Development and validation of a metastasis-related Gene Signature for predicting the Overall Survival in patients with Pancreatic Ductal Adenocarcinoma

The path of GPR87: from a P2Y-like receptor to its role in cancer progression

GPR87 is a G protein-coupled seven-transmembrane receptor first described as an orphan receptor in 2001. Despite its high structural homology to several extracellular nucleotide-activated P2Y receptors and sharing conserved sequence motifs in transmembrane regions, identification of endogenous ligands from the class of nucleotides and their analogues has failed for GPR87. Although lysophosphatidic acid was proposed to be a natural ligand for this cell surface receptor, these data are preliminary and inconsistent, and IUPHAR is currently considering GPR87 as an orphan receptor. Thus, the endogenous ligands and physiological functions of GPR87 are still required to be determined and/or confirmed. The remarkably higher expression of GPR87 in human malignant tissues compared to the normal healthy ones clearly suggests that this receptor may be involved in the development and progression of cancerous neoplasms. Therefore, in this review article, the main focus is placed on the oncogenic role of GPR87 in various human malignancies, presenting it as a potential novel target site for therapeutic interventions using both humanized monoclonal antibodies and gene therapy but also selective antagonists which are still waiting for their identification. Furthermore, the importance of the expression of GPR87 as a predictive biomarker for evaluating the prognosis and overall survival of cancer patients is also highlighted.

Gene Dysregulation and Islet Changes in PDAC-Associated Type 3c Diabetes

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy, often associated with new-onset diabetes. The relationship between PDAC and diabetes, particularly type 3c diabetes, remains poorly understood. This study investigates whether PDAC-associated diabetes represents a distinct subtype by integrating transcriptomic and histological analyses. Whole-tumour RNA sequencing data from The Cancer Genome Atlas (TCGA) were analysed to compare gene expression profiles between PDAC patients with and without diabetes. Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) deconvolution was employed to assess immune cell populations. Histopathological evaluations of pancreatic tissues were conducted to assess fibrosis and islet morphology. Histological analysis revealed perivascular fibrosis and islet basement membrane thickening in both PDAC cohorts. Transcriptomic data indicated significant downregulation of islet hormone genes insulin (INS) and glucagon (GCG) but not somatostatin (SST) in PDAC-associated diabetes, consistent with a type 3c diabetes phenotype. Contrary to previous reports, no distinct immunogenic signature was identified in PDAC with diabetes, as key immune checkpoint genes (Programmed Cell Death Protein 1 (PDCD1), Cytotoxic T-Lymphocyte Associated Protein 4 (CTLA4), Programmed Death-Ligand 1(PD-L1)) were not differentially expressed. The findings suggest that PDAC-associated diabetes arises through neoplastic alterations in islet physiology rather than immune-mediated mechanisms. The observed reductions in endocrine markers reinforce the concept of PDAC-driven β-cell dysfunction as a potential early indicator of malignancy. Given the poor response of PDAC to PD-L1 checkpoint inhibitors, further research is needed to elucidate alternative therapeutic strategies targeting tumour-islet interactions.

Tetraspanins in digestive‑system cancers: Expression, function and therapeutic potential (Review)

The tetraspanin family of membrane proteins is essential for controlling different biological processes such as cell migration, penetration, adhesion, growth, apoptosis, angiogenesis and metastasis. The present review summarized the current knowledge regarding the expression and roles of tetraspanins in different types of cancer of the digestive system, including gastric, liver, colorectal, pancreatic, esophageal and oral cancer. Depending on the type and context of cancer, tetraspanins can act as either tumor promoters or suppressors. In the present review, the importance of tetraspanins in serving as biomarkers and targets for different types of digestive system‑related cancer was emphasized. Additionally, the molecular mechanisms underlying the involvement of tetraspanins in cancer progression and metastasis were explored. Furthermore, the current challenges are addressed and future research directions for advancing investigations related to tetraspanins in the context of digestive system malignancies are proposed.

Comprehensive analysis and experimental verification of the mechanism of anoikis related genes in pancreatic cancer

Background

Pancreatic cancer (PC), characterized by its aggressive nature and low patient survival rate, remains a challenging malignancy. Anoikis, a process inhibiting the spread of metastatic cancer cells, is closely linked to cancer progression and metastasis through anoikis-related genes. Nonetheless, the precise mechanism of action of these genes in PC remains unclear.

Methods

Study data were acquired from the Cancer Genome Atlas (TCGA) database, with validation data accessed at the Gene Expression Omnibus (GEO) database. Differential expression analysis and univariate Cox analysis were performed to determine prognostically relevant differentially expressed genes (DEGs) associated with anoikis. Unsupervised cluster analysis was then employed to categorize cancer samples. Subsequently, a least absolute shrinkage and selection operator (LASSO) Cox regression analysis was conducted on the identified DEGs to establish a clinical prognostic gene signature. Using risk scores derived from this signature, patients with cancer were stratified into high-risk and low-risk groups, with further assessment conducted via survival analysis, immune infiltration analysis, and mutation analysis. External validation data were employed to confirm the findings, and Western blot and immunohistochemistry were utilized to validate risk genes for the clinical prognostic gene signature.

Results

A total of 20 prognostic-related DEGs associated with anoikis were obtained. The TCGA dataset revealed two distinct subgroups: cluster 1 and cluster 2. Utilizing the 20 DEGs, a clinical prognostic gene signature comprising two risk genes (CDKN3 and LAMA3) was constructed. Patients with pancreatic adenocarcinoma (PAAD) were classified into high-risk and low-risk groups per their risk scores, with the latter exhibiting a superior survival rate. Statistically significant variation was noted across immune infiltration and mutation levels between the two groups. Validation cohort results were consistent with the initial findings. Additionally, experimental verification confirmed the high expression of CDKN3 and LAMA3 in tumor samples.

Conclusion

Our study addresses the gap in understanding the involvement of genes linked to anoikis in PAAD. The clinical prognostic gene signature developed herein accurately stratifies patients with PAAD, contributing to the advancement of precision medicine for these patients.

A cuproptosis-related lncRNAs signature predicts prognosis and reveals pivotal interactions between immune cells in colon cancer

Copper-mediated cell death presents distinct pathways from established apoptosis processes, suggesting alternative therapeutic approaches for colon cancer. Our research aims to develop a predictive framework utilizing long-noncoding RNAs (lncRNAs) related to cuproptosis to predict colon cancer outcomes while examining immune interactions and intercellular signaling. We obtained colon cancer-related human mRNA expression profiles and clinical information from the Cancer Genome Atlas repository. To isolate lncRNAs involved in cuproptosis, we applied Cox proportional hazards modeling alongside the least absolute shrinkage and selection operator technique. We elucidated the underlying mechanisms by examining the tumor mutational burden, the extent of immune cell penetration, and intercellular communication dynamics. Based on the model, drugs were predicted and validated with cytological experiments. A 13 lncRNA-cuproptosis-associated risk model was constructed. Two colon cancer cell lines were used to validate the predicted representative mRNAs with high correlation coefficients with copper-induced cell death. Survival enhancement in the low-risk cohort was evidenced by the trends in Kaplan-Meier survival estimates. Analysis of immune cell infiltration suggested that survival was induced by the increased infiltration of naïve CD4+ T cells and a reduction of M2 macrophages within the low-risk faction. Decreased infiltration of naïve B cells, resting NK cells, and M0 macrophages was significantly associated with better overall survival. Combined single-cell analysis suggested that CCL5-ACKR1, CCL2-ACKR1, and CCL5-CCR1 pathways play key roles in mediating intercellular dialogues among immune constituents within the neoplastic microhabitat. We identified three drugs with a high sensitivity in the high-risk group. In summary, this discovery establishes the possibility of using 13 cuproptosis-associated lncRNAs as a risk model to assess the prognosis, unravel the immune mechanisms and cell communication, and improve treatment options, which may provide a new idea for treating colon cancer.

A Novel Pancreatic Cancer Hypoxia Status Related Gene Signature for Prognosis and Therapeutic Responses

Pancreatic cancer (PAC) is a highly fatal and aggressive type of cancer. Hypoxia is a common feature of PAC. The aim of this study was to develop a hypoxia status-related prognostic model for predicting the survival outcomes in PAC. The data sets of PAC from The Cancer Genome Atlas and the International Cancer Genome Consortium were used to construct and validate the signature. A 6 hypoxia status-related differential expression genes prognostic model for predicting the survival outcomes was established. The Kaplan-Meier analysis and Received operating characteristic curve indicated the good performance of the signature at predicting overall survival. Univariate and Multivariate Cox regression revealed that the signature was an independent prognostic factor in PAC. Weighted Gene Co-expression Network Analysis and immune infiltration analysis indicated that Immune-related pathways and immune cell infiltration was mostly enriched in the low-risk group, which presented a better prognosis. We also evaluated the predictive of the signature for immunotherapy and chemoradiotherapy. Risk gene LY6D may be a potential prognostic predictor of PAC. This model can be used as an independent prognostic factor for predicting clinical outcomes and a possible classifier for response to chemotherapy.

The Potential Significance of the EMILIN3 Gene in Augmenting the Aggressiveness of Low-Grade Gliomas is Noteworthy

Purpose

Low-grade gliomas (LGG) are common brain tumors with high mortality rates. Cancer cell invasion is a significant factor in tumor metastasis. Novel biomarkers are urgently needed to predict LGG prognosis effectively.

Methods

The data for LGG were obtained from the Bioinformatics database. A consensus clustering analysis was performed to identify molecular subtypes linked with invasion in LGG. Differential expression analysis was performed to identify differentially expressed genes (DEGs) between the identified clusters. Enrichment analyses were then conducted to explore the function for DEGs. Prognostic signatures were placed, and their predictive power was assessed. Furthermore, the invasion-related prognostic signature was validated using the CGGA dataset. Subsequently, clinical specimens were procured in order to validate the expression levels of the distinct genes examined in this research, and to further explore the impact of these genes on the glioma cell line LN229 and HS-683.

Results

Two invasion-related molecular subtypes of LGG were identified, and we sifted 163 DEGs between them. The enrichment analyses indicated that DEGs are mainly related to pattern specification process. Subsequently, 10 signature genes (IGF2BP2, SRY, CHI3L1, IGF2BP3, MEOX2, ABCC3, HOXC4, OTP, METTL7B, and EMILIN3) were sifted out to construct a risk model. Besides, the survival (OS) in the high-risk group was lower. The performance of the risk model was verified. Furthermore, a highly reliable nomogram was generated. Cellular experiments revealed the ability to promote cell viability, value-addedness, migratory ability, invasive ability, and colony-forming ability of the glioma cell line LN229 and HS-683. The qRT-PCR analysis of clinical glioma samples showed that these 10 genes were expressed at higher levels in high-grade gliomas than in low-grade gliomas, suggesting that these genes are associated with poor prognosis of gliomas.

Conclusion

Our study sifted out ten invasion-related biomarkers of LGG, providing a reference for treatments and prognostic prediction in LGG.

Identifying and evaluating a disulfidptosis-related gene signature to predict prognosis in colorectal adenocarcinoma patients

Disulfidptosis, a regulated form of cell death, has been recently reported in cancers characterized by high SLC7A11 expression, including invasive breast carcinoma, lung adenocarcinoma, and hepatocellular carcinoma. However, its role in colon adenocarcinoma (COAD) has been infrequently discussed. In this study, we developed and validated a prognostic model based on 20 disulfidptosis-related genes (DRGs) using LASSO and Cox regression analyses. The robustness and practicality of this model were assessed via a nomogram. Subsequent correlation and enrichment analysis revealed a relationship between the risk score, several critical cancer-related biological processes, immune cell infiltration, and the expression of oncogenes and cell senescence-related genes. POU4F1, a significant component of our model, might function as an oncogene due to its upregulation in COAD tumors and its positive correlation with oncogene expression. In vitro assays demonstrated that POU4F1 knockdown noticeably decreased cell proliferation and migration but increased cell senescence in COAD cells. We further investigated the regulatory role of the DRG in disulfidptosis by culturing cells in a glucose-deprived medium. In summary, our research revealed and confirmed a DRG-based risk prediction model for COAD patients and verified the role of POU4F1 in promoting cell proliferation, migration, and disulfidptosis.

Mitochondrial energy metabolism-related gene signature as a prognostic indicator for pancreatic adenocarcinoma

Background: Pancreatic adenocarcinoma (PAAD) is a highly malignant gastrointestinal tumor and is associated with an unfavorable prognosis worldwide. Considering the effect of mitochondrial metabolism on the prognosis of pancreatic cancer has rarely been investigated, we aimed to establish prognostic gene markers associated with mitochondrial energy metabolism for the prediction of survival probability in patients with PAAD. Methods: Gene expression data were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases, and the mitochondrial energy metabolism-related genes were obtained from the GeneCards database. Based on mitochondrial energy metabolism score (MMs), differentially expressed MMRGs were established for MMs-high and MMs-low groups using ssGSEA. After the univariate Cox and least absolute and selection operator (LASSO) analyses, a prognostic MMRG signature was used in the multivariate Cox proportional regression model. Survival and immune cell infiltration analyses were performed. In addition, a nomogram based on the risk model was used to predict the survival probability of patients with PAAD. Finally, the expression of key genes was verified using quantitative polymerase chain reaction and immunohistochemical staining. Intro cell experiments were performed to evaluated the proliferation and invasion of pancreatic cancer cells. Results: A prognostic signature was constructed consisting of two mitochondrial energy metabolism-related genes (MMP11, COL10A1). Calibration and receiver operating characteristic (ROC) curves verified the good predictability performance of the risk model for the survival rate of patients with PAAD. Finally, immune-related analysis explained the differences in immune status between the two subgroups based on the risk model. The high-risk score group showed higher estimate, immune, and stromal scores, expression of eight checkpoint genes, and infiltration of M0 macrophages, which might indicate a beneficial response to immunotherapy. The qPCR results confirmed high expression of MMP11 in pancreatic cancer cell lines, and IHC also verified high expression of MMP11 in clinical pancreatic ductal adenocarcinoma tissues. In vitro cell experiments also demonstrated the role of MMP11 in cell proliferation and invasion. Conclusion: Our study provides a novel two-prognostic gene signature-based on MMRGs-that accurately predicted the survival of patients with PAAD and could be used for mitochondrial energy metabolism-related therapies in the future.

Construction and validation of a RARRES3-based prognostic signature related to the specific immune microenvironment of pancreatic cancer

Background

Tumor immune microenvironment (TiME) is prognostically instructive in Pancreatic adenocarcinoma (PAAD). However, the potential value of TiME-related genes in the individualized immunotherapy of PAAD has not been clarified.

Methods

Correlation between Immune-Related Genes (IRGs) and immune-related transcription factors (TFs) was performed to prove the immune correlation of selected genes. Immune-related molecular subtypes were identified by consensus clustering. The TiME-score, an immune microenvironment-related prognostic signature for PAAD, was constructed using minimum absolute contraction and selection operator regression (Lasso-Cox). The International Cancer Genome Consortium (ICGC) dataset validated the reliability of TiME-score as external validation. Single-cell samples from GSE197177 confirmed microenvironment differences of TiME-score hub genes between tumor and its paracancer tissues. Then, RARRES3, a hub gene in TiME-score, was further analyzed about its upstream TP53 mutation and the specific immune landscape of itself in transcriptome and Single-cell level. Eventually, TiME-score were validated in different therapeutic cohorts of PAAD mice models.

Results

A 14-genes PAAD immune-related risk signature, TiME-score, was constructed based on IRGs. The differences of TiME-score hub genes in single-cell samples of PAAD cancer tissues and adjacent tissues were consistent with the transcriptome. Single-cell samples of cancer tissues showed more pronounced immune cell infiltration. The upstream mutation factor TP53 of RARRES3 was significantly enriched in immune-related biological processes. High RARRES3 expression was correlated with a worse prognosis and high macrophages M1 infiltration. Additionally, the immunohistochemistry of hub genes AGT, DEFB1, GH1, IL20RB, and TRAF3 in different treatment cohorts of mice PAAD models were consistent with the predicted results. The combination of immunotherapy, chemotherapy and targeted therapy has shown significantly better therapeutic effects than single drug therapy in PAAD.

Conclusion

TiME-score, as a prognostic signature related to PAAD-specific immune microenvironment constructed based on RARRES3, has predictive value for prognosis and the potential to guide individualized immunotherapy for PAAD patients.

Netrin-1 feedforward mechanism promotes pancreatic cancer liver metastasis via hepatic stellate cell activation, retinoid, and ELF3 signaling

The biology of metastatic pancreatic ductal adenocarcinoma (PDAC) is distinct from that of the primary tumor due to changes in cell plasticity governed by a distinct transcriptome. Therapeutic strategies that target this distinct biology are needed. We detect an upregulation of the neuronal axon guidance molecule Netrin-1 in PDAC liver metastases that signals through its dependence receptor (DR), uncoordinated-5b (Unc5b), to facilitate metastasis in vitro and in vivo. The mechanism of Netrin-1 induction involves a feedforward loop whereby Netrin-1 on the surface of PDAC-secreted extracellular vesicles prepares the metastatic niche by inducing hepatic stellate cell activation and retinoic acid secretion that in turn upregulates Netrin-1 in disseminated tumor cells via RAR/RXR and Elf3 signaling. While this mechanism promotes PDAC liver metastasis, it also identifies a therapeutic vulnerability, as it can be targeted using anti-Netrin-1 therapy to inhibit metastasis using the Unc5b DR cell death mechanism.

Genome, Metabolism, or Immunity: Which Is the Primary Decider of Pancreatic Cancer Fate through Non-Apoptotic Cell Death?

Pancreatic ductal adenocarcinoma (PDAC) is a solid tumor characterized by poor prognosis and resistance to treatment. Resistance to apoptosis, a cell death process, and anti-apoptotic mechanisms, are some of the hallmarks of cancer. Exploring non-apoptotic cell death mechanisms provides an opportunity to overcome apoptosis resistance in PDAC. Several recent studies evaluated ferroptosis, necroptosis, and pyroptosis as the non-apoptotic cell death processes in PDAC that play a crucial role in the prognosis and treatment of this disease. Ferroptosis, necroptosis, and pyroptosis play a crucial role in PDAC development via several signaling pathways, gene expression, and immunity regulation. This review summarizes the current understanding of how ferroptosis, necroptosis, and pyroptosis interact with signaling pathways, the genome, the immune system, the metabolism, and other factors in the prognosis and treatment of PDAC.

A prospective prognostic signature for pancreatic adenocarcinoma based on ubiquitination-related mRNA-lncRNA with experimental validation in vitro and vivo

Ubiquitination-related genes (URGs) exerted a crucial part in a variety of human disease disorders; however, their association with pancreatic adenocarcinoma (PAAD) had yet to be clearly described. We aimed to comprehensively characterize the contributions of URGs in PAAD through in silico analysis and experimental validation, and then identified a robust mRNA-lncRNA-based molecular prognostic panel for patients with PAAD using bulk RNA-sequencing and single-cell RNA-sequencing data. Initially, we collected the multi-omics data from TCGA platform to depict a comprehensive landscape of URGs in pan-cancer. Furthermore, we were accurate to PAAD for in-depth analysis. Significant differences of the activation of ubiquitination pathways and the expression of URGs were detected between normal and malignant cells. Unsupervised hierarchical clustering determined two PAAD subtypes with distinct clinical outcomes, ubiquitination pathway activities, immune microenvironment, and functional annotation characteristics. The expression profiles of ubiquitination-associated mRNAs and lncRNAs in the training and validation datasets were utilized to develop and verify a novel ubiquitination-related mRNA-lncRNA prognostic panel, which had a satisfied prediction efficiency. Our ubiquitination-associated model could function as an effective prognostic index and outperformed four other recognized panels in evaluating PAAD patients' survival status. Tumor immune microenvironment, mutation burden, and chemotherapy response were intensively explored to demonstrate the underlying mechanism of prognostic difference according to our panel. Our findings also revealed that FTI-277, a farnesyltransferase inhibitor, had a better curative effect in high-risk patients, while MK-2206, an Akt allosteric inhibitor, had a superior therapeutic effect in low-risk patients. The real-time PCR results uncovered the RNA expression of AC005062.1 in all the three PAAD cell lines was elevated several thousandfold. In conclusion, our URGs-based classification panel could be triumphantly served as a prediction tool for survival evaluation in patients with PAAD, and the genes in this panel could be developed as a potential target in PAAD therapy.

Immune gene signatures as prognostic criteria for cancer patients

Recently, the possibility of using immune gene signatures (IGSs) has been considered as a novel prognostic tool for numerous cancer types. State-of-the-art methods of genomic, transcriptomic, and protein analysis have allowed the identification of a number of immune signatures correlated to disease outcome. The major adaptive and innate immune components are the T lymphocytes and macrophages, respectively. Herein, we collected essential data on IGSs consisting of subsets of T cells and tumor-associated macrophages and indicating cancer patient outcomes. We discuss factors that can introduce errors in the recognition of immune cell types and explain why the significance of immune signatures can be interpreted with uncertainty. The unidirectional functions of cell types should be entirely addressed in the signatures constructed by the combination of innate and adaptive immune cells. The state of the antitumor immune response is the key basis for IGSs and should be considered in gene signature construction. We also analyzed immune signatures for the prediction of immunotherapy response. Finally, we attempted to explain the present-day limitations in the use of immune signatures as robust criteria for prognosis.

Telomere-related prognostic biomarkers for survival assessments in pancreatic cancer

Human telomeres are linked to genetic instability and a higher risk of developing cancer. Therefore, to improve the dismal prognosis of pancreatic cancer patients, a thorough investigation of the association between telomere-related genes and pancreatic cancer is required. Combat from the R package "SVA" was performed to correct the batch effects between the TCGA-PAAD and GTEx datasets. After differentially expressed genes (DEGs) were assessed, we constructed a prognostic risk model through univariate Cox regression, LASSO-Cox regression, and multivariate Cox regression analysis. Data from the ICGC, GSE62452, GSE71729, and GSE78229 cohorts were used as test cohorts for validating the prognostic signature. The major impact of the signature on the tumor microenvironment and its response to immune checkpoint drugs was also evaluated. Finally, PAAD tissue microarrays were fabricated and immunohistochemistry was performed to explore the expression of this signature in clinical samples. After calculating 502 telomere-associated DEGs, we constructed a three-gene prognostic signature (DSG2, LDHA, and RACGAP1) that can be effectively applied to the prognostic classification of pancreatic cancer patients in multiple datasets, including TCGA, ICGC, GSE62452, GSE71729, and GSE78229 cohorts. In addition, we have screened a variety of tumor-sensitive drugs targeting this signature. Finally, we also found that protein levels of DSG2, LDHA, and RACGAP1 were upregulated in pancreatic cancer tissues compared to normal tissues by immunohistochemistry analysis. We established and validated a telomere gene-related prognostic signature for pancreatic cancer and confirmed the upregulation of DSG2, LDHA, and RACGAP1 expression in clinical samples, which may provide new ideas for individualized immunotherapy.

The biomarkers related to immune infiltration to predict distant metastasis in breast cancer patients

Background: The development of distant metastasis (DM) results in poor prognosis of breast cancer (BC) patients, however, it is difficult to predict the risk of distant metastasis. Methods: Differentially expressed genes (DEGs) were screened out using GSE184717 and GSE183947. GSE20685 were randomly assigned to the training and the internal validation cohort. A signature was developed according to the results of univariate and multivariate Cox regression analysis, which was validated by using internal and external (GSE6532) validation cohort. Gene set enrichment analysis (GSEA) was used for functional analysis. Finally, a nomogram was constructed and calibration curves and concordance index (C-index) were compiled to determine predictive and discriminatory capacity. The clinical benefit of this nomogram was revealed by decision curve analysis (DCA). Finally, we explored the relationships between candidate genes and immune cell infiltration, and the possible mechanism. Results: A signature containing CD74 and TSPAN7 was developed according to the results of univariate and multivariate Cox regression analysis, which was validated by using internal and external (GSE6532) validation cohort. Mechanistically, the signature reflect the overall level of immune infiltration in tissues, especially myeloid immune cells. The expression of CD74 and TSPAN7 is heterogeneous, and the overexpression is positively correlated with the infiltration of myeloid immune cells. CD74 is mainly derived from myeloid immune cells and do not affect the proportion of CD8+T cells. Low expression levels of TSPAN7 is mainly caused by methylation modification in BC cells. This signature could act as an independent predictive factor in patients with BC (p = 0.01, HR = 0.63), and it has been validated in internal (p = 0.023, HR = 0.58) and external (p = 0.0065, HR = 0.67) cohort. Finally, we constructed an individualized prediction nomogram based on our signature. The model showed good discrimination in training, internal and external cohort, with a C-index of 0.742, 0.801, 0.695 respectively, and good calibration. DCA demonstrated that the prediction nomogram was clinically useful. Conclusion: A new immune infiltration related signature developed for predicting metastatic risk will improve the treatment and management of BC patients.

Construction of a metabolism-related gene prognostic model to predict survival of pancreatic cancer patients

Pancreatic cancer (PC) is one of the most fatal malignant tumors, and is commonly diagnosed at an advanced stage with no effective therapy. Metabolism-related genes (MRGs) and immune-related genes (IRGs) play considerable roles in the tumor microenvironment. Therefore, an effective prediction model based on MRGs and IRGs could aid in the prognosis of PC. In this study, differential expression analysis was performed to gain 25 intersectional genes from 857 differentially expressed MRGs (DEMRGs), and 1353 differentially expressed IRGs, from The Cancer Genome Atlas database of PC. Cox and Lasso regression were applied and a five-DEMRGs prognostic model constructed. Survival analysis, ROC values, risk curve and validation analysis showed that the model could independently predict PC prognosis. In addition, the correlation analysis suggested that the five-DEMRGs prognostic model could reflect the status of the immune microenvironment, including Tregs, M1 macrophages and Mast cell resting. Therefore, our study provides new underlying predictive biomarkers and associated immunotherapy targets.

Construction and validation of a novel apoptosis-associated prognostic signature related to osteosarcoma metastasis and immune infiltration

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Osteosarcoma is one of the most aggressive diseases which often develops metastasis.

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Apoptosis relates to the recurrence and metastasis of osteosarcoma and the related signature could predict the prognosis of patients.

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A novel apoptosis-associated prognosis signature related to osteosarcoma metastasis and immune infiltration has been developed.

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The signature could help to predict the prognosis of osteosarcoma patients and serve as the potential targets for anti-cancer treatment.

Background

Apoptosis played vital roles in the formation and progression of osteosarcoma. However, no studies elucidated the prognostic relationships between apoptosis-associated genes (AAGs) and osteosarcoma.

Methods

The differentially expressed genes associated with osteosarcoma metastasis and apoptosis were identified from GEO and MSigDB databases. The apoptosis-associated prognostic signature was established through univariate and multivariate cox regression analyses. The Kaplan–Meier (KM) survival curve, ROC curve and nomogram were constructed to investigate the predictive value of this signature. CIBERSORT algorithm and ssGSEA were used to explore the relationships between immune infiltration and AAG signature. The above results were validated in another GEO dataset and the expression of AAGs was also validated in osteosarcoma patient samples by immunohistochemistry.

Results

HSPB1 and IER3 were involved in AAG signature. In training and validation datasets, apoptosis-associated risk scores were negatively related to patient survival rates and the AAG signature was regarded as the independent prognostic factor. ROC and calibration curves demonstrated the signature and nomogram were reliable. GSEA revealed the signature related to immune-associated pathways. ssGSEA indicated that one immune cell and three immune functions were significantly dysregulated. The immunohistochemistry analyses of patients’ samples revealed that AAGs were significantly differently expressed between metastasis and non-metastasis osteosarcomas.

Conclusions

The present study identified and validated a novel apoptosis-associated prognostic signature related to osteosarcoma metastasis. It could serve as the potential biomarker and therapeutic targets for osteosarcoma in the future.

The expression of m6A regulators correlated with the immune microenvironment plays an important role in the prognosis of pancreatic ductal adenocarcinoma

BACKGROUND

The relationship between N6-methyladenosine (m6A) RNA methylation regulators and the tumor immune microenvironment has been extensively studied. Nevertheless, the potential function of m6A regulators in the tumor immune landscape of pancreatic ductal adenocarcinoma (PDAC) remains to be fully elucidated.

METHODS

Here, we systematically evaluated the expression of 19 m6A regulators in PDAC patients from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database. Utilizing consensus clustering, the PDAC patients were segmented into two subgroups according to the expression of 19 m6A regulators. A prognostic risk signature of 5 m6A methylation regulators (ALKBH5, IGF2BP2, IGF2BP3, LRPPRC, and KIAA1429) was then built, and the PDAC patients were divided into high-risk and low-risk groups. Subsequently, differences in independent prognostic parameters, risk score distribution, survival, and cluster analysis between high-risk and low-risk groups were analyzed.

RESULTS

We found two subgroups with dramatically different immune landscapes and prognoses. Subsequently, differences in independent prognostic parameters, risk score distribution, survival, and cluster analysis between the high-risk and low-risk groups were found. Moreover, these gene signatures displayed good discriminative performances in the GEO datasets. We also found that the risk score was positively correlated with the tumor mutation burden (TMB), and the TMB value was higher in the high-risk scoring group. The low-risk scoring group was linked by a stronger response to anti-programmed cell death ligand 1 (anti-PD-L1) immunotherapy and clinical advantages in the immunotherapeutic advanced urothelial cancer (IMvigor210) cohort. Ultimately, we found that these 5 m6A regulators had a fatal regulatory role on the tumor immune microenvironment in PDAC patients.

CONCLUSIONS

The construction signature based on the m6A regulators may be crucial regulators of the tumor immune microenvironment in PDAC, providing a new approach to improving the immunotherapy strategy for PDAC patients.

Identification of a 3-gene signature based on differentially expressed invasion genes related to cancer molecular subtypes to predict the prognosis of osteosarcoma patients

Invasion is a critical pathway leading to tumor metastasis. This study constructed an invasion-related polygenic signature to predict osteosarcoma prognosis. We initially determined two molecular subtypes of osteosarcoma, Cluster1 (C1) and Cluster2 (C2).. A 3 invasive-gene signature was established by univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) Cox regression analysis of the differentially expressed genes (DEGs) between the two subtypes, and was validated in internal and two external data sets (GSE21257 and GSE39058). Patients were divided into high- and low-risk groups by their signature, and the prognosis of osteosarcoma patients in the high-risk group was poor. Based on the time-independent receiver operating characteristic (ROC) curve, the area under the curve (AUC) for 1-year and 2-year OS were higher than 0.75 in internal and external cohorts. This signature also showed a high accuracy and independence in predicting osteosarcoma prognosis and a higher AUC in predicting 1-year osteosarcoma survival than other four existing models. In a word, a 3 invasive gene-based signature was developed, showing a high performance in predicting osteosarcoma prognosis. This signature could facilitate clinical prognostic analysis of osteosarcoma.

A Novel Ferroptosis-Related Gene Signature Predicts Recurrence in Patients With Pancreatic Ductal Adenocarcinoma

Background: Recurrence after surgery is largely responsible for the extremely poor outcomes for patients with pancreatic ductal adenocarcinoma (PDAC). Ferroptosis is implicated in chemotherapy sensitivity and tumor recurrence, we aimed to find out survival-associated ferroptosis-related genes and use them to build a practical risk model with the purpose to predict PDAC recurrence. Methods: Univariate Cox regression analysis was conducted to obtain prognostic ferroptosis-related genes in The Cancer Genome Atlas (TCGA, N = 140) cohort. Multivariate Cox regression analysis was employed to construct a reliable and credible gene signature. The prognostic performance was verified in a MTAB-6134 (N = 286) validation cohort and a PACA-CA (N = 181) validation cohort. The stability of the signature was tested in TCGA and MTAB-6134 cohorts by ROC analyses. Pathway enrichment analysis was adopted to preliminary illuminate the biological relevance of the gene signature. Results: Univariate and multivariate Cox regression analyses identified a 5-gene signature that contained CAV1, DDIT4, SLC40A1, SRXN1 and TFAP2C. The signature could efficaciously stratify PDAC patients with different recurrence-free survival (RFS), both in the training and validation cohorts. Results of subgroup receiver operating characteristic curve (ROC) analyses confirmed the stability and the independence of this signature. Our signature outperformed clinical indicators and previous reported models in predicting RFS. Moreover, the signature was found to be closely associated with several cancer-related and drug response pathways. Conclusion: This study developed a precise and concise prognostic model with the clinical implication in predicting PDAC recurrence. These findings may facilitate individual management of postoperative recurrence in patients with PDAC.

Comprehensive Analysis of Glycolysis-Related Genes for Prognosis, Immune Features, and Candidate Drug Development in Colon Cancer

The dysregulated expression of glycolysis-related genes (GRGs) is closely related to the occurrence of diverse tumors and regarded as a novel target of tumor therapy. However, the role of GRGs in colon cancer is unclear. We obtained 226 differential GRGs (DE-GRGs) from The Cancer Genome Atlas (TCGA) database. Cox regression analysis was used to construct a DE-GRG prognostic model, including P4HA1, PMM2, PGM2, PPARGC1A, PPP2CB, STC2, ENO3, and CHPF2. The model could accurately predict the overall survival rate of TCGA and GSE17536 patient cohorts. The risk score of the model was closely related to a variety of clinical traits and was an independent risk factor for prognosis. Enrichment analysis revealed the activation of a variety of glycolysis metabolism and immune-related signaling pathways in the high-risk group. High-risk patients displayed low expression of CD4+ memory resting T cells and resting dendritic cells and high expression of macrophages M0 compared with the expression levels in the low-risk patients. Furthermore, patients in the high-risk group had a higher tumor mutation load and tumor stem cell index and were less sensitive to a variety of chemotherapeutic drugs. Quantitative reverse transcription polymerase chain reaction and immunohistochemistry analyses validated the expression of eight GRGs in 43 paired clinical samples. This is the first multi-omics study on the GRGs of colon cancer. The establishment of the risk model may benefit the prognosis and drug treatment of patients.

A Novel Five-Gene Signature for Prognosis Prediction in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) has been a global health issue and attracted wide attention due to its high incidence and poor outcomes. In this study, our purpose was to explore an effective prognostic marker for HCC. Five cohort profile datasets from GEO (GSE25097, GSE36376, GSE62232, GSE76427 and GSE101685) were integrated with TCGA-LIHC and GTEx dataset to identify differentially expressed genes (DEGs) between normal and cancer tissues in HCC patients, then 5 upregulated differentially expressed genes and 32 downregulated DEGs were identified as common DEGs in total. Next, we systematically explored the relationship between the expression of 37 common DEGs in tumor tissues and overall survival (OS) rate of HCC patients in TCGA and constructed a novel prognostic model composed of five genes (AURKA, PZP, RACGAP1, ACOT12 and LCAT). Furthermore, the predicted performance of the five-gene signature was verified in ICGC and another independent clinical samples cohort, and the results demonstrated that the signature performed well in predicting the OS rate of patients with HCC. What is more, the signature was an independent hazard factor for HCC patients when considering other clinical factors in the three cohorts. Finally, we found the signature was significantly associated with HCC immune microenvironment. In conclusion, the prognostic five-gene signature identified in our present study could efficiently classify patients with HCC into subgroups with low and high risk of longer overall survival time and help clinicians make decisions for individualized treatment.

Current Perspectives on the Role of Matrix Metalloproteinases in the Pathogenesis of Basal Cell Carcinoma

Basal cell carcinoma (BCC) is the most common skin malignancy, which rarely metastasizes but has a great ability to infiltrate and invade the surrounding tissues. One of the molecular players involved in the metastatic process are matrix metalloproteinases (MMPs). MMPs are enzymes that can degrade various components of the extracellular matrix. In the skin, the expression of MMPs is increased in response to various stimuli, including ultraviolet (UV) radiation, one of the main factors involved in the development of BCC. By modulating various processes that are linked to tumor growth, such as invasion and angiogenesis, MMPs have been associated with UV-related carcinogenesis. The sources of MMPs are multiple, as they can be released by both neoplastic and tumor microenvironment cells. Inhibiting the action of MMPs could be a useful therapeutic option in BCC management. In this review that reunites the latest advances in this domain, we discuss the role of MMPs in the pathogenesis and evolution of BCC, as molecules involved in tumor aggressiveness and risk of recurrence, in order to offer a fresh and updated perspective on this field.

Prognostic Significance of KIF11 and KIF14 Expression in Pancreatic Adenocarcinoma

Simple Summary

Prognostic markers for survival stratification of patients with pancreatic adenocarcinoma (PAC) are missing yet. Therefore, the primary aim of this study was to assess the expression, clinical associations, and survival implications of KIF11 and KIF14 in PACs. In addition, the genes co-expressed with KIF11 or KIF14 were predicted and functionally annotated. Herein, we found that the expression patterns of KIF11 and KIF14 alter significantly in PACs, at both protein and mRNA levels, and this may be harnessed for patient prognosis. KIF11 and KIF14 could be defined as positive prognostic biomarkers based on the protein-based immunohistochemistry data, while they were associated with adverse prognosis based on the transcriptomic data. We also captured a five-gene prognostic signature and the biology associated with it. The findings of the present study suggest that KIF11 or KIF14 proteins, as well as a new five-gene panel, may serve as potentially useful prognostic biomarkers for PAC.

Abstract

Available biomarkers for pancreatic adenocarcinoma (PAC) are inadequate to guide individual patient prognosis or therapy. Therefore, herein we aimed to verify the hypothesis that differences in the expression of KIF11 and KIF14, i.e., molecular motor proteins being primarily implicated in cell division events could account for the differences in the clinical outcome of PAC patients. In-house immunohistochemistry was used to evaluate the protein expressions of KIF11 and KIF14 in PAC, whereas RNA-seq datasets providing transcript expression data were obtained from public sources. IHC and mRNA results were correlated with clinicopathological features and overall survival (OS). Furthermore, the genes co-expressed with KIF11 or KIF14 were predicted and functionally annotated. In our series, malignant ducts displayed more intense but less abundant KIF11 staining than normal-appearing ducts. The former was also true for KIF14, whereas the prevalence of positive staining was similar in tumor and normal adjacent tissues. Based on categorical immunoreactive scores, we found KIF11 and KIF14 to be frequently downregulated or upregulated in PAC cases, respectively, and those with elevated levels of either protein, or both together, were associated with better prognosis. Specifically, we provide the first evidence that KIF11 or KIF14 proteins can robustly discriminate between patients with better and worse OS, independently of other relevant clinical risk factors. In turn, mRNA levels of KIF11 and KIF14 were markedly elevated in tumor tissues compared to normal tissues, and this coincided with adverse prognosis, even after adjusting for multiple confounders. Tumors with low predicted KIF11 or KIF14 expression were seen to have enrichment for circadian clock, whereas those with high levels were enriched for the genomic instability-related gene set. KIF11 and KIF14 were strongly correlated with one another, and CEP55, ASPM, and GAMT were identified as the main hub genes. Importantly, the combined expression of these five genes emerged as the most powerful independent prognostic indicator associated with poor survival outcome compared to classical clinicopathological factors and any marker alone. In conclusion, our study identifies novel prognostic biomarkers for PAC, which await validation.

Gene expression of cytokinesis regulators PRC1, KIF14 and CIT has no prognostic role in colorectal and pancreatic cancer

Colorectal cancer is one of the most common cancers and pancreatic cancer is among the most fatal and difficult to treat. New prognostic biomarkers are urgently needed to improve the treatment of colorectal and pancreatic cancer. Protein regulating cytokinesis 1 (PRC1), kinesin family member 14 (KIF14) and citron Rho-interacting serine/threonine kinase (CIT) serve important roles in cytokinesis, are strongly associated with cancer progression and have prognostic potential. The present study aimed to investigate the prognostic relevance of the PRC1, KIF14 and CIT genes in colorectal and pancreatic cancer. PRC1, KIF14 and CIT transcript expression was assessed by reverse transcription-quantitative PCR in tumors and paired distant unaffected mucosa from 67 patients with colorectal cancer and tumors and paired non-neoplastic control tissues from 48 patients with pancreatic cancer. The extent of transcript dysregulation between tumor and control tissues and between groups of patients divided by main clinical characteristics, namely patients' age and sex, disease stage, localization and grade, was determined. Finally, the associations of transcript levels in tumors with disease-free interval and overall survival time were evaluated. PRC1, KIF14 and CIT transcripts were upregulated in tumors compared with control tissues. PRC1, KIF14 and CIT levels strongly correlated to each other in both colorectal and pancreatic tumor and control tissues after correction for multiple testing. However, no significant associations were found among the transcript levels of PRC1, KIF14 and CIT and disease-free interval or overall survival time. In summary, the present study demonstrated mutual correlation of PRC1, KIF14 and CIT cytokinesis regulators with no clear prognostic value in pancreatic and colorectal cancers. Hence, according to the results of the present study, transcript levels of these genes cannot be clinically exploited as prognostic biomarkers in colorectal or pancreatic cancer patients.

Tumor DDR1 deficiency reduces liver metastasis by colon carcinoma and impairs stromal reaction

Tumor DDR1 acts as a key factor during the desmoplastic response surrounding hepatic colorectal metastasis. Hepatic sinusoidal cell-derived soluble factors stimulate tumor DDR1 activation. DDR1 modulates matrix remodeling to promote metastasis in the liver through the interaction with hepatic stromal cells, specifically liver sinusoidal endothelial cells and hepatic stellate cells.

Near-infrared photoimmunotherapy targeting GPR87: Development of a humanised anti-GPR87 mAb and therapeutic efficacy on a lung cancer mouse model

BACKGROUND

GPR87 is a G-protein receptor that is specifically expressed in tumour cells, such as lung cancer, and rarely expressed in normal cells. GPR87 is a promising target for cancer therapy, but its ligand is controversial. Near-infrared photoimmunotherapy (NIR-PIT) is a novel cancer therapy in which a photosensitiser, IRDye700DX (IR700), binds to antibodies and specifically destroys target cells by irradiating them with near-infrared-light. Here, we aimed to develop a NIR-PIT targeting GPR87.

METHODS

We evaluated the expression of GPR87 in resected specimens of lung cancer and malignant pleural mesothelioma (MPM) resected at Nagoya University Hospital using immunostaining. Humanised anti-GPR87 antibody (huGPR87) was generated by introducing CDRs from mouse anti-GPR87 antibody generated by standard hybridoma method. HuGPR87 was conjugated with IR700 and the therapeutic effect of NIR-PIT was evaluated in vitro and in vivo using lung cancer or MPM cell lines.

FINDINGS

Among the surgical specimens, 54% of lung cancer and 100% of MPM showed high expression of GPR87. It showed therapeutic effects on lung cancer and MPM cell lines in vitro, and showed therapeutic effects in multiple models in vivo.

INTERPRETATION

These results suggest that NIR-PIT targeting GPR87 is a promising therapeutic approach for the treatment of thoracic cancer.

FUNDING

This research was supported by the Program for Developing Next-generation Researchers (Japan Science and Technology Agency), KAKEN (18K15923, 21K07217, JSPS), FOREST-Souhatsu, CREST (JST).

Three Immune-Related Prognostic mRNAs as Therapeutic Targets for Pancreatic Cancer

Objective: Pancreatic cancer is a highly lethal malignancy globally. This study aimed to probe and validate immune-related prognostic mRNAs as therapeutic targets for pancreatic cancer.

Methods: Gene transcriptome data of pancreatic cancer and normal pancreas were retrieved from TCGA-GTEx projects. Two thousand four hundred and ninety-eight immune-related genes were obtained from the IMMUPORT database. Abnormally expressed immune-related genes were then identified. Under univariate and multivariate cox models, a gene signature was constructed. Its predictive efficacy was assessed via ROCs. The interactions between the 21 genes were analyzed by Spearson analysis and PPI network. Using the GEPIA and The Human Protein Atlas databases, their expression and prognostic value were evaluated. The TIMER database was utilized to determine the relationships between MET, OAS1, and OASL mRNAs and immune infiltrates. Finally, their mRNA expression was externally verified in the GSE15471 and GSE62452 datasets.

Results: An immune-related 21-gene signature was developed for predicting patients' prognosis. Following verification, this signature exhibited the well predictive performance. There were physical and functional interactions between them. MET, OAS1, and OASL mRNAs were all up-regulated in pancreatic cancer and associated with unfavorable prognosis. They showed strong correlations with tumor progression. Furthermore, the three mRNAs were distinctly associated with immune infiltrates. Their up-regulation was confirmed in the two external datasets.

Conclusion: These findings identified three immune-related prognostic mRNAs MET, OAS1, and OASL, which may assist clinicians to choose targets for immunotherapy and make personalized treatment strategy for pancreatic cancer patients.

A novel tp53-associated nomogram to predict the overall survival in patients with pancreatic cancer

BACKGROUND

Gene mutations play critical roles in tumorigenesis and cancer development. Our study aimed to screen survival-related mutations and explore a novel gene signature to predict the overall survival in pancreatic cancer.

METHODS

Somatic mutation data from three cohorts were used to identify the common survival-related gene mutation with Kaplan-Meier curves. RNA-sequencing data were used to explore the signature for survival prediction. First, Weighted Gene Co-expression Network Analysis was conducted to identify candidate genes. Then, the ICGC-PACA-CA cohort was applied as the training set and the TCGA-PAAD cohort was used as the external validation set. A TP53-associated signature calculating the risk score of every patient was developed with univariate Cox, least absolute shrinkage and selection operator, and stepwise regression analysis. Kaplan-Meier and receiver operating characteristic curves were plotted to verify the accuracy. The independence of the signature was confirmed by the multivariate Cox regression analysis. Finally, a prognostic nomogram including 359 patients was constructed based on the combined expression data and the risk scores.

RESULTS

TP53 mutation was screened to be the robust and survival-related mutation type, and was associated with immune cell infiltration. Two thousand, four hundred fifty-five genes included in the six modules generated in the WGCNA were screened as candidate survival related TP53-associated genes. A seven-gene signature was constructed: Risk score = (0.1254 × ERRFI1) - (0.1365 × IL6R) - (0.4400 × PPP1R10) - (0.3397 × PTOV1-AS2) + (0.1544 × SCEL) - (0.4412 × SSX2IP) - (0.2231 × TXNL4A). Area Under Curves of 1-, 3-, and 5-year ROC curves were 0.731, 0.808, and 0.873 in the training set and 0.703, 0.677, and 0.737 in the validation set. A prognostic nomogram including 359 patients was constructed and well-calibrated, with the Area Under Curves of 1-, 3-, and 5-year ROC curves as 0.713, 0.753, and 0.823.

CONCLUSIONS

The TP53-associated signature exhibited good prognostic efficacy in predicting the overall survival of PC patients.

Development and Validation of a Hypoxia-Associated Prognostic Signature Related to Osteosarcoma Metastasis and Immune Infiltration

Background

Increasing evidence has shown that hypoxia microenvironment relates to tumor initiation and progression. However, no studies focus on the application of hypoxia-associated genes in predicting osteosarcoma patients’ prognosis. This research aims to identify the hypoxia-associated genes related to osteosarcoma metastasis and construct a gene signature to predict osteosarcoma prognosis.

Methods

The differentially expressed messenger RNAs (DEmRNAs) related to osteosarcoma metastasis were identified from Therapeutically Applicable Research to Generate Effective Treatments (Target) database. Univariate and multivariate cox regression analyses were performed to develop the hypoxia-associated prognostic signature. The Kaplan–Meier (KM) survival analyses of patients with high and low hypoxia risk scores were conducted. The nomogram was constructed and the gene signature was validated in the external Gene Expression Omnibus (GEO) cohort. Single-sample gene set enrichment analysis (ssGSEA) was conducted to investigate the relationships between immune infiltration and gene signature.

Results

Two genes, including decorin (DCN) and prolyl 4-hydroxylase subunit alpha 1 (P4HA1), were involved in the hypoxia-associated gene signature. In training and testing datasets, patients with high-risk scores showed lower survival rates and the gene signature was identified as the independent prognostic factor. Receiver operating characteristic (ROC) curves demonstrated the robustness of signature. Functional analyses of DEmRNAs among high- and low-risk groups revealed that immune-associated functions and pathways were significantly enriched. Furthermore, ssGSEA showed that five immune cells (DCs, macrophages, neutrophils, pDCs, and TIL) and three immune features (CCR, APC co inhibition, and Check-point) were down-regulated in the high-risk group.

Conclusion

The current study established and validated a novel hypoxia-associated gene signature in osteosarcoma. It could act as a prognostic biomarker and serve as therapeutic guidance in clinical applications.