Prognostic Implication of a Novel Metabolism-Related Gene Signature in Hepatocellular Carcinoma

Exploring bile acid transporters as key players in cancer development and treatment: Evidence from preclinical and clinical studies

Bile acid transporters (BATs) are integral membrane proteins belonging to various families, such as solute carriers, organic anion transporters, and ATP-binding cassette families. These transporters play a crucial role in bile acid transportation within the portal and systemic circulations, with expression observed in tissues, including the liver, kidney, and small intestine. Bile acids serve as signaling molecules facilitating the absorption and reabsorption of fats and lipids. Dysregulation of bile acid concentration has been implicated in tumorigenesis, yet the role of BATs in this process remains underexplored. Emerging evidence suggests that BATs may modulate various stages of cancer progression, including initiation, development, proliferation, metastasis, and tumor microenvironment regulation. Targeting BATs using siRNAs, miRNAs, and small compound inhibitors in preclinical models and their polymorphisms are well-studied for transporters like BSEP, MDR1, MRP2, OATP1A2, etc., and have shed light on their involvement in tumorigenesis, particularly in cancers such as those affecting the liver and gastrointestinal tract. While BATs' role in diseases like Alagille syndrome, biliary atresia, and cirrhosis have been extensively studied, their implications in cancer warrant further investigation. This review highlights the expression and function of BATs in cancer development and emphasizes the potential of targeting these transporters as a novel therapeutic strategy for various malignancies.

Aldo-keto reductases: Role in cancer development and theranostics

Aldo-keto reductases (AKRs) are a superfamily of enzymes that play crucial roles in various cellular processes, including the metabolism of xenobiotics, steroids, and carbohydrates. A growing body of evidence has unveiled the involvement of AKRs in the development and progression of various cancers. AKRs are aberrantly expressed in a wide range of malignant tumors. Dysregulated expression of AKRs enables the acquisition of hallmark traits of cancer by activating oncogenic signaling pathways and contributing to chemoresistance. AKRs have emerged as promising oncotherapeutic targets given their pivotal role in cancer development and progression. Inhibition of aldose reductase (AR), either alone or in combination with chemotherapeutic drugs, has evolved as a pragmatic therapeutic option for cancer. Several classes of synthetic aldo-keto reductase (AKR) inhibitors have been developed as potential anticancer agents, some of which have shown promise in clinical trials. Many AKR inhibitors from natural sources also exhibit anticancer effects. Small molecule inhibitors targeting specific AKR isoforms have shown promise in preclinical studies. These inhibitors disrupt the activation of oncogenic signaling by modulating transcription factors and kinases and sensitizing cancer cells to chemotherapy. In this review, we discuss the physiological functions of human AKRs, the aberrant expression of AKRs in malignancies, the involvement of AKRs in the acquisition of cancer hallmarks, and the role of AKRs in oncogenic signaling, and drug resistance. Finally, the potential of aldose reductase inhibitors (ARIs) as anticancer drugs is summarized.

Predictive value of a stemness-based classifier for prognosis and immunotherapy response of hepatocellular carcinoma based on bioinformatics and machine-learning strategies

OBJECTIVE

Significant advancements have been made in hepatocellular carcinoma (HCC) therapeutics, such as immunotherapy for treating patients with HCC. However, there is a lack of reliable biomarkers for predicting the response of patients to therapy, which continues to be challenging. Cancer stem cells (CSCs) are involved in the oncogenesis, drug resistance, and invasion, as well as metastasis of HCC cells. Therefore, in this study, we aimed to create an mRNA expression-based stemness index (mRNAsi) model to predict the response of patients with HCC to immunotherapy.

METHODS

We retrieved gene expression and clinical data of patients with HCC from the GSE14520 dataset and the Cancer Genome Atlas (TCGA) database. Next, we used the "one-class logistic regression (OCLR)" algorithm to obtain the mRNAsi of patients with HCC. We performed "unsupervised consensus clustering" to classify patients with HCC based on the mRNAsi scores and stemness subtypes. The relationships between the mRNAsi model, clinicopathological features, and genetic profiles of patients were compared using various bioinformatic methods. We screened for differentially expressed genes to establish a stemness-based classifier for predicting the patient's prognosis. Next, we determined the effect of risk scores on the tumor immune microenvironment (TIME) and the response of patients to immune checkpoint blockade (ICB). Finally, we used qRT-PCR to investigate gene expression in patients with HCC.

RESULTS

We screened CSC-related genes using various bioinformatics tools in patients from the TCGA-LIHC cohort. We constructed a stemness classifier based on a nine-gene (PPARGC1A, FTCD, CFHR3, MAGEA6, CXCL8, CABYR, EPO, HMMR, and UCK2) signature for predicting the patient's prognosis and response to ICBs. Further, the model was validated in an independent GSE14520 dataset and performed well. Our model could predict the status of TIME, immunogenomic expressions, congenic pathway, and response to chemotherapy drugs. Furthermore, a significant increase in the proportion of infiltrating macrophages, Treg cells, and immune checkpoints was observed in patients in the high-risk group. In addition, tumor cells in patients with high mRNAsi scores could escape immune surveillance. Finally, we observed that the constructed model had a good expression in the clinical samples. The HCC tumor size and UCK2 genes expression were significantly alleviated and decreased, respectively, by treatments of anti-PD1 antibody. We also found knockdown UCK2 changed expressions of immune genes in HCC cell lines.

CONCLUSION

The novel stemness-related model could predict the prognosis of patients and aid in creating personalized immuno- and targeted therapy for patients in HCC.

Prognostic value of preoperative serum ferritin in hepatocellular carcinoma patients undergoing transarterial chemoembolization

The present study investigated the prognostic impact of preoperative serum ferritin (SF) levels on the survival of patients with hepatocellular carcinoma (HCC) undergoing transarterial chemoembolization (TACE). Clinicopathological characteristics and laboratory biomarkers of 223 patients with HCC who underwent TACE were retrospectively reviewed. The Kaplan-Meier method was used to calculate the overall survival (OS), and the log-rank test was used to evaluate statistical significance. Univariate and multivariate analyses were performed using Cox proportional hazards regression to evaluate the prognostic impact of SF in these patients. The present findings identified extrahepatic metastases [hazard ratio (HR)=0.490,95%; confidence interval (CI)=0.282-0.843; P=0.010)] and vascular invasion (HR=0.373; 95% CI=0.225-0.619; P<0.0001) as independent prognostic factors for OS. However, preoperative SF levels could not independently predict OS when compared with other prognostic factors (HR=0.810; 95% CI=0.539-1.216; P=0.309). In conclusion, preoperative SF level is an unreliable biochemical predictor of survival in patients with HCC undergoing TACE.

A novel telomere-related genes model for predicting prognosis and treatment responsiveness in diffuse large B-cell lymphoma

Diffuse large B cell lymphoma (DLBCL) is a highly heterogeneous disease with diverse clinical and molecular features. Telomere maintenance is widely present in tumors, but there is a lack of relevant reports on the role of telomere-related genes (TRGs) in DLBCL. In this study, we used consensus clustering based on TRGs expression to identify two molecular clusters with distinct prognoses and immune cell infiltration. We developed a TRGs scoring model using univariate Cox regression and LASSO regression in the GSE10846 training cohort. DLBCL patients in the high-risk group had a worse prognosis than those in the low-risk group, as revealed by Kaplan-Meier curves. The scoring model was validated in the GSE10846 testing cohort and GSE87371 cohort, respectively. The high-risk group was characterized by elevated infiltration of activated DCs, CD56 dim natural killer cells, myeloid-derived suppressor cells, monocytes, and plasmacytoid DCs, along with reduced infiltration of activated CD4 T cells, Type 2 T helper cells, γδ T cells, NK cells, and neutrophils. Overexpression of immune checkpoints, such as PDCD1, CD274, and LAG3, was observed in the high-risk group. Furthermore, high-risk DLBCL patients exhibited increased sensitivity to bortezomib, rapamycin, AZD6244, and BMS.536924, while low-risk DLBCL patients showed sensitivity to cisplatin and ABT.263. Using RT-qPCR, we found that three protective model genes, namely TCEAL7, EPHA4, and ELOVL4, were down-regulated in DLBCL tissues compared with control tissues. In conclusion, our novel TRGs-based model has great predictive value for the prognosis of DLBCL patients and provides a promising direction for treatment optimization.

A novel model based on disulfidptosis-related genes to predict prognosis and therapy of bladder urothelial carcinoma

PURPOSE

Disulfidptosis is a novel type of cell death induced by disulphide stress that depends on the accumulation of cystine disulphide, causing cytotoxicity and triggering cell death. However, the direct prognostic effect and regulatory mechanism of disulfidptosis-related genes in bladder urothelial carcinoma (BLCA) remain unclear.

METHODS

To explore the role of 10 disulfidptosis-related genes, the multiomic data of 10 genes were comprehensively analysed. Next, based on seven disulfidptosis-related differentially expressed genes, a novel disulfidptosis-related gene score was developed to help predict the prognosis of BLCA. Immunohistochemistry, EDU, Real-time PCR and western blot were used to verify the model.

RESULTS

Significant functional differences were found between the high- and low-risk score groups, and samples with a higher risk score were more malignant. Furthermore, the tumour exclusion and Tumour Immune Dysfunction and Exclusion scores of the high-risk score group were higher than those of the low-risk score group. The risk score was positively correlated with the expression of immune checkpoints. Drug sensitivity analyses revealed that the low-risk score group had a higher sensitivity to cisplatin, doxorubicin, docetaxel and gemcitabine than the high-risk score group. Moreover, the expression of the TM4SF1 was positively correlated with the malignancy degree of BLCA, and the proliferation ability of BLCA cells was reduced after knockdown TM4SF1.

CONCLUSION

The present study results suggest that disulfidptosis-related genes influence the prognosis of BLCA through their involvement in immune cell infiltration. Thus, these findings indicate the role of disulfidptosis in BLCA and its potential regulatory mechanisms.

CENPA-driven STMN1 Transcription Inhibits Ferroptosis in Hepatocellular Carcinoma

Background and Aims

The growing knowledge of ferroptosis has suggested the regulatory role of ferroptosis in hepatocellular carcinoma (HCC), but the pertinent molecular mechanisms remain unclear. Herein, this study investigated the mechanistic basis of ferroptosis-related genes (ferrGenes) in the growth of HCC.

Methods

Differentially expressed human ferrGenes and tumor-related transcription factors (TFs) were obtained from the The Cancer Genome Atlas (TCGA) dataset and the GTEx dataset. Spearman method-based correlation analysis were conducted to construct TF-ferrGene coexpression regulatory network. Key genes associated with prognosis were singled out with Lasso regression and multivariate Cox analysis to construct the prognostic risk model. Then the accuracy and independent prognostic ability of the model were evaluated. Expression of CENPA and STMN1 was determined in clinical HCC tissues and HCC cells, and their binding was analyzed with dual-luciferase and chromatin immunoprecipitation (ChIP) assays. Furthermore, ectopic expression and knockdown assays were performed in HCC cells to assess the effect of CENPA and STMN1 on ferroptosis and malignant phenotypes.

Results

The prognostic risk model constructed based on the eight TF-ferrGene regulatory network-related genes accurately predicted the prognosis of HCC patients. It was strongly related to the clinical characteristics of HCC patients. Moreover, CENPA/STMN1 might be a key TF-ferrGene regulatory network in ferroptosis of HCC. CENPA and STMN1 were overexpressed in HCC tissues and cells. Additionally, CENPA facilitated STMN1 transcription by binding to STMN1 promoter, thus facilitating the malignant phenotypes and suppressing the ferroptosis of HCC cells.

Conclusions

Taken together, CENPA curbs the ferroptosis of HCC cells by upregulating STMN1 transcription, thereby promoting HCC growth.

A pyroptosis-related gene signature provides an alternative for predicting the prognosis of patients with hepatocellular carcinoma

BACKGROUND

Hepatocellular Carcinoma (HCC) is a common malignant neoplasm with limited treatment options and poor outcomes. Thus, there is an urgent need to find sensitive biomarkers for HCC.

METHODS

Gene expression and clinicopathological information were obtained from public databases, based on which a pyroptosis-related gene signature was constructed by the least absolute shrinkage and selection operator Cox regression. The applicability of the signature was evaluated via Kaplan-Meier curve and time-dependent ROC curve. TIMER, QUANTISEQ, MCPCOUNTER, EPIC, CIBERSORT, ssGSEA, and ESTIMATE were employed to assess the immune status. Comparisons between groups were analyzed with Wilcoxon test. Pearson and Spearman correlation analyses were adopted for linear correlation analysis. Genetic knockdown was conducted using siRNA transfection and the mRNA expression levels of interest genes were measured using quantitative reverse transcription PCR. Finally, protein levels in 10 paired tumor tissues and adjacent non-tumor tissues from HCC patients were measured using immunohistochemistry.

RESULTS

A pyroptosis-related gene signature was established successfully to calculate independent prognostic risk scores. It was found that survival outcomes varied significantly between different risk groups. In addition, an attenuated antitumor immune response was found in the high-risk group. Meanwhile, multiple immune checkpoints were up-regulated in high-risk score patients. Cell cycle-related genes, angiogenesis-related genes and tumor drug resistance genes were also markedly elevated. Knockdown of prognostic genes in the signature significantly inhibited the expression of immune checkpoint genes and angiogenesis-related genes. Besides, each prognostic gene was expressed at a higher level in HCC tissues than in adjacent normal tissues.

CONCLUSIONS

We successfully established a novel pyroptosis-related gene signature which could help predict the overall survival and assess the immune status of HCC patients.

Genes Modulating Butyrate Metabolism for Assessing Clinical Prognosis and Responses to Systematic Therapies in Hepatocellular Carcinoma

Butyrate, one of the major products of the gut microbiota, has played notable roles in diverse therapies for multiple tumors. Our study aimed to determine the roles of genes that modulate butyrate metabolism (BM) in predicting the clinical prognosis and responses to systemic therapies in hepatocellular carcinoma (HCC). The genes modulating BM were available from the GeneCard database, and gene expression and clinical information were obtained from TCGA-LIHC, GEO, ICGC-JP, and CCLE databases. Candidate genes from these genes that regulate BM were then identified by univariate Cox analysis. According to candidate genes, the patients in TCGA were grouped into distinct subtypes. Moreover, BM- related gene signature (BMGs) was created via the LASSO Cox algorithm. The roles of BMGs in identifying high-risk patients of HCC, assessing the prognoses, and predicting systematic therapies were determined in various datasets. The statistical analyses were fulfilled with R 4.1.3, GraphPad Prism 8.0 and Perl 5.30.0.1 software. In the TCGA cohort, most butyrate-related genes were over-expressed in the B cluster, and patients in the B cluster showed worse prognoses. BMGs constructed by LASSO were composed of eight genes. BMGs exhibited a strong performance in evaluating the prognoses of HCC patients in various datasets, which may be superior to 33 published biomarkers. Furthermore, BMGs may contribute to the early surveillance of HCC, and BMGs could play active roles in assessing the effectiveness of immunotherapy, TACE, ablation therapy, and chemotherapeutic drugs for HCC. BMGs may be served as novel promising biomarkers for early identifying high-risk groups of HCC, as well as assessing prognoses, drug sensitivity, and the responses to immunotherapy, TACE, and ablation therapy in patients with HCC.

A bile acid-related prognostic signature in hepatocellular carcinoma

Due to the high mortality of hepatocellular carcinoma (HCC), its prognostic models are urgently needed. Bile acid (BA) metabolic disturbance participates in hepatocarcinogenesis. We aim to develop a BA-related gene signature for HCC patients. Research data of HCC were obtained from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) online databases. After least absolute shrinkage and selection operator (LASSO) regression analysis, we developed a BA-related prognostic signature in TCGA cohort based on differentially expressed prognostic BA-related genes. Then, the predictive performance of the signature was evaluated and verified in TCGA and ICGC cohort respectively. We obtained the risk score of each HCC patient according to the model. The differences of immune status and drug sensitivity were compared in patients that were stratified based on risk score. The protein and mRNA levels of the modeling genes were validated in the Human Protein Atlas database and our cell lines, respectively. In TCGA cohort, we selected 4 BA-related genes to construct the first BA-related prognostic signature. The risk signature exhibited good discrimination and predictive ability, which was verified in ICGC cohort. Patients were classified into high- and low-risk groups according to their median scores. The occurrence of death increased with increasing risk score. Low-risk patients owned favorable overall survival. High-risk patients possessed high immune checkpoint expression and low IC50 values for sorafenib, cisplatin and doxorubicin. Real-time quantitative PCR and immunohistochemical results validate expression of modeling genes in the signature. We constructed the first BA-related gene signature, which might help to identify HCC patients with poor prognosis and guide individualized treatment.

Prognostic significance and immune landscape of a fatty acid metabolism-related gene signature in colon adenocarcinoma

Background: Fatty acid metabolism (FAM), as a hallmark of caner, plays important roles in tumor initiation and carcinogenesis. However, the significance of fatty acid metabolism-related genes in colon adenocarcinoma (COAD) are largely unknown. Methods: RNA sequencing data and clinical information were downloaded from the Cancer Genome Atlas (TCGA) cohort. Univariate and multivariate Cox regression analyses were utilized to construct a fatty acid metabolism-related gene signature. Kaplan-Meier survival and receiver operating characteristic (ROC) analyses were used to verify the performance of this signature. GEO datasets were applied to validate the signature. Maftools package was utilized to analyze the mutation profiles of this signature. Correlation between the risk signature and stemness scores was compared by RNA stemness score (RNAss). Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene set variation analysis (GSVA) were performed to explore the potential functions and signaling pathways. Immune landscape of the signature was explored by analyzing different immune cells infiltration, immune functions and microsatellite instability. A nomogram was constructed by combining the risk signature and multiple clinical factors. Expression levels and prognostic values of the risk genes were revealed in the cancer genome atlas and GEO databases. Moreover, the expression the risk genes were measured in cell lines using real time quantitative PCR (qRT-PCR). Results: Eight fatty acid metabolism-related genes (CD36, ENO3, MORC2, PTGR1, SUCLG2, ELOVL3, ELOVL6 and CPT2) were used to construct a risk signature. This signature demonstrated better prognostic value than other clinicopathological parameters, with AUC value was 0.734 according to the cancer genome atlas database. There was negative correlation between the riskscore and RNA stemness score. The patients in the high-risk group demonstrated higher infiltration of M0 macrophages, and less infiltration of activated CD4 memory T cells and Eosinophils. There were more MSI patients in the high-risk group than those in the low-risk group (38% vs. 30%). The risk scores of patients in the MSI group were slightly higher than those in the microsatellite stability group. Gene ontology, kyoto encyclopedia of genes and genomes and gene set variation analysis enrichment analyses showed that several metabolism-related functions and signaling pathways were enriched. A nomogram showed good predictive capability of the signature. Moreover, qRT-PCR revealed upregulated expression of ENO3, MORC2, SUCLG2 and ELOVL6, and downregulated expression of CPT2 in all examined colon adenocarcinoma cell lines. Conclusion: This study provided novel insights into a fatty acid metabolism-related signature in the prognosis an immune landscape of colon adenocarcinoma patients.

CD5L-associated gene analyses highlight the dysregulations, prognostic effects, immune associations, and drug-sensitivity predicative potentials of LCAT and CDC20 in hepatocellular carcinoma

BACKGROUND

The dysregulation of CD5L has been reported in hepatocellular carcinoma (HCC). However, its functions in HCC were controversial. In this study, we aimed to identify CD5L-associated pathways and markers and explore their values in HCC diagnosis, prognosis and treatment.

METHODS

HCC datasets with gene expression profiles and clinical data in TCGA and ICGC were downloaded. The immune/stroma cell infiltrations were estimated with xCell. CD5L-associated pathways and CD5L-associated genes (CD5L-AGs) were identified with gene expression comparisons and gene set enrichment analysis (GSEA). Cox regression, Kaplan-Meier survival analysis, and least absolute shrinkage and selection operator (LASSO) regression analysis were performed. The correlations of the key genes with immune/stroma infiltrations, immunoregulators, and anti-cancer drug sensitivities in HCC were investigated. At protein level, the key genes dysregulations, their correlations and prognostic values were validated in clinical proteomic tumor analysis consortium (CPTAC) database. Serum CD5L and LCAT activity in 50 HCC and 30 normal samples were evaluated and compared. The correlations of serum LCAT activity with alpha-fetoprotein (AFP), albumin (ALB) and high-density lipoprotein (HDL) in HCC were also investigated.

RESULTS

Through systemic analyses, 14 CD5L-associated biological pathways, 256 CD5L-AGs and 28 CD5L-associated prognostic and diagnostic genes (CD5L-APDGs) were identified. A risk model consisting of LCAT and CDC20 was constructed for HCC overall survival (OS), which could discriminate HCC OS status effectively in both the training and the validation sets. CD5L, LCAT and CDC20 were shown to be significantly correlated with immune/stroma cell infiltrations, immunoregulators and 31 anti-cancer drug sensitivities in HCC. At protein level, the dysregulations of CD5L, LCAT and CDC20 were confirmed. LCAT and CDC20 were shown to be significantly correlated with proliferation marker MKI67. In serum, no significance of CD5L was shown. However, the lower activity of LCAT in HCC serum was obvious, as well as its significant positive correlations ALB and HDL concentrations.

CONCLUSIONS

CD5L, LCAT and CDC20 were dysregulated in HCC both at mRNA and protein levels. The LCAT-CDC20 signature might be new predicator for HCC OS. The associations of the three genes with HCC microenvironment and anti-cancer drug sensitivities would provide new clues for HCC immunotherapy and chemotherapy.

Telomere-related gene risk model for prognosis and drug treatment efficiency prediction in kidney cancer

Kidney cancer is one of the most common urological cancers worldwide, and kidney renal clear cell cancer (KIRC) is the major histologic subtype. Our previous study found that von-Hippel Lindau (VHL) gene mutation, the dominant reason for sporadic KIRC and hereditary kidney cancer-VHL syndrome, could affect VHL disease-related cancers development by inducing telomere shortening. However, the prognosis role of telomere-related genes in kidney cancer has not been well discussed. In this study, we obtained the telomere-related genes (TRGs) from TelNet. We obtained the clinical information and TRGs expression status of kidney cancer patients in The Cancer Genome Atlas (TCGA) database, The International Cancer Genome Consortium (ICGC) database, and the Clinical Proteomic Tumor Analysis Consortium (CPTAC) database. Totally 353 TRGs were differential between tumor and normal tissues in the TCGA-KIRC dataset. The total TCGA cohort was divided into discovery and validation TCGA cohorts and then using univariate cox regression, lasso regression, and multivariate cox regression method to conduct data analysis sequentially, ten TRGs (ISG15, RFC2, TRIM15, NEK6, PRKCQ, ATP1A1, ELOVL3, TUBB2B, PLCL1, NR1H3) risk model had been constructed finally. The kidney patients in the high TRGs risk group represented a worse outcome in the discovery TCGA cohort (p<0.001), and the result was validated by these four cohorts (validation TCGA cohort, total TCGA cohort, ICGC cohort, and CPTAC cohort). In addition, the TRGs risk score is an independent risk factor for kidney cancer in all these five cohorts. And the high TRGs risk group correlated with worse immune subtypes and higher tumor mutation burden in cancer tissues. In addition, the high TRGs risk group might benefit from receiving immune checkpoint inhibitors and targeted therapy agents. Moreover, the proteins NEK6, RF2, and ISG15 were upregulated in tumors both at the RNA and protein levels, while PLCL1 and PRKCQ were downregulated. The other five genes may display the contrary expression status at the RNA and protein levels. In conclusion, we have constructed a telomere-related genes risk model for predicting the outcomes of kidney cancer patients, and the model may be helpful in selecting treatment agents for kidney cancer patients.

Identification of FABP7 as a Potential Biomarker for Predicting Prognosis and Antiangiogenic Drug Efficacy of Glioma

Objective

Glioma is a common malignant tumor of the central nervous system with extremely poor prognosis. An efficient molecular marker for diagnosis and treatment is urgently needed. Fatty acid binding protein 7(FABP7), which regulates intracellular lipid metabolism, is highly expressed in nervous system tumors, but its prognostic value remains undetermined. The present study investigated the relationship between FABP7 expression and prognosis in glioma patients by bioinformatics analysis, as well as immunohistochemically evaluating the effect of FABP7 expression on the efficacy of antiangiogenic drugs.

Methods

Gene expression and clinical data on patients with glioma were collected from the China Glioma Genome Atlas (CGGA) database, The Cancer Genome Atlas (TCGA), and the Gene Expression Omnibus (GEO) databases. Levels of FABP7 expression and their association with the clinicopathologic characteristics of glioma patients were analyzed in the CGGA database. The relationships between FABP7 expression and clinical findings, such as survival and prognostic, were determined and used for nomogram construction. Mechanisms of action of FABP7 were assessed using GSEA software. FABP7 expression in the tissues of glioma patients treated with apatinib was evaluated immunohistochemically.

Results

FABP7 was highly expressed in glioma samples, with higher FABP7 expression associated with poorer patient prognosis and more advanced clinicopathological features. Bioinformatics analysis, including survival, receiver operating characteristic curve, and univariate and multivariate Cox analyses, showed that FABP7 was independently prognostic of outcomes in glioma patients. GSEA analysis showed that FABP7 was associated with angiogenesis, with FABP7 having correlation coefficients > 0.4 with seven factors in the angiogenic pathway, POSTN, TIMP1, PDGFA, FGFR1, S100A4, COL5A2, and STC1, and the expression of these factors related to patient prognosis. Immunohistochemistry showed that FABP7 expression was higher in glioma patients with poor survival after apatinib treatment.

Conclusions

High FABP7 expression is associated with poor prognosis in glioma patients. FABP7, which is important for glioma angiogenesis, may serve as an independent prognostic predictor in glioma patients.

Integrated Bioinformatic Analysis of DNA Methylation and Immune Infiltration in Endometrial Cancer

Background

Endometrial cancer greatly threatens the health of female. Emerging evidences have demonstrated that DNA methylation and immune infiltration are involved in the occurrence and development of endometrial cancer. However, the mechanism and prognostic biomarkers of endometrial cancer are still unclear. In this study, we assess DNA methylation and immune infiltration via bioinformatic analysis.

Methods

The latest RNA-Seq, DNA methylation data, and clinical data related to endometrial cancer were downloaded from the UCSC Xena dataset. The methylation-driven genes were selected, and then the risk score was obtained using “MethylMix” and “corrplot” R packages. The connection between methylated genes and the expression of screened driven genes were explored using “survminer” and “beeswarm” packages, respectively. Finally, the role of VTCN1in immune infiltration was analyzed using “CIBERSORT” package.

Results

In this study, 179 upregulated genes, and 311 downregulated genes were identified and found to be related to extracellular matrix organization, cell–cell junctions, and cell adhesion molecular binding. The methylation-driven gene VTCN1 was selected, and patients classified to the hypomethylation and high expression group displayed poor prognosis. The VTCN1 gene exhibited highest correlation coefficient between methylation and expression. More importantly, the hypomethylation of promoter of VTCN1 led to its high expression, thereby induce tumor development by inhibiting CD8+ T cell infiltration.

Conclusions

Overall, our study was the first to reveal the mechanism of endometrial cancer by assessing DNA methylation and immune infiltration via integrated bioinformatic analysis. In addition, we found a pivotal prognostic biomarker for the disease. Our study provides potential targets for the diagnosis and prognosis of endometrial cancer in the future.

Prognostic Value and Correlation With Tumor Immune Infiltration of a Novel Metabolism-Related Gene Signature in Pancreatic Cancer

Background

Energy metabolism has been considered as one of the novel features of neoplasms. This study aimed to establish the prognostic signature for pancreatic cancer (PC) based on metabolism-related genes (MRGs).

Methods

We obtained MRGs from the Molecular Signatures Database (MSigDB) and gene sequence data in the Cancer Genome Atlas (TCGA) databases. Then, differentially expressed MRGs (DE-MRGs) were identified utilizing the R software. We built the prognostic model via multivariate Cox regression. Moreover, external validation of the prognostic signature was also performed. Nomogram was created to predict the overall survival (OS). Next, this study analyzed the prognostic value, clinical relationship, and metabolism-related signaling pathways of the prognostic signature. The role in tumor infiltration was further evaluated. Eventually, the expression level of the three MRGs along with the function of NT5E was validated.

Results

Twenty-two MRGs were chosen, eight of which were identified to be most significantly correlated with the prognosis of PC. Meanwhile, a 3-MRG prognostic signature was established, and we verified this prognostic model in two separate external cohorts. What is more, the nomogram was used to predict 1-/2-/3-year OS of PC patients. In addition, the immune cell infiltration and expression of immune checkpoint were significantly influenced by the risk score. Finally, three MRGs were highly expressed in PC cell lines, and NT5E was associated with the proliferation and migration ability of PC.

Conclusion

To sum up, the study established and validated a 3-MRG prognostic signature for PC, and the signature could be utilized to predict the prognosis and assist the individualized clinical management of patients with PC.

Identification and Validation of Pyroptosis-Related Gene Signature to Predict Prognosis and Reveal Immune Infiltration in Hepatocellular Carcinoma

Background: Hepatocellular carcinoma (HCC) is characterized by a poor prognosis and accounts for the fourth common cause of cancer-related deaths. Recently, pyroptosis has been revealed to be involved in the progression of multiple cancers. However, the role of pyroptosis in the HCC prognosis remains elusive.

Methods: The clinical information and RNA-seq data of the HCC patients were collected from the TCGA-LIHC datasets, and the differential pyroptosis-related genes (PRG) were firstly explored. The univariate Cox regression and consensus clustering were applied to recognize the HCC subtypes. The prognostic PRGs were then submitted to the LASSO regression analysis to build a prognostic model in the TCGA training cohort. We further evaluated the predictive model in the TCGA test cohort and ICGC validation cohort (LIRI-JP). The accuracy of prediction was validated using the Kaplan—Meier (K-M) and receiver operating characteristic (ROC) analyses. The single-sample gene set enrichment analysis (ssGSEA) was used to determine the differential immune cell infiltrations and related pathways. Finally, the expression of the prognostic genes was validated by qRT-PCR in vivo and in vitro.

Results: We identified a total of 26 differential PRGs, among which three PRGs comprising GSDME, GPX4, and SCAF11 were subsequently chosen for constructing a prognostic model. This model significantly distinguished the HCC patients with different survival years in the TCGA training, test, and ICGC validation cohorts. The risk score of this model was confirmed as an independent prognostic factor. A nomogram was generated indicating the survival years for each HCC patient. The ssGSEA demonstrated several tumor-infiltrating immune cells to be remarkably associated with the risk scores. The qRT-PCR results also showed the apparent dysregulation of PRGs in HCC. Finally, the drug sensitivity was analyzed, indicating that Lenvatinib might impact the progression of HCC via targeting GSDME, which was also validated in human Huh7 cells.

Conclusion: The PRG signature comprised of GSDME, GPX4, and SCAF11 can serve as an independent prognostic factor for HCC patients, which would provide further evidence for more clinical and functional studies.

Identification and Validation of the Pyroptosis-Related Molecular Subtypes of Lung Adenocarcinoma by Bioinformatics and Machine Learning

Lung cancer remains the leading cause of cancer death globally, with lung adenocarcinoma (LUAD) being its most prevalent subtype. Due to the heterogeneity of LUAD, patients given the same treatment regimen may have different responses and clinical outcomes. Therefore, identifying new subtypes of LUAD is important for predicting prognosis and providing personalized treatment for patients. Pyroptosis-related genes play an essential role in anticancer, but there is limited research investigating pyroptosis in LUAD. In this study, 33 pyroptosis gene expression profiles and clinical information were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. By bioinformatics and machine learning analyses, we identified novel subtypes of LUAD based on 10 pyroptosis-related genes and further validated them in the GEO dataset, with machine learning models performing up to an AUC of 1 for classifying in GEO. A web-based tool was established for clinicians to use our clustering model (http://www.aimedicallab.com/tool/aiml-subphe-luad.html). LUAD patients were clustered into 3 subtypes (A, B, and C), and survival analysis showed that B had the best survival outcome and C had the worst survival outcome. The relationships between pyroptosis gene expression and clinical characteristics were further analyzed in the three molecular subtypes. Immune profiling revealed significant differences in immune cell infiltration among the three molecular subtypes. GO enrichment and KEGG pathway analyses were performed based on the differential genes of the three subtypes, indicating that differentially expressed genes (DEGs) were involved in multiple cellular and biological functions, including RNA catabolic process, mRNA catabolic process, and pathways of neurodegeneration-multiple diseases. Finally, we developed an 8-gene prognostic model that accurately predicted 1-, 3-, and 5-year overall survival. In conclusion, pyroptosis-related genes may play a critical role in LUAD, and provide new insights into the underlying mechanisms of LUAD.