Establishment of a prognostic model of four genes in gastric cancer based on multiple data sets

Comprehensive analysis of CPNE1 predicts prognosis and drug resistance in gastric adenocarcinoma

BACKGROUND

Recent studies have confirmed that Copines-1 (CPNE1) is associated with many malignancies. However, the role of CPNE1 in stomach adenocarcinoma (STAD) is currently unclear.

METHODS

TIMER2.0, TCGA, UALCAN databases were used to investigate the expression of CPNE1 in STAD and normal tissues. KM-plotter database was used to explore the relationship between CPNE1 expression and prognosis in STAD. Immunohistochemistry (IHC) was used to assess the protein levels of CPNE1 in both normal and cancer tissues, as well as to confirm the prognostic significance of CPNE1. In order to assess the viability of CPNE1 as a divider, the Recipient Operating Characteristics (ROC) curve was employed and the assessment based on the AUC score (below the curve). To investigate the potential function of CPNE1, correlation analysis and enrichment analysis were performed with the clusterProfiler package in R software. The CPNE1 binding protein network was constructed by STRING and GeneMANIA. The relationship between methylation and prognosis was explored by Methsurv database. The Genomics of Drug Sensitivity in Cancer (GDSC) was employed to predict drug responsiveness in STAD. Ultimately, CCK-8 assays and RT-qPCR were performed to confirm the correlation between CPNE1 expression and the IC50 of Axitinib in the AGS cell line.

RESULT

CPNE1 is highly expressed in various cancers, including STAD. High expression of CPNE1 indicated poor overall survival (OS) of STAD (P < 0.05). The ROC curve suggested that CPNE1 was a potential diagnostic biomarker (AUC = 0.925). The functions of CPNE1 were enriched in DNA-acting catalytic activity, sulfur transferase activity, Ran GTPase binding, DNA helicase activity, helicase activity and eukaryotic ribosome biosynthesis. Hyper-methylated CPNE1 predicts better prognosis in STAD (P < 0.05). Additionally, STAD patients with high-expression CPNE1 seemed to be more resistant to the chemotherapeutic agents, including A-770041, WH-4-023, AZD-2281, AG-014699, AP-24534, Axitinib, AZD6244, RDEA119, AZD8055, Temsirolimus, Pazopanib and Roscovitine. In vitro experiments demonstrated the involvement of CPNE1 in Axitinib chemoresistance.

CONCLUSION

CPNE1 could be a predictive biomarker and a potential target for biological therapy in STAD.

Prediction of all-cause death and specific causes of death in patients with gastric cancer with liver metastasis: a Surveillance, Epidemiology, and End Results-based study

BACKGROUND

Gastric cancer (GC), considered the fifth most prevalent malignancy, is the fourth leading cause of cancer death worldwide. This cancer is heterogeneous and invasive and often metastasizes to the liver. The survival of patients with GC, especially cancer-specific survival (CSS), is a matter of concern to their families and medical workers in clinical practice. However, efficient tools for early risk prediction are lacking. Thus, this study aimed to develop a nomogram for forecasting the overall survival (OS) and CSS of patients with GC with liver metastasis (GCLM) based on the Surveillance, Epidemiology, and End Results (SEER) database.

METHODS

Information on individuals with GCLM was acquired from the SEER database from January 2000 to December 2015. Patients' data were randomized into the train cohort and the test cohort. The independent factors for CSS and OS were determined by univariate and multivariate competing risk analyses and Cox proportional hazards analysis, and the nomograms for predicting CSS and OS were constructed. The receiver operating characteristic curve and calibration curve were used to measure the accuracy and calibration of nomograms.

RESULTS

Our study included 4372 patients with GCLM, with 3060 patients in the train set and 1312 in the test set. The mean follow-up period was 12.31 months. The independent factors influencing the OS of patients with GCLM were age, bone metastasis, chemotherapy, grade, lung metastasis, stage, primary site, radiotherapy, surgical primary site, T stage, and tumor size. The concordance Index (C-index) of the constructed nomogram for OS were 0.718 (SE, 0.004) in the train set and 0.0.680 (SE, 0.006) in the test set. The independent factors affecting the CSS of patients with GCLM were age, chemotherapy, grade, lung metastasis, stage, radiotherapy, regional lymph node positive, surgical primary site, and total number of tumors. The C-index for the constructed nomogram for CSS were 0.696 (SE, 0.005) in the train set and 0.696 (SE, 0.008) in the test set.

CONCLUSION

The constructed nomograms showed satisfactory performance in predicting the OS and CSS of patients with GCLM, which can help clinicians formulate follow-up and rehabilitation strategies conducive to survival. At the same time, it can provide more family and social support for high-risk groups.

Risk stratification in gastric cancer lung metastasis: Utilizing an overall survival nomogram and comparing it with previous staging

BACKGROUND

Gastric cancer (GC) is prevalent and aggressive, especially when patients have distant lung metastases, which often places patients into advanced stages. By identifying prognostic variables for lung metastasis in GC patients, it may be possible to construct a good prediction model for both overall survival (OS) and the cumulative incidence prediction (CIP) plot of the tumour.

AIM

To investigate the predictors of GC with lung metastasis (GCLM) to produce nomograms for OS and generate CIP by using cancer-specific survival (CSS) data.

METHODS

Data from January 2000 to December 2020 involving 1652 patients with GCLM were obtained from the Surveillance, epidemiology, and end results program database. The major observational endpoint was OS; hence, patients were separated into training and validation groups. Correlation analysis determined various connections. Univariate and multivariate Cox analyses validated the independent predictive factors. Nomogram distinction and calibration were performed with the time-dependent area under the curve (AUC) and calibration curves. To evaluate the accuracy and clinical usefulness of the nomograms, decision curve analysis (DCA) was performed. The clinical utility of the novel prognostic model was compared to that of the 7th edition of the American Joint Committee on Cancer (AJCC) staging system by utilizing Net Reclassification Improvement (NRI) and Integrated Discrimination Improvement (IDI). Finally, the OS prognostic model and Cox-AJCC risk stratification model modified for the AJCC system were compared.

RESULTS

For the purpose of creating the OS nomogram, a CIP plot based on CSS was generated. Cox multivariate regression analysis identified eleven significant prognostic factors (P < 0.05) related to liver metastasis, bone metastasis, primary site, surgery, regional surgery, treatment sequence, chemotherapy, radiotherapy, positive lymph node count, N staging, and time from diagnosis to treatment. It was clear from the DCA (net benefit > 0), time-dependent ROC curve (training/validation set AUC > 0.7), and calibration curve (reliability slope closer to 45 degrees) results that the OS nomogram demonstrated a high level of predictive efficiency. The OS prediction model (New Model AUC = 0.83) also performed much better than the old Cox-AJCC model (AUC difference between the new model and the old model greater than 0) in terms of risk stratification (P < 0.0001) and verification using the IDI and NRI.

CONCLUSION

The OS nomogram for GCLM successfully predicts 1- and 3-year OS. Moreover, this approach can help to appropriately classify patients into high-risk and low-risk groups, thereby guiding treatment.

Key Roles of p53 Signaling Pathway-Related Factors GADD45B and SERPINE1 in the Occurrence and Development of Gastric Cancer

p53 can function as an independent and unfavorable prognosis biomarker in cancer patients. We tried to identify the key factors of the p53 signaling pathway involved in gastric cancer (GC) occurrence and development based on the genotype-tissue expression (GTEx) and the Cancer Genome Atlas (TCGA) screening. We downloaded gene expression data and clinical data of GC included in the GTEx and TCGA databases, followed by differential analysis. Then, the key factors in the p53 signaling pathway were identified, followed by an analysis of the correlation between key factors and the prognosis of GC patients. Human GC cell lines were selected for in vitro cell experiments to verify the effects of key prognostic factors on the proliferation, migration, invasion, and apoptosis of GC cells. We found 4,944 significantly differentially expressed genes (DEGs), of which 2,465 were upregulated and 2,479 downregulated in GC. Then, 27 DEGs were found to be involved in the p53 signaling pathway. GADD45B and SERPINE1 genes were prognostic high-risk genes. The regression coefficients of GADD45B and SERPINE1 were positive. GADD45B was poorly expressed, while SERPINE1 was highly expressed in GC tissues, highlighting their prognostic role in GC. The in vitro cell experiments confirmed that overexpression of GADD45B or silencing of SERPINE1 could inhibit the proliferation, migration, and invasion and augment the apoptosis of GC cells. Collectively, the p53 signaling pathway-related factors GADD45B and SERPINE1 may be key genes that participate in the development of GC.

Comprehensive analysis of chemokines family and related regulatory ceRNA network in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is one of the world's commonest malignancies with a high fatality rate. Chemokines not only regulate immune response but also participate in tumor development and metastasis and yet the mechanism of chemokines in LUAD remains unclear. In this study, transcriptional expression profiles, mutation data, and copy number variation data were downloaded from The Cancer Genome Atlas (TCGA). Risk gene protein expression was assessed by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and the Human Protein Atlas (HPA). Gene Expression Omnibus (GEO) data was used to validate the prognostic model. We summarized the genetic mutation variation landscape of chemokines. The risk prognosis model was developed based on differentially expressed chemokines, and patients in the high-risk score (RS) group had lower survival rates. Gene Set Enrichment Analysis (GSEA) revealed that high-RS patients were associated with metabolic transformation pathways, while low-RS patients were associated with immune-related pathways. Compared with the high-RS group, the low-RS group had higher immune/stromal/estimate scores calculated by the ESTIMATE package. The proportion of immune cells obtained using the CIBERSORT package was significantly different between the two groups. Most of the immune checkpoints were highly expressed in low-RS samples. Finally, we discovered that the lncRNA MIR17HG/AC009299.3/miR-21–5p/CCL20 regulatory network might be crucial in the pathogenesis of LUAD. In conclusion, we developed a risk signature and chemokine-related competing endogenous RNA (ceRNA) network.

System analysis based on the pyroptosis-related genes identifies GSDMC as a novel therapy target for pancreatic adenocarcinoma

Background

Pancreatic adenocarcinoma (PAAD) is one of the most common malignant tumors of the digestive tract. Pyroptosis is a newly discovered programmed cell death that highly correlated with the prognosis of tumors. However, the prognostic value of pyroptosis in PAAD remains unclear.

Methods

A total of 178 pancreatic cancer PAAD samples and 167 normal samples were obtained from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. The “DESeq2” R package was used to identify differntially expressed pyroptosis-related genes between normal pancreatic samples and PAAD samples. The prognostic model was established in TCGA cohort based on univariate Cox and the least absolute shrinkage and selection operator (LASSO) Cox regression analyses, which was validated in test set from Gene Expression Omnibus (GEO) cohort. Univariate independent prognostic analysis and multivariate independent prognostic analysis were used to determine whether the risk score can be used as an independent prognostic factor to predict the clinicopathological features of PAAD patients. A nomogram was used to predict the survival probability of PAAD patients, which could help in clinical decision-making. The R package "pRRophetic" was applied to calculate the drug sensitivity of each samples from high- and low-risk group. Tumor immune infiltration was investigated using an ESTIMATE algorithm. Finally, the pro‐tumor phenotype of GSDMC was explored in PANC-1 and CFPAC-1 cells.

Result

On the basis of univariate Cox and LASSO regression analyses, we constructed a risk model with identified five pyroptosis-related genes (IL18, CASP4, NLRP1, GSDMC, and NLRP2), which was validated in the test set. The PAAD samples were divided into high-risk and low-risk groups on the basis of the risk score's median. According to Kaplan Meier curve analysis, samples from high-risk groups had worse outcomes than those from low-risk groups. The time-dependent receiver operating characteristics (ROC) analysis revealed that the risk model could predict the prognosis of PAAD accurately. A nomogram accompanied by calibration curves was presented for predicting 1-, 2-, and 3-year survival in PAAD patients. More importantly, 4 small molecular compounds (A.443654, PD.173074, Epothilone. B, Lapatinib) were identified, which might be potential drugs for the treatment of PAAD patients. Finally, the depletion of GSDMC inhibits the proliferation, invasion, and migration of pancreatic adenocarcinoma cells.

Conclusion

In this study, we developed a pyroptosis-related prognostic model based on IL18, CASP4, NLRP1, NLRP2, and GSDMC , which may be helpful for clinicians to make clinical decisions for PAAD patients and provide valuable insights for individualized treatment. Our result suggest that GSDMC may promote the proliferation and migration of PAAD cell lines. These findings may provide new insights into the roles of pyroptosis-related genes in PAAD, and offer  new therapeutic targets for the treatment of PAAD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03632-z.

Identification of a chemotherapy-associated gene signature for a risk model of prognosis in gastric adenocarcinoma through bioinformatics analysis

BACKGROUND

Over the past few years, the overall survival rate of patients with gastric adenocarcinoma who have received different chemotherapy regimens has increased. However, not all gastric cancer patients who receive chemotherapy have a longer survival. We need better predictive biomarkers. This study is to construct a new risk model of chemotherapy-associated genes in gastric adenocarcinoma (GA) for prognostication.

METHODS

RNA-seq data and clinical information of GSE26901 (containing 44 chemotherapy samples and 65 patients without chemotherapy) in Gene Expression Omnibus (GEO) and stomach adenocarcinoma (STAD, containing 360 cancer tissue samples and 50 paired normal tissue samples) in The Cancer Genome Atlas (TCGA) were selected for screening differentially expressed genes (DEGs). Multivariate Cox regression was conducted to screen prognosis-associated genes and its link to patients' prognosis were screened by least absolute shrinkage and selection operator (LASSO) regression analysis. Based on the key genes, a risk scoring equation for the prognosis model was established, and constructed survival prognosis model. The model was tested for predictive ability through training set (TCGA datasets) and validation set (GSE84437). The correlations of the risk score with clinical pathological features, immune score and drug sensitivity score were evaluated.

RESULTS

In total, 179 overlapping genes were obtained by screening DEGs. Univariate Cox analysis revealed 36 prognosis-related genes, and LASSO regression analysis revealed 8 key genes (KCNJ2, GATA5, CLDN1, SERPINE1, FCER2, PMEPA1, TMEM37 and CRTAC1). Kaplan-Meier (K-M) analysis uncovered a relatively short overall survival time in the high-risk group. The model was verified to possess favourable predictive ability. In addition, the nomogram model were demonstrated good predictability with area under the curve (AUC) for 1-5 years in training set were 0.78, 0.78, 0.76, 0.79 and 0.81. The high-risk group was less likely to get benefits from immunotherapy and less sensitive to cisplatin.

CONCLUSIONS

According to the results of our training set and validation set, the risk model based on the eight chemotherapy-related gene signatures predicting prognosis has certain predictive accuracy in predicting the survival of GA patients which can be a promising prognostic parameter for GA. However, its efficacy remains to be proved in clinical practice.

Discoidin domain receptor 1 is a potential target correlated with tumor invasion and immune infiltration in gastric cancer

Discoidin domain receptor 1 (DDR1) has been demonstrated to be able to promote tumor invasion and metastasis and being closely related to tumor immune infiltration. However, DDR1 has rarely been studied in gastric cancer. Here, we primarily evaluated DDR1 expression in gastric cancer and its cell lines using multiple databases. Subsequently, the cancer prognosis was investigated in relation to DDR1 expression. After analysis, we discovered that DDR1 was highly expressed and significantly connected with poor prognosis in gastric cancer. To comprehensively understand the molecular mechanism of DDR1, we explored genes and proteins interacting with DDR1 in gastric cancer using databases. Additionally, we found that the expression level of DDR1 was inversely correlated with immune infiltration and significantly relative to various immune cell markers. Overall, DDR1 was implicated in invasion, metastasis, and immune infiltration of gastric cancer. Inhibition of DDR1 may have the potential to alleviate the strong invasiveness and metastasis of advanced gastric cancer. Meanwhile, immune exclusion by DDR1 may also provide a new strategy for improving the efficacy of immune checkpoints inhibitors (ICIs), such as programmed cell death protein 1 (PD-1) antibody.

Development and validation of two nomograms for predicting overall survival and cancer-specific survival in gastric cancer patients with liver metastases: A retrospective cohort study from SEER database

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Nomograms constructed by overall survival and cancer-specific survival can predict more accurately than AJCC stage system for GCLM patients.

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The study includes the prognostic factor as many as possible and evaluated all of them in the cohort.

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In our cohort, surgery is a beneficial factor associated with survival.

Background

Gastric cancer is heterogeneous and aggressive, especially with liver metastasis. This study aims to develop two nomograms to predict the overall survival (OS) and cancer-specific survival (CSS) of gastric cancer with liver metastasis (GCLM) patients.

Methods

From January 2000 to December 2018, a total of 1936 GCLM patients were selected from the Surveillance, Epidemiology, and End Results Program (SEER) database. They were further divided into a training cohort and a validation cohort, with the OS and CSS serving as the study's endpoints. The correlation analyses were used to determine the relationship between the variables. The univariate and multivariate Cox analyses were used to confirm the independent prognostic factors. To discriminate and calibrate the nomogram, calibration curves and the area under the time-dependent receiver operating characteristic curve (time-dependent AUC) were used. DCA curves were used to examine the accuracy and clinical benefits. The clinical utility of the nomogram and the AJCC Stage System was compared using net reclassification improvement (NRI) and integrated differentiation improvement (IDI) (IDI). Finally, the nomogram and the AJCC Stage System risk stratifications were compared.

Results

There was no collinearity among the variables that were screened. The results of multivariate Cox regression analysis showed that six variables (bone metastasis, lung metastasis, surgery, chemotherapy, grade, age) and five variables (lung metastasis, surgery, chemotherapy, grade, N stage) were identified to establish the nomogram for OS and CSS, respectively. The calibration curves, time-dependent AUC curves, and DCA revealed that both nomograms had pleasant predictive power. Furthermore, NRI and IDI confirmed that the nomogram outperformed the AJCC Stage System.

Conclusion

Both nomograms had satisfactory accuracy and were validated to assist clinicians in evaluating the prognosis of GCLM patients.

Collagen type X alpha 1 promotes proliferation, invasion and epithelial-mesenchymal transition of cervical cancer through activation of TGF-β/Smad signaling

Background

Collagen type X alpha 1 (COL10A1) belongs to the collagen family and constitutes the main component of the interstitial matrix. COL10A1 was found to be dysregulated in various cancers, and to participate in tumorigenesis. However, the role of COL10A1 in cervical cancer (CC) remains unclear.

Methods

Expression of COL10A1 in CC cells and tissues was detected by western blot and qRT-PCR. CC cells were transfected with pcDNA-COL10A1 or si-COL10A1, and the effect of COL10A1 on cell proliferation of CC was assessed by MTT and colony formation assays. Cell metastasis was detected by wound healing and transwell assays. Western blot was applied to evaluate epithelial-mesenchymal transition.

Results

COL10A1 was significantly elevated in CC tissues and cells (P < 0.001). Over-expression of COL10A1 increased cell viability of CC (P < 0.001), and enhanced the number of colonies (P < 0.001). However, knockdown of COL10A1 reduced the cell proliferation of CC (P < 0.001). Over-expression of COL10A1 also promoted cell migration (P < 0.001) and invasion (P < 0.001) of CC, whereas silencing of COL10A1 suppressed cell metastasis (P < 0.001). Protein level of E-cadherin in CC was reduced (P < 0.05), whereas N-cadherin and vimentin were enhanced by COL10A1 over-expression (P < 0.001). Silencing of COL10A1 reduced the protein level of TGF-β1 (P < 0.01), and down-regulated the phosphorylation of Smad2 and Smad3 in CC (P < 0.001).

Conclusion

Down-regulation of COL10A1 suppressed cell proliferation, metastasis, and epithelial-mesenchymal transition of CC through inactivation of TGF-β/Smad signaling.

Identification of Potential Diagnoses Based on Immune Infiltration and Autophagy Characteristics in Major Depressive Disorder

Background: Major depressive disorder (MDD) is a serious mental illness characterized by mood changes and high suicide rates. However, no studies are available to support a blood test method for MDD diagnosis. The objective of this research was to identify potential peripheral blood biomarkers for MDD and characterize the novel pathophysiology.

Methods: We accessed whole blood microarray sequencing data for MDD and control samples from public databases. Biological functions were analysed by GO and KEGG pathway enrichment analyses using the clusterprofile R package. Infiltrated immune cell (IIC) proportions were identified using the CIBERSORT algorithm. Clustering was performed using the ConsensusClusterPlus R package. Protein–protein interactions (PPI) were assessed by constructing a PPI network using STRING and visualized using Cytoscape software. Rats were exposed to chronic unpredictable mild stress (CUMS) for 6 weeks to induce stress behaviour. Stress behaviour was evaluated by open field experiments and forced swimming tests. Flow cytometry was used to analyse the proportion of CD8⁺ T cells. The expression of the corresponding key genes was detected by qRT–PCR.

Results: We divided MDD patients into CD8H and CD8L clusters. The functional enrichment of marker genes in the CD8H cluster indicated that autophagy-related terms and pathways were significantly enriched. Furthermore, we obtained 110 autophagy-related marker genes (ARMGs) in the CD8H cluster through intersection analysis. GO and KEGG analyses further showed that these ARMGs may regulate a variety of autophagy processes and be involved in the onset and advancement of MDD. Finally, 10 key ARMGs were identified through PPI analysis: RAB1A, GNAI3, VAMP7, RAB33B, MYC, LAMP2, RAB11A, HIF1A, KIF5B, and PTEN. In the CUMS model, flow cytometric analysis confirmed the above findings. qRT–PCR revealed significant decreases in the mRNA levels of Gnai3, Rab33b, Lamp2, and Kif5b in the CUMS groups.

Conclusion: In this study, MDD was divided into two subtypes. We combined immune infiltrating CD8⁺ T cells with autophagy-related genes and screened a total of 10 ARMG genes. In particular, RAB1A, GNAI3, RAB33B, LAMP2, and KIF5B were first reported in MDD. These genes may offer new hope for the clinical diagnosis of MDD.

Unsupervised learning of cross-modal mappings in multi-omics data for survival stratification of gastric cancer

Aims: This study presents a survival stratification model based on multi-omics integration using bidirectional deep neural networks (BiDNNs) in gastric cancer. Methods: Based on the survival-related representation features yielded by BiDNNs through integrating transcriptomics and epigenomics data, K-means clustering analysis was performed to cluster tumor samples into different survival subgroups. The BiDNNs-based model was validated using tenfold cross-validation and in two independent confirmation cohorts. Results: Using the BiDNNs-based survival stratification model, patients were grouped into two survival subgroups with log-rank p-value = 9.05E-05. The subgroups classification was robustly validated in tenfold cross-validation (C-index = 0.65 ± 0.02) and in two confirmation cohorts (E-GEOD-26253, C-index = 0.609; E-GEOD-62254, C-index = 0.706). Conclusion: We propose and validate a robust and stable BiDNN-based survival stratification model in gastric cancer.