A 6‑gene risk score system constructed for predicting the clinical prognosis of pancreatic adenocarcinoma patients

Comprehensive Analysis of Tertiary Lymphoid Structures in Pancreatic Cancer: Molecular Characteristics and Prognostic Implications

Purpose:

The molecular properties of TLSs in pancreatic cancer are still not well comprehended. This research delved into the molecular properties of intratumoral TLSs in pancreatic cancer through the exploration of multi-omics data.

Methods:

Seven key genes were identified through Cox regression analysis and random survival forest analysis from a total of 5908 genes related to TLSs. These genes were utilized to construct a prognosis model, which was subsequently validated in two independent cohorts. Additionally, the study investigated the molecular features of different populations of TLSs from multiple perspectives. The model’ s forecasting accuracy was verified by analyzing nomogram and decision curves, taking into account the patients’ clinical traits.

Results:

The analysis of immune cell infiltration showed a notably greater presence of Macrophage M0 cells in the group at high risk than in the low-risk group. The pathway enrichment analysis demonstrated the activation among common cancer-related pathways, including ECM receptor interaction, pathways in cancer, and focal adhesion, in the high-risk group. Additionally, the methylation study revealed notable disparities in DNA methylation between two TLS groups across four regions: TSS200, 5’ UTR, 1stExon, and Body. A variety of notably distinct sites were linked with PVT1. Furthermore, by constructing a competing endogenous RNA network, several mRNAs and lncRNAs were identified that compete for the binding of hsa-mir-221.

Conclusion:

Overall, this research sheds light on the molecular properties of TLSs across various pancreatic cancer stages and suggests possible focal points for the treatment of pancreatic cancer.

Function and prognostic value of basement membrane -related genes in lung adenocarcinoma

Background: Lung adenocarcinoma (LUAD) has become a common cause of cancer-related death. Many studies have shown that the basement membrane (BM) is associated with the development of cancer. However, BM-related gene expression and its relationship to LUAD prognosis remains unclear. Methods: BM-related genes from previous studies were used. Clinical and mRNA expression information were obtained from TCGA database. Cox, minimum absolute contraction, and selection operator regression were applied to analyze the selected genes affecting LUAD prognosis. A prognostic-risk model was then established. Furthermore, this study applied Kaplan-Meier analysis to assess the outcomes of high- and low-risk groups, then explored their differences in drug sensitivity. The DSigDB database was used to screen for therapeutic small-molecule drugs. Results: Fourteen prognostic models based on BM-related genes were successfully constructed and validated in patients with LUAD. We also found that independence was a prognostic factor in all 14 BM-based models. Functional analysis showed that the enrichment of BM-related genes mainly originated from signaling pathways related to cancer. The BM-based model also suggested that immune cell infiltration is associated with checkpoints. The low-risk patients may benefit from cyclopamine and docetaxel treatments. Conclusion: This study identified a reliable biomarker to predict survival in patients with LUAD and offered new insights into the function of BM-related genes in LUAD.

A novel immune checkpoint score system for prognostic evaluation in pancreatic adenocarcinoma

BACKGROUND

Pancreatic adenocarcinoma (PAAD) remains a lethal malignancy making the detection of novel prognostic biomarkers urgent. Limited studies have investigated the predictive capability of immune checkpoints in PAAD.

METHOD

Gene expression data and correlative clinical information of PAAD cohort were obtained from public databases, including TCGA, ICGC, GTEX and GEO databases. Risk factors were screened and used to establish a risk score model through LASSO and Cox regression analyses. The prognostic ability of the risk score model was demonstrated. The association between risk score with immune cells infiltration, immune checkpoint genes expression, immunogenic cell death, somatic mutations and signaling pathways enrichment were analysed. scRNA-seq data were collected to confirmed the immune checkpoints expression in PAAD samples. The prognosis prediction ability of OX40/TNFRSF4 was identified. The mRNA and protein expression of OX40 in our clinical specimens were examined by RT-PCR and IHC method and its prognosis ability was verified.

RESULTS

First of all, the difference of immune microenvironment between pancreatic cancer and adjacent tissues was shown. A risk score system based on three immune checkpoints (OX40, TNFSF14 and KIR3DL1) was established. The risk score model was an independent prognostic factor and performed well regarding overall survival (OS) predictions among PAAD patients. A nomogram was established to facilitate the risk model application in clinical prognosis. Immune cells including naive B cells, CD8+ T cells and Tregs were negatively correlated with the risk score. The risk score was associated with expression of immune checkpoint genes, immunogenic cell death related genes and somatic mutations. Glycolysis processes, IL-2-STAT5, IL-6-STAT3, and mTORC1 signaling pathways were enriched in the high-risk score group. Furthermore, scRNA-seq data confirmed that TNFRSF4, TNFSF14 and KIR3DL1 were expressed on immune cells in PAAD samples. We then identified OX40 as an independent prognosis-related gene, and a higher OX40 expression was associated with increased survival rate and immune environment change. In 84 PAAD clinical specimens collected from our center, we confirmed that higher OX40 mRNA expression levels were related to a good prognosis. The protein expression of OX40 on tumor-infiltrating immune cells (TIICs), endothelial cells and tumor cells was verified in PAAD tissues by immunohistochemistry (IHC) method.

CONCLUSIONS

Overall, our findings strongly suggested that the three-immune checkpoints score system might be useful in the prognosis and design of personalized treatments for PAAD patients. Finally, we identified OX40 as an independent potential biomarker for PAAD prognosis prediction.

Development of a basement membrane gene signature and identification of the potential candidate therapeutic targets for pancreatic cancer

Background

Pancreatic cancer is a deadly cancer with a poor prognosis. In light of mounting evidence that basement membrane genes (BMGs) play a role in the development of cancer, we sought to examine the prognostic importance and role of BMGs in pancreatic ductal adenocarcinoma (PDAC) patients.

Methods

BMGs were obtained from previous top research studies. The clinical and messenger ribonucleic acid expression data were retrieved from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) data sets, respectively. Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses were used for the PDAC risk modeling and gene identification. The Kaplan-Meier method was used to compare outcomes between the low- and high-risk groups. Finally, we analyzed small-molecule drugs that could be used to target BMGs for treatment using the Enrichr data set and validated the function of the tubulointerstitial nephritis antigen (TINAG) in pancreatic cancer.

Results

We successfully constructed and validated a 7 BMG-based model to predict PDAC patient outcomes. Additionally, we discovered that 7 BMG-based model was an independent predictive factor for PDAC. According to our functional analysis, the majority of the signaling pathways enriched in BMGs were those connected to malignancy. Immune cell infiltration and immunological checkpoints were also linked to the BMG-based model. Further, we identified 5 small-molecule drugs that may be useful in treating PDAC patients. We also found that TINAG promoted cell proliferation in pancreatic cancer.

Conclusions

Our study extended understandings of how BMGs work in PDAC. We identified a credible predictive biomarker for PDAC patients' survival.

Construction and comprehensive analysis of a novel prognostic signature associated with pyroptosis molecular subtypes in patients with pancreatic adenocarcinoma

Background

Treatment of cancer with pyroptosis is an emerging strategy. Molecular subtypes based on pyroptosis-related genes(PRGs) seem to be considered more conducive to individualized therapy. It is meaningful to construct a pyroptosis molecular subtypes-related prognostic signature (PMSRPS) to predict the overall survival (OS) of patients with pancreatic adenocarcinoma(PAAD) and guide treatment.

Methods

Based on the transcriptome data of 23 PRGs, consensus clustering was applied to divide the TCGA and GSE102238 combined cohort into three PRGclusters. Prognosis-related differentially expressed genes(DEGs) among PRGclusters were subjected to LASSO Cox regression analysis to determine a PMSRPS. External cohort and in vitro experiments were conducted to verify this PMSRPS. The CIBERSORT algorithm, the ESTIMATE algorithm and the Immunophenoscore (IPS) were used to analyze the infiltrating abundance of immune cells, the tumor microenvironment (TME), and the response to immunotherapy, respectively. Wilcoxon analysis was used to compare tumor mutational burden (TMB) and RNA stemness scores (RNAss) between groups. RT-qPCR and in vitro functional experiments were used for evaluating the expression and function of SFTA2.

Results

Based on three PRGclusters, 828 DEGs were obtained and a PMSRPS was subsequently constructed. In internal and external validation, patients in the high-risk group had significantly lower OS than those in the low-risk group and PMSRPS was confirmed to be an independent prognostic risk factor for patients with PAAD with good predictive performance. Immune cell infiltration abundance and TME scores indicate patients in the high-risk group have typical immunosuppressive microenvironment characteristics. Analysis of IPS suggests patients in the high-risk group responded better to novel immune checkpoint inhibitors (ICIs) than PD1/CTLA4. The high-risk group had higher TMB and RNAss. In addition, 10 potential small-molecule compounds were screened out. Finally, we found that the mRNA expression of SFTA2 gene with the highest risk coefficient in PMSRPS was significantly higher in PAAD than in paracancerous tissues, and knockdown of it significantly delayed the progression of PAAD.

Conclusions

PMSRPS can well predict the prognosis, TME and immunotherapy response of patients with PAAD, identify potential drugs, and provide treatment guidance based on individual needs.

Prognosis-related genes participate in immunotherapy of renal clear cell carcinoma possibly by targeting dendritic cells

Tumor immunotherapy has become one of the most promising approaches to tumor treatment. This study aimed to screen genes involved in the response of clear cell renal cell carcinoma (ccRCC) to immunotherapy and analyze their function. Based on the Gene Expression Omnibus and The Cancer Genome Atlas datasets, we screened out nine differentially expressed genes (TYROBP, APOC1, CSTA, LY96, LAPTM5, CD300A, ALOX5, C1QA, and C1QB) associated with clinical traits and prognosis. A risk signature constructed by these nine genes could predict the survival probability for patients at 1 year, 3 years, and 5 years. The immune checkpoint blockade response rate in the high-risk group was significantly higher than in the low-risk group (49.25% vs. 24.72%, p ≤ 0.001). The nine prognosis-related genes were negatively correlated with activated dendritic cells in the low-risk group but not in the high-risk group. qRT-PCR, immunohistochemistry, and immunofluorescence showed that the nine prognosis-related genes were associated with dendritic cell activity and the PD-1 positive staining rate. In conclusion, the nine prognosis-related genes have a high prognostic value. The patients in the high-risk group were more likely to benefit from immunotherapy, and the mechanism might be related to the release of dendritic cell-mediated immunosuppression.

Prognostic significance of AP-2α/γ targets as cancer therapeutics

Identifying genes with prognostic importance could improve cancer treatment. An increasing number of reports suggest the existence of successful strategies based on seemingly "untargetable" transcription factors. In addition to embryogenesis, AP-2 transcription factors are known to play crucial roles in cancer development. Members of this family can be used as prognostic factors in oncological patients, and AP-2α/γ transcription factors were previously investigated in our pan-cancer comparative study using their target genes. The present study investigates tumors that were previously found similar with an emphasis on the possible role of AP-2 factors in specific cancer types. The RData workspace was loaded back to R environment and 3D trajectories were built via Monocle3. The genes that met the requirement of specificity were listed using top_markers(), separately for mutual and unique targets. Furthermore, the candidate genes had to meet the following requirements: correlation with AP-2 factor (through Correlation AnalyzeR) and validated prognostic importance (using GEPIA2 and subsequently KM-plotter or LOGpc). Eventually, the ROC analysis was applied to confirm their predictive value; co-dependence of expression was visualized via BoxPlotR. Some similar tumors were differentiated by AP-2α/γ targets with prognostic value. Requirements were met by only fifteen genes (EMX2, COL7A1, GRIA1, KRT1, KRT14, SLC12A5, SEZ6L, PTPRN, SCG5, DPP6, NTSR1, ARX, COL4A3, PPEF1 and TMEM59L); of these, the last four were excluded based on ROC curves. All the above genes were confronted with the literature, with an emphasis on the possible role played by AP-2 factors in specific cancers. Following ROC analysis, the genes were verified using immunohistochemistry data and progression-related signatures. Staining differences were observed, as well as co-dependence on the expression of e.g. CTNNB1, ERBB2, KRAS, SMAD4, EGFR or MKI67. In conclusion, prognostic value of targets suggested AP-2α/γ as candidates for novel cancer treatment. It was also revealed that AP-2 targets are related to tumor progression and that some mutual target genes could be inversely regulated.

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

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

The Sez6 Family Inhibits Complement by Facilitating Factor I Cleavage of C3b and Accelerating the Decay of C3 Convertases

The Sez6 family consists of Sez6, Sez6L, and Sez6L2. Its members are expressed throughout the brain and have been shown to influence synapse numbers and dendritic morphology. They are also linked to various neurological and psychiatric disorders. All Sez6 family members contain 2-3 CUB domains and 5 complement control protein (CCP) domains, suggesting that they may be involved in complement regulation. We show that Sez6 family members inhibit C3b/iC3b opsonization by the classical and alternative pathways with varying degrees of efficacy. For the classical pathway, Sez6 is a strong inhibitor, Sez6L2 is a moderate inhibitor, and Sez6L is a weak inhibitor. For the alternative pathway, the complement inhibitory activity of Sez6, Sez6L, and Sez6L2 all equaled or exceeded the activity of the known complement regulator MCP. Using Sez6L2 as the representative family member, we show that it specifically accelerates the dissociation of C3 convertases. Sez6L2 also functions as a cofactor for Factor I to facilitate the cleavage of C3b; however, Sez6L2 has no cofactor activity toward C4b. In summary, the Sez6 family are novel complement regulators that inhibit C3 convertases and promote C3b degradation.

Identification of small proline-rich protein 1B (SPRR1B) as a prognostically predictive biomarker for lung adenocarcinoma by integrative bioinformatic analysis

BACKGROUND

With the ongoing development of targeted therapy and immunotherapy in recent years, the overall five-year survival rate of NSCLC patients has not improved, and the search for novel diagnostic and prognostic markers for lung adenocarcinoma continues.

METHODS

Lung adenocarcinoma (LUAD) gene expression data and relevant clinical information were obtained from the TCGA. Hub genes were identified with weighted gene co-expression network analysis (WGCNA) and protein-protein interaction network (PPI). Survival analyses were also performed using GEPIA. The 536 LUAD patients were divided into two groups according to the SPRR1B expression level and analyzed by gene set enrichment analysis (GSEA) and verified by immunoblotting. The effects of SPRR1B on cell proliferation and cell metastasis and apoptosis were evaluated by 5-ethynyl-2'-deoxyuridine (EdU) staining, colony formation assay, transwell migration and invasion assay, and flow cytometry, respectively.

RESULTS

A total of 2269 DEGs were analyzed by WGCNA and five hub genes (CCK, FETUB, PCSK9, SPRR1B, and SPRR2D) were identified. Among them, SPRR1B was selected as one of the most significant prognostic genes in LUAD. SPRR1B was found to be highly expressed in lung adenocarcinoma cells compared with that in normal bronchial epithelial cells. In addition, silencing of SPRR1B could inhibit the cell proliferation, invasion, and migration of lung adenocarcinoma cells, but induced cell apoptosis and G2/M phase arrest in vitro. The result of GSEA and immunoblotting revealed that SPRR1B activated the MAPK signaling pathway involved in the proliferation and metastasis of lung cancer.

CONCLUSIONS

Our findings demonstrate that SPRR1B may function as a prognosis predictor in lung adenocarcinoma.

Molecular epidemiology and diagnostics of KRAS mutations in human cancer

RAS mutation is the most frequent oncogenic alteration in human cancers. KRAS is the most frequently mutated followed by NRAS. The emblematic KRAS mutant cancers are pancreatic, colorectal, lung adenocarcinomas and urogenital cancers. KRAS mutation frequencies are relatively stable worldwide in various cancer types with the one exception of lung adenocarcinoma. The frequencies of KRAS variant alleles appears cancer type specific, reflecting the various carcinogenic processes. In addition to point mutation KRAS, allelic imbalances are also frequent in human cancers leading to the predominance of a mutant allele. KRAS mutant cancers are characterized by typical, cancer-type-specific co-occurring mutations and distinct gene expression signatures. The heterogeneity of KRAS mutant primary cancers is significant, affecting the variant allele frequency, which could lead to unpredictable branching development in metastases. Selection of minute mutant subclones in the primary tumors or metastases during target therapies can also occur frequently in lung or colorectal cancers leading to acquired resistance. Ultrahigh sensitivity techniques are now routinely available for diagnostic purposes, but the proper determination of mutant allele frequency of KRAS in the primary or metastatic tissues may have larger clinical significance.

Identification of Spindle and Kinetochore-Associated Family Genes as Therapeutic Targets and Prognostic Biomarkers in Pancreas Ductal Adenocarcinoma Microenvironment

Aim

The role of spindle and kinetochore-associated (SKA) genes in tumorigenesis and cancer progression has been widely studied. However, so far, the oncogenic involvement of SKA family genes in pancreatic cancer and their prognostic potential remain unknown.

Methods

Here, we carried out a meta-analysis of the differential expression of SKA genes in normal and tumor tissue. Univariate and multivariate survival analyses were done to evaluate the correlation between SKA family gene expression and pancreas ductal adenocarcinoma (PDAC) prognosis. Joint-effect and stratified survival analysis as well as nomogram analysis were used to estimate the prognostic value of genes. The underlying regulatory and biological mechanisms were identified by Gene set enrichment analysis. Interaction between SKA prognosis-related genes and immune cell infiltration was assessed using the Tumor Immune Estimation Resource tool.

Results

We find that SKA1-3 are highly expressed in PDAC tissues relative to non-cancer tissues. Survival analysis revealed that high expression of SKA1 and SKA3 independently indicate poor prognosis but they are not associated with relapse-free survival. The prognostic value of SKA1 and SKA3 was further confirmed by the nomogram, joint-effect, and stratified survival analysis. Analysis of underlying mechanisms reveals that these genes influence cancer-related signaling pathways, kinases, miRNA, and E2F family genes. Notably, prognosis-related genes are inversely correlated with several immune cells infiltrating levels.

Conclusion

We find that SKA1 and SKA3 expression correlates with prognosis and immune cell infiltration in PDAC, highlighting their potential as pancreatic cancer prognostic biomarkers.

Identification of genomic alterations and associated transcriptomic profiling reveal the prognostic significance of MMP14 and PKM2 in patients with pancreatic cancer

Pancreatic cancer is characterized by multiple genomic alterations, including KRAS mutations, TP53 mutations and CDKN2A deletion. However, the prognostic relevance of those genomic alterations and associated transcriptomic profiling in pancreatic cancer are unclear. Integrated analysis of The Cancer Genome Atlas (TCGA) datasets revealed that KRAS mutation, TP53 mutation and CDKN2A deletion were all bad prognostic factors in pancreatic cancer. And KRAS mutation, TP53 mutation and CDKN2A deletion were coordinated and co-occurred in pancreatic cancer. Transcriptomic analysis showed that MMP14 and PKM2 were both up-regulated by KRAS mutation, TP53 mutation or CDKN2A deletion. Also, MMP14 and PKM2 were both associated with unfavorable outcomes in pancreatic cancer. Compared with normal tissues, MMP14 and PKM2 were up-regulated in pancreatic cancer tissues. Moreover, MMP14 and PKM2 were highly expressed in high grade of pancreatic cancer. Furthermore, MMP14 and PKM2 were correlated with each other, and the combination of MMP14 and PKM2 could be used as better prognostic markers than MMP14 or PKM2 alone. At last, the high expression and bad prognostic effects of MMP14 and PKM2 in pancreatic cancer were validated using tissue microarray. Overall, the genomic alterations and associated transcriptomic profiling analysis suggested new prognostic makers of MMP14 and PKM2 in pancreatic cancer.

Pathogenesis and Treatment of Pancreatic Cancer Related Pain

Pancreatic cancer is often diagnosed due to the patient seeking medical attention for abdominal pain. It is among the most painful cancers, with pain severity strongly correlating with prognosis. Perineural invasion is a prominent feature of pancreatic cancer and often the first route of metastasis resulting in neuropathic pain. While surgical pain is present, it is generally short-lived; chemo- and radio-therapy associated side effect pain is often longer lasting and more difficult to manage. Treatment-induced mucositis in response to chemotherapy occurs throughout the GI tract resulting in infection-prone ulcers on the lip, buccal mucosa, palate or tongue. Cisplatin treatment is associated with axonal neuropathy in the dorsal root ganglion, although other large sensory fibers can be affected. Opioid-induced hyperalgesia can also emerge in patients. Along with traditional means to address pain, neurolytic celiac plexus block of afferent nociceptive fibers has been reported to be effective in 74% of patients. Moreover, as cancer treatments become more effective and result in improved survival, treatment-related side effects become more prevalent. Here, pancreatic cancer and treatment associated pain are reviewed along with current treatment strategies. Potential future therapeutic strategies to target the pathophysiology underlying pancreatic cancer and pain induction are also presented.

Identification of seven-gene signature for prediction of lung squamous cell carcinoma

Background and aim: Lung squamous cell carcinoma (LUSC), is a pathological subtype of lung cancer, accounting for 30% of the lung cancers. A reliable model was constructed, based on the whole gene expression profiles, to predict the prognosis of patients with LUSC. Methods: The RNA-Seq data of LUSC was downloaded from the TCGA database, and differentially expressed genes (p<0.05, |log2fold change| >1) were screened out. By univariate and multivariate Cox regression analysis, we identified seven prognosis-related genes. Then, we established a risk score staging system to predict the prognosis of patients with LUSC. Compared with other clinical parameters, the risk score was an independent prognostic factor and had a better performance in predicting prognosis. Finally, GSEA analysis was carried out to determine the enrichment pathway significantly. The risk score models were established by Cox proportional hazard regression analysis; the ROC curve was applied to test the performance of risk score model. All the statistical analysis was accomplished by R packages. Results: In this study, a model was constructed to predict prognosis, which contains seven genes: CSRNP1, CLEC18B, MIR27A, AC130456.4, DEFA6, ARL14EPL, and ZFP42. Based on the model, the risk score of each patient was calculated with LUSC (hazard ratio [HR]=2.673, 95% CI=1.871-3.525). It was found that the risk score can distinguish high-risk and low-risk groups in prognosis of LUSC patients, independently. Furthermore, the model was validated by ROC curves in the testing dataset and the whole dataset. Lastly, by gene set enrichment analysis (GSEA), we showed the main enrichment pathways were DNA damage stimulus, DNA repair, and DNA replication. It was suggested that the risk score may provide a new and reliable method for prognosis prediction. Conclusion: The results of this study suggested that the risk score based on seven-genes could indicate a promising and independent prognostic biomarker for LUSC patients.