Prognostic Value and Immune Infiltrates of ABCA8 and FABP4 in Stomach Adenocarcinoma

The SARIFA biomarker in the context of basic research of lipid-driven cancers

SARIFA was very recently introduced as a histomorphological biomarker with strong prognostic power for colorectal, gastric, prostate, and pancreatic cancer. It is characterized by the direct contact between tumor cells and adipocytes due to a lack of stromal reaction. This can be easily evaluated on routinely available H&E-slides with high interobserver agreement. SARIFA also reflects a specific tumor biology driven by metabolic reprogramming. Tumor cells in SARIFA-positive tumors benefit from direct interaction with adipocytes as an external source of lipids. Numerous studies have shown that lipid metabolism is crucial in carcinogenesis and cancer progression. We found that the interaction between tumor cells and adipocytes was not triggered by obesity, as previously assumed. Instead, we believe that this is due to an immunological mechanism. Knowledge about lipid metabolism in cancer from basic experiments can be transferred to develop strategies targeting this reprogramed metabolism.

ABCA8: A potential therapeutic target in the treatment of colorectal cancer?

None.

Tumour suppressor ABCA8 inhibits malignant progression of colorectal cancer via Wnt/β-catenin pathway

BACKGROUND

Colorectal cancer (CRC) is one of the most commonly diagnosed malignant tumours of the digestive tract, and new therapeutic targets and prognostic markers are still urgently required. However, the role and molecular mechanisms of ATP binding cassette subfamily A member 8 (ABCA8) in CRC remain unclear.

METHODS

Databases and clinical specimens were analysed to determine the expression level of ABCA8 in CRC. Colony formation, CCK-8 and Transwell assays were conducted to determine cell proliferation, viability, migration and invasion. Flow cytometry was used to detect cell cycle progression and apoptosis. Western blot and rescue experiments were performed to determine the specific mechanisms of action of ABCA8.

RESULTS

ABCA8 expression is dramatically down-regulated in CRC tissues and cell lines. Ectopic expression of ABCA8 induced apoptosis and cell cycle arrest in vitro, inhibited cell growth, suppressed migration and invasion, reversed epithelial-mesenchymal transition and suppressed xenograft tumour growth and metastasis in vivo. Mechanistically, ABCA8 inhibited CRC cell proliferation and metastasis through the Wnt/β-catenin signalling pathway, both in vitro and in vivo.

CONCLUSION

We verified that ABCA8 inhibits the malignant progression of CRC through the Wnt/β-catenin pathway. This newly discovered ABCA8-Wnt-β-catenin signalling axis is probably helpful in guiding the clinical diagnosis and treatment of CRC.

Stroma AReactive Invasion Front Areas (SARIFA) improves prognostic risk stratification of perioperative chemotherapy treated oesophagogastric cancer patients from the MAGIC and the ST03 trial

BACKGROUND

Tumour-associated fat cells without desmoplastic stroma reaction at the invasion front (Stroma AReactive Invasion Front Areas (SARIFA)) is a prognostic biomarker in gastric and colon cancer. The clinical utility of the SARIFA status in oesophagogastric cancer patients treated with perioperative chemotherapy is currently unknown.

METHODS

The SARIFA status was determined in tissue sections from patients recruited into the MAGIC (n = 292) or ST03 (n = 693) trials treated with surgery alone (S, MAGIC) or perioperative chemotherapy (MAGIC, ST03). The relationship between SARIFA status, clinicopathological factors, overall survival (OS) and treatment was analysed.

RESULTS

The SARIFA status was positive in 42% MAGIC trial S patients, 28% MAGIC and 48% ST03 patients after pre-operative chemotherapy. SARIFA status was related to OS in MAGIC trial S patients and was an independent prognostic biomarker in ST03 trial patients (HR 1.974, 95% CI 1.555-2.507, p < 0.001). ST03 patients with lymph node metastasis (ypN + ) and SARIFA-positive tumours had poorer OS than patients with ypN+ and SARIFA-negative tumours (plogrank < 0.001).

CONCLUSIONS

The SARIFA status has clinical utility as prognostic biomarker in oesophagogastric cancer patients irrespective of treatment modality. Whilst underlying biological mechanisms warrant further investigation, the SARIFA status might be used to identify new drug targets, potentially enabling repurposing of existing drugs targeting lipid metabolism.

Nine-Gene Prognostic Signature Related to Gut Microflora for Predicting the Survival in Gastric Cancer Patients

BACKGROUND/AIMS

The purpose of this study is to screen the feature genes related to gut microflora and explore the role of the genes in predicting the prognosis of patients with gastric cancer.

MATERIALS AND METHODS

We downloaded the gene profile of gastric cancer from the University of California Santa Cruz, the gut microflora related to gastric cancer from The Cancer Microbiome Atlas. The GSE62254 dataset was downloaded from National Center for Biotechnology Information Gene Expression Omnibus as a validation dataset. A correlation network between differentially expressed genes and gut microflora was constructed using Cytoscape. The optimized prognostic differentially expressed genes were identified through least absolute shrinkage and selection operator (LASSO) algorithm and univariate Cox regression analysis. The risk score model was established and then measured via Kaplan-Meier and area under the curve. Finally, the nomogram model was constructed according to the independent clinical factors, which was evaluated using C-index.

RESULTS

A total of 754 differentially expressed genes and 8 gut microflora were screened, based on which we successfully constructed the correlation network. We obtained 9 optimized prognostic differentially expressed genes, including HSD17B3, GNG7, CHAD, ARHGAP8, NOX1, YY2, GOLGA8A, DNASE1L3, and ABCA8. Moreover, Kaplan-Meier curves indicated the risk score model correctly predicted the prognosis of gastric cancer in both University of California Santa Cruz and GSE62254 dataset (area under the curve >0.8; area under the curve >0.7). Finally, we constructed the nomogram, in which the C index of 1, 3, and 5 years was 0.824, 0.772, and 0.735 representing that the nomogram was consistent with the actual situation.

CONCLUSIONS

These results indicate the 9 differentially expressed genes related to gut microflora might predict the survival time of patients with gastric cancer. Both risk signature and nomogram could effectively predict the prognosis for patients with gastric cancer.

ABCA8 Elevation Predicts the Prognosis and Exerts the Anti-oncogenic Effects on the Malignancy of Non-small Cell Lung Cancer via TCF21-Mediated Inactivation of PI3K/AKT

The malignant growth and metastatic potential of non-small-cell lung cancer (NSCLC) are the major causes for its poor prognosis. ATP-binding cassette (ABC) subfamily A member 8 (ABCA8) exerts contradictive roles in the development of several cancers. Nevertheless, its role in NSCLC remains unclear. In this study, three GEO datasets and bioinformatics databases (GEPIA2 and UALCAN) revealed the obvious down-regulation of ABCA8 in NSCLC tissues and cells, and this expression was associated with cancer stages and lymph node metastasis. Low expression of ABCA8 predicted poor survival in NSCLC. ABCA8 elevation inhibited cell proliferation and induced cell apoptosis. Moreover, ABCA8 overexpression suppressed cancer cell invasion. Mechanistically, ABCA8 was associated with TCF21 in NSCLC specimens and its overexpression enhanced TCF21 expression. ABCA8 elevation inactivated the PI3K/AKT signaling, which was reversed after TCF21 knockdown. Additionally, targeting TCF21 overturned the anti-oncogenic effects of ABCA8 elevation on cell proliferation, apoptosis and invasion. Thus, the current findings highlight that ABCA8 may be a promising prognostic marker and may act as a suppressor gene to regulate the malignancy of NSCLC cells via TCF21-mediated inactivation of PI3K/AKT signaling, supporting a new promising target for the treatment of NSCLC.

Delineation of fatty acid metabolism in gastric cancer: Therapeutic implications

BACKGROUND

The prognosis of gastric cancer is extremely poor. Metabolic reprogramming involving lipids has been associated with cancer occurrence and progression.

AIM

To illustrate fatty acid metabolic mechanisms in gastric cancer, detect core genes, develop a prognostic model, and provide treatment options.

METHODS

Raw data from The Cancer Genome Atlas and Gene Expression Omnibus databases were collected and analyzed. Differentially expressed fatty acid metabolism genes were identified and incorporated into a risk model based on least absolute shrinkage and selection operator regression analysis. Then, patients from The Cancer Genome Atlas were assigned to high- and low-risk cohorts according to the mean value of the risk score as the threshold, which was verified in the Gene Expression Omnibus database. Relationships between chemotherapeutic sensitivity and tumor microenvironment features were assessed.

RESULTS

An integrated evaluation was performed in this study. Fatty acid metabolism-related genes were used to construct the risk model. Patients classified into the high-risk cohort were considered to be resistant to chemotherapy based on results of the "pRRophetic" R package. Patients in the high-risk cohort were associated with type I/II interferon activation, increased inflammation level, immune cell infiltration, and tumor immune dysfunction based on the exclusion algorithm, indicating the potential benefit of immunotherapy in these patients.

CONCLUSION

We constructed a fatty acid-related risk score model to assess the comprehensive fatty acid features in gastric cancer and validated its vital role in prognosis, chemotherapy sensitivity, and immunotherapy.

Delineation of fatty acid metabolism in gastric cancer: Therapeutic implications

BACKGROUND

The prognosis of gastric cancer is extremely poor. Metabolic reprogramming involving lipids has been associated with cancer occurrence and progression.

AIM

To illustrate fatty acid metabolic mechanisms in gastric cancer, detect core genes, develop a prognostic model, and provide treatment options.

METHODS

Raw data from The Cancer Genome Atlas and Gene Expression Omnibus databases were collected and analyzed. Differentially expressed fatty acid metabolism genes were identified and incorporated into a risk model based on least absolute shrinkage and selection operator regression analysis. Then, patients from The Cancer Genome Atlas were assigned to high- and low-risk cohorts according to the mean value of the risk score as the threshold, which was verified in the Gene Expression Omnibus database. Relationships between chemotherapeutic sensitivity and tumor microenvironment features were assessed.

RESULTS

An integrated evaluation was performed in this study. Fatty acid metabolism-related genes were used to construct the risk model. Patients classified into the high-risk cohort were considered to be resistant to chemotherapy based on results of the “pRRophetic” R package. Patients in the high-risk cohort were associated with type I/II interferon activation, increased inflammation level, immune cell infiltration, and tumor immune dysfunction based on the exclusion algorithm, indicating the potential benefit of immunotherapy in these patients.

CONCLUSION

We constructed a fatty acid-related risk score model to assess the comprehensive fatty acid features in gastric cancer and validated its vital role in prognosis, chemotherapy sensitivity, and immunotherapy.

Identidication of novel biomarkers in non-small cell lung cancer using machine learning

Lung cancer is one of the leading causes of cancer-related deaths worldwide, and non-small cell lung cancer (NSCLC) accounts for a large proportion of lung cancer cases, with few diagnostic and therapeutic targets currently available for NSCLC. This study aimed to identify specific biomarkers for NSCLC. We obtained three gene-expression profiles from the Gene Expression Omnibus database (GSE18842, GSE21933, and GSE32863) and screened for differentially expressed genes (DEGs) between NSCLC and normal lung tissue. Enrichment analyses were performed using Gene Ontology, Disease Ontology, and the Kyoto Encyclopedia of Genes and Genomes. Machine learning methods were used to identify the optimal diagnostic biomarkers for NSCLC using least absolute shrinkage and selection operator logistic regression, and support vector machine recursive feature elimination. CIBERSORT was used to assess immune cell infiltration in NSCLC and the correlation between biomarkers and immune cells. Finally, using western blot, small interfering RNA, Cholecystokinin-8, and transwell assays, the biological functions of biomarkers with high predictive value were validated. A total of 371 DEGs (165 up-regulated genes and 206 down-regulated genes) were identified, and enrichment analysis revealed that these DEGs might be linked to the development and progression of NSCLC. ABCA8, ADAMTS8, ASPA, CEP55, FHL1, PYCR1, RAMP3, and TPX2 genes were identified as novel diagnostic biomarkers for NSCLC. Monocytes were the most visible activated immune cells in NSCLC. The knockdown of the TPX2 gene, a biomarker with a high predictive value, inhibited A549 cell proliferation and migration. This study identified eight potential diagnostic biomarkers for NSCLC. Further, the TPX2 gene may be a therapeutic target for NSCLC.

Novel insight on predicting prognosis of gastric cancer based on inflammation

BACKGROUND

The tumor microenvironment (TME) and inflammation play vital roles in the development and progression of gastric cancer (GC). However, there are no inflammation-related models that can predict the prognosis and immunotherapy response of GC patients. We aimed to establish a prognostic model based on an inflammation-related gene (IRG) signature that can predict poor clinical outcomes in GC.

METHODS

We searched IRGs in The Cancer Genome Atlas (TCGA) database and identified genes differentially expressed in GC. The model was constructed using univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analysis and validated using Gene Expression Omnibus (GEO) database. Receiver operating characteristic (ROC) curve, principal component analysis (PCA), and t-distribution stochastic neighbor embedding (t-SNE) analysis were performed to evaluate model performance. Independent prognostic factor, immune infiltration, cancer stemness, immunotherapy response analysis and gene set enrichment analysis (GSEA) were performed for functional evaluation.

RESULTS

An inflammation-related risk model was established based on 8 genes (F2, LBP, SERPINE1, ADAMTS12, FABP4, PROC, TNFSF18, and CYSLTR1). Risk score significantly correlated with poor outcomes and independently predicted prognosis. It was also associated with immune infiltration and reflected immunotherapy response.

CONCLUSIONS

We established and validated an inflammation-related prognostic model that predicts immune escape and patient prognosis in GC. Our model is expected to improve clinical outcomes by facilitating clinical decision making and the development of individualized treatments.

Identification and Validation of Prognostic Related Hallmark ATP-Binding Cassette Transporters Associated With Immune Cell Infiltration Patterns in Thyroid Carcinoma

ATP-binding cassette (ABC) transporters are a large superfamily of membrane proteins that facilitate the translocation of heterogeneous substrates. Studies indicate that ABC transporters may play important roles in various carcinomas. However, the correlation between ABC transporters and immunomodulation in thyroid carcinoma (TC), as well as the prognoses for this disease, is poorly understood.TC data from The Cancer Genome Atlas (TCGA) database were used to identify prognostic hallmark ABC transporters associated with immune cell infiltration patterns via multiple bioinformatic analyses. Thereafter, quantitative real-time polymerase chain reaction (qRT-PCR) was performed to validate the expression of these selected hallmark ABC transporters in both TC and para-cancerous thyroid tissues. Of a total of 49 ABC transporters, five (ABCA8, ABCA12, ABCB6, ABCB8, and ABCC10) were identified as hallmark ABC transporters. All five were differentially expressed in TC and associated with the relapse-free survival rates of patients with TC. Immunoregulation by these five hallmark ABC transporters involved the modulation of various aspects of immune cell infiltration, such as hot or cold tumor subsets and the abundances of infiltrating immune cells, as well as specific immunomodulators and chemokines. Besides the diverse significantly correlated factors, the five hallmark ABC transporters and correlated genes were most highly enriched in plasma membrane, transporter activity, and transmembrane transport of small molecules. In addition, many chemicals, namely bisphenol A and vincristine, affected the expression of these five transporters. The qRT-PCR results of collected TC and para-cancerous thyroid tissues were consistent with those of TCGA. The findings in this study may reveal the role played by these five hallmark ABC transporters in regulating immune cell infiltration patterns in TC as well as the molecular mechanisms underlying their functions, leading to a better understanding of their potential prognostic and immunotherapeutic values.

Identification of ABCA5 among ATP-Binding Cassette Transporter Family as a New Biomarker for Colorectal Cancer

Background

The increasing incidence and mortality of colorectal cancer (CRC) urgently requires updated biomarkers. The ABC transporter family is a widespread family of membrane-bound proteins involved in the transportation of substrates associated with ATP hydrolysis, including metabolites, amino acids, peptides and proteins, sterols and lipids, organic and inorganic ions, sugars, metals, and drugs. They play an important role in the maintenance of homeostasis in the body.

Purpose

This study aims to search for new markers in the ABC transporter gene family for diagnostic and prognostic purposes through data mining of The Cancer Genome Atlas (TCGA) and GEO (Gene Expression Omnibus) datasets.

Methods

A total of 980 samples, including 684 CRC patients and 296 controls from five different datasets, were included for analysis. The construction of the PPI (protein-protein interaction) network and pathway analysis were performed in STRING database and DAVID (database for annotation, visualization, and integrated discovery), respectively. In addition, GSEA (gene set enrichment analysis) and WGCNA (weighted gene co-expression network analysis) were also used for functional analysis.

Results

After several rounds of screening and validation, only the ABCB5 gene was retained among the 49 genes.

Conclusions

The results demonstrated that ABCA5 expression is reduced in CRC and patients with high ABCA5 expression have better OS, which can provide guidance for better management and treatment of CRC in the future.

Prognostic and tumor immunity implication of inflammatory bowel disease-associated genes in colorectal cancer

BACKGROUND

Epidemiologic studies continue to emphasize that increasing patients with inflammatory bowel disease (IBD) develop to colorectal cancer (CRC). Although the function and mechanisms of IBD-associated genes (IBDGs) in CRC tumorigenesis have been extensively researched, the implications of IBDGs in the prognosis value and tumor immunity of CRC remain unclear.

RESULTS

In this study, the expression, pathological stages and prognostic value of IBDGs in CRC were systematically analyzed, and 7 prognostic genes including CDH1, CCL11, HLA-DRA, NOS2, NAT2, TIMP1 and TP53 were screened through LASSO-Cox regression analysis. Then, a prognostic signature was established based on the 7 prognostic genes, and the model exhibited a good ability in risk stratification of CRC patients. Subsequent results showed that the genetic alterations of the 7 prognostic genes exhibited more significant and extensive influence on immune cells infiltration in colon adenocarcinoma than that in rectal adenocarcinoma. Meanwhile, immune cells infiltration also showed a significant difference between low-risk group and high-risk group. What's more, 7 prognostic genes-based risk stratification was associated with microsatellite instability, and its prognostic characteristics were significantly negatively correlated with mismatch repair genes.

CONCLUSIONS

This study provided a promising insight that the 7 IBDGs could be used as valuable biomarkers for prognostic diagnosis and personalized immunotherapy of CRC patients.

ABCA8 inhibits breast cancer cell proliferation by regulating the AMP activated protein kinase/mammalian target of rapamycin signaling pathway

ATP-binding cassette (ABC) subfamily A member 8 (ABCA8) has been reported to play a vital role in cancer development. Our study aimed to explore the role and the molecular mechanism of ABCA8 in breast cancer (BC) progression. GSE65194, GSE15852, and GSE45827 datasets were used to identify differentially expressed genes (DEGs) in BC. The diagnosis and prognosis value were determined using ROC curve analysis and Kaplan-Meier plotter, respectively. The relationship between ABCA8 expression and clinicopathological features in BC was analyzed by TCGA. Co-expressed genes of ABCA8 in BC were screened out through GEPIA and subjected to KEGG pathway enrichment analysis. Cell proliferation was evaluated by CCK-8 and EdU incorporation assays. Proliferating cell nuclear antigen (PCNA) expression and the changes of the AMP activated protein kinase (AMPK)/mammalian target of rapamycin (mTOR) pathway were measured by western blot analysis. Totally 4 overlapping DEGs were identified and all reduced in BC samples. ABCA8 with high diagnostic and prognostic values was selected for further exploration. Low ABCA8 expression was correlated with clinicopathological features in BC patients. ABCA8 overexpression inhibited BC cell proliferation. The top 20 co-expressed genes of ABCA8 were identified by GEPIA and significantly enriched in AMPK signaling pathway. Inhibition of AMPK/mTOR pathway reversed the suppressive effect of ABCA8 on BC cell growth. These results suggested that ABCA8 overexpression repressed BC cell proliferation through regulating the AMPK/mTOR signaling pathway.

Key Molecules of Fatty Acid Metabolism in Gastric Cancer

Fatty acid metabolism is closely linked to the progression of gastric cancer (GC), a very aggressive and life-threatening tumor. This study examines linked molecules, such as Sterol Regulatory Element-Binding Protein 1 (SREBP1), ATP Citrate Lyase (ACLY), Acetyl-CoA Synthases (ACSs), Acetyl-CoA Carboxylase (ACC), Fatty Acid Synthase (FASN), Stearoyl-CoA Desaturase 1 (SCD1), CD36, Fatty Acid Binding Proteins (FABPs), and Carnitine palmitoyltransferase 1 (CPT1), as well as their latest studies and findings in gastric cancer to unveil its core mechanism. The major enzymes of fatty acid de novo synthesis are ACLY, ACSs, ACC, FASN, and SCD1, while SREBP1 is the upstream molecule of fatty acid anabolism. Fatty acid absorption is mediated by CD36 and FABPs, and fatty acid catabolism is mediated by CPT1. If at all possible, we will discover novel links between fatty acid metabolism and a prospective gastric cancer target.

Identification of Immune-Related Prognostic Markers in Gastric Cancer

Gastric cancer (GC) is a malignant tumor with a high fatality rate. Poor prognosis is the main cause of death caused by GC. In this study, the gene expression difference between GC and the control group was analyzed. Differentially expressed genes (DEGs) related to immunity were screened for enrichment analysis. The differences in immune cell infiltration and immune function between GC and normal were identified. Cox regression analysis and survival analysis were used to determine the prognostic genes of GC in TCGA and GSE62254. The potential prognostic role of genes was further evaluated by risk score. Difference genes in GC were analyzed in TCGA. Candidate genes in TCGA and GSE62254 are analyzed, and prognostic genes are determined. The potential prognostic role of genes was further evaluated by risk score. The immune-related prognostic markers in GC were determined. FABP4, LBP, LCN1, CMA1, INHBA, ANGPTL1, ACKR1, GHR, and OGN may be used as markers for monitoring the prognosis of GC in the future.

A Transcription Factor-Based Risk Model for Predicting the Prognosis of Prostate Cancer and Potential Therapeutic Drugs

Background

Prostate cancer (PC) is one of the most critical cancers affecting men's health worldwide. The development of many cancers involves dysregulation or mutations in key transcription factors. This study established a transcription factor-based risk model to predict the prognosis of PC and potential therapeutic drugs.

Materials and Methods

In this study, RNA-sequencing data were downloaded and analyzed using The Cancer Genome Atlas dataset. A total of 145 genes related to the overall survival rate of PC patients were screened using the univariate Cox analysis. The Kdmist clustering method was used to classify prostate adenocarcinoma (PRAD), thereby determining the cluster related to the transcription factors. The support vector machine-recursive feature elimination method was used to identify genes related to the types of transcription factors and the key genes specifically upregulated or downregulated were screened. These genes were further analyzed using Lasso to establish a model. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used for the functional analysis. The TIMER algorithm was used to quantify the abundance of immune cells in PRAD samples. The chemotherapy response of each GBM patient was predicted based on the public pharmacogenomic database, Genomics of Drug Sensitivity in Cancer (GDSC, http://www.cancerrxgene.org). The R package "pRRophetic" was applied to drug sensitivity (IC50) value prediction.

Results

We screened 10 genes related to prognosis, including eight low-risk genes and two high-risk genes. The receiver operating characteristic (ROC) curve was 0.946. Patients in the high-risk score group had a poorer prognosis than those in the low-risk score group. The average area under the curve value of the model at different times was higher than 0.8. The risk score was an independent prognostic factor. Compared with the low-risk score group, early growth response-1 (EGR1), CACNA2D1, AC005831.1, SLC52A3, TMEM79, IL20RA, CRACR2A, and FAM189A2 expressions in the high-risk score group were decreased, while AC012181.1 and TRAPPC8 expressions were increased. GO and KEGG analyses showed that prognosis was related to various cancer signaling pathways. The proportion of B_cell, T_cell_CD4, and macrophages in the high-risk score group was significantly higher than that in the low-risk score group. A total of 25 classic immune checkpoint genes were screened out to express abnormally high-risk scores, and there were significant differences. Thirty mutant genes were identified; in the high- and low-risk score groups, SPOP, TP53, and TTN had the highest mutation frequency, and their mutations were mainly missense mutations. A total of 36 potential drug candidates for the treatment of PC were screened and identified.

Conclusions

Ten genes of both high-and low-risk scores were associated with the prognosis of PC. PC prognosis may be related to immune disorders. SPOP, TP53, and TTN may be potential targets for the prognosis of PC.

PANX1 is a potential prognostic biomarker associated with immune infiltration in pancreatic adenocarcinoma: A pan-cancer analysis

Pannexin 1 (PANX1) channel is a critical ATP-releasing pathway that modulates tumor immunity, progression, and prognosis. However, the roles of PANX1 in different cancers remain unclear. We analyzed the expression of PANX1 in human pan-cancer in the Oncomine and GEPIA2.0 databases. The prognostic value of PANX1 expression was determined using Kaplan-Meier plotter and OncoLnc tools. The correlation between PANX1 and tumor-infiltrating immune cells was investigated using the TIMER 2.0. In addition, the relationship between PANX1 and immunomodulators was explored using TISIDB. Finally, gene set enrichment analysis (GSEA) was performed utilizing LinkedOmics. The results indicated that PANX1 was overexpressed in most cancers compared to normal tissues. The high expression of PANX1 was associated with poor prognosis in multiple tumors, especially in pancreatic adenocarcinoma (PAAD). In addition, PANX1 was correlated with a variety of immunomodulators, such as CD274, IL10, CD276, IL2RA, TAP1, and TAP2. PANX1 expression level was significantly related to infiltration of multiple immune cells in many cancers, including cancer associated fibroblast, macrophage, and neutrophil cells. Further analysis revealed that PANX1 was significantly associated with T cells CD8+ (rho = 0.524, P = 1.94e-13) and Myeloid dendritic cell (rho = 0.564, P = 9.45e-16). GSEA results showed that PANX1 was closely associated with leukocyte cell-cell adhesion, endoplasmic reticulum lumen, ECM-receptor interaction, and Focal adhesion pathways in PAAD. PANX1 expression was higher in pan-cancer samples than in normal tissues. The high expression of PANX1 was associated with poor outcome and immune infiltration in multiple cancers, especially in PAAD.

Construction of a ceRNA Network and Analysis of Tumor Immune Infiltration in Pancreatic Adenocarcinoma

Pancreatic adenocarcinoma (PAAD) is characterized by high malignancy, frequent metastasis, and recurrence with an unfavorable prognosis. This study is aimed at constructing a prognostic model for tumor-infiltrating immune cells and a competing endogenous RNA (ceRNA) network in PAAD and analyzing susceptibilities of chemotherapy and immunotherapy of PAAD. Gene expression profiles and clinical information of PAAD were downloaded from The Cancer Genome Atlas (TCGA) database and divided into the tumor group and the normal group. A total of five PAAD survival-related key genes in the ceRNA network and three survival-related immune infiltrating cells were uncovered, and two survival risk models and nomograms were constructed. The efficiency and performance of the two models were verified using multi-index area under the curve analysis at different time points, decision curve analysis, and calibration curves. Co-expression analysis showed that LRRC1, MIR600HG, and RNF166 in the ceRNA network and tumor-infiltrating immune cells including CD8 T cells and M1 macrophages were likely related to the PAAD prognosis, and the expression of key ceRNA-related genes was experimently validated in tissues and cell lines by RT-qPCR. Patients with low risk scores for key genes in the ceRNA network displayed a positive response to anti-programmed death-1 (PD-1) treatment and greater sensitivity to chemotherapeutic drugs such as docetaxel, lapatinib, and paclitaxel. More importantly, our results suggested that the IC50 values of gemcitabine in PAAD were not significantly different between the high and low risk groups. The expression levels of immune checkpoints were significantly different in the high-risk and low-risk groups. The prognostic model, nomogram, and drug analysis may provide an essential reference for PAAD patient management in the clinic.

Comprehensive Analysis to Identify Enhancer-Regulated Inflammation-Associated Genes in Lung Adenocarcinoma

Objective

The purpose of this study was to identify prognostic inflammatory markers regulated by enhancers in lung adenocarcinoma (LUAD).

Methods

Inflammatory indices of 490 LUAD patients in TCGA database were calculated using genomic variation analysis (GSVA). Patients were divided into high- and low-inflammatory index groups. Fraction of 22 infiltrating immune cells was estimated using the Cell type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT). Gene set enrichment analysis (GSEA) was used to analyze gene enrichment. Differentially expressed genes were screened based on TCGA database. The H3K27ac ChIP-seq of A549 cells in GEO database (GSE42374) was analyzed to identify super enhancers. Kaplan-Meier method and multivariate Cox proportional hazards models were used for survival analysis. CCK8 and RT-qPCR were used for cellular level verification.

Results

Inflammation was associated with better outcome in LUAD patients. Anti-cancer immune cell fractions were upregulated in high-inflammatory index group. Genes enriched in inflammation-related signaling pathways were positively correlated with high-inflammatory index group. A total of 146 upregulated genes regulated by enhancers were screened, of which five genes including GDF10, HPGDS, ABCA8, SLIT3 and ADAMTS8 had significant influence on prognosis. ChIP-seq analysis showed that TGFβ+TNFα treatment promoted the enhancer activation of the five genes. Cellular experiments revealed that there was no significant effect of TGFβ treatment on the five genes expression. TNFα treatment upregulated the five genes expression, while the BET-bromodomain inhibitor JQ1 restored the effect of TNFα. Overexpression of the five genes significantly inhibited the proliferation of A549 and H1299 cells.

Conclusion

GDF10, HPGDS, ABCA8, SLIT3 and ADAMTS8 were identified as enhancer-regulated prognostic inflammation-related biomarkers, and the expression of these genes inhibited proliferation of LUAD cells.

Screening and Identification of Key Microenvironment-Related Genes in Non-functioning Pituitary Adenoma

Purpose

Non-functioning pituitary adenoma (NFPA) is a very common type of intracranial tumor, which can be locally invasive and can have a high recurrence rate. The tumor microenvironment (TME) shows a high correlation with tumor pathogenesis and prognosis. The current study aimed to identify microenvironment-related genes in NFPAs and assess their prognostic value.

Methods

73 NFPA tumor samples were collected from Beijing Tiantan Hospital and transcriptional expression profiles were obtained through microarray analysis. The immune and stromal scores of each sample were calculated through the ESTIMATE algorithm, and the patients were divided into high and low immune/stromal score groups. Intersection differentially expressed genes (DEGs) were then obtained to construct a protein–protein interaction (PPI) network. Potential functions and pathways of intersection DEGs were then analyzed through Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes. The prognostic value of these genes was evaluated. The quantitative real-time polymerase chain reaction in another set of NFPA samples was used to confirm the credibility of the bioinformatics analysis.

Results

The immune/stromal scores were significantly correlated with cavernous sinus (CS) invasion. The Kaplan–Meier curve indicated that the high immune score group was significantly related to poor recurrence-free survival. We identified 497 intersection DEGs based on the high vs. low immune/stromal score groups. Function enrichment analyses of 497 DEGs and hub genes from the PPI network showed that these genes are mainly involved in the immune/inflammatory response, T cell activation, and the phosphatidylinositol 3 kinase-protein kinase B signaling pathway. Among the intersection DEGs, 88 genes were further verified as significantly expressed between the CS invasive group and the non-invasive group, and five genes were highly associated with NFPA prognosis.

Conclusion

We screened out a series of critical genes associated with the TME in NFPAs. These genes may play a fundamental role in the development and prognosis of NFPA and may yield new therapeutic targets.