Development and Validation of a 7-Gene Prognostic Signature to Improve Survival Prediction in Pancreatic Ductal Adenocarcinoma

Identification of Cuproptosis-Associated Prognostic Gene Expression Signatures from 20 Tumor Types

We investigated the mRNA expression of 124 cuproptosis-associated genes in 7489 biopsies from 20 different tumor types of The Cancer Genome Atlas (TCGA). The KM plotter algorithm has been used to calculate Kaplan-Meier statistics and false discovery rate (FDR) corrections. Interaction networks have been generated using Ingenuity Pathway Analysis (IPA). High mRNA expression of 63 out of 124 genes significantly correlated with shorter survival times of cancer patients across all 20 tumor types. IPA analyses revealed that their gene products were interconnected in canonical pathways (e.g., cancer, cell death, cell cycle, cell signaling). Four tumor entities showed a higher accumulation of genes than the other cancer types, i.e., renal clear cell carcinoma (n = 21), renal papillary carcinoma (n = 13), kidney hepatocellular carcinoma (n = 13), and lung adenocarcinoma (n = 9). These gene clusters may serve as prognostic signatures for patient survival. These signatures were also of prognostic value for tumors with high mutational rates and neoantigen loads. Cuproptosis is of prognostic significance for the survival of cancer patients. The identification of specific gene signatures deserves further exploration for their clinical utility in routine diagnostics.

DDX21 functions as a potential novel oncopromoter in pancreatic ductal adenocarcinoma: a comprehensive analysis of the DExD box family

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal tumor with an ill-defined pathogenesis. DExD box (DDX) family genes are widely distributed and involved in various RNA metabolism and cellular biogenesis; their dysregulation is associated with aberrant cellular processes and malignancies. However, the prognostic significance and expression patterns of the DDX family in PDAC are not fully understood. The present study aimed to explore the clinical value of DDX genes in PDAC.

METHODS

Differentially expressed DDX genes were identified. DDX genes related to prognostic signatures were further investigated using LASSO Cox regression analysis. DDX21 protein expression was analyzed using the UALCAN and human protein atlas (HPA) online tools and confirmed in 40 paired PDAC and normal tissues through Tissue Microarrays (TMA). The independent prognostic significance of DDX21 in PDAC was determined through the construction of nomogram models and calibration curves. The functional roles of DDX21 were investigated using gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA). Cell proliferation, invasion, and migration were assessed using Cell Counting Kit-8, colony formation, Transwell, and wound healing assays.

RESULTS

Upregulation of genes related to prognostic signatures (DDX10, DDX21, DDX60, and DDX60L) was significantly associated with poor prognosis of patients with PDAC based on survival and recurrence time. Considering the expression profile and prognostic values of the signature-related genes, DDX21 was finally selected for further exploration. DDX21 was overexpressed significantly at both the mRNA and protein levels in PDAC compared to normal pancreatic tissues. DDX21 expression, pathological stage, and residual tumor were significant independent prognostic indicators in PDAC. Moreover, functional enrichment analysis revealed that Genes co-expressed with DDX21 are predominantly involved in RNA metabolism, helicase activity, ribosome biogenesis, cell cycle, and various cancer-related pathways, such as PI3K/Akt signaling pathway and TGF-β signaling pathway. Furthermore, in vitro experiments confirmed that the knockdown of DDX21 significantly reduced MIA PaCa-2 cell viability, proliferation, migration, and invasion.

CONCLUSIONS

Four signature-related genes could relatively precisely predict the prognosis of patients with PDAC. Specifically, DDX21 upregulation may signal an unfavorable prognosis by negatively affecting the biological properties of PDAC cells. DDX21 may be considered as a candidate therapeutic target in PDAC.

Sense and anti-sense: Role of FAM83A and FAM83A-AS1 in Wnt, EGFR, PI3K, EMT pathways and tumor progression

An increasing number of studies have shown that FAM83A, a member of the family with sequence similarity 83 (FAM83), which consists of eight members, is a key tumor therapeutic target involved in multiple signaling pathways. It has been reported that FAM83A plays essential roles in the regulation of Wnt/β-catenin, EGFR, MAPK, EMT, and other signaling pathways and physiological processes in models of pancreatic cancer, lung cancer, breast cancer, and other malignant tumors. Moreover, the expression of FAM83A could be significantly affected by multiple noncoding RNAs that are dysregulated in malignant tumors, the dysregulation of which is essential for the malignant process. Among these noncoding RNAs, the most noteworthy is the antisense long noncoding (Lnc) RNA of FAM83A itself (FAM83A-AS1), indicating an outstanding synergistic carcinogenic effect between FAM83A and FAM83A-AS1. In the present study, the specific mechanisms by which FAM83A and FAM83A-AS1 cofunction in the Wnt/β-catenin and EGFR signaling pathways were reviewed in detail, which will guide subsequent research. We also described the applications of FAM83A and FAM83A-AS1 in tumor therapy and provided a certain theoretical basis for subsequent drug target development and combination therapy strategies.

Signaling lymphocytic activation molecule family receptors as potential immune therapeutic targets in solid tumors

Recently, cancer immunotherapy has revolutionized cancer treatment. Various forms of immunotherapy have a manageable safety profile and result in prolongation of overall survival in patients with solid tumors, but only in a proportion of patients. Various factors in the tumor microenvironment play critical roles and may be responsible for this lack of therapeutic response. Signaling lymphocytic activation molecule family (SLAMF) members are increasingly being studied as factors impacting the tumor immune microenvironment. SLAMF members consist of nine receptors mainly expressed in immune cells. However, SLAMF receptors have also been detected in cancer cells, and they may be involved in a spectrum of anti-tumor immune responses. Here, we review the current knowledge of the expression of SLAMF receptors in solid tumors and tumor-infiltrating immune cells and their association with patient outcomes. Furthermore, we discuss the therapeutic potential of targeting SLAMF receptors to improve outcomes of cancer therapy in solid tumors. We believe the research on SLAMF receptor-targeted strategies may enhance anti-cancer immunity in patients with solid tumors and improve clinical outcomes.

A novel autoantibody signatures for enhanced clinical diagnosis of pancreatic ductal adenocarcinoma

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is a devastating disease that requires precise diagnosis for effective treatment. However, the diagnostic value of carbohydrate antigen 19 - 9 (CA19-9) is limited. Therefore, this study aims to identify novel tumor-associated autoantibodies (TAAbs) for PDAC diagnosis.

METHODS

A three-phase strategy comprising discovery, test, and validation was implemented. HuProt™ Human Proteome Microarray v3.1 was used to screen potential TAAbs in 49 samples. Subsequently, the levels of potential TAAbs were evaluated in 477 samples via enzyme-linked immunosorbent assay (ELISA) in PDAC, benign pancreatic diseases (BPD), and normal control (NC), followed by the construction of a diagnostic model.

RESULTS

In the discovery phase, protein microarrays identified 167 candidate TAAbs. Based on bioinformatics analysis, fifteen tumor-associated antigens (TAAs) were selected for further validation using ELISA. Ten TAAbs exhibited differentially expressed in PDAC patients in the test phase (P < 0.05), with an area under the curve (AUC) ranging from 0.61 to 0.76. An immunodiagnostic model including three TAAbs (anti-HEXB, anti-TXLNA, anti-SLAMF6) was then developed, demonstrating AUCs of 0.81 (58.0% sensitivity, 86.0% specificity) and 0.78 (55.71% sensitivity, 87.14% specificity) for distinguishing PDAC from NC. Additionally, the model yielded AUCs of 0.80 (58.0% sensitivity, 86.25% specificity) and 0.83 (55.71% sensitivity, 100% specificity) for distinguishing PDAC from BPD in the test and validation phases, respectively. Notably, the combination of the immunodiagnostic model with CA19-9 resulted in an increased positive rate of PDAC to 92.91%.

CONCLUSION

The immunodiagnostic model may offer a novel serological detection method for PDAC diagnosis, providing valuable insights into the development of effective diagnostic biomarkers.

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.

Based on the Cancer Genome Atlas Database Development of a prognostic model of RNA binding protein in stomach adenocarcinoma

The purpose of this study was to identify potential RNA binding proteins associated with the survival of gastric adenocarcinoma, as well as the corresponding biological characteristics and signaling pathways of these RNA binding proteins. RNA sequencing and clinical data were obtained from the cancer genome map (N = 32, T = 375) and the comprehensive gene expression database (GSE84437, N = 433). The samples in The Cancer Genome Atlas were randomly divided into a development group and a test group. A total of 1495 RNA binding protein related genes were extracted. Using nonparametric tests to analyze the difference of RNA binding protein related genes, 296 differential RNA binding proteins were obtained, 166 were up-regulated and 130 were down regulated. Twenty prognosis-related RNA binding proteins were screened using Cox regression, including 14 high-risk genes (hazard ratio > 1.0) and 6 low-risk genes (hazard ratio < 1.0). Seven RNA binding protein related genes were screened from the final prognostic model and used to construct a new prognostic model. Using the development group and test group, the model was verified with survival analysis, receiver operating characteristics curves and prognosis analysis curves. A prediction nomogram was finally developed and showed good prediction performance.

Involvement of FAM83 Family Proteins in the Development of Solid Tumors: An Update Review

FAM83 family members are a group of proteins that have been implicated in various solid tumors. In this updated review, we mainly focus on the cellular localization, molecular composition, and biological function of FAM83 family proteins in solid tumors. We discussed the factors that regulate abnormal protein expression and alterations in the functional activities of solid tumor cells (including non-coding microRNAs and protein modifiers) and potential mechanisms of tumorigenesis (including the MAPK, WNT, and TGF-β signaling pathways). Further, we highlighted the application of FAM83 family proteins in the diagnoses and treatment of different cancers, such as breast, lung, liver, and ovarian cancers from two aspects: molecular marker diagnosis and tumor drug resistance. We described the overexpression of FAM83 genes in various human malignant tumor cells and its relationship with tumor proliferation, migration, invasion, transformation, and drug resistance. Moreover, we explored the prospects and challenges of using tumor treatments based on the FAM83 proteins. Overall, we provide a theoretical basis for harnessing FAM83 family proteins as novel targets in cancer treatment. We believe that this review opens up open new directions for solid tumor treatment in clinical practice.

Macrophages M1-Related Prognostic Signature in Hepatocellular Carcinoma

A large number of studies have found that macrophages M1 play an important role in the occurrence and development of tumors. The aim of our study is to explore the causes of differential infiltration of macrophages M1 in hepatocellular carcinoma from the perspective of transcriptome and establish a prognostic model of hepatocellular carcinoma. We downloaded gene expression and clinical data from the public database, estimated the content of macrophages M1 in different samples with R software, and found the different genes between high- and low-infiltration groups. Using differentially expressed genes, we constructed a model composed of 7 genes. The risk score of the model has a good ability to predict the prognosis, has a positive correlation with immune checkpoints, and is closely related to other immune cells and immune function. Our model shows good prognostic function and has wide application value.

Development and Verification of the Hypoxia- and Immune-Associated Prognostic Signature for Pancreatic Ductal Adenocarcinoma

We aim to construct a hypoxia- and immune-associated risk score model to predict the prognosis of patients with pancreatic ductal adenocarcinoma (PDAC). By unsupervised consensus clustering algorithms, we generate two different hypoxia clusters. Then, we screened out 682 hypoxia-associated and 528 immune-associated PDAC differentially expressed genes (DEGs) of PDAC using Pearson correlation analysis based on the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression project (GTEx) dataset. Seven hypoxia and immune-associated signature genes (S100A16, PPP3CA, SEMA3C, PLAU, IL18, GDF11, and NR0B1) were identified to construct a risk score model using the Univariate Cox regression and the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression, which stratified patients into high- and low-risk groups and were further validated in the GEO and ICGC cohort. Patients in the low-risk group showed superior overall survival (OS) to their high-risk counterparts (p < 0.05). Moreover, it was suggested by multivariate Cox regression that our constructed hypoxia-associated and immune-associated prognosis signature might be used as the independent factor for prognosis prediction (p < 0.001). By CIBERSORT and ESTIMATE algorithms, we discovered that patients in high-risk groups had lower immune score, stromal score, and immune checkpoint expression such as PD-L1, and different immunocyte infiltration states compared with those low-risk patients. The mutation spectrum also differs between high- and low-risk groups. To sum up, our hypoxia- and immune-associated prognostic signature can be used as an approach to stratify the risk of PDAC.