Development and validation of glycolysis-related prognostic score for prediction of prognosis and chemosensitivity of pancreatic ductal adenocarcinoma

Interplay of Sphingolipid Metabolism in Predicting Prognosis of GBM Patients: Towards Precision Immunotherapy

Background: In spite of numerous existing bio-surveillance systems for predicting glioma (GBM) prognosis, enhancing the efficacy of immunotherapy remains an ongoing conundrum. The continual scrutiny of the dynamic interplay between the sphingolipid metabolic pathway and tumor immunophenotypes has unveiled potential implications. However, the intricate orchestration of functional and regulatory mechanisms by long non-coding RNAs (lncRNAs) in GBM, particularly in the context of sphingolipid metabolism, remains cryptic. Methods: We harnessed established R packages to intersect gene expression profiles of GBM patients within the The Cancer Genome Atlas (TCGA) database with the compilation of sphingolipid metabolism genes from GeneCards. This enabled us to discern markedly distinct lncRNAs, which were subsequently deployed to construct a robust prognostic model utilizing Lasso-Cox regression analysis. We then scrutinized the immune microenvironment across various risk strata using the ssGSEA and CIBERSORT algorithms. To evaluate mutation patterns and drug resistance profiles within patient subgroups, we devised the "Prophytic" and "Maftools" packages, respectively. Results: Our investigation scrutinized lncRNAs linked to sphingolipid metabolism, utilizing glioma specimens from TCGA. We meticulously curated 1224 sphingolipid-associated genes gleaned from GeneCards and pinpointed 272 differentially expressed mRNAs via transcriptomic analysis. Enrichment analyses underscored their significance in sphingolipid processes. A prognostic model founded on 17 meticulously selected lncRNAs was systematically constructed and validated. This model adeptly stratified GBM patients into high- and low-risk categories, yielding highly precise prognostic insights. We also discerned correlations between immune cell infiltration and genetic mutation discrepancies, along with distinct therapeutic responses through drug sensitivity analysis. Notably, computational findings were corroborated through experimental validation by RT-PCR. Conclusion: In summation, our exhaustive inquiry underscores the multifaceted utility of the sphingolipid metabolic pathway as an autonomous diagnostic and prognostic indicator for glioma patients. Furthermore, we amalgamate a profusion of substantiated evidence concerning immune infiltration and gene mutations, thereby reinforcing the proposition that sphingolipid metabolism may function as a pivotal determinant in the panorama of immunotherapeutic interventions.

The role of ARL4C in predicting prognosis and immunotherapy drug susceptibility in pan-cancer analysis

Background: ARLs, which are a class of small GTP-binding proteins, play a crucial role in facilitating tumor tumorigenesis and development. ARL4C, a vital member of the ARLs family, has been implicated in the progression of tumors, metastatic dissemination, and development of resistance to therapeutic drugs. Nevertheless, the precise functional mechanisms of ARL4C concerning tumor prognosis and immunotherapy drug susceptibility remain elusive. Methods: By combining the GTEx and TCGA databases, the presence of ARL4C was examined in 33 various types of cancer. Immunohistochemistry and immunofluorescence staining techniques were utilized to confirm the expression of ARL4C in particular tumor tissues. Furthermore, the ESTIMATE algorithm and TIMER2.0 database were utilized to analyze the tumor microenvironment and immune infiltration associated with ARL4C. The TISCH platform facilitated the utilization of single-cell RNA-seq datasets for further analysis. ARL4C-related immune escape was investigated using the TISMO tool. Lastly, drug sensitivity analysis was conducted to assess the sensitivity of different types of tumors to compounds based on the varying levels of ARL4C expression. Results: The study found that ARL4C was highly expressed in 23 different types of cancer. Moreover, the presence of high ARL4C expression was found to be associated with a poor prognosis in BLCA, COAD, KIRP, LGG, and UCEC. Notably, ARL4C was also expressed in immune cells, and its high expression was found to be correlated with cancer immune activation. Most importantly, the drug sensitivity analysis revealed a positive correlation between ARL4C expression and the heightened sensitivity of tumors to Staurosporine, Midostaurin, and Nelarabine. Conclusion: The findings from our study indicate that the expression level of ARL4C may exert an influence on cancer development, prognosis, and susceptibility to immunotherapy drugs. In addition, the involvement of ARL4C in the tumor immune microenvironment has expanded the concept of ARL4C-targeted immunotherapy.

Early and late recurrence patterns of pancreatic ductal adenocarcinoma after pancreaticoduodenectomy: a multicenter study

BACKGROUND

Survival after pancreaticoduodenectomy (PD) for pancreatic ductal adenocarcinoma (PDAC) remains poor because of high incidences of recurrence. The risk factors, patterns, and long-term prognosis in patients with early recurrence and late recurrence (ER and LR) for PDAC after PD were studied.

METHODS

Data from patients who underwent PD for PDAC were analyzed. Recurrence was divided into ER (ER ≤1 years) and LR (LR >1 years) using the time to recurrence after surgery. Characteristics and patterns of initial recurrence, and postrecurrence survival (PRS) were compared between patients with ER and LR.

RESULTS

Among the 634 patients, 281 (44.3%) and 249 (39.3%) patients developed ER and LR, respectively. In the multivariate analysis, preoperative CA19-9 levels, resection margin status, and tumor differentiation were significantly associated with both ER and LR, while lymph node metastasis and perineal invasion were associated with LR. Patients with ER, when compared with patients with LR, showed a significantly higher proportion of liver-only recurrence ( P <0.05), and worse median PRS (5.2 vs. 9.3 months, P <0.001). Lung-only recurrence had a significantly longer PRS when compared with liver-only recurrence ( P <0.001). Multivariate analysis demonstrated that ER and irregular postoperative recurrence surveillance were independently associated with a worse prognosis ( P <0.001).

CONCLUSION

The risk factors for ER and LR after PD are different for PDAC patients. Patients who developed ER had worse PRS than those who developed LR. Patients with lung-only recurrence had a significantly better prognosis than those with other recurrent sites.

A novel online calculator to predict early recurrence and long-term survival of patients with resectable pancreatic ductal adenocarcinoma after pancreaticoduodenectomy: A multicenter study

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is prone to relapse even after radical pancreaticoduodenectomy (PD) (including robotic, laparoscopic and open approach). This study aimed to develop an online nomogram calculator to predict early recurrence (ER) (within one year after surgery) and long-term survival in patients with PDAC.

METHODS

Patients with PDAC after radical PD were included. Univariate and multivariate logistic regression analysis was used to identify independent risk factors. An online nomogram calculator was developed based on independent risk factors in the training cohort and then tested in the internal and external validation cohorts.

RESULTS

Of the 569 patients who met the inclusion criteria, 310, 155, and 104 patients were in the training, internal and external validation cohorts, respectively. Multivariate analysis revealed that preoperative carbohydrate antigen19-9 (CA19-9) [Odds Ratio (OR) 1.002; 95% confidence interval (CI) 1.001-1.003; P = 0.001], fibrinogen/albumin (FAR) (OR 1.132; 95% CI 1.012-1.266; P = 0.029), N stage (OR 2.291; 95% CI 1.283-4.092; P = 0.005), and tumor differentiation (OR 3.321; 95% CI 1.278-8.631; P = 0.014) were independent risk factors for ER. Nomogram based on the above four factors achieved good C-statistics of 0.772, 0.767 and 0.765 in predicting ER in the training, internal and external validation cohorts, respectively. Time-dependent ROC analysis (timeROC) and decision curve analysis (DCA) revealed that the nomogram provided superior diagnostic capacity and net benefit compared with other staging systems.

CONCLUSION

This multi-center study developed and validated an online nomogram calculator that can predict ER and long-term survival in patients with PDAC with high degrees of stability and accuracy.

The immune escape signature predicts the prognosis and immunotherapy sensitivity for pancreatic ductal adenocarcinoma

Background

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest malignancies worldwide. Immune escape is considered to be a reason for immunotherapy failure in PDAC. In this study, we explored the correlation between immune escape-related genes and the prognosis of PDAC patients.

Methods

1163 PDAC patients from four public databases, including The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), Array-express, and Gene Expression Omnibus (GEO), were included in our study. Cox regression analysis was used to identify the 182 immune genes which were significantly associated with overall survival (OS). And then we established an immune escape-related gene prognosis index (IEGPI) score using several datasets as the training cohort and validated it using the validation cohort. Kaplan-Meier (KM) and Cox regression analysis were used to detect the relationship of IEGPI score with OS. We further explored the relationship between the IEGPI and immune indexes. And the prediction value of response for immunotherapy in Tumor Immune Dysfunction and Exclusion (TIDE) dataset.

Results

We establish an IEGPI score based on 27 immune escape genes which were significantly related to the prognosis of OS in PDAC patients. Patients in the high-IEGPI group had a significantly worse overall survival rate compared with that in the low-IEGPI groups by KM curves and cox-regression. 5 of the 32 cancer types in TCGA could be significantly distinguished in survival rates through the low- and high-IEGPI groups. Moreover, the correlation between the IEGPI score was negatively correlated with an immune score in several datasets. And higher IEGPI better recurrence-free survival (RFS) and OS in the patients after patients were treated with both PD-1 and CTLA4 in the public datasets (P<0.05). Intriguingly, by using RT-PCR, we verified that the gene of PTPN2, CEP55, and JAK2 were all higher in the BxPC-3 and PANC-1 than HPDE5 cells. Lastly, we found that the IEGPI score was higher in K-rasLSL.G12D/+, p53LSL.R172H/+, Pdx1Cre (KPC) mice model with anti-PD-L1 than that without anti-PD-L1.

Conclusion

Using the immune escape-related genes, our study established and validated an IEGPI score in PDAC patients from the public dataset. IEGPI score has the potential to serve as a prognostic marker and as a tool for selecting tumor patients suitable for immunotherapy in clinical practice.

AQP5 Is a Novel Prognostic Biomarker in Pancreatic Adenocarcinoma

Background

Pancreatic adenocarcinoma (PAAD) is a highly malignant tumor with a poor prognosis. The identification of effective molecular markers is of great significance for diagnosis and treatment. Aquaporins (AQPs) are a family of water channel proteins that exhibit several properties and play regulatory roles in human carcinogenesis. However, the association between Aquaporin-5 (AQP5) expression and prognosis and tumor-infiltrating lymphocytes in PAAD has not been reported.

Methods

AQP5 mRNA expression, methylation, and protein expression data in PAAD were analyzed using GEPIA, UALCAN, HAP, METHSURV, and UCSC databases. AQP5 expression in PAAD patients and cell lines from our cohort was examined using immunohistochemistry and Western blotting. The LinkedOmics database was used to study signaling pathways related to AQP5 expression. TIMER and TISIDB were used to analyze correlations among AQP5, tumor-infiltrating immune cells, and immunomodulators. Survival was analyzed using TCGA and Kaplan-Meier Plotter databases.

Results

In this study, we investigated AQP5 expression in PAAD and determined whether the expression of AQP5 is a strong prognostic biomarker for PAAD. We searched and analyzed public cancer databases (GEO, TCGA, HAP, UALCAN, GEPIA, etc.) to conclude that AQP5 expression levels were upregulated in PAAD. Kaplan-Meier curve analysis showed that high AQP5 expression positively correlated with poor prognosis. Using TIMER and TISIDB, we found that the expression of AQP5 was associated with different tumor-infiltrating immune cells, especially macrophages. We found that hypomethylation of the AQP5 promoter region was responsible for its high expression in PAAD.

Conclusions

AQP5 can serve as a novel biomarker to predict prognosis and immune infiltration in PAAD.

A Glycolysis-Related Gene Signature Correlates With the Characteristics of the Tumor Immune Microenvironment and Predicts Prognosis in Patients With Hepatocellular Carcinoma

Aim: To develop a glycolysis-related gene signature that correlated with the characteristics of the tumor immune microenvironment and had good predictive power for overall survival (OS) in hepatocellular carcinoma (HCC).

Methods: Gene expression profiles, RNA sequencing data, clinical characteristics and survival information for 407 patients with HCC and 58 healthy controls were downloaded from the TCGA database. GSEA 4.1.0 software was used to evaluate the glycolysis-related pathways enriched in HCC compared to normal liver tissue. Univariate Cox, Least Absolute Shrinkage, Selection Operator, and two-step multivariate Cox analyses were used to construct a glycolysis-related gene signature for prognostic prediction. The glycolysis-related gene signature was combined with clinical characteristics to generate a nomogram. Tumor-infiltrating immune cell profiles and PD-L1 protein expression in HCC tissues were investigated.

Results: The gene expression profiles of HCC tissues were enriched in glycolysis-related pathways. A glycolysis-related gene signature was used to categorize patients as high-risk or low-risk, where high-risk patients had significantly worse OS. Receiver operating characteristic curves confirmed the predictive capability of the glycolysis-related gene signature for OS (AUC >0.80). There was a significant difference in M0 macrophage (p = 0.017), dendritic cell (p = 0.043), B cell (p = 0.0018), CD4 T cell (p = 0.003), Treg (p = 0.01) and mast cell (p = 0.02) content and PD-L1 protein expression (p = 0.019) between HCC tissues in patients in the high-risk and low-risk groups.

Conclusion: We established a glycolysis-related gene signature for OS in HCC that was predictive in training and test TCGA cohorts and correlated with the characteristics of the HCC tumor immune microenvironment. The glycolysis-related gene signature may guide clinical decision-making concerning patient selection for immunotherapy in HCC.

Identification and Validation of Three PDAC Subtypes and Individualized GSVA Immune Pathway-Related Prognostic Risk Score Formula in Pancreatic Ductal Adenocarcinoma Patients

Background

With the progress of precision medicine treatment in pancreatic ductal adenocarcinoma (PDAC), individualized cancer-related medical examination and prediction are of great importance in this high malignant tumor and tumor-immune microenvironment with changed pathways highly enrolled in the carcinogenesis of PDAC.

Methods

High-throughput data of pancreatic ductal adenocarcinoma were downloaded from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) database. After batch normalization, the enrichment pathway and relevant scores were identified by the enrichment of immune-related pathway signature using gene set variation analysis (GSVA). Then, cancerous subtype in TCGA and GEO samples was defined through the NMF methods by cancertypes packages in R software, respectively. Subsequently, the significance between the characteristics of each TCGA sample and cancer type and the significant prognosis-related pathway with risk score formula is calculated through t-test and univariate Cox analysis. Next, the prognostic value of gained risk score formula and each significant prognosis-related pathway were validated in TCGA and GEO samples by survival analysis. The pivotal hub genes in the enriched significant prognosis-related pathway are identified and validated, and the TIMER database was used to identify the potential role of hub genes in the PDAC immune environment. The potential role of hub genes is promoting the transdifferentiation of cancer-associated fibroblasts.

Results

The enrichment pathway and relevant scores were identified by GSVA, and 3 subtypes of pancreatic ductal adenocarcinoma were defined in TCGA and GEO samples. The clinical stage, tumor node metastasis classification, and tumor grade are strongly relative to the subtype above in TCGA samples. A risk formula about GSVA significant pathway “GSE45365_WT_VS_IFNAR_KO_CD11B_DC_MCMV_INFECTION_DN ∗ 0.80 + HALLMARK_GLYCOLYSIS ∗ 16.8 + GSE19888_CTRL_VS_T_CELL_MEMBRANES_ACT_MAST_CELL_DN ∗ 14.4” was identified and validated in TCGA and GEO samples through survival analysis with significance. DCN, VCAN, B4GALT7, SDC1, SDC2, B3GALT6, B3GAT3, SDC3, GPC1, and XYLT2 were identified as hub genes in these GSVA significant pathways and validated in silico.

Conclusions

Three pancreatic ductal adenocarcinoma subtypes are identified, and an individualized GSVA immune pathway score-related prognostic risk score formula with 10 hub genes is identified and validated. The predicted function of the 10 upregulated hub genes in tumor-immune microenvironment was promoting the infiltration of cancer-associated fibroblasts. These findings will contribute to the precision medicine of pancreatic ductal adenocarcinoma treatment and tumor immune-related basic research.

A Prognostic Model of Differentiated Thyroid Cancer Based on Up-Regulated Glycolysis-Related Genes

OBJECTIVE

This study aims to identify reliable prognostic biomarkers for differentiated thyroid cancer (DTC) based on glycolysis-related genes (GRGs), and to construct a glycolysis-related gene model for predicting the prognosis of DTC patients.

METHODS

We retrospectively analyzed the transcriptomic profiles and clinical parameters of 838 thyroid cancer patients from 6 public datasets. Single factor Cox proportional risk regression analysis and Least Absolute Shrinkage and Selection Operator (LASSO) were applied to screen genes related to prognosis based on 2528 GRGs. Then, an optimal prognostic model was developed as well as evaluated by Kaplan-Meier and ROC curves. In addition, the underlying molecular mechanisms in different risk subgroups were also explored via The Cancer Genome Atlas (TCGA) Pan-Cancer study.

RESULTS

The glycolysis risk score (GRS) outperformed conventional clinicopathological features for recurrence-free survival prediction. The GRS model identified four candidate genes (ADM, MKI67, CD44 and TYMS), and an accurate predictive model of relapse in DTC patients was established that was highly correlated with prognosis (AUC of 0.767). In vitro assays revealed that high expression of those genes increased DTC cancer cell viability and invasion. Functional enrichment analysis indicated that these signature GRGs are involved in remodelling the tumour microenvironment, which has been demonstrated in pan-cancers. Finally, we generated an integrated decision tree and nomogram based on the GRS model and clinicopathological features to optimize risk stratification (AUC of the composite model was 0.815).

CONCLUSIONS

The GRG signature-based predictive model may help clinicians provide a prognosis for DTC patients with a high risk of recurrence after surgery and provide further personalized treatment to decrease the chance of relapse.

Development and validation of glycolysis-related prognostic score for prediction of prognosis and chemosensitivity of pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy with aggressive biological behaviour. Its rapid proliferation and tumour growth require reprogramming of glucose metabolism or the Warburg effect. However, the association between glycolysis-related genes with clinical features and prognosis of PDAC is still unknown. Here, we used the meta-analysis to correlate the hazard ratios (HR) of 106 glycolysis genes from MSigDB by the cox proportional hazards regression analysis in 6 clinical data sets of PDAC patients to form a training cohort, and a single group of PDAC patients from the TCGA, ICGC, Arrayexpress and GEO databases to form the validation cohort. Then, a glycolysis-related prognosis (GRP) score based on 29 glycolysis prognostic genes was established in 757 PDAC patients from the training composite cohort and validated in 267 ICGC-CA validation cohort (all P < .05). In addition, including PADC, the prognostic value was also confirmed in other 7 out of 30 pan-cancer cohorts. The GRP score was significantly related to specific metabolism pathways, immune genes and immune cells in the patients with PADC (all P < .05). Finally, by combining with immune cells, the GRP score also well-predicted the chemosensitivity of patients with PADC in the TCGA cohort (AUC = 0.709). In conclusion, this study developed a GRP score for patients with PDAC in predicting prognosis and chemosensitivity for PDAC.