Development and validation of a hypoxia-stemness-based prognostic signature in pancreatic adenocarcinoma

ANKZF1 knockdown inhibits glioblastoma progression by promoting intramitochondrial protein aggregation through mitoRQC

Protein homeostasis is fundamental to the development of tumors. Ribosome-associated quality-control (RQC) is able to add alanine and threonine to the stagnant polypeptide chain C-terminal (CAT-tail) when protein translation is hindered, while Ankyrin repeat and zinc-finger domain-containing-protein 1 (ANKZF1) can counteract the formation of the CAT-tail, preventing the aggregation of polypeptide chains. In particular, ANKZF1 plays an important role in maintaining mitochondrial protein homeostasis by mitochondrial RQC (mitoRQC) after translation stagnation of precursor proteins targeting mitochondria. However, the role of ANKZF1 in glioblastoma is unclear. Therefore, the current study was aimed to investigate the effects of ANKZF1 in glioblastoma cells and a nude mouse glioblastoma xenograft model. Here, we reported that knockdown of ANKZF1 in glioblastoma cells resulted in the accumulation of CAT-tail in mitochondria, leading to the activated mitochondrial unfolded protein response (UPRmt) and inhibits glioblastoma malignant progression. Excessive CAT-tail sequestered mitochondrial chaperones HSP60, mtHSP70 and proteases LONP1 as well as mitochondrial respiratory chain subunits ND1, Cytb, mtCO2 and ATP6, leading to mitochondrial oxidative phosphorylation dysfunction, membrane potential impairment, and mitochondrial apoptotic pathway activation. Our study highlights ANKZF1 as a valuable target for glioblastoma intervention and provides an innovative insight for the treatment of glioblastoma through the regulating of mitochondrial protein homeostasis.

Comprehensive analysis of bulk and single-cell transcriptomic data reveals a novel signature associated with endoplasmic reticulum stress, lipid metabolism, and liver metastasis in pancreatic cancer

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy with high probability of recurrence and distant metastasis. Liver metastasis is the predominant metastatic mode developed in most pancreatic cancer cases, which seriously affects the overall survival rate of patients. Abnormally activated endoplasmic reticulum stress and lipid metabolism reprogramming are closely related to tumor growth and metastasis. This study aims to construct a prognostic model based on endoplasmic reticulum stress and lipid metabolism for pancreatic cancer, and further explore its correlation with tumor immunity and the possibility of immunotherapy.

METHODS

Transcriptomic and clinical data are acquired from TCGA, ICGC, and GEO databases. Potential prognostic genes were screened by consistent clustering and WGCNA methods, and the whole cohort was randomly divided into training and testing groups. The prognostic model was constructed by machine learning method in the training cohort and verified in the test, TCGA and ICGC cohorts. The clinical application of this model and its relationship with tumor immunity were analyzed, and the relationship between endoplasmic reticulum stress and intercellular communication was further explored.

RESULTS

A total of 92 characteristic genes related to endoplasmic reticulum stress, lipid metabolism and liver metastasis were identified in pancreatic cancer. We established and validated a prognostic model for pancreatic cancer with 7 signatures, including ADH1C, APOE, RAP1GAP, NPC1L1, P4HB, SOD2, and TNFSF10. This model is considered to be an independent prognosticator and is a more accurate predictor of overall survival than age, gender, and stage. TIDE score was increased in high-risk group, while the infiltration levels of CD8+ T cells and M1 macrophages were decreased. The number and intensity of intercellular communication were increased in the high ER stress group.

CONCLUSIONS

We constructed and validated a novel prognostic model for pancreatic cancer, which can also be used as an instrumental variable to predict the prognosis and immune microenvironment. In addition, this study revealed the effect of ER stress on cell-cell communication in the tumor microenvironment.

CYB5R1 is a potential biomarker that correlates with stemness and drug resistance in gastric cancer

BACKGROUND

Drug resistance is a major obstacle in the treatment of gastric cancers (GC). In recent years, the prognostic value of the mRNA expression-based stemness score (mRNAss) across cancers has been reported. We intended to search for the key genes associated with Cancer stem cells (CSCs) and drug resistance.

METHODS

All GC samples from The Cancer Genome Atlas (TCGA) were then divided into low- and high-mRNAss groups based on the median value of mRNAss. A weighted correlation network analysis (WCGNA) was used to identify co-expressed genes related to mRNAss groups. Differential gene expression analysis with Limma was performed in the GSE31811. The correlations between CYB5R1 and the immune cells and macrophage infiltration were analyzed by TIMER database. Spheroid formation assay was used to evaluate the stemness of gastric cancer cells, and transwell assay was used to detect the invasion and migration ability of gastric cancer cells.

RESULTS

GC patients with high mRNAss values had a worse prognosis than those with low mRNAss values. 584 genes were identified by WGCNA analysis. 668 differentially expressed genes (DEGs) (|logFC|>1) with 303 down-regulated and 365 up-regulated were established in drug-effective patients compared to controls. TCGA-STAD samples were divided into 3 subtypes based on 303 down-regulated genes. CYB5R1 was a potential biomarker that correlated with the response to drugs in GC (AUC=0.83). CYB5R1 participated in drug resistance and tumorigenesis through NFS1 in GC.

CONCLUSIONS

Our study highlights the clinical importance of CYB5R1 in GC and the CYB5R1-NFS1 signaling-targeted therapy might be a feasible strategy for the treatment of GC.

Characterization of cancer-associated fibroblasts (CAFs) and development of a CAF-based risk model for triple-negative breast cancer

Triple-negative breast Cancer (TNBC) is a highly malignant cancer with unclear pathogenesis. Within the tumor microenvironment (TME), cancer-associated fibroblasts (CAFs) vitally influence tumor onset and progression. Thus, this research aimed to identify distinct subgroups of CAF using single-cell and TNBC-related information from the GEO and TCGA databases, respectively. The primary aim was to establish a novel predictive model based on the CAF features and their clinical relevance. Moreover, the CAFs were analyzed for their immune characteristics, response to immunotherapy, and sensitivity to different drugs. The developed predictive model demonstrated significant effectiveness in determining the prognosis of patients with TNBC, TME, and the immune landscape of the tumor. Of note, the expression of GPR34 was significantly higher in TNBC tissues compared to that in other breast cancer (non-TNBC) tissues, indicating that GPR34 plays a crucial role in the onset and progression of TNBC. In summary, this research has yielded a novel predictive model for TNBC that holds promise for the accurate prediction of prognosis and response to immunotherapy in patients with TNBC.

Novel prognostic gene signature for pancreatic ductal adenocarcinoma based on hypoxia

BACKGROUND

Currently, there is lack of marker to accurately assess the prognosis of patients diagnosed with pancreatic ductal adenocarcinoma (PDAC). This study aims to establish a hypoxia-related risk scoring model that can effectively predict the prognosis and chemotherapy outcomes of PDAC patients.

METHODS

Using unsupervised consensus clustering algorithms, we comprehensively analyzed The Cancer Genome Atlas (TCGA) data to identify two distinct hypoxia clusters and used the weighted gene co-expression network analysis (WGCNA) to examine gene sets significantly associated with these hypoxia clusters. Then univariate Cox regression, the least absolute shrinkage and selection operator (LASSO) Cox regression and multivariate Cox regression were used to construct a signature and its efficacy was evaluated using the International Cancer Genome Consortium (ICGC) PDAC cohort. Further, the correlation between the risk scores obtained from the signature and carious clinical, pathological, immunophenotype, and immunoinfiltration factors as well as the differences in immunotherapy potential and response to common chemotherapy drugs between high-risk and low-risk groups were evaluated.

RESULTS

From a total of 8 significantly related modules and 4423 genes, 5 hypoxia-related signature genes were identified to construct a risk model. Further analysis revealed that the overall survival rate (OS) of patients in the low-risk group was significantly higher than the high-risk group. Univariate and multivariate Cox regression analysis showed that the risk scoring signature was an independent factor for prognosis prediction. Analysis of immunocyte infiltration and immunophenotype showed that the immune score and the anticancer immune response in the high-risk were significantly lower than that in the low-risk group.

CONCLUSION

The constructed hypoxia-associated prognostic signature demonstrated could be used as a potential risk classifier for PDAC.

Establishment and validation of a ubiquitination-related gene signature associated with prognosis in pancreatic duct adenocarcinoma

Background

Patients with pancreatic duct adenocarcinoma (PDAC) have varied prognoses that depend on numerous variables. However, additional research is required to uncover the latent impact of ubiquitination-related genes (URGs) on determining PDAC patients' prognoses.

Methods

The URGs clusters were discovered via consensus clustering, and the prognostic differentially expressed genes (DEGs) across clusters were utilized to develop a signature using a least absolute shrinkage and selection operator (LASSO) regression analysis of data from TCGA-PAAD. Verification analyses were conducted across TCGA-PAAD, GSE57495 and ICGC-PACA-AU to show the robustness of the signature. RT-qPCR was used to verify the expression of risk genes. Lastly, we formulated a nomogram to improve the clinical efficacy of our predictive tool.

Results

The URGs signature, comprised of three genes, was developed and was shown to be highly correlated with the prognoses of PAAD patients. The nomogram was established by combining the URGs signature with clinicopathological characteristics. We discovered that the URGs signature was remarkably superior than other individual predictors (age, grade, T stage, et al). Also, the immune microenvironment analysis indicated that ESTIMATEscore, ImmuneScores, and StromalScores were elevated in the low-risk group. The immune cells that infiltrated the tissues were different between the two groups, as did the expression of immune-related genes.

Conclusion

The URGs signature could act as the biomarker of prognosis and selecting appropriate therapeutic drugs for PDAC patients.

Identification of gene signatures related to hypoxia and angiogenesis in pancreatic cancer to aid immunotherapy and prognosis

Background

One of the most diverse tumors is pancreatic cancer (PC), which makes predicting the prognosis challenging. PC development is directly related to hypoxia, angiogenesis, and immunotherapy. It is still unclear how the three features are related.

Methods

The Genotype-Tissue Expression (GTEx) and the Cancer Genome Atlas (TCGA) database were employed to obtain sequencing data for healthy pancreatic tissues and PC tissues, respectively. According to the constructed hypoxic prognostic model (HPM) and angiogenic prognostic model (APM), 4 subtypes of PC were identified. Hypoxia and angiogenesis prognostic model (HAPM) was established based on differentially expressed genes (DEGs) between high-angiogenesis/high-hypoxia (HH) and low-angiogenesis/low-hypoxia (LL) subgroups. Base on the median risk score, PC patients were separated into high-risk and low-risk groups, and clinical traits, prognosis, percentage of immune cell infiltration, PD-1 expression, and the fraction of T-cell depletion were compared between the groups. Finally, the predictive accuracy of the tumor immune dysfunction and rejection (TIDE) and tumor inflammatory signature (TIS) models, as well as HAPM, was compared.

Result

We analyzed the mRNA sequencing data from 178 PC tissues and 171 normal pancreatic tissues to obtain 9527 DEGs. We discovered 200 genes linked with hypoxia and 36 genes involved with angiogenesis through the literature. We found the core genes related with hypoxia and angiogenesis in PC by intersecting the DEGs of the HH and LL subgroups with those of PC via WGCNA. IL-17 signaling pathway, ECM-receptor interactions, cytokine receptor interactions, etc. were all enriched in the Kyoto Encyclopedia of Genes and Genomes (KEGG) results of core genes. HAPM has good predictive efficiency, according to an evaluation of KM survival curves and ROC curves. The external dataset also validated the model’s ability to anticipate outcomes. Patients in the high- and low-risk groups were compared for PD1 expression and T-cell exclusion scores, which suggested that the model might be used to forecast which PC patients might benefit from immunotherapy.

Conclusions

The probable molecular processes connecting hypoxia and angiogenesis are described in this work, and a model is developed that may be utilized to forecast the prognosis for PC patients and the benefits of immunotherapy.

Hypoxia, a Targetable Culprit to Counter Pancreatic Cancer Resistance to Therapy

Pancreatic ductal adenocarcinoma (PDAC) is the most common type of pancreatic cancer, and it is a disease of dismal prognosis. While immunotherapy has revolutionized the treatment of various solid tumors, it has achieved little success in PDAC. Hypoxia within the stroma-rich tumor microenvironment is associated with resistance to therapies and promotes angiogenesis, giving rise to a chaotic and leaky vasculature that is inefficient at shuttling oxygen and nutrients. Hypoxia and its downstream effectors have been implicated in immune resistance and could be contributing to the lack of response to immunotherapy experienced by patients with PDAC. Paradoxically, increasing evidence has shown hypoxia to augment genomic instability and mutagenesis in cancer, suggesting that hypoxic tumor cells could have increased production of neoantigens that can potentially enable their clearance by cytotoxic immune cells. Strategies aimed at relieving this condition have been on the rise, and one such approach opts for normalizing the tumor vasculature to reverse hypoxia and its downstream support of tumor pathogenesis. An important consideration for the successful implementation of such strategies in the clinic is that not all PDACs are equally hypoxic, therefore hypoxia-detection approaches should be integrated to enable optimal patient selection for achieving improved patient outcomes.

Profiling of a novel circadian clock-related prognostic signature and its role in immune function and response to molecular targeted therapy in pancreatic cancer

BACKGROUND

Pancreatic ductal adenocarcinoma (PADA) represents a devastating type of pancreatic cancer with high mortality. Defining a prognostic gene signature that can stratify patients with different risk will benefit cancer treatment strategies.

METHODS

Gene expression profiles of PADA patients were acquired from the Cancer Genome Atlas and Gene Expression Omnibus, including GSE62452 and GSE28735. Differential expression analysis was carried out using the package edgeR in R. Intro-tumor immune infiltrates were quantified by six different computational algorithms XCELL, TIMER, QUANTISEQ, MCPCOUNTER, EPIC, and CIBERSORT. Biological processes were investigated based on R package "clusterProfiler".

RESULTS

13 genes (ARNTL2, BHLHE40, FBXL17, FBXL8, PPP1CB, RBM4B, ADRB1, CCAR2, CDK1, CSNK1D, KLF10, PSPC1, SIAH2) were eligible for the development of a prognostic gene signature. Performance of the prognostic gene signature was assessed in the discovery set (n = 210), validation set (n = 52), and two external data set (GSE62452, n = 65, and GSE28735, n = 84). Area under the curve (AUC) for predicting 3-year overall survival was 0.727, 0.732, 0.700, and 0.658 in the training set, the validation set, and the two test sets, respectively. KM curve revealed that the low-risk group had an improved prognosis than the high-risk group in all four datasets. PCA analysis demonstrated that the low-risk group was apparently separated from the high-risk group. CD8 T cell and B cell were significantly reduced in the high-risk group than in the low-risk group, while neutrophils were significantly augmented in the high-risk group than in the low-risk group. BMS-536924, Foretinib, Linsitinib, and Sabutoclax were more sensitive in the low-risk group, whereas Erlotinib was more effective in the high-risk group.

CONCLUSIONS

We successfully established and verified a novel circadian clock-related gene signature, which could stratify patients with different risk and be reflective of the therapeutic effect of molecular targeted therapy. Our findings could incorporate the pharmacological modulation of circadian clock into future therapeutic strategies.

Cuprotosis Programmed-Cell-Death-Related lncRNA Signature Predicts Prognosis and Immune Landscape in PAAD Patients

In terms of mortality and survival, pancreatic cancer is one of the worst malignancies. Known as a unique type of programmed cell death, cuprotosis contributes to tumor cell growth, angiogenesis, and metastasis. Cuprotosis programmed-cell-death-related lncRNAs (CRLs) have been linked to PAAD, although their functions in the tumor microenvironment and prognosis are not well understood. This study included data from the TCGA-PAAD cohort. Random sampling of PAAD data was conducted, splitting the data into two groups for use as a training set and test set (7:3). We searched for differentially expressed genes that were substantially linked to prognosis using univariate Cox and Lasso regression analysis. Through the use of multivariate Cox proportional risk regression, a risk-rating system for prognosis was developed. Correlations between the CRL signature and clinicopathological characteristics, tumor microenvironment, immunotherapy response, and chemotherapy sensitivity were further evaluated. Lastly, qRT-PCR was used to compare CRL expression in healthy tissues to that in tumors. Some CRLs are thought to have strong correlations with PAAD outcomes. These CRLs include AC005332.6, LINC02041, LINC00857, and AL117382.1. The CRL-based signature construction exhibited outstanding predictive performance and offers a fresh approach to evaluating pre-immune effectiveness, paving the way for future studies in precision immuno-oncology.