Integrated transcriptomic analysis reveals hub genes involved in diagnosis and prognosis of pancreatic cancer

Identification of key biomarkers of the glomerulus in focal segmental glomerulosclerosis and their relationship with immune cell infiltration based on WGCNA and the LASSO algorithm

OBJECTIVE

The aim of our study was to identify key biomarkers of glomeruli in focal glomerulosclerosis (FSGS) and analyze their relationship with the infiltration of immune cells.

METHODS

The expression profiles (GSE108109 and GSE200828) were obtained from the GEO database. The differentially expressed genes (DEGs) were filtered and analyzed by gene set enrichment analysis (GSEA). MCODE module was constructed. Weighted gene coexpression network analysis (WGCNA) was performed to obtain the core gene modules. Least absolute shrinkage and selection operator (LASSO) regression was applied to identify key genes. ROC curves were employed to explore their diagnostic accuracy. Transcription factor prediction of the key biomarkers was performed using the Cytoscape plugin IRegulon. The analysis of the infiltration of 28 immune cells and their correlation with the key biomarkers were performed.

RESULTS

A total of 1474 DEGs were identified. Their functions were mostly related to immune-related diseases and signaling pathways. MCODE identified five modules. The turquoise module of WGCNA had significant relevance to the glomerulus in FSGS. TGFB1 and NOTCH1 were identified as potential key glomerular biomarkers in FSGS. Eighteen transcription factors were obtained from the two hub genes. Immune infiltration showed significant correlations with T cells. The results of immune cell infiltration and their relationship with key biomarkers implied that NOTCH1 and TGFB1 were enhanced in immune-related pathways.

CONCLUSION

TGFB1 and NOTCH1 may be strongly correlated with the pathogenesis of the glomerulus in FSGS and are new candidate key biomarkers. T-cell infiltration plays an essential role in the FSGS lesion process.

CAP2 promotes gastric cancer metastasis by mediating the interaction between tumor cells and tumor-associated macrophages

The metastasis of cancer cells is the main cause of death in patients with gastric cancer (GC). Mounting evidence has demonstrated the vital importance of tumor-associated macrophages in promoting tumor invasion and metastasis; however, the interaction between tumor cells and macrophages in GC is largely unknown. In this study, we demonstrated that cyclase-associated protein 2 (CAP2) was upregulated in GC, especially in cases with lymph node metastasis, and was correlated with a poorer prognosis. The transcription factor JUN directly bound to the promoter region of CAP2 and activated CAP2 transcription. The N-terminal domain of CAP2 bound to the WD5 to WD7 domains of receptor for activated C kinase 1 (RACK1) and induced M2 macrophage polarization by activating the SRC/focal adhesion kinase (FAK)/ERK signaling pathway, which resulted in IL-4 and IL-10 secretion. Polarized M2 macrophages induced premetastatic niche formation and promoted GC metastasis by secreting TGFB1, which created a TGFB1/JUN/CAP2 positive-feedback loop to activate CAP2 expression continuously. Furthermore, we identified salvianolic acid B as an inhibitor of CAP2, which effectively inhibited GC cell invasion capabilities by suppressing the SRC/FAK/ERK signaling pathway. Our data suggest that CAP2, a key molecule mediating the interaction between GC cells and tumor-associated macrophages, may be a promising therapeutic target for suppressing tumor metastasis in GC.

Exosome and lipid metabolism-related genes in pancreatic adenocarcinoma: a prognosis analysis

OBJECTIVE

The purpose of the study was to investigate the role of exosome and lipid metabolism-related genes (EALMRGs) mRNA levels in the diagnosis and prognosis of Pancreatic Adenocarcinoma (PAAD).

METHODS

The mRNA expression pattern of PAAD and pan-cancers with prognostic data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. EALMRGs were acquired from GeneCards and MSigDB database after merging and deduplication. Prognostic EALMRGs were screened through univariate COX regression analysis, and a prognostic model was constructed based on these genes by least absolute shrinkage and selection operator (LASSO) regression. The prognostic value of EALMRGs was then validated in pan-cancer data. The time characteristics ROC curve analysis was performed to evaluate the effectiveness of the prognostic genes.

RESULTS

We identified 5 hub genes (ABCB1, CAP1, EGFR, PPARG, SNCA) according to high and low-risk groups of prognoses. The risk formula was verified in three other cohort of pancreatic cancer patients and was explored in pan-cancer data. Additionally, T cell and dendritic cell infiltration was significantly increased in low-risk group. The expression of the 5 hub genes was also identified in single-cell sequencing data of pancreatic cancer with pivotal pathways. Additionally, functional enrichment analysis based on pancreatic cancer data in pancreatic cancer showed that protein serine/threonine kinase activity, focal adhesion, actin binding, cell-substrate junction, organic acid transport, and regulation of transporter activity were significant related to the expression of genes in EALMRGs.

CONCLUSIONS

Our risk formula shows potential prognostic value in multiple cancers and manifest pivotal alterations in immune infiltration and biological pathway in pancreatic cancer.

YY2/PHGDH axis suppresses tumorigenesis by inhibiting tumor cell de novo serine biosynthesis

Metabolic reprogramming is one of the key features of tumors facilitating their rapid proliferation and adaptation to harsh microenvironments. Yin Yang 2 (YY2) has recently been reported as a tumor suppressor downregulated in various types of tumors; however, the molecular mechanisms underlying its tumor-suppressive activity remain poorly understood. Furthermore, the involvement of YY2 in tumor cell metabolic reprogramming remains unclear. Herein, we aimed to elucidate the novel regulatory mechanism of YY2 in the suppression of tumorigenesis. Using transcriptomic analysis, we uncovered an unprecedented link between YY2 and tumor cell serine metabolism. YY2 alteration could negatively regulate the expression level of phosphoglycerate dehydrogenase (PHGDH), the first enzyme in the serine biosynthesis pathway, and consequently, tumor cell de novo serine biosynthesis. Mechanistically, we revealed that YY2 binds to the PHGDH promoter and suppresses its transcriptional activity. This, in turn, leads to decreased production of serine, nucleotides, and cellular reductants NADH and NADPH, which subsequently suppresses tumorigenic potential. These findings reveal a novel function of YY2 as a regulator of the serine metabolic pathway in tumor cells and provide new insights into its tumor suppressor activity. Furthermore, our findings suggest the potential of YY2 as a target for metabolic-based antitumor therapeutic strategies.

The role of amino acid metabolism alterations in pancreatic cancer: From mechanism to application

The incidence of pancreatic cancer is increasing in both developed and developing Nations. In recent years, various research evidence suggested that reprogrammed metabolism may play a key role in pancreatic cancer tumorigenesis and development. Therefore, it has great potential as a diagnostic, prognostic and therapeutic target. Amino acid metabolism is deregulated in pancreatic cancer, and changes in amino acid metabolism can affect cancer cell status, systemic metabolism in malignant tumor patients and mistakenly involved in different biological processes including stemness, proliferation and growth, invasion and migration, redox state maintenance, autophagy, apoptosis and even tumor microenvironment interaction. Generally, the above effects are achieved through two pathways, energy metabolism and signal transduction. This review aims to highlight the current research progress on the abnormal alterations of amino acids metabolism in pancreatic cancer, how they affect tumorigenesis and development of pancreatic cancer and the application prospects of them as diagnostic, prognostic and therapeutic targets.

Robust identification of common genomic biomarkers from multiple gene expression profiles for the prognosis, diagnosis, and therapies of pancreatic cancer

Pancreatic cancer (PC) is one of the leading causes of cancer-related death globally. So, identification of potential molecular signatures is required for diagnosis, prognosis, and therapies of PC. In this study, we detected 71 common differentially expressed genes (cDEGs) between PC and control samples from four microarray gene-expression datasets (GSE15471, GSE16515, GSE71989, and GSE22780) by using robust statistical and machine learning approaches, since microarray gene-expression datasets are often contaminated by outliers due to several steps involved in the data generating processes. Then we detected 8 cDEGs (ADAM10, COL1A2, FN1, P4HB, ITGB1, ITGB5, ANXA2, and MYOF) as the PC-causing key genes (KGs) by the protein-protein interaction (PPI) network analysis. We validated the expression patterns of KGs between case and control samples by box plot analysis with the TCGA and GTEx databases. The proposed KGs showed high prognostic power with the random forest (RF) based prediction model and Kaplan-Meier-based survival probability curve. The KGs regulatory network analysis detected few transcriptional and post-transcriptional regulators for KGs. The cDEGs-set enrichment analysis revealed some crucial PC-causing molecular functions, biological processes, cellular components, and pathways that are associated with KGs. Finally, we suggested KGs-guided five repurposable drug molecules (Linsitinib, CX5461, Irinotecan, Timosaponin AIII, and Olaparib) and a new molecule (NVP-BHG712) against PC by molecular docking. The stability of the top three protein-ligand complexes was confirmed by molecular dynamic (MD) simulation studies. The cross-validation and some literature reviews also supported our findings. Therefore, the finding of this study might be useful resources to the researchers and medical doctors for diagnosis, prognosis and therapies of PC by the wet-lab validation.

Transcriptomic analysis of pancreatic adenocarcinoma specimens obtained from Black and White patients

In pancreatic cancer clinical trials, Black patients are under-represented while having higher morbidity and mortality rates as compared to other racial groups. Multiple factors, including socioeconomic and lifestyle factors may contribute to this disparity, but genomic contributions remain unclear. In an exploratory project to identify genes that may contribute to differences in survival between Black (n = 8) and White (n = 20) patients with pancreatic cancer, transcriptomic sequencing of over 24,900 genes was performed in human pancreatic tumor and non-tumor tissue obtained from Black and White patients. Over 4,400 genes were differentially expressed in tumor and non-tumor tissue, irrespective of race. To validate these results, the expression of four genes (AGR2, POSTN, TFF1, and CP) reported to be up-regulated in pancreatic tumor tissue as compared to non-tumor tissue were confirmed using quantitative PCR. Transcriptomic analysis that compared pancreatic tumor tissue from Black and White patients revealed differential expression in 1,200 genes, while a comparison of the non-tumor and tumor gene expression differences within each race revealed over 1,500 tumor-specific differentially expressed genes in pancreatic tumor and non-tumor tissue from Black patients. We identified TSPAN8 as a potential tumor-specific gene significantly overexpressed in pancreatic tumor tissue in Black patients as compared to White patients. Using Ingenuity Pathway Analysis software to compare the race-associated gene expression profiles, over 40 canonical pathways were identified to be potentially impacted by the gene expression differences between the races. Heightened expression of TSPAN8 was associated with poor overall survival, suggesting TSPAN8 as one potential genetic factor contributing to the differential outcomes in Black patients with pancreatic cancer, supporting the potential utility of larger genomic studies to further explore the role of TSPAN8 in pancreatic cancer.

The connection between innervation and metabolic rearrangements in pancreatic cancer through serine

Pancreatic cancer is a kind of aggressive tumor famous for its lethality and intractability, and pancreatic ductal adenocarcinoma is the most common type. Patients with pancreatic cancer often suffer a rapid loss of weight and abdominal neuropathic pain in their early stages and then go through cachexia in the advanced stage. These features of patients are considered to be related to metabolic reprogramming of pancreatic cancer and abundant nerve innervation responsible for the pain. With increasing literature certifying the relationship between nerves and pancreatic ductal adenocarcinoma (PDAC), more evidence point out that innervation's role is not limited to neuropathic pain but explore its anti/pro-tumor functions in PDAC, especially the neural-metabolic crosstalks. This review aims to unite pancreatic cancer's innervation and metabolic rearrangements with terminated published articles. Hopefully, this article could explore the pathogenesis of PDAC and further promote promising detecting or therapeutic measurements for PDAC according to the lavish innervation in PDAC.

Identification of immune-related features involved in Duchenne muscular dystrophy: A bidirectional transcriptome and proteome-driven analysis

Background

We aimed to investigate the biological mechanism and feature genes of Duchenne muscular dystrophy (DMD) by multi-omics and experimental verification strategy.

Methods

We integrated the transcriptomic and proteomic methods to find the differentially expressed mRNAs (DEMs) and proteins (DEPs) between DMD and Control groups. Weighted gene co-expression network analysis (WGCNA) was then used to identify modules of highly correlated genes and hub genes. In the following steps, the immune and stromal cells infiltrations were accomplished by xCELL algorithm. Furthermore, TF and miRNA prediction were performed with Networkanalyst. ELISA, western blot and external datasets were performed to verify the key proteins/mRNAs in DMD patient and mouse. Finally, a nomogram model was established based on the potential biomarkers.

Results

4515 DEMs and 56 DEPs were obtained from the transcriptomic and proteomic study respectively. 14 common genes were identified, which is enriched in muscle contraction and inflammation-related pathways. Meanwhile, we observed 33 significant differences in the infiltration of cells in DMD. Afterwards, a total of 22 miRNAs and 23 TF genes interacted with the common genes, including TFAP2C, MAX, MYC, NFKB1, RELA, hsa-miR-1255a, hsa-miR-130a, hsa-miR-130b, hsa-miR-152, and hsa-miR-17. In addition, three genes (ATP6AP2, CTSS, and VIM) showed excellent diagnostic performance on discriminating DMD in GSE1004, GSE3307, GSE6011 and GSE38417 datasets (all AUC > 0.8), which is validated in patients (10 DMD vs. 10 controls), DMD with exon 55 mutations, mdx mouse, and nomogram model.

Conclusion

Taken together, ATP6AP2, CTSS, and VIM play important roles in the inflammatory response in DMD, which may serve as diagnostic biomarkers and therapeutic targets.

LAMC2 marks a tumor-initiating cell population with an aggressive signature in pancreatic cancer

BACKGROUND

Tumor-initiating cells (TIC), also known as cancer stem cells, are considered a specific subpopulation of cells necessary for cancer initiation and metastasis; however, the mechanisms by which they acquire metastatic traits are not well understood.

METHODS

LAMC2 transcriptional levels were evaluated using publicly available transcriptome data sets, and LAMC2 immunohistochemistry was performed using a tissue microarray composed of PDAC and normal pancreas tissues. Silencing and tracing of LAMC2 was performed using lentiviral shRNA constructs and CRISPR/Cas9-mediated homologous recombination, respectively. The contribution of LAMC2 to PDAC tumorigenicity was explored in vitro by tumor cell invasion, migration, sphere-forming and organoids assays, and in vivo by tumor growth and metastatic assays. mRNA sequencing was performed to identify key cellular pathways upregulated in LAMC2 expressing cells. Metastatic spreading induced by LAMC2- expressing cells was blocked by pharmacological inhibition of transforming growth factor beta (TGF-β) signaling.

RESULTS

We report a LAMC2-expressing cell population, which is endowed with enhanced self-renewal capacity, and is sufficient for tumor initiation and differentiation, and drives metastasis. mRNA profiling of these cells indicates a prominent squamous signature, and differentially activated pathways critical for tumor growth and metastasis, including deregulation of the TGF-β signaling pathway. Treatment with Vactosertib, a new small molecule inhibitor of the TGF-β type I receptor (activin receptor-like kinase-5, ALK5), completely abrogated lung metastasis, primarily originating from LAMC2-expressing cells.

CONCLUSIONS

We have identified a highly metastatic subpopulation of TICs marked by LAMC2. Strategies aimed at targeting the LAMC2 population may be effective in reducing tumor aggressiveness in PDAC patients. Our results prompt further study of this TIC population in pancreatic cancer and exploration as a potential therapeutic target and/or biomarker.

Fibroblast activation protein-based theranostics in pancreatic cancer

Fibroblast activation protein-α (FAP) is a type II transmembrane serine protease that has specific endopeptidase activity. Given its well-established selective expression in the activated stromal fibroblasts of epithelial cancers, although not in quiescent fibroblasts, FAP has received substantial research attention as a diagnostic marker and therapeutic target. Pancreatic cancer is characterized by an abundant fibrotic or desmoplastic stroma, leading to rapid progression, therapeutic resistance, and poor clinical outcomes. Numerous studies have revealed that the abundant expression of FAP in cancer cells, circulating tumor cells, stromal cells, and cancer-associated fibroblasts (CAFs) of pancreatic adenocarcinoma is implicated in diverse cancer-related signaling pathways, contributing to cancer progression, invasion, migration, metastasis, immunosuppression, and resistance to treatment. In this article, we aim to systematically review the recent advances in research on FAP in pancreatic adenocarcinoma, including its utility as a diagnostic marker, therapeutic potential, and correlation with prognosis. We also describe the functional role of FAP-overexpressing stromal cells, particulary CAFs, in tumor immuno- and metabolic microenvironments, and summarize the mechanisms underlying the contribution of FAP-overexpressing CAFs in pancreatic cancer progression and treatment resistance. Furthermore, we discuss whether targeting FAP-overexpressing CAFs could represent a potential therapeutic strategy and describe the development of FAP-targeted probes for diagnostic imaging. Finally, we assess the emerging basic and clinical studies regarding the bench-to-bedside translation of FAP in pancreatic cancer.

Keep a watchful eye on methionine adenosyltransferases, novel therapeutic opportunities for hepatobiliary and pancreatic tumours

Methionine adenosyltransferases (MATs) synthesize S-adenosylmethionine (SAM) from methionine, which provides methyl groups for DNA, RNA, protein, and lipid methylation. MATs play a critical role in cellular processes, including growth, proliferation, and differentiation, and have been implicated in tumour development and progression. The expression of MATs is altered in hepatobiliary and pancreatic (HBP) cancers, which serves as a rare biomarker for early diagnosis and prognosis prediction of HBP cancers. Independent of SAM depletion in cells, MATs are often dysregulated at the transcriptional, post-transcriptional, and post-translational levels. Dysregulation of MATs is involved in carcinogenesis, chemotherapy resistance, T cell exhaustion, activation of tumour-associated macrophages, cancer stemness, and activation of tumourigenic pathways. Targeting MATs both directly and indirectly is a potential therapeutic strategy. This review summarizes the dysregulations of MATs, their proposed mechanism, diagnostic and prognostic roles, and potential therapeutic effects in context of HBP cancers.

A 70‑RNA model based on SVR and RFE for predicting the pancreatic cancer clinical prognosis

Researches on the prognosis of pancreatic cancer is of great significance to improve the patient treatment effect and survival. Current researches mainly focus on the prediction of the survival status and the determination of prognostic markers. Each patient has its own characteristics, there is no report about the prediction of survival time. However, accurate prediction of survival time is critical for personalized medicine. In this paper, a hybrid algorithm of Support Vector Regression (SVR) and Recursive Feature Elimination (RFE) was used to construct a quantitative prediction model of Overall Survival (OS) for pancreatic cancer patients, 70 RNAs related to OS were determined, including 33 mRNAs, 28 lncRNAs, and 9 miRNAs. The results of 10-fold cross-validation (R2 is 0.9693) and the generalization ability (R2 is 0.9666) showed that the model has reliable predictive performance and these 70 RNAs are important factors influencing the OS of pancreatic cancer patients. To further study the relationship between RNA-RNA interaction and the survival, competitive endogenous RNA (ceRNA) regulation network was constructed. Degree centrality, betweenness centrality and closeness centrality of nodes in the ceRNA network showed that hsa-mir-570, hsa-mir-944, hsa-mir-6506, hsa-mir-3136, MMP16, PLGLB2, HPGD, FUT1, MFSD2A, SULT1E1, SLC13A5, ZNF488, F2RL2, TNFRSF8, TNFSF11, FHDC1, ISLR2 and THSD7B are hub nodes, which are key RNAs closely determining the OS of pancreatic cancer patients.

Bioinformatics Analysis Revealing the Correlation between NF-κB Signaling Pathway and Immune Infiltration in Gastric Cancer

Although the emerging of immunotherapy conferred a new landscape of gastric cancer (GC) treatment, its response rate was of significant individual differences. Insight into GC immune microenviroment may contribute to breaking the dilemma. To this end, the enrichment score of NF-κB signaling pathway was calculated in each GC sample from The Cancer Genome Atlas (TCGA) via ssGSEA algorithm, and its association with immune infiltration was estimated. Based on NF-κB-related genes, a risk score was established and its involvement in immune infiltration, tumor mutational burden (TMB), and N6-methyladenosine (M6A) modification was analyzed in GC. The results showed that NF-κB signaling pathway promoted the infiltration of immune cells in GC. In addition, GC samples were divided into low- and high-risk groups according to a seven-gene (CARD11, CCL21, GADD45B, LBP, RELB, TRAF1, and VCAM1) risk score. Although the high-risk group displayed high immune infiltration and high expression of M6A regulatory genes, it remains in an immunosuppressive microenviroment and whereby suffers a poorer outcome. Of note, most of hub genes were related to immune infiltration and could serve as an independent prognostic biomarker. Conclusively, our study emphasized the crucial role of NF-κB signaling pathway in GC immune microenviroment and provided several candidate genes that may participate in immune infiltration.

Identifying DUSP-1 and FOSB as hub genes in immunoglobulin A nephropathy by WGCNA and DEG screening and validation

Background

The mechanism of immunoglobulin A nephropathy (IgAN) is still unknown. A bioinformatics analysis is a powerful method to identify the biomarkers and possible therapeutic targets of a certain disease from related datasets.

Methods

The GSE93973 dataset, obtained from the Gene Expression Omnibus (GEO) database, was used to construct a weighted gene co-expression network (WGCNA) and filter differentially expressed genes (DEGs). The biological process (BP) enrichment among all the genes in the key modules was analyzed through a Gene Ontology (GO) enrichment analysis. We selected the overlap of hub genes in the WGCNA and Protein-Protein Interaction (PPI) network as the final hub genes in IgAN. We verified the final hub genes in two other datasets and in clinical kidney tissue specimens. A receiver operating characteristic (ROC) curve was used to evaluate the diagnostic efficacy of hub genes for IgAN.

Results

The turquoise module, which contained 1,806 genes, was the module with the highest correlation coefficient with IgAN in the GSE93973 dataset. The GO enrichment analysis showed that these 1,806 genes were mainly enriched in inflammation and immune responses. There were five hub genes identified by WGCNA and 34 hub genes identified in a DEG analysis in the GSE93973 dataset. DUSP1 and FOSB were identified as the final hub genes in IgAN. The validation results of the final hub genes in two other databases and clinical kidney tissue specimens validated the result that, compared to the control group, FOSB and DUSP1 were expressed at lower levels in the glomerulus of IgAN patients. The ROC curve indicated that DUSP1 and FOSB were good diagnostic indicators for IgAN.

Conclusions

Our analysis identified two hub genes that might be potential targets for the intervention and treatment of IgAN.

Establishment and Verification of a Gene Signature for Diagnosing Type 2 Diabetics by WGCNA, LASSO Analysis, and In Vitro Experiments

Objective

The incidence and prevalence of type 2 diabetes are increasing with age. Nevertheless, there is lack of sensitive diagnostic tools and effective therapeutic regimens. We aimed to establish and verify a practical and valid diagnostic tool for this disease.

Methods

WGCNA was presented on the expression profiling of type 2 diabetic and normal islets in combined GSE25724 and GSE38642 datasets. By LASSO Cox regression analyses, a gene signature was constructed based on the genes in diabetes-related modules. ROC curves were plotted for assessing the diagnostic efficacy. Correlations between the genes and immune cell infiltration and pathways were analyzed. BST2 and BTBD1 expression was verified in glucotoxicity-induced and normal islet β cells. The influence of BST2 on β cell dysfunction was investigated under si-BST2 transfection.

Results

Totally, 14 coexpression modules were constructed, and red and cyan modules displayed the correlations to diabetes. The LASSO gene signature (BST2, BTBD1, IFIT1, IFIT3, and RTP4) was developed. The AUCs in the combined datasets and GSE20966 dataset were separately 0.914 and 0.910, confirming the excellent performance in diagnosing type 2 diabetes. Each gene in the model was distinctly correlated to immune cell infiltration and key signaling pathways (TGF-β and P53, etc.). The abnormal expression of BST2 and BTBD1 was confirmed in glucotoxicity-induced β cells. BST2 knockdown ameliorated β cell dysfunction and altered the activation of TGF-β and P53 pathways.

Conclusion

Our findings propose a gene signature with high efficacy to diagnose type 2 diabetes, which could assist and improve early diagnosis and therapy.

Identification and prognostic analysis of biomarkers to predict the progression of pancreatic cancer patients

BACKGROUND

Pancreatic cancer (PC) is a malignancy with a poor prognosis and high mortality. Surgical resection is the only "curative" treatment. However, only a minority of patients with PC can obtain surgery. Improving the overall survival (OS) rate of patients with PC is still a major challenge. Molecular biomarkers are a significant approach for diagnostic and predictive use in PCs. Several prediction models have been developed for patients newly diagnosed with PC that is operable or patients with advanced and metastatic PC; however, these models require further validation. Therefore, precise biomarkers are urgently required to increase the efficiency of predicting a disease-free survival (DFS), OS, and sensitivity to immunotherapy in PC patients and to improve the prognosis of PC.

METHODS

In the present study, we first evaluated the highly and selectively expressed targets in PC, using the GeoMxTM Digital Spatial Profiler (DSP) and then, we analyzed the roles of these targets in PCs using TCGA database.

RESULTS

LAMB3, FN1, KRT17, KRT19, and ANXA1 were defined as the top five upregulated targets in PC compared with paracancer. The TCGA database results confirmed the expression pattern of LAMB3, FN1, KRT17, KRT19, and ANXA1 in PCs. Significantly, LAMB3, FN1, KRT19, and ANXA1 but not KRT17 can be considered as biomarkers for survival analysis, univariate and multivariate Cox proportional hazards model, and risk model analysis. Furthermore, in combination, LAMB3, FN1, KRT19, and ANXA1 predict the DFS and, in combination, LAMB3, KRT19, and ANXA1 predict the OS. Immunotherapy is significant for PCs that are inoperable. The immune checkpoint blockade (ICB) analysis indicated that higher expressions of FN1 or ANXA1 are correlated with lower ICB response. In contrast, there are no significant differences in the ICB response between high and low expression of LAMB3 and KRT19.

CONCLUSIONS

In conclusion, LAMB3, FN1, KRT19, and ANXA1 are good predictors of PC prognosis. Furthermore, FN1 and ANXA1 can be predictors of immunotherapy in PCs.

Whole Exome Sequencing Study in a Family with Type 2 Diabetes Mellitus

Background

Type 2 diabetes mellitus (T2DM) is characterized by β cell decline in the pancreas and insulin resistance. This study aimed to investigate the possible pathogenic gene mutation sites of T2DM patients using whole exome sequencing.

Materials and Methods

We recruited a Chinese family with 3-generation history of diabetes. The whole blood genomic DNA of seven members of the family was extracted and sent for whole exome sequencing. Biological information was analyzed with in silico prediction methods, including significance analysis of single nucleotide polymorphism (SNP)/Indel site, and analysis of specific SNP/Indel proteins and their potential mechanisms.

Results

Six out of seven members of the family were diagnosed with diabetes. All DNA samples (23 kb) met quality requirements of library construction. Clean reads of each sample demonstrated high Q20 and Q30 (>80%), indicating good sequencing quality of sequencing data. A total of 130,693 SNPs and 15,928 Indels were found in DNA samples. A total of 22 significant SNPs and Indel mutation sites located on 19 genes were obtained, including ZCCHC3, SYN2, RPL14, SRRD, AMD1, CAMKK2, ZNF787, RNF157, NPIPB15, ALG3, KIAA0040, MAST2, ESRRA, C8orf58, PNLIPRP1, DACH1, MACC1, CAPN9 and DMKN. An rs2305205 mutation of PNLIPRP1 gene and an rs778701848 mutation of CAMKK2 gene may be associated with the pathogenesis of T2DM in this family.

Conclusion

Exons of these diabetic patients demonstrated an rs2305205 mutation in PNLIPRP1 gene and an rs778701848 mutation in CAMKK2 gene. These two mutations might promote T2DM occurrence through reducing sensitivity of peripheral tissue to insulin and reducing insulin secretion.

Bioinformatic Analysis and Integration of Transcriptome and Proteome Results Identify Key Coding and Noncoding Genes Predicting Malignancy in Intraductal Papillary Mucinous Neoplasms of the Pancreas

BACKGROUND

Intraductal papillary mucinous neoplasms (IPMNs) are precursor lesions of pancreatic ductal adenocarcinoma (PDAC). IPMNs are generally associated with high risk of developing malignancy and therefore need to be diagnosed and assessed accurately, once detected. Existing diagnostic methods are inadequate, and identification of efficient biomarker capable of detecting high-risk IPMNs is necessitated. Moreover, the mechanism of development of malignancy in IPMNs is also elusive.

METHODS

Gene expression meta-analysis conducted using 12 low-risk IPMN and 23 high-risk IPMN tissue samples. We have also listed all the altered miRNAs and long noncoding RNAs (lncRNAs), identified their target genes, and performed pathway analysis. We further enlisted cyst fluid proteins detected to be altered in high-risk or malignant IPMNs and compared them with fraction of differentially expressed genes secreted into cyst fluid.

RESULTS

Our meta-analysis identified 270 upregulated and 161 downregulated genes characteristically altered in high-risk IPMNs. We further identified 61 miRNAs and 14 lncRNAs and their target genes and key pathways contributing towards understanding of the gene regulation during the progression of the disease. Most importantly, we have detected 12 genes altered significantly both in cystic lesions and cyst fluid.

CONCLUSION

Our study reports, for the first time, a meta-analysis identifying key changes in gene expression between low-risk and high-risk IPMNs and also explains the regulatory aspect through construction of a miRNA-lncRNA-mRNA interaction network. The 12-gene-signature could function as potential biomarker in cyst fluid for detection of IPMN with a high risk of developing malignancy.

Comprehensive analysis of abnormal expression, prognostic value and oncogenic role of the hub gene FN1 in pancreatic ductal adenocarcinoma via bioinformatic analysis and in vitro experiments

Background

Pancreatic ductal adenocarcinoma (PDAC) is one of the most commonly diagnosed cancers with a poor prognosis worldwide. Although the treatment of PDAC has made great progress in recent years, the therapeutic effects are still unsatisfactory. Methods. In this study, we identified differentially expressed genes (DEGs) between PDAC and normal pancreatic tissues based on four Gene Expression Omnibus (GEO) datasets (GSE15471, GSE16515, GSE28735 and GSE71729). A protein–protein interaction (PPI) network was established to evaluate the relationship between the DEGs and to screen hub genes. The expression levels of the hub genes were further validated through the Gene Expression Profiling Interactive Analysis (GEPIA), ONCOMINE and Human Protein Atlas (HPA) databases, as well as the validation GEO dataset GSE62452. Additionally, the prognostic values of the hub genes were evaluated by Kaplan–Meier plotter and the validation GEO dataset GSE62452. Finally, the mechanistic roles of the most remarkable hub genes in PDAC were examined through in vitro experiments.

Results

We identified the following nine hub genes by performing an integrated bioinformatics analysis: COL1A1, COL1A2, FN1, ITGA2, KRT19, LCN2, MMP9, MUC1 and VCAN. All of the hub genes were significantly upregulated in PDAC tissues compared with normal pancreatic tissues. Two hub genes (FN1 and ITGA2) were associated with poor overall survival (OS) rates in PDAC patients. Finally, in vitro experiments indicated that FN1 plays vital roles in PDAC cell proliferation, colony formation, apoptosis and the cell cycle.

Conclusions

In summary, we identified two hub genes that are associated with the expression and prognosis of PDAC. The oncogenic role of FN1 in PDAC was first illustrated by performing an integrated bioinformatic analysis and in vitro experiments. Our results provide a fundamental contribution for further research aimed finding novel therapeutic targets for overcoming PDAC.

A prognostic score based on long-term survivor unique transcriptomic signatures predicts patient survival in pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is known for its poor prognosis with few long-term survivors. This study aimed to establish a prognostic score using unique transcriptomic profiles of long-term survivors to be used as a patient selection tool for meaningful clinical intervention in PDAC. In TCGA PDAC cohort, 16 genes were significantly upregulated in the long-term survivor tumors. A prognostic score was established using these 16 genes by LASSO Cox regression, and PHKG1, HOXA4, ISL2, DMRT3 and TRA2A gene expressions were included in the score. The prognostic value was confirmed in both testing and validation cohorts. The characteristics of the high score tumor was investigated by bioinformatical approach. The high score tumor was associated with TP53 mutation but not with other commonly enhanced signaling pathways in PDAC. The high score tumor was associated with higher tumor mutational burden and unfavorable tumor microenvironment (TME), such as lower infiltration of CD8-positive T cells and dendritic cells, and less cell composition of mature blood vessels and fibroblasts. The high score tumor was also associated with enhanced cell proliferation and margin positivity after surgery. The impact of score component genes on the cell proliferation was investigated by in vitro experiments. Silencing of the score component genes promoted cell proliferation. In conclusion, the prognostic score predicted PDAC patient survival and was associated with cancer aggressiveness such as unfavorable TME and enhanced cell proliferation.

Identification of PLA2G7 as a novel biomarker of diffuse large B cell lymphoma

BACKGROUND

Diffuse large B-cell lymphoma is the most common form of non-Hodgkin lymphoma globally, and patients with relapsed or refractory DLBCL typically experience poor long-term outcomes.

METHODS

Differentially expressed genes associated with DLBCL were identified using two GEO datasets in an effort to detect novel diagnostic or prognostic biomarkers of this cancer type, after which receiver operating characteristic curve analyses were conducted. Genes associated with DLBCL patient prognosis were additionally identified via WCGNA analyses of the TCGA database. The expression of PLA2G7 in DLBCL patient clinical samples was further assessed, and the functional role of this gene in DLBCL was assessed through in vitro and bioinformatics analyses.

RESULTS

DLBCL-related DEGs were found to be most closely associated with immune responses, cell proliferation, and angiogenesis. WCGNA analyses revealed that PLA2G7 exhibited prognostic value in DLBCL patients, and the upregulation of this gene in DLBCL patient samples was subsequently validated. PLA2G7 was also found to be closely linked to tumor microenvironmental composition such that DLBCL patients expressing higher levels of this gene exhibited high local monocyte and gamma delta T cell levels. In vitro experiments also revealed that knocking down PLA2G7 expression was sufficient to impair the migration and proliferation of DLBCL cells while promoting their apoptotic death. Furthmore, the specific inhibitor of PLA2G7, darapladib, could noticeably restrained the DLBCL cell viability and induced apoptosis.

CONCLUSIONS

PLA2G7 may represent an important diagnostic, prognostic, or therapeutic biomarker in patients with DLBCL.

Nuclear Pyruvate Kinase M2 (PKM2) Contributes to Phosphoserine Aminotransferase 1 (PSAT1)-Mediated Cell Migration in EGFR-Activated Lung Cancer Cells

Simple Summary

Alternative functions for metabolic proteins have recently been shown to drive cancer growth. These may include differential enzymatic activity or novel protein associations. Phosphoserine aminotransferase 1 (PSAT1) participates in cellular serine synthesis and has been observed to be elevated in different tumor types. In this study, we aimed to identify new putative PSAT1 activities and determine their contribution to lung tumor progression. We found a direct association for PSAT1 with another enzyme, pyruvate kinase M2. While this appears not to affect PKM2’s metabolic activity, PSAT1 is required for the specific cellular localization of PKM2 upon tumorigenic signaling. Further, the depletion of PSAT1 suppresses lung cancer cell movement that can be partially restored by the compartment expression of PKM2. These findings reveal a novel mechanism that is able to promote the spread of this deadly disease.

Abstract

An elevated expression of phosphoserine aminotransferase 1 (PSAT1) has been observed in multiple tumor types and is associated with poorer clinical outcomes. Although PSAT1 is postulated to promote tumor growth through its enzymatic function within the serine synthesis pathway (SSP), its role in cancer progression has not been fully characterized. Here, we explore a putative non-canonical function of PSAT1 that contributes to lung tumor progression. Biochemical studies found that PSAT1 selectively interacts with pyruvate kinase M2 (PKM2). Amino acid mutations within a PKM2-unique region significantly reduced this interaction. While PSAT1 loss had no effect on cellular pyruvate kinase activity and PKM2 expression in non-small-cell lung cancer (NSCLC) cells, fractionation studies demonstrated that the silencing of PSAT1 in epidermal growth factor receptor (EGFR)-mutant PC9 or EGF-stimulated A549 cells decreased PKM2 nuclear translocation. Further, PSAT1 suppression abrogated cell migration in these two cell types whereas PSAT1 restoration or overexpression induced cell migration along with an elevated nuclear PKM2 expression. Lastly, the nuclear re-expression of the acetyl-mimetic mutant of PKM2 (K433Q), but not the wild-type, partially restored cell migration in PSAT1-silenced cells. Therefore, we conclude that, in response to EGFR activation, PSAT1 contributes to lung cancer cell migration, in part, by promoting nuclear PKM2 translocation.

Construction of a prognostic model with histone modification-related genes and identification of potential drugs in pancreatic cancer

Background

Pancreatic cancer (PC) is a highly fatal and aggressive disease with its incidence and mortality quite discouraging. An effective prediction model is urgently needed for the accurate assessment of patients’ prognosis to assist clinical decision-making.

Methods

Gene expression data and clinicopathological data of the samples were acquired from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Gene Expression Omnibus (GEO) databases. Differential expressed genes (DEGs) analysis, univariate Cox regression analysis, least absolute shrinkage and selection operator (LASSO) regression analysis, random forest screening and multivariate Cox regression analysis were applied to construct the risk signature. The effectiveness and independence of the model were validated by time-dependent receiver operating characteristic (ROC) curve, Kaplan–Meier (KM) survival analysis and survival point graph in training set, test set, TCGA entire set and GSE57495 set. The validity of the core gene was verified by immunohistochemistry and our own independent cohort. Meanwhile, functional enrichment analysis of DEGs between the high and low risk groups revealed the potential biological pathways. Finally, CMap database and drug sensitivity assay were utilized to identify potential small molecular drugs as the risk model-related treatments for PC patients.

Results

Four histone modification-related genes were identified to establish the risk signature, including CBX8, CENPT, DPY30 and PADI1. The predictive performance of risk signature was validated in training set, test set, TCGA entire set and GSE57495 set, with the areas under ROC curve (AUCs) for 3-year survival were 0.773, 0.729, 0.775 and 0.770 respectively. Furthermore, KM survival analysis, univariate and multivariate Cox regression analysis proved it as an independent prognostic factor. Mechanically, functional enrichment analysis showed that the poor prognosis of high-risk population was related to the metabolic disorders caused by inadequate insulin secretion, which was fueled by neuroendocrine aberration. Lastly, a cluster of small molecule drugs were identified with significant potentiality in treating PC patients.

Conclusions

Based on a histone modification-related gene signature, our model can serve as a reliable prognosis assessment tool and help to optimize the treatment for PC patients. Meanwhile, a cluster of small molecule drugs were also identified with significant potentiality in treating PC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-01928-6.

Identification of Regeneration and Hub Genes and Pathways at Different Time Points after Spinal Cord Injury

Spinal cord injury (SCI) is a neurological injury that can cause neuronal loss around the lesion site and leads to locomotive and sensory deficits. However, the underlying molecular mechanisms remain unclear. This study aimed to verify differential gene time-course expression in SCI and provide new insights for gene-level studies. We downloaded two rat expression profiles (GSE464 and GSE45006) from the Gene Expression Omnibus database, including 1 day, 3 days, 7 days, and 14 days post-SCI, along with thoracic spinal cord data for analysis. At each time point, gene integration was performed using "batch normalization." The raw data were standardized, and differentially expressed genes at the different time points versus the control were analyzed by Gene Ontology enrichment analysis, the Kyoto Encyclopedia of Genes and Genomes pathway analysis, and gene set enrichment analysis. A protein-protein interaction network was then built and visualized. In addition, ten hub genes were identified at each time point. Among them, Gnb5, Gng8, Agt, Gnai1, and Psap lack correlation studies in SCI and deserve further investigation. Finally, we screened and analyzed genes for tissue repair, reconstruction, and regeneration and found that Anxa1, Snap25, and Spp1 were closely related to repair and regeneration after SCI. In conclusion, hub genes, signaling pathways, and regeneration genes involved in secondary SCI were identified in our study. These results may be useful for understanding SCI-related biological processes and the development of targeted intervention strategies.

A retrospective overview of PHGDH and its inhibitors for regulating cancer metabolism

Emerging evidence suggests that cancer metabolism is closely associated to the serine biosynthesis pathway (SSP), in which glycolytic intermediate 3-phosphoglycerate is converted to serine through a three-step enzymatic transformation. As the rate-limiting enzyme in the first step of SSP, phosphoglycerate dehydrogenase (PHGDH) is overexpressed in various diseases, especially in cancer. Genetic knockdown or silencing of PHGDH exhibits obvious anti-tumor response both in vitro and in vivo, demonstrating that PHGDH is a promising drug target for cancer therapy. So far, several types of PHGDH inhibitors have been identified as a significant and newly emerging option for anticancer treatment. Herein, this comprehensive review summarizes the recent achievements of PHGDH, especially its critical role in cancer and the development of PHGDH inhibitors in drug discovery.

HEMGN and SLC2A1 might be potential diagnostic biomarkers of steroid-induced osteonecrosis of femoral head: study based on WGCNA and DEGs screening

BACKGROUND

Steroid-induced osteonecrosis of the femoral head (SONFH) is a chronic and crippling bone disease. This study aims to reveal novel diagnostic biomarkers of SONFH.

METHODS

The GSE123568 dataset based on peripheral blood samples from 10 healthy individuals and 30 SONFH patients was used for weighted gene co-expression network analysis (WGCNA) and differentially expressed genes (DEGs) screening. The genes in the module related to SONFH and the DEGs were extracted for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Genes with |gene significance| > 0.7 and |module membership| > 0.8 were selected as hub genes in modules. The DEGs with the degree of connectivity ≥5 were chosen as hub genes in DEGs. Subsequently, the overlapping genes of hub genes in modules and hub genes in DEGs were selected as key genes for SONFH. And then, the key genes were verified in another dataset, and the diagnostic value of key genes was evaluated by receiver operating characteristic (ROC) curve.

RESULTS

Nine gene co-expression modules were constructed via WGCNA. The brown module with 1258 genes was most significantly correlated with SONFH and was identified as the key module for SONFH. The results of functional enrichment analysis showed that the genes in the key module were mainly enriched in the inflammatory response, apoptotic process and osteoclast differentiation. A total of 91 genes were identified as hub genes in the key module. Besides, 145 DEGs were identified by DEGs screening and 26 genes were identified as hub genes of DEGs. Overlapping genes of hub genes in the key module and hub genes in DEGs, including RHAG, RNF14, HEMGN, and SLC2A1, were further selected as key genes for SONFH. The diagnostic value of these key genes for SONFH was confirmed by ROC curve. The validation results of these key genes in GSE26316 dataset showed that only HEMGN and SLC2A1 were downregulated in the SONFH group, suggesting that they were more likely to be diagnostic biomarkers of SOFNH than RHAG and RNF14.

CONCLUSIONS

Our study identified that two key genes, HEMGN and SLC2A1, might be potential diagnostic biomarkers of SONFH.

Identifying Drug Targets in Pancreatic Ductal Adenocarcinoma Through Machine Learning, Analyzing Biomolecular Networks, and Structural Modeling

Pancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of cancer-related death and has an extremely poor prognosis. Thus, identifying new disease-associated genes and targets for PDAC diagnosis and therapy is urgently needed. This requires investigations into the underlying molecular mechanisms of PDAC at both the systems and molecular levels. Herein, we developed a computational method of predicting cancer genes and anticancer drug targets that combined three independent expression microarray datasets of PDAC patients and protein-protein interaction data. First, Support Vector Machine–Recursive Feature Elimination was applied to the gene expression data to rank the differentially expressed genes (DEGs) between PDAC patients and controls. Then, protein-protein interaction networks were constructed based on the DEGs, and a new score comprising gene expression and network topological information was proposed to identify cancer genes. Finally, these genes were validated by “druggability” prediction, survival and common network analysis, and functional enrichment analysis. Furthermore, two integrins were screened to investigate their structures and dynamics as potential drug targets for PDAC. Collectively, 17 disease genes and some stroma-related pathways including extracellular matrix-receptor interactions were predicted to be potential drug targets and important pathways for treating PDAC. The protein-drug interactions and hinge sites predication of ITGAV and ITGA2 suggest potential drug binding residues in the Thigh domain. These findings provide new possibilities for targeted therapeutic interventions in PDAC, which may have further applications in other cancer types.

Proteomic and lipidomic analysis of exosomes derived from ovarian cancer cells and ovarian surface epithelial cells

BACKGROUND

The limitation of current biomarker of early stage ovarian cancer and the anatomical location of ovarian (depths of the pelvic) make ovarian cancer difficult to be detected in early stage. Growing evidence shows exosomes as key information transmitters, it carried molecules, such as miRNAs, proteins, lipids, double-stranded DNA have been reported as promising biomarkers in many diseases. However, little is known about the protein and lipid composition of ovarian cancer.

METHODS

Here, we report proteomic and lipidomic analysis of exosomes derived from ovarian cancer cells (SKOV-3) and ovarian surface epithelial cells (HOSEPiC).

RESULTS

A total of 1433 proteins and 1227 lipid species were identified from two cell line derived exosomes. Several lipid species and proteins significantly differ in SKOV-3 derived exosomes compared to those from HOSEPiC. For example, we noted that ChE and ZyE species were in general more abundant in exosomes from SKOV-3 than from HOSEPiC; Collagen type V alpha 2 chain (COL5A2) and lipoprotein lipase (LPL) were significantly higher in SKOV-3 derived exosomes than HOSEpic (p < 0.05).

CONCLUSIONS

Our research indicates the promising role of exosomal proteins and lipids in the early diagnosis of ovarian cancer.