCC.

Methods

Combining the lncRNA expression profiles and somatic mutation profiles in The Cancer Genome Atlas database, we identified GI-related lncRNAs (GILncRNAs) and obtained the prognosis-related GILncRNAs through univariate regression analysis. These lncRNAs obtained risk coefficients through multivariate regression analysis for constructing GI-associated lncRNA signature (GILncSig). ROC curves were used to evaluate signature performance. The International Cancer Genomics Consortium (ICGC) cohort, and in vitro experiments were used for signature external validation. Immunotherapy efficacy, tumor microenvironments, the half-maximal inhibitory concentration (IC50), and immune infiltration were compared between the high- and low-risk groups with TIDE, ESTIMATE, pRRophetic, and ssGSEA program.

Results

Five GILncRNAs were used to construct a GILncSig. It was confirmed that the GILncSig has good prognostic evaluation performance for patients with HCC by drawing a time-dependent ROC curve. Patients were divided into high- and low-risk groups according to the GILncSig risk score. The prognosis of the low-risk group was significantly better than that of the high-risk group. Independent prognostic analysis showed that the GILncSig could independently predict the prognosis of patients with HCC. In addition, the GILncSig was correlated with the mutation rate of the HCC genome, indicating that it has the potential to measure the degree of genome instability. In GILncSig, LUCAT1 with the highest risk factor was further validated as a risk factor for HCC in vitro. The ESTIMATE analysis showed a significant difference in stromal scores and ESTIMATE scores between the two groups. Multiple immune checkpoints had higher expression levels in the high-risk group. The ssGSEA results showed higher levels of tumor-antagonizing immune cells in the low-risk group compared with the high-risk group. Finally, the GILncSig score was associated with chemotherapeutic drug sensitivity and immunotherapy efficacy of patients with HCC.

Conclusion

Our research indicates that GILncSig can be used for prognostic evaluation of patients with HCC and provide new insights for clinical decision-making and potential therapeutic strategies.