Integrated bioinformatics analysis identified COL11A1 as an immune infiltrates correlated prognosticator in pancreatic adenocarcinoma

Identification of potential biomarkers for pancreatic ductal adenocarcinoma: a bioinformatics analysis

PDA is an aggressive cancer with a 5-year survival rate, which is very low. There is no effective prognosis or therapy for PDA because of the lack of target biomarkers. The objective of this article is to identify the target biomarkers for PDA using a bioinformatics approach. In this work, we have analysed the three microarray datasets from the NCBI GEO database. We used the Geo2R tool to analyse the microarray data with the Benjamini and Hochberg false discovery rate method, and the significance level cut-off was set to 0.05. We have identified 659 DEGs from the datasets. There are a total of 15 hub genes that were selected from the PPI network constructed using the STRING application. Furthermore, these 15 genes were evaluated on PDA patients using TCGA and GTEx databases in (GEPIA). The online tool DAVID was used to analyse the functional annotation information for the DEGs. The functional pathway enrichment was performed on the GO and KEGG. The hub genes were mainly enriched for cell division, chromosome segregation, protein binding and microtubule binding. Further, the gene alteration study was performed using the cBioportal tool and screened out six hub genes (ASPM, CENPF, BIRC5, TTK, DLGAP5, and TOP2A) with a high alteration rate in PDA samples. Furthermore, Kaplan-Meier survival analysis was performed on the six hub genes and identified poor-survival outcomes that may be involved in tumorigenesis and PDA development. So, this study concludes that, these six hub genes may be potential prognostic biomarkers for PDA.

A Comprehensive Transcriptional Signature in Pancreatic Ductal Adenocarcinoma Reveals New Insights into the Immune and Desmoplastic Microenvironments

Pancreatic ductal adenocarcinoma (PDAC) prognoses and treatment responses remain devastatingly poor due partly to the highly heterogeneous, aggressive, and immunosuppressive nature of this tumor type. The intricate relationship between the stroma, inflammation, and immunity remains vaguely understood in the PDAC microenvironment. Here, we performed a meta-analysis of stroma-, and immune-related gene expression in the PDAC microenvironment to improve disease prognosis and therapeutic development. We selected 21 PDAC studies from the Gene Expression Omnibus and ArrayExpress databases, including 922 samples (320 controls and 602 cases). Differential gene enrichment analysis identified 1153 significant dysregulated genes in PDAC patients that contribute to a desmoplastic stroma and an immunosuppressive environment (the hallmarks of PDAC tumors). The results highlighted two gene signatures related to the immune and stromal environments that cluster PDAC patients into high- and low-risk groups, impacting patients' stratification and therapeutic decision making. Moreover, HCP5, SLFN13, IRF9, IFIT2, and IFI35 immune genes are related to the prognosis of PDAC patients for the first time.

A mutational signature and significantly mutated driver genes associated with immune checkpoint inhibitor response across multiple cancers

Immune checkpoint inhibitor (ICI) treatments dramatically prolong the survival outcomes of several advanced cancers. However, as multiple studies reported, only a subset of patients could benefit from the ICI treatment. In this study, we aim to uncover novel molecular biomarkers predictive of immunotherapy efficacy across multiple cancers. Pre-treatment somatic mutational profiles and immunotherapy clinical information were obtained from 1097 samples of multiple cancers, including melanoma, non-small cell lung cancer (NSCLC), clear cell renal cell carcinoma (ccRCC), bladder carcinoma (BLCA), and head and neck squamous cell carcinoma (HNSCC). Mutational signatures, molecular subtypes, and significantly mutated genes (SMGs) were determined, and their connections with ICI response and outcome were also evaluated. We extracted a total of six mutational signatures across all samples. Among, a mutational signature featured by T > C substitutions was identified to associate with an ICI resistance. A molecular subtype determined based on mutational activities was connected with a significantly improved ICI response rate and outcome. Totaling 50 SMGs were identified, and we observed that patients with COL11A1 or COL4A6 mutations exhibited a superior ICI treatment efficacy than those without such mutations. In this study, we uncovered several novel molecular determinants of cancer immunotherapy response under a multiple-cancer setting, which provides clues for enrolling patients to receive immunotherapy and customizing personalized treatment strategies.

A new machine learning based computational framework identifies therapeutic targets and unveils influential genes in pancreatic islet cells

Pancreatic islets comprise a group of cells that produce hormones regulating blood glucose levels. Particularly, the alpha and beta islet cells produce glucagon and insulin to stabilize blood glucose. When beta islet cells are dysfunctional, insulin is not secreted, inducing a glucose metabolic disorder. Identifying effective therapeutic targets against the disease is a complicated task and is not yet conclusive. To close the wide gap between understanding the molecular mechanism of pancreatic islet cells and providing effective therapeutic targets, we present a computational framework to identify potential therapeutic targets against pancreatic disorders. First, we downloaded three transcriptome expression profiling datasets pertaining to pancreatic islet cells (GSE87375, GSE79457, GSE110154) from the Gene Expression Omnibus database. For each dataset, we extracted expression profiles for two cell types. We then provided these expression profiles along with the cell types to our proposed constrained optimization problem of a support vector machine and to other existing methods, selecting important genes from the expression profiles. Finally, we performed (1) an evaluation from a classification perspective which showed the superiority of our methods against the baseline; and (2) an enrichment analysis which indicated that our methods achieved better outcomes. Results for the three datasets included 44 unique genes and 10 unique transcription factors (SP1, HDAC1, EGR1, E2F1, AR, STAT6, RELA, SP3, NFKB1, and ESR1) which are reportedly related to pancreatic islet functions, diseases, and therapeutic targets.

COL11A1 as an novel biomarker for breast cancer with machine learning and immunohistochemistry validation

Machine learning (ML) algorithms were used to identify a novel biological target for breast cancer and explored its relationship with the tumor microenvironment (TME) and patient prognosis. The edgR package identified hub genes associated with overall survival (OS) and prognosis, which were validated using public datasets. Of 149 up-regulated genes identified in tumor tissues, three ML algorithms identified COL11A1 as a hub gene. COL11A1was highly expressed in breast cancer samples and associated with a poor prognosis, and positively correlated with a stromal score (r=0.49, p<0.001) and the ESTIMATE score (r=0.29, p<0.001) in the TME. Furthermore, COL11A1 negatively correlated with B cells, CD4 and CD8 cells, but positively associated with cancer-associated fibroblasts. Forty-three related immune-regulation genes associated with COL11A1 were identified, and a five-gene immune regulation signature was built. Compared with clinical factors, this gene signature was an independent risk factor for prognosis (HR=2.591, 95%CI 1.831–3.668, p=7.7e-08). A nomogram combining the gene signature with clinical variables, showed better predictive performance (C-index=0.776). The model correction prediction curve showed little bias from the ideal curve. COL11A1 is a potential therapeutic target in breast cancer and may be involved in the tumor immune infiltration; its high expression is strongly associated with poor prognosis.

COL11A1 as a potential prognostic target for oral squamous cell carcinoma

Oral squamous cell carcinoma (OSCC) is a malignant tumor occurring in the oral cavity. However, the molecular mechanism of OSCC is not clear. Bioinformatics was used to screen and identify role of collagen type X1 alpha 1 (COL11A1) on OSCC. 200 patients with OSCC were recruited. Clinical and follow-up data were recorded and COL11A1 expression levels were tested. Pearson chi-square test and Spearman correlation coefficient were used to analyze relationship between prognosis and related parameters in patients with OSCC. Univariate and multivariate Logistic regression, univariate and multivariate Cox proportional risk regression were used for further analysis, survival curve was drawn. Through bioinformatics analysis, OSCC patients with higher expression of COL11A1 have poor overall survival compare with OSCC patients with lower expression of COL11A1 (hazard ratios [HR] = 1.32, P = .047). Pearson chi-square test showed that age (P = .011), tumor grade (P = .023), COL11A1 (P < .001) was significantly correlated with prognosis of OSCC. Univariate Logistic regression analysis showed age (odds ratio [OR] = 2.102, 95% confidence intervals [95%CI]: 1.180-3.746, P = .012), tumor grade (OR = 1.919, 95%CI: 1.093-3.372, P = .023) and COL11A1 (OR = 12.775, 95%CI: 6.509-25.071, P < .001). Multivariate Logistic regression analysis showed that COL11A1 (OR = 12.066, 95%CI: 6.042-24.096, P < .001) was significantly associated with prognosis of patients with OSCC. Univariate Cox regression analysis showed that age (HR = 1.592, 95%CI: 1.150-2.205, P = .005), tumor grade (HR = 1.460, 95%CI: 1.067-1.999, P = .018) and COL11A1 (HR = 1.848, 95%CI: 1.340-2.548, P < .001) were significantly correlated with survival time of OSCC patients. Multivariate Cox regression analysis showed that tumor grade (HR = 1.466, 95%CI: 1.064-2.020, P = .019) and COL11A1 (HR = 1.645, 95%CI: 1.164-2.325, P = .005) were significantly correlated with survival time of OSCC patients. COL11A1 is significantly correlated with occurrence of OSCC. When COL11A1 is highly expressed, prognosis of patients with OSCC is worse and the survival time is shorter.

Collagen Remodeling along Cancer Progression Providing a Novel Opportunity for Cancer Diagnosis and Treatment

The extracellular matrix (ECM) is a significant factor in cancer progression. Collagens, as the main component of the ECM, are greatly remodeled alongside cancer development. More and more studies have confirmed that collagens changed from a barrier to providing assistance in cancer development. In this course, collagens cause remodeling alongside cancer progression, which in turn, promotes cancer development. The interaction between collagens and tumor cells is complex with biochemical and mechanical signals intervention through activating diverse signal pathways. As the mechanism gradually clears, it becomes a new target to find opportunities to diagnose and treat cancer. In this review, we investigated the process of collagen remodeling in cancer progression and discussed the interaction between collagens and cancer cells. Several typical effects associated with collagens were highlighted in the review, such as fibrillation in precancerous lesions, enhancing ECM stiffness, promoting angiogenesis, and guiding invasion. Then, the values of cancer diagnosis and prognosis were focused on. It is worth noting that several generated fragments in serum were reported to be able to be biomarkers for cancer diagnosis and prognosis, which is beneficial for clinic detection. At a glance, a variety of reported biomarkers were summarized. Many collagen-associated targets and drugs have been reported for cancer treatment in recent years. The new targets and related drugs were discussed in the review. The mass data were collected and classified by mechanism. Overall, the interaction of collagens and tumor cells is complicated, in which the mechanisms are not completely clear. A lot of collagen-associated biomarkers are excavated for cancer diagnosis. However, new therapeutic targets and related drugs are almost in clinical trials, with merely a few in clinical applications. So, more efforts are needed in collagens-associated studies and drug development for cancer research and treatment.

LINC00665/miRNAs axis-mediated collagen type XI alpha 1 correlates with immune infiltration and malignant phenotypes in lung adenocarcinoma

Collagen type XI alpha 1 (COL11A1) as an oncogene has been reported in several malignant tumors. Herein, we aimed to explore the function of COL11A1 and its upstream regulators in lung adenocarcinoma (LUAD). COL11A1 expression prognostic significance, gene ontology, Kyoto Encyclopedia of Genes and Genomes, and immune infiltration were explored in LUAD. In vitro experimental measurements were implemented to validate the function of COL11A1 and LINC00665 in LUAD cells. Our study demonstrated that LINC00665-2 and COL11A1 were significantly upregulated in LUAD tissues compared with nontumor tissues. COL11A1 was positively correlated with multiple immune cell enrichment, suggesting that COL11A1 may be a prospective therapeutic target to enhance the efficacy of immunotherapy in LUAD. A regulatory mechanism LINC00665-2/microRNAs (miRNAs)/COL11A1 axis was identified to facilitate the tumorigenesis of LUAD. si-LINC00665 transfection induced the inhibition of growth and migration, and apoptosis was reversed by the overexpression of COL11A1 in LUAD cells. In conclusion, LINC00665 as a competing endogenous RNA sponging multiple miRNAs to modulate COL11A1 expression in LUAD, suggesting that LINC00665/miRNAs/COL11A1 axis may contribute to the pathogenesis of LUAD.

RAGE is a potential biomarker implicated in immune infiltrates and cellular senescence in lung adenocarcinoma

Background

Receptor for Advanced Glycation End‐products (RAGE) is an oncogene abnormally expressed in various cancers. However, the clinical value of RAGE and the biological role of RAGE in lung cancer have not been fully investigated.

Methods

We compared the RAGE expression using several public databases. The relationship between RAGE expression and clinicopathological variables was assessed. The R software package was used to carry out enrichment analyses of RAGE co‐expression and gene set enrichment analysis (GSEA). Additionally, we used the TIMER database to assess the association between immune infiltration and RAGE expression. The correlation between RAGE expression and senescence biomarkers in lung adenocarcinoma was analyzed using the TCGA database.

Results

Our findings indicated that the expression of RAGE was downregulated in lung adenocarcinoma, and down‐regulation of RAGE was related to poor overall survival and disease‐free survival. Functional enrichment analysis indicated that RAGE co‐expression genes were mainly associated with neutrophil activation involved in immune response, neutrophil degranulation, and regulation of leukocyte‐mediated immunity. Correlation analysis revealed that RAGE expression was closely related to the purity of the tumor and immune infiltration. GSEA indicated that the RAGE‐related differential genes were mainly enriched in senescence‐related pathways. Besides, the RAGE expression was significantly associated with senescence‐related genes.

Conclusion

Down‐regulation of RAGE expression was associated with poor prognosis, as well as defective immune infiltration and cellular senescence in lung adenocarcinoma.

Systematic Characterization of the Clinical Relevance of KPNA4 in Pancreatic Ductal Adenocarcinoma

Background

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies with poor prognosis. Karyopherin subunit alpha 4 (KPNA4) is a nuclear transport factor and plays tumor-promoting roles in multiple cancers. However, the roles of KPNA4 in PDAC still remain unknown. This study investigated the prognostic value of KPNA4 and its potential functions in PDAC and tumor microenvironment.

Methods

LinkedOmics was utilized to screen genes with survival significance in PDAC. KPNA4 expression was analyzed using multiple datasets and verified in PDAC cells and clinical samples by qRT-PCR and immunohistochemistry. Clinical correlation and survival analyses were conducted to identify the clinical significance and prognostic value of KPNA4 in PDAC patients. Subsequently, KPNA4 was knocked down in PDAC cell lines, and CCK-8, colony formation and wound healing assays were performed to test the functions of KPNA4 in vitro. Immune infiltration analysis was performed to explore the potential roles of KPNA4 in the tumor microenvironment of PDAC. Moreover, functional analyses were conducted to explore the underlying mechanism of KPNA4 in the progression of PDAC.

Results

We found KPNA4 was significantly upregulated in PDAC cells and tissues. KPNA4 expression was associated with tumor progression in PDAC patients. Survival analyses further revealed that KPNA4 could act as an independent predictor of unfavorable survival for PDAC patients. KPNA4 knockdown suppressed the viability, colony formation and migration of PDAC cells. Moreover, KPNA4 was correlated with immunosuppressive cells infiltration and T cell exhaustion in the tumor microenvironment of PDAC. Finally, functional analyses indicated the association of KPNA4 with focal adhesion kinase (FAK) signaling, and KPNA4 silencing significantly decreased the expression of FAK and PD-L1.

Conclusions

This study revealed that KPNA4 is an independent prognostic biomarker for PDAC and plays a tumor-promoting role by facilitating proliferation and migration of cancer cells and participating in immune infiltration, which may be mediated by FAK signaling and PD-L1 expression. These results provide a novel and potential therapeutic target for pancreatic cancer.

Development and validation of a novel 3-gene prognostic model for pancreatic adenocarcinoma based on ferroptosis-related genes

Background

Molecular markers play an important role in predicting clinical outcomes in pancreatic adenocarcinoma (PAAD) patients. Analysis of the ferroptosis-related genes may provide novel potential targets for the prognosis and treatment of PAAD.

Methods

RNA-sequence and clinical data of PAAD was downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) public databases. The PAAD samples were clustered by a non-negative matrix factorization (NMF) algorithm. The differentially expressed genes (DEGs) between different subtypes were used by “limma_3.42.2” package. The R software package clusterProfiler was used for functional enrichment analysis. Then, a multivariate Cox proportional and LASSO regression were used to develop a ferroptosis-related gene signature for pancreatic adenocarcinoma. A nomogram and corrected curves were constructed. Finally, the expression and function of these signature genes were explored by qRT-PCR, immunohistochemistry (IHC) and proliferation, migration and invasion assays.

Results

The 173 samples were divided into 3 categories (C1, C2, and C3) and a 3-gene signature model (ALOX5, ALOX12, and CISD1) was constructed. The prognostic model showed good independent prognostic ability in PAAD. In the GSE62452 external validation set, the molecular model also showed good risk prediction. KM-curve analysis showed that there were significant differences between the high and low-risk groups, samples with a high-risk score had a worse prognosis. The predictive efficiency of the 3-gene signature-based nomogram was significantly better than that of traditional clinical features. For comparison with other models, that our model, with a reasonable number of genes, yields a more effective result. The results obtained with qPCR and IHC assays showed that ALOX5 was highly expressed, whether ALOX12 and CISD1 were expressed at low levels in tissue samples. Finally, function assays results suggested that ALOX5 may be an oncogene and ALOX12 and CISD1 may be tumor suppressor genes.

Conclusions

We present a novel prognostic molecular model for PAAD based on ferroptosis-related genes, which serves as a potentially effective tool for prognostic differentiation in pancreatic cancer patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02431-8.

GNG7 and ADCY1 as diagnostic and prognostic biomarkers for pancreatic adenocarcinoma through bioinformatic-based analyses

Pancreatic adenocarcinoma (PAAD) is one of the most lethal malignant tumors in the world. The GSE55643 and GSE15471 microarray datasets were downloaded to screen the diagnostic and prognostic biomarkers for PAAD. 143 downregulated genes and 118 upregulated genes were obtained. Next, we performed gene ontology (GO) and The Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis on these genes and constructed a protein-protein interaction (PPI) network. We screened out two important clusters of genes, including 13 upregulated and 5 downregulated genes. After the survival analysis, 3 downregulated genes and 10 upregulated genes were identified as the selected key genes. The KEGG analysis on 13 selected genes showed that GNG7 and ADCY1 enriched in the Pathway in Cancer. Next, the diagnostic and prognostic value of GNG7 and ADCY1 was investigated using independent cohort of the Cancer Genome Atlas (TCGA), GSE84129 and GSE62452. We observed that the expression of the GNG7 and ADCY1 was decreased in PAAD. The diagnostic receiver operating characteristic (ROC) analysis indicated that the GNG7 and ADCY1 could serve as sensitive diagnostic markers in PAAD. Survival analysis suggested that expression of GNG7, ADCY1 were significantly associated with PAAD overall survival (OS). The multivariate cox regression analysis showed that the expression of GNG7, ADCY1 were independent risk factors for PAAD OS. Our study indicated GNG7 and ADCY1 may be potential diagnostic and prognostic biomarkers in patients with PAAD.

Identification of the Real Hub Gene and Construction of a Novel Prognostic Signature for Pancreatic Adenocarcinoma Based on the Weighted Gene Co-expression Network Analysis and Least Absolute Shrinkage and Selection Operator Algorithms

Background: Pancreatic adenocarcinoma (PAAD) has a considerably bad prognosis, and its pathophysiologic mechanism remains unclear. Thus, we aimed to identify real hub genes to better explore the pathophysiology of PAAD and construct a prognostic panel to better predict the prognosis of PAAD using the weighted gene co-expression network analysis (WGCNA) and the least absolute shrinkage and selection operator (LASSO) algorithms. Methods: WGCNA identified the modules most closely related to the PAAD stage and grade based on the Gene Expression Omnibus. The module genes significantly associated with PAAD progression and prognosis were considered as the real hub genes. Eligible genes in the most significant module were selected for construction and validation of a multigene prognostic signature based on the LASSO-Cox regression analysis in The Cancer Genome Atlas and the International Cancer Genome Consortium databases, respectively. Results: The brown module identified by WGCNA was most closely associated with the clinical characteristics of PAAD. Scaffold attachment factor B (SAFB) was significantly associated with PAAD progression and prognosis, and was identified as the real hub gene of PAAD. Moreover, both transcriptional and translational levels of SAFB were significantly lower in PAAD tissues compared with normal pancreatic tissues. In addition, a novel multigene-independent prognostic signature consisting of SAFB, SP1, and SERTAD3 was identified and verified. The predictive accuracy of our signature was superior to that of previous studies, especially for predicting 3- and 5-year survival probabilities. Furthermore, a prognostic nomogram based on independent prognostic variables was developed and validated using calibration curves. The predictive ability of this nomogram was also superior to the well-established AJCC stage and histological grade. The potential mechanisms of different prognoses between the high- and low-risk subgroups were also investigated using functional enrichment analysis, GSEA, ssGSEA, immune checkpoint analysis, and mutation profile analysis. Conclusion: SAFB was identified as the real hub gene of PAAD. A novel multigene-independent prognostic signature was successfully identified and validated to better predict PAAD prognosis. An accurate nomogram was also developed and verified to aid in the accurate treatment of tumors, as well as in early intervention.

Collagen XI alpha 1 chain, a potential therapeutic target for cancer

Extracellular matrix (ECM) plays an important role in the progression of cancer. Collagen is the most abundant component in ECM, and it is involved in the biological formation of cancer. Although type XI collagen is a minor fibrillar collagen, collagen XI alpha 1 chain (COL11A1) has been found to be upregulated in a variety of cancers including ovarian cancer, breast cancer, thyroid cancer, pancreatic cancer, non-small-cell lung cancer, and transitional cell carcinoma of the bladder. High levels of COL11A1 usually predict poor prognosis, while COL11A1 is related to angiogenesis, invasion, and drug resistance of cancer. However, little is known about the specific mechanism by which COL11A1 regulates tumor progression. Here, we have organized and summarized the recent developments regarding elucidation of the relationship between COL11A1 and various cancers, as well as the interaction between COL11A1 and intracellular signaling pathways. In addition, we have selected therapeutic agents targeting COL11A1. All these indicate the possibility of using COL11A1 as a target for cancer treatment.

The Burden of Post-Translational Modification (PTM)-Disrupting Mutations in the Tumor Matrisome

BACKGROUND

To evaluate the occurrence of mutations affecting post-translational modification (PTM) sites in matrisome genes across different tumor types, in light of their genomic and functional contexts and in comparison with the rest of the genome.

METHODS

This study spans 9075 tumor samples and 32 tumor types from The Cancer Genome Atlas (TCGA) Pan-Cancer cohort and identifies 151,088 non-silent mutations in the coding regions of the matrisome, of which 1811 affecting known sites of hydroxylation, phosphorylation, N- and O-glycosylation, acetylation, ubiquitylation, sumoylation and methylation PTM.

RESULTS

PTM-disruptive mutations (PTMmut) in the matrisome are less frequent than in the rest of the genome, seem independent of cell-of-origin patterns but show dependence on the nature of the matrisome protein affected and the background PTM types it generally harbors. Also, matrisome PTMmut are often found among structural and functional protein regions and in proteins involved in homo- and heterotypic interactions, suggesting potential disruption of matrisome functions.

CONCLUSIONS

Though quantitatively minoritarian in the spectrum of matrisome mutations, PTMmut show distinctive features and damaging potential which might concur to deregulated structural, functional, and signaling networks in the tumor microenvironment.