AUNIP Expression Is Correlated With Immune Infiltration and Is a Candidate Diagnostic and Prognostic Biomarker for Hepatocellular Carcinoma and Lung Adenocarcinoma

Identification of copper death-associated molecular clusters and immunological profiles for lumbar disc herniation based on the machine learning

Lumbar disc herniation (LDH) is a common clinical spinal disorder, yet its etiology remains unclear. We aimed to explore the role of cuproptosis-related genes (CRGs) and identify potential diagnostic biomarkers. Our analysis involved interrogating the GSE124272 and GSE150408 datasets for differential gene expression profiles associated with CRGs and immune characteristics. Molecular clustering was performed on LDH samples, followed by expression and immune infiltration analyses. Using the WGCNA algorithm, specific genes within CRG clusters were identified. After selecting the most predictive genes from the optimal model, four machine learning models were constructed and validated. This study identified nine CRGs associated with copper-regulated cell death. Two copper-containing molecular clusters linked to death were detected in LDH samples. Elevated expression and immune infiltration levels were found in LDH patients, particularly in CRG cluster C2. Utilizing XGB, five genes were identified for constructing a diagnostic model, achieving an area under the curve values of 0.715. In conclusion, this research provides valuable insights into the association between LDH and copper-regulated cell death, alongside proposing a promising predictive model.

Integrated bioinformatics analysis of nucleotide metabolism based molecular subtyping and biomarkers in lung adenocarcinoma

Background

Lung adenocarcinoma (LUAD), a predominant subtype of non-small cell lung cancers, continues to challenge treatment outcomes due to its heterogeneity and complex tumor microenvironment (TME). Dysregulation in nucleotide metabolism has been identified as a significant factor in tumorigenesis, suggesting its potential as a therapeutic target.

Methods

This study analyzed LUAD samples from The Cancer Genome Atlas (TCGA) using Non-negative Matrix Factorization (NMF) clustering, Weighted Correlation Network Analysis (WGCNA), and various machine learning techniques. We investigated the role of nucleotide metabolism in relation to clinical features and immune microenvironment through large-scale data analysis and single-cell sequencing. Using in vivo and in vitro experiments such as RT-qPCR, Western Blot, immunohistochemistry, and subcutaneous tumor formation in mice, we further validated the functions of key nucleotide metabolism genes in cell lines and animals.

Results

Nucleotide metabolism genes classified LUAD patients into two distinct subtypes with significant prognostic differences. The 'C1' subtype associated with active nucleotide metabolism pathways showed poorer prognosis and a more aggressive tumor phenotype. Furthermore, a nucleotide metabolism-related score (NMRS) calculated from the expression of 28 key genes effectively differentiated between patient outcomes and predicted associations with oncogenic pathways and immune responses. By integrating various immune infiltration algorithms, we delineated the associations between nucleotide metabolism signature genes and the tumor microenvironment, and characterized their distribution differences at the cellular level by analyzing single-cell sequencing dataset related to immunochemotherapy. Finally, we demonstrated the differential expression of the key nucleotide metabolism gene AUNIP acts as an oncogene to promote LUAD cell proliferation and is associated with tumor immune infiltration.

Conclusion

The study underscores the pivotal role of nucleotide metabolism in LUAD progression and prognosis, highlighting the NMRS as a valuable biomarker for clinical outcomes and therapeutic responses. Specifically, AUNIP functions as a critical oncogene, offering a promising target for novel treatment strategies in LUAD.

Systematic Multiomic Analysis of PKHD1L1 Gene Expression and Its Role as a Predicting Biomarker for Immune Cell Infiltration in Skin Cutaneous Melanoma and Lung Adenocarcinoma

The identification of genetic factors that regulate the cancer immune microenvironment is important for understanding the mechanism of tumor progression and establishing an effective treatment strategy. Polycystic kidney and hepatic disease 1-like protein 1 (PKHD1L1) is a large transmembrane protein that is highly expressed in immune cells; however, its association with tumor progression remains unclear. Here, we systematically analyzed the clinical relevance of PKHD1L1 in the tumor microenvironment in multiple cancer types using various bioinformatic tools. We found that the PKHD1L1 mRNA expression levels were significantly lower in skin cutaneous melanoma (SKCM) and lung adenocarcinoma (LUAD) than in normal tissues. The decreased expression of PKHD1L1 was significantly associated with unfavorable overall survival (OS) in SKCM and LUAD. Additionally, PKHD1L1 expression was positively correlated with the levels of infiltrating B cells, cluster of differentiation (CD)-8+ T cells, and natural killer (NK) cells, suggesting that the infiltration of immune cells could be associated with a good prognosis due to increased PKHD1L1 expression. Gene ontology (GO) analysis also revealed the relationship between PKHD1L1-co-altered genes and the activation of lymphocytes, including B and T cells. Collectively, this study shows that PKHD1L1 expression is positively correlated with a good prognosis via the induction of immune infiltration, suggesting that PKHD1L1 has potential prognostic value in SKCM and LUAD.

Stroma-associated FSTL3 is a factor of calcium channel-derived tumor fibrosis

Hepatocellular carcinoma (HCC) is the most widespread histological form of primary liver cancer, and it faces great diagnostic and therapeutic difficulties owing to its tumor diversity. Herein, we aim to establish a unique prognostic molecular subtype (MST) and based on this to find potential therapeutic targets to develop new immunotherapeutic strategies. Using calcium channel molecules expression-based consensus clustering, we screened 371 HCC patients from The Cancer Genome Atlas to screen for possible MSTs. We distinguished core differential gene modules between varying MSTs, and Tumor Immune Dysfunction and Exclusion scores were employed for the reliable assessment of HCC patient immunotherapeutic response rate. Immunohistochemistry and Immunofluorescence staining were used for validation of predicted immunotherapy outcomes and underlying biological mechanisms, respectively. We identified two MSTs with different clinical characteristics and prognoses. Based on the significant differences between the two MSTs, we further identified Follistatin-like 3 (FSTL3) as a potential indicator of immunotherapy resistance and validated this result in our own cohort. Finally, we found that FSTL3 is predominantly expressed in HCC stromal components and that it is a factor in enhancing fibroblast-M2 macrophage signaling crosstalk, the function of which is relevant to the pathogenesis of HCC. The presence of two MSTs associated with the calcium channel phenotype in HCC patients may provide promising directions for overcoming immunotherapy resistance in HCC, and the promotion of FSTL3 expressed in stromal components for HCC hyperfibrosis may be responsible for the poor response rate to immunotherapy in Cluster 2 (C2) patients.

Link between m6A modification and infiltration characterization of tumor microenvironment in lung adenocarcinoma

N6-methyladenosine (m6A) RNA methylation plays a pivotal role in immune responses and the onset and advancement of cancer. Nonetheless, the precise impact of m6A modification in lung adenocarcinoma (LUAD) and its associated tumor microenvironment (TME) remains to be fully elucidated. Here, we distinguished distinct m6A modification patterns within two separate LUAD cohorts using a set of 21 m6A regulators. The TME characteristics associated with these two patterns align with the immune-inflamed and immune-excluded phenotypes, respectively. We identified 2064 m6A-related genes, which were used as a basis to divide all LUAD samples into three distinct m6A gene clusters. We applied a scoring system to evaluate the m6A gene signature of the m6A modification pattern in individual patients. To authenticate the categorization significance of m6A modification patterns, we established a correlation between m6A score and TME infiltration profiling, tumor somatic mutations, and responses to immunotherapy. A high level of m6A modification may be associated with the aggressiveness and poor prognosis of LUAD. Further studies should investigate the mechanism of action of m6A regulators and m6A-related genes to improve the diagnosis and treatment of patients with LUAD.

Exploring tumor-normal cross-talk with TranNet: Role of the environment in tumor progression

There is a growing awareness that tumor-adjacent normal tissues used as control samples in cancer studies do not represent fully healthy tissues. Instead, they are intermediates between healthy tissues and tumors. The factors that contribute to the deviation of such control samples from healthy state include exposure to the tumor-promoting factors, tumor-related immune response, and other aspects of tumor microenvironment. Characterizing the relation between gene expression of tumor-adjacent control samples and tumors is fundamental for understanding roles of microenvironment in tumor initiation and progression, as well as for identification of diagnostic and prognostic biomarkers for cancers. To address the demand, we developed and validated TranNet, a computational approach that utilizes gene expression in matched control and tumor samples to study the relation between their gene expression profiles. TranNet infers a sparse weighted bipartite graph from gene expression profiles of matched control samples to tumors. The results allow us to identify predictors (potential regulators) of this transition. To our knowledge, TranNet is the first computational method to infer such dependencies. We applied TranNet to the data of several cancer types and their matched control samples from The Cancer Genome Atlas (TCGA). Many predictors identified by TranNet are genes associated with regulation by the tumor microenvironment as they are enriched in G-protein coupled receptor signaling, cell-to-cell communication, immune processes, and cell adhesion. Correspondingly, targets of inferred predictors are enriched in pathways related to tissue remodelling (including the epithelial-mesenchymal Transition (EMT)), immune response, and cell proliferation. This implies that the predictors are markers and potential stromal facilitators of tumor progression. Our results provide new insights into the relationships between tumor adjacent control sample, tumor and the tumor environment. Moreover, the set of predictors identified by TranNet will provide a valuable resource for future investigations.

Identification of INSRR as an immune-related gene in the tumor microenvironment of glioblastoma by integrated bioinformatics analysis

Gliomas are the most common malignant tumors in the central nervous system. The tumor microenvironment (TME) plays a crucial role in tumor proliferation, invasion, angiogenesis, and immune escape. However, little is known about TME in gliomas. The purpose of this study was to explore the biomarkers associated with TME in glioblastoma (GBM) to predict immunotherapy effectiveness and prognosis in patients. Based on RNA-seq transcriptome data and clinical features of 1222 samples (113 normal samples and 1109 tumor samples) in The Cancer Genome Atlas (TCGA) database, the ImmuneScore, StromalScore, and ESTIMATEScore were calculated by ESTIMATE algorithm. The differentially expressed genes (DEGs) and differentially mutated genes (DMGs) were determined in the TCGA GBM cohort. Furthermore, gene set enrichment analysis (GSEA) was used to investigate the enrichment pathways of INSRR genes with abnormal expression. The proportion of tumor-infiltrating immune cells (TIICs) was evaluated by CIBERSORT. Frequent mutations of TP53, EGFR, and PTEN occurred in high and low immune scores. The cross-analysis of DEGs and DMGs revealed that INSRR was an immune-related biomarker in the TCGA GBM cohort. According to GSEA, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway with INSRR abnormal expression were IgA-produced intestinal immune network and Alzheimer's disease, oxidative phosphorylation, and Parkinson's disease, respectively. Additionally, INSRR expression was correlated with dendritic cells activated, dendritic cells resting, T cells CD8, and T cell gamma delta. INSRR is associated with the immune microenvironment in GBM and is used as a biomarker to predict immune invasion.

A human pan-cancer system analysis of regulator of chromatin condensation 2

Regulation of chromosome condensation 2 (RCC2) is associated with the cell cycle and is a crucial regulator of the chromatin condensation 1 (RCC1) family. The members of this family were normally regulators in the process of DNA replication and nucleocytoplasmic transport. RCC2 overexpression may lead to tumor formation and poor prognosis in some tumors including breast cancer and lung adenocarcinoma. However, the possible role of RCC2 in tumor formation and its prognostic function remains unclear. In this study, expression analysis from databases including The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC) were combined to perform the first integrative and comprehensive analysis of RCC2 in human pan-cancer. RCC2 was highly expressed in most tumors which may lead to a poor prognosis. RCC2 expression was associated with immune/stromal infiltration, immune checkpoints, tumor mutational burden, and microsatellite instability. Thus, RCC2 could be a novel biomarker for prognosis and a promising cancer therapy target.

Molecular mechanism of Ganji Fang in the treatment of hepatocellular carcinoma based on network pharmacology, molecular docking and experimental verification technology

Background: Hepatocellular carcinoma (HCC) is a malignant tumor harmful to human health. Ganji Fang (GJF) has good clinical efficacy in the treatment of HCC, but its mechanism is still unclear. Objective: The aim of this study was to investigate the mechanism of action of GJF in the treatment of HCC through network pharmacology, molecular docking and in vitro experiments. Methods: A series of network pharmacology methods were used to identify the potential targets and key pathways of GJF in the treatment of HCC. Then, molecular docking technology was used to explore the binding ability of key active ingredients and targets in GJF. Multiple external databases were used to validate the key targets. In in vitro experiments, we performed MTT assays, wound-healing assays, cell cycle assays, apoptosis assays and RT‒qPCR to verify the inhibitory effect of GJF on the Human hepatoma G2 (HepG2) cells. Result: A total of 162 bioactive components and 826 protein targets of GJF were screened, and 611 potential targets of HCC were identified. Finally, 63 possible targets of GJF acting on HCC were obtained. KEGG enrichment analyses showed that the top five pathways were the cell cycle, cellular senescence, p53 signaling pathway, PI3K/Akt signaling pathway, and progesterone-mediated oocyte maturation. Among them, we verified the PI3K/Akt signaling pathway. CCNE1, PKN1, CCND2, CDK4, EPHA2, FGFR3, CDK6, CDK2 and HSP90AAI were enriched in the PI3K/Akt pathway. The molecular docking results showed that the docking scores of eight active components of GJF with the two targets were all less than -5.0, indicating that they had certain binding activity. In vitro cell experiments showed that GJF could inhibit the proliferation and migration of HepG2 cells, block the cell cycle and induce apoptosis of HepG2 cells, which may be related to the PI3K/Akt signaling pathway. In summary, EPHA2 may be an important target of GJF in HCC, and pachymic acid may be an important critical active compound of GJF that exerts anticancer activity. Conclusion: In general, we demonstrated, for the first time, that the molecular mechanism of GJF in HCC may involve induction of G0/G1 phase cycle arrest through inhibition of the PI3K/Akt signaling pathway and promote apoptosis of hepatoma cell lines. This study provides a scientific basis for the subsequent clinical application of GJF and the in-depth study of its mechanism.

Prognostic biomarkers of pancreatic cancer identified based on a competing endogenous RNA regulatory network

BACKGROUND

Pancreatic cancer is an insidious and heterogeneous malignancy with poor prognosis that is often locally unresectable. Therefore, determining the underlying mechanisms and effective prognostic indicators of pancreatic cancer may help optimize clinical management. This study was conducted to develop a prognostic model for pancreatic cancer based on a competing endogenous RNA (ceRNA) network.

METHODS

We obtained transcriptomic data and corresponding clinicopathological information of pancreatic cancer samples from The Cancer Genome Atlas (TCGA) database (training set). Based on the ceRNA interaction network, we screened candidate genes to build prediction models. Univariate Cox regression analysis was performed to screen for genes associated with prognosis, and least absolute shrinkage and selection operator (LASSO) regression analysis was conducted to construct a predictive model. A receiver operating characteristic (ROC) curve was drawn, and the C-index was calculated to evaluate the accuracy of the prediction model. Furthermore, we downloaded transcriptomic data and related clinical information of pancreatic cancer samples from the Gene Expression Omnibus database (validation set) to evaluate the robustness of our prediction model.

RESULTS

Eight genes (ANLN, FHDC1, LY6D, SMAD6, ACKR4, RAB27B, AUNIP, and GPRIN3) were used to construct the prediction model, which was confirmed as an independent predictor for evaluating the prognosis of patients with pancreatic cancer through univariate and multivariate Cox regression analysis. By plotting the decision curve, we found that the risk score model is an independent predictor has the greatest impact on survival compared to pathological stage and targeted molecular therapy.

CONCLUSIONS

An eight-gene prediction model was constructed for effectively and independently predicting the prognosis of patients with pancreatic cancer. These eight genes identified show potential as diagnostic and therapeutic targets.

Evaluation of CD4+ tumor-infiltrating lymphocyte association with some clinicopathological indices of oral squamous cell carcinoma

BACKGROUND

The delayed diagnosis of oral squamous cell carcinoma (OSCC) affects therapeutic and prognostic strategies, and provides regional recurrence or distant metastasis. The tumor-infiltrating lymphocytes (TILs) are known as a critical diagnostic biomarker in antitumor immune response. We evaluated the association between CD4⁺ T-lymphocyte marker, some clinicopathological indices, and the impact of TILs on the stage and grade of OSCC.

MATERIALS AND METHODS

In this cross-sectional study, 37 OSCC specimens including 16 early and 21 advanced stages (categorized base-on recent clinical oncology references) and their related healthy surgical margin (as internal control group) were collected. Obtained histochemical data were analyzed by SPSS V.23 software. The expression of CD4⁺ marker in tumor microenvironment (TME) was compared by nonparametric Mann-Whitney and Kruskal-Wallis as well as Fisher's exact tests. P < 0.05 was remarked statistically significant.

RESULTS

The low-grade patients represented more CD4⁺ TIL that was statistically significant (P = 0.011). However, there was no statistically significant difference in CD4⁺ TIL between various stages (P = 0.404), tumor size, and lymph node involvement (P > 0.05). Moreover, there was no significant relation between TIL infiltration, age, and tumor localization (P > 0.05), however CD4⁺ expression in women was more than men (P = 0.008). The CD4⁺ T-lymphocyte infiltration in TME was more significant than healthy surgical margin (P < 0.001). There was a statistically significant difference between healthy surgical margin and different grades and stages of OSCCs that lower grades demonstrated more CD4⁺ TIL infiltration (P < 0.001).

CONCLUSION

The CD4⁺ T-lymphocytes may play important role in differentiation and maturity of epithelial cell, tumorigenesis, and progression of OSCC.

[CD40LG is a novel immune- and stroma-related prognostic biomarker in the tumor microenvironment of invasive breast cancer]

OBJECTIVE

To identify tumor microenvironment (TME)- related genes associated with the occurrence of invasive breast cancer as potential prognostic biomarkers and therapeutic targets.

METHODS

RNA transcriptome data and clinically relevant data were retrieved from TCGA database, and the StromalScore and ImmuneScore were calculated using the ESTIMATE algorithm. The differentially expressed genes (DEGs) were screened by taking the intersection. A protein- protein interaction network was established, and univariate COX regression analysis was used to identify the core genes among the DEGs. A core gene was selected for GSEA and CIBERSORT analysis to determine the function of the core gene and the proportion of tumor-infiltrating immune cells, respectively. Western blotting and qRT-PCR were performed to verify the expression level of CD40LG in breast cancer cell lines and clinical specimens.

RESULTS

A total of 1222 samples (124 normal and 1098 tumor samples) were extracted from TCGA for analysis, from which 487 DEGs were identified. These genes were mainly enriched in immune-related pathways, and crossover analysis identified 11 key genes (CD40LG, ITK, CD5, CD3E, SPN, IL7R, CD48, CCL19, CD2, CD52, and CD2711) associated with breast cancer TME status. CD40LG was selected as the core gene, whose high expression was found to be associated with a longer overall survival of breast cancer patients (P=0.002), and its expression level differed significantly with TNM stage and tumor size (P < 0.05). GSEA and CIBERSORT analyses indicated that CD40LG expression level was associated with immune activity in the TME. Western blotting and qRT-PCR showed that the protein and mRNA expression of CD40LG were significantly lower in breast cancer cells and cancer tissues than in normal breast cells and adjacent tissues.

CONCLUSIONS

The high expression of CD40LG in TME is positively correlated with the survival of patients with invasive breast cancer, suggesting its value as a potential new biomarker for predicting prognosis of the patients.

DDX59-AS1 is a prognostic biomarker and correlated with immune infiltrates in OSCC

Background: lncRNAs play a critical role in multiple steps of gene regulation associated with tumor progression. However, the engagement of DDX59-AS1, a lncRNA, remains equivocal, particularly in oral squamous cell carcinoma (OSCC). In this study, the expression of DDX59-AS1 and its association with immune infiltration were investigated, and its prognostic value in OSSC was evaluated.

Methods: OSCC patients were collected from The Cancer Genome Atlas (TCGA) database. The expression of DDX59-AS1 in OSCC and healthy tissue was compared using Wilcoxon rank sum test. The relationship between DDX59-AS1 and clinicopathological features was analyzed using Logistic regression. Gene ontology (GO) terminology analysis, gene set enrichment analysis (GSEA), and single sample GSEA (ssGSEA) were utilized to interpret the enrichment pathway and functionality and to quantify the immune cell infiltration of DDX59-AS1. The correlation between survival and DDA59-AS1 was evaluated by Kaplan-Meier analysis and Cox regression. The prognostic impact of DDX59-AS1 was predicted by the nomogram based on Cox multivariate analysis.

Results: High expression of DDX59-AS1 was significantly correlated with T stage, clinical stage, race, and age (p < 0.05). Multivariate survival analysis demonstrated that the high expression of DDX59-AS1 was associated with lower overall and specific survival rates. The prognosis prediction was validated by the nomogram and calibration curves. The expression of DDX59-AS1 was negatively correlated with Mast cells, Tfh, T cells, Treg, and B cells, and positively related with the Tgd infiltration level.

Conclusion: DDX59-AS1 played a crucial role in the progression and prognosis of OSCC and was potentially a predictive biomarker for OSCC.

Identification of Potential Antigens for Developing mRNA Vaccine for Immunologically Cold Mesothelioma

Messenger RNA vaccines are considered to be a promising strategy in cancer immunotherapy, while their application on mesothelioma is still largely uncharacterized. This study aimed to identify potential antigens in mesothelioma for anti-mesothelioma mRNA vaccine development, and further determine the immune subtypes of mesothelioma for selection of suitable candidates from an extremely heterogeneous population. Gene expression data and corresponding clinicopathological information were obtained from the TCGA and gene expression omnibus, respectively. Then, the genetic alterations were compared and visualized using cBioPortal, and differentially expressed genes and their prognostic signatures were identified by GEPIA. The relationship between tumor-infiltrating immune cells and the expression of tumor antigens was systematically evaluated by TIMER online. Finally, the immune subtypes and immune landscape of mesothelioma were separately analyzed using consensus cluster and graph learning-based dimensional reduction. A total of five potential tumor antigens correlated with prognosis and infiltration of antigen-presenting cells, including AUNIP, FANCI, LASP1, PSMD8, and XPO5 were identified. Based on the expression of immune-related genes, patients with mesothelioma were divided into two immune subtypes (IS1 and IS2). Each subtype exhibited differential molecular, cellular and clinical properties. Patients with the IS1 subtype were characterized by an immune “cold” phenotype, displaying superior survival outcomes, whereas those with the IS2 subtype were characterized by an immune “hot” and immunosuppressive phenotype. Furthermore, immune checkpoints and immunogenic cell death modulators were differentially expressed between the IS1 and IS2 immune subtype tumors. The immunogenomic landscape of mesothelioma revealed a complex tumor immune microenvironment between individual patients. AUNIP, FANCI, LASP1, PSMD8, and XPO5 are putative antigens for the development of anti-mesothelioma mRNA vaccine and patients with the IS1 subtype may be considered for vaccination.

Prognostic potential of the small GTPase Ran and its methylation in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is a common malignant tumor with high mortality. The prognostic significance of Ran, a member of Ras superfamily, remains unclear in HCC patients.

METHODS

Based on The Cancer Genome Atlas (TCGA) database and Tumor Immune Estimation Resource (TIMER), we analyzed the correlations among Ran expression, promoter methylation and immune cell infiltration. We also investigated the Ran expression levels in HCC tissues and normal tissues by using quantitative real-time PCR.

RESULTS

Ran mRNA expression was significantly increased in HCC tissues compared with the normal tissues (P < 0.001). Time-dependent receiver operating characteristic (ROC) curves showed that Ran expression had predictive value of the 1-, 3- and 5-year overall survival for HCC patients, and the areas under the curves (AUC) were 0.747, 0.634 and 0.704, respectively. Cox regression analysis showed that Ran expression was an independent prognostic factor for HCC patients (HR = 1.492, 95% CI: 1.129-1.971, P = 0.005). We also found a negative relationship between Ran mRNA expression and its promoter methylation (r = -0.36, P < 0.001). High Ran expression and promoter hypomethylation predicted worse overall survival and progression-free survival (P < 0.05) and were involved in the progression of HCC. Ran expression exhibited significant correlations with immune infiltrates and prognostic immune-related genes.

CONCLUSIONS

The present study provides further insight into the prognosis of HCC, and Ran could serve as a biomarker for predicting the survival of HCC patients.

RPL19 Is a Prognostic Biomarker and Promotes Tumor Progression in Hepatocellular Carcinoma

Background

Hepatocellular carcinoma (HCC) is one of the most common malignancies, and the therapeutic outcome remains undesirable due to its recurrence and metastasis. Gene dysregulation plays a pivotal role in the occurrence and progression of cancer, and the molecular mechanisms are largely unknown.

Methods

The differentially expressed genes of HCC screened from the GSE39791 dataset were used to conduct weighted gene co-expression network analysis. The selected hub genes were validated in The Cancer Genome Atlas (TCGA) database and 11 HCC datasets from the Gene Expression Omnibus (GEO) database. Then, a tissue microarray comprising 90 HCC specimens and 90 adjacent normal specimens was used to validate the hub genes. Moreover, the Hallmark, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases were used to identify enriched pathways. Then, we conducted the immune infiltration analysis.

Results

A total of 17 co-expression modules were obtained by weighted gene co-expression network analysis. The green, blue, and purple modules were the most relevant to HCC samples. Four hub genes, RPL19, RPL35A, RPL27A, and RPS12, were identified. Interestingly, we found that all four genes were highly expressed in HCC and that their high expression was related to a poor prognosis by analyzing the TCGA and GEO databases. Furthermore, we investigated RPL19 in HCC tissue microarrays and demonstrated that RPL19 was overexpressed in tumor tissues compared with non-tumor tissues (p = 0.016). Moreover, overexpression of RPL19 predicted a poor prognosis in hepatocellular carcinoma (p < 0.0007). Then, enrichment analysis revealed that cell cycle pathways were significantly enriched, and bile acid metabolism-related pathways were significantly down-regulated when RPL19 was highly expressed. Furthermore, immune infiltration analysis showed that immune response was suppressed.

Conclusion

Our study demonstrates that RPL19 may play an important role in promoting tumor progression and is correlated with a poor prognosis in HCC. RPL19 may serve as a promising biomarker and therapeutic target for the precise diagnosis and treatment of HCC in the future.

Characteristics of hypoxic tumor microenvironment in non-small cell lung cancer, involving molecular patterns and prognostic signature

Background

The mechanisms of hypoxia or immune microenvironment in cancer have been studied respectively, but the role of hypoxia immune microenvironment in non-small cell lung cancer (NSCLC) still needs further exploration.

Methods

By applying the K-means algorithm, 1,121 patients with NSCLC were divided into three categories. We evaluated the constructed signature in order to link it with the prognosis, which was constructed by univariate and least absolute shrinkage operator (LASSO) Cox regression analysis.

Results

A total of three clusters were obtained by clustering five Gene Expression Omnibus (GEO) data sets. Gene Set Variation Analysis (GSVA) and immune infiltration analysis were performed to explore the biological behavior. Cluster one presented an activated state of oncogenic pathways, and compared with the other two clusters, the median risk score was the highest, which was the reason for its poor survival. Cluster three showed that the immune pathway was active and the median risk score was the lowest, so the survival was the best. However, cluster two presented a state in which both immune and matrix pathways were activate. This was manifested as mutual antagonism, and its risk score was in the middle. Its survival was in the middle.

Conclusions

This work revealed the role of hypoxia related genes (HRGs) modification in tumor microenvironment, which was conducive to our comprehensive analysis of the prognosis of NSCLC, and provided direction and guidance for clinical immunotherapy.