GEO Cohorts Research Articles

Integrated Analysis of Genomic and Transcriptomic Profiles Identified the Role of GTP Binding Protein-4 (GTPBP4) in Breast Cancer.

Purpose: To explore the significance of GTP-binding protein 4 (GTPBP4) in breast cancer. Methods: Firstly, GTPBP4 expression analysis was performed in TIMER and UALCAN databases. Subsequently, the TCGA cohort and multiple Gene Expression Omnibus Cohorts were used as validation for GTPBP4 expression. Besides, we also evaluated the diagnostic value of GTPBP4 in TCGA Cohort and multiple GEO Cohorts. The predictive effect of GTPBP4 in breast cancer was then assessed using survival analysis. Then we look at the role of GTPBP4 in the immune milieu and create a Nomogram to help patients with breast cancer understand their prognosis. Finally, in vitro tests were carried out to look at GTPBP4 expression and function in breast cancer cell lines. Results: GTPBP4 is an independent breast cancer prognostic factor that is upregulated in the disease (p < 0.05). Enrichment analysis showed that GTPBP4 was associated with multiple functions and pathways. In addition, GTPBP4 is associated with a variety of immune cell types (p < 0.05). PCR assay showed that GTPBP4 expression was up-regulated in breast cancer cell lines. The activity, migration, and proliferation of breast cancer cells were considerably reduced after GTPBP4 knockdown in the CCK-8, Transwell, and Scratch assays. Conclusions: Our research discovered a new breast cancer biomarker that can be used as a guide for breast cancer diagnosis and treatment.

Identification of Prognostic and Tumor Microenvironment by Shelterin Complex-Related Signatures in Oral Squamous Cell Carcinoma.

Oral squamous cell carcinoma (OSCC) is a common malignant tumor of the oral cavity. Shelterin complex gene (SG) has an important role in regulating telomere structure and length. SG is considered promising as a novel prognostic marker for cancer and a potential target for tumor therapy. However, SGs have not been systematically studied in OSCC. We analyzed SGs based on public data from OSCC patients and showed that SGs are closely associated with the prognosis of OSCC patients. Two different subtypes of SGs were identified in the TCGA and GEO cohorts, and LASSO regression analysis was used to further construct an SGs-related prognostic model. Randomized cohorts and different clinical subgroups validated the model's accuracy. The assessment of clinical characteristics, tumor mutational burden (TMB), and tumor microenvironment (TME) between high- and low-risk scores groups showed lower TMB, more abundant immune cell infiltration, and better prognosis in the low-risk group. According to the IPS analysis, patients in the low-risk group were more responsive to immunotherapy. This study establishes a foundation for research on SG and confirms that risk scores can predict prognosis and guide clinical treatment in OSCC patients.

3D Collagen Fiber Concentration Regulates Treg Cell Infiltration in Triple Negative Breast Cancer.

BackgroundTriple negative breast cancer (TNBC) is characterized by poor prognosis and a lack of effective therapeutic agents owing to the absence of biomarkers. A high abundance of tumor-infiltrating regulatory T cells (Tregs) was associated with worse prognosis in malignant disease. Exploring the association between Treg cell infiltration and TNBC will provide new insights for understanding TNBC immunosuppression and may pave the way for developing novel immune-based treatments.Materials and MethodsPatients from TCGA were divided into Treg-high (Treg-H) and Treg-low (Treg-L) groups based on the abundance of Tregs according to CIBERSORT analysis. The association between expression level of Tregs and the clinical characteristics as well as prognosis of breast cancer were evaluated. Next, a Treg-related prognostic model was established after survival-dependent univariate Cox and LASSO regression analysis, companied with an external GEO cohort validation. Then, GO, KEGG and GSEA analyses were performed between the Treg-H and Treg-L groups. Masson and Sirius red/Fast Green staining were applied for ECM characterization. Accordingly, Jurkat T cells were encapsulated in 3D collagen to mimic the ECM microenvironment, and the expression levels of CD4, FOXP3 and CD25 were quantified according to immunofluorescence staining.ResultsThe expression level of Tregs is significantly associated with the clinical characteristics of breast cancer patients, and a high level of Treg cell expression indicates a poor prognosis in TNBC. To further evaluate this, a Treg-related prognostic model was established that accurately predicted outcomes in both TCGA training and GEO validation cohorts of TNBC patients. Subsequently, ECM-associated signaling pathways were identified between the Treg-H and Treg-L groups, indicating the role of ECM in Treg infiltration. Since we found increasing collagen concentrations in TNBC patients with distant migration, we encapsulated Jurkat T cells within a 3D matrix with different collagen concentrations and observed that increasing collagen concentrations promoted the expression of Treg biomarkers, supporting the regulatory role of ECM in Treg infiltration.ConclusionOur results support the association between Treg expression and breast cancer progression as well as prognosis in the TNBC subtype. Moreover, increasing collagen density may promote Treg infiltration, and thus induce an immunosuppressed TME.

Significance of a Tumor Mutation Burden Gene Signature with Prognosis and Immune Feature of Gastric Cancer Patients.

Gastric cancer (GC) is a common digestive tumor which ranks the fourth most common malignancy worldwide. Immunotherapy is a promising treatment for GC, especially for advanced gastric cancer (AGC). However, in clinical practice, not all patients are sensitive to immunotherapy. Recent studies showed that tumor mutation burden (TMB) is closely correlated with the response of immunotherapy. The current study identified a TMB-related genes' signature to predict the prognosis and immune feature of GC patients. Firstly, we acquired the TMB data and expression data from The Cancer Genome Atlas (TCGA) and the National Center for Biotechnology Information (NCBI) GEO databases. Then, we extracted TMB-related genes from the expression data of TCGA and two GEO cohorts. By using univariate Cox analysis, we identified that the 429 genes were correlated to GC patients' overall survival. Subsequently, an immune prognostic signature was constructed by using the least absolute shrinkage and selection operator analysis (LASSO) and multivariate Cox regression analysis. The signature could be utilized to predict the prognosis of GC patients. In addition, the signature showed a closed correlation with immune feature of GC patients. In conclusion, our risk signature could offer hints for the prognosis of GC patients and might provide insights to formulate new immunotherapy strategies for GC patients.

Pyroptosis is related to immune infiltration and predictive for survival of colon adenocarcinoma patients

Pyroptosis is a novel type of programmed cell death, initiated by inflammasome. Pyroptosis inhibits the development and metastasis of colon cancer and is associated with patients’ prognosis. However, how the pyroptosis-related genes predict the survival of patients is still unclear. In the study, colon adenocarcinoma (COAD) patients were divided into two groups according to the expression of pyroptosis-related regulators through consensus clustering. DEGs between two clusters were analyzed by using COX and Lasso regression. Then, regression coefficients in Lasso were used to calculate the risk score for every patient. Patients were classified into two types: low- and high-risk group according to their risk score. The difference of immune microenvironment infiltration and clinicopathological characteristics between subgroups was performed. Moreover, the nomogram model was built on the bases of risk model and clinicopathological factors. The TCGA-COAD cohort and GEO cohort were used as training and validating set respectively. 398 COAD patients in TCGA training set were identified as two regulation patterns via unsupervised clustering method. Patients in cluster 2 showed better prognosis (P = 0.002). Through differentiated expression analysis, COX and Lasso regression, a 5-gene prognostic risk model was constructed. This risk model was significantly associated with OS (HR: 2.088, 95% CI: 1.183–3.688, P = 0.011), validated in GEO set (HR:1.344, 95%CI: 1.061–1.704, P = 0.014), and patients with low risk had better prognosis (P < 0.001 in TCGA; P = 0.038 in GEO). Through ROC analysis, it can be found that this model presented better predictive accuracy for long-term survival. Clinical analyses demonstrated that high-risk group had more advanced N stage, higher risk of metastasis and later pathological stage. Immune-related analysis illustrated that low-risk group had more immune cell infiltration and more activated immune pathways. The pyroptosis-related risk model can be predictive for the survival of COAD patients. That patients with higher risk had poorer prognosis was associated with more advanced tumor stage and higher risk of metastasis, and resulted from highly activated pro-tumor pathways and inhibited immune system and poorer integrity of intestinal epithelial. This study proved the relationship between pyroptosis and immune, which offered basis for future studies.

Exploration of Different Hypoxia Patterns and Construction of a Hypoxia-Related Gene Prognostic Index in Colorectal Cancer.

IntroductionImmune checkpoint inhibitor (ICI) therapy has been proven to be a highly efficacious treatment for colorectal adenocarcinoma (COAD). However, it is still unclear how to identify those who might benefit the most from ICI therapy. Hypoxia facilitates the progression of the tumor from different aspects, including proliferation, metabolism, angiogenesis, and migration, and improves resistance to ICI. Therefore, it is essential to conduct a comprehensive understanding of the influences of hypoxia in COAD and identify a biomarker for predicting the benefit of ICI.MethodsAn unsupervised consensus clustering algorithm was used to identify distinct hypoxia-related patterns for COAD patients from TCGA and the GEO cohorts. The ssGSEA algorithm was then used to explore the different biological processes, KEGG pathways, and immune characteristics among distinct hypoxia-related clusters. Some hypoxia-related hub genes were then selected by weighted gene coexpression network analysis (WGCNA). Subsequently, univariate Cox regression analysis, multivariate Cox regression analysis, and least absolute shrinkage and selection operator (LASSO) regression were utilized to construct a hypoxia-related gene prognostic index (HRGPI). Finally, validation was also conducted for HRGPI in prognostic value, distinguishing hypoxia-related characteristics and benefits of ICI.ResultsWe identified four hypoxia-related clusters and found that different hypoxia response patterns induced different prognoses significantly. Again, we found different hypoxia response patterns presented distinct characteristics of biological processes, signaling pathways, and immune features. Severe hypoxia conditions promoted activation of some cancer-related signaling pathways, including Wnt, Notch, ECM-related pathways, and remodeled the tumor microenvironment of COAD, tending to present as an immune-excluded phenotype. Subsequently, we selected nine genes (ANO1, HOXC6, SLC2A4, VIP, CD1A, STC2, OLFM2, ATP6V1B1, HMCN2) to construct our HRGPI, which has shown an excellent prognostic value. Finally, we found that HRGPI has an advantage in distinguishing immune and molecular characteristics of hypoxia response patterns, and it could also be an excellent predictive indicator for clinical response to ICI therapy.ConclusionDifferent hypoxia response patterns activate different signaling pathways, presenting distinct biological processes and immune features. HRGPI is an independent prognostic factor for COAD patients, and it could also be used as an excellent predictive indicator for clinical response to ICI therapy.

A panel of necroptosis-related genes predicts the prognosis of pancreatic adenocarcinoma

Pancreatic adenocarcinoma (PAAD) has become one of the deadliest malignancies in the world. Since necroptosis plays a crucial role in regulating the immune system, it is necessary to develop novel prognostic biomarkers associated with necroptosis and explore its potential role in PAAD. The transcriptome RNA-seq data of PAAD were downloaded from the TCGA and GTEx databases. A prognostic signature was constructed by the least absolute shrinkage and selection operator (LASSO) Cox regression, and its prognostic value was evaluated by nomogram and validated in an independent GEO cohort. We identified a total of 24 differentially expressed NRGs in PAAD, and constructed a prognostic signature with 5 NRGs, which showed good performance in predicting the prognosis of PAAD patients. The ROC curves for 1-, 3-, and 5-year survival rate were 0.652, 0.778, and 0.817, respectively. This prognostic signature showed consistent prognosis prediction in an independent patient cohort. Furthermore, the correlations between 5-NRGs signature and TMB, MSI, histopathological classification, immune infiltration, immune types, and immunomodulators were all significant. Notably, the expression profiles of the five NRGs in exosomes of serum were consistent with their expression in tumor tissues. These data suggested that the 5-NRGs signature is a promising biomarker for predicting the prognosis of PAAD.

Characterization of the Immune Cell Infiltration Landscape Uncovers Prognostic and Immunogenic Characteristics in Lung Adenocarcinoma.

The immune cell infiltration in TME has been reported to be associated with prognosis and immunotherapy efficiency of lung cancers. However, to date, the immune infiltrative landscape of lung adenocarcinoma (LUAD) has not been elucidated yet. Therefore, this study aimed to identify a new transcriptomic-based TME classification and develop a risk scoring system to predict the clinical outcomes of patients with LUAD. We applied “CIBERSORT” algorithm to analyze the transcriptomic data of LUAD samples and classified LUAD into four discrete subtypes according to the distinct immune cell infiltration patterns. Furthermore, we established a novel predictive tool (TMEscore) to quantify the immune infiltration patterns for each LUAD patient by principal component analysis. The TMEscore displayed as a reliable and independent prognostic biomarker for LUAD, with worse survival in TMEscrore-high patients and better survival in TMEscrore-low patients in both TCGA and other five GEO cohorts. In addition, enriched pathways and genomic alterations were also analyzed and compared in different TMEscore subgroups, and we observed that high TMEscore was significantly correlated with more aggressive molecular changes, while the low TMEscore subgroup enriched in immune active-related pathways. The TMEscore-low subtype showed overexpression of PD-1, CTLA4, and associations of other markers of sensitivity to immunotherapy, including TMB, immunophenoscore (IPS) analysis, and tumor immune dysfunction and exclusion (TIDE) algorithm. Conclusively, TMEscore is a promising and reliable biomarker to distinguish the prognosis, the molecular and immune characteristics, and the benefit from ICIs treatments in LUAD.

Identification of A Risk Signature Based on Lactic Acid Metabolism-Related LncRNAs in Patients With Esophageal Squamous Cell Carcinoma

Lactic acid, formerly thought of as a byproduct of glycolysis or a metabolic waste produced, has now been identified as a key regulator of cancer growth, maintenance, and progression. However, the results of investigations on lactic acid metabolism-related long non-coding RNAs (LRLs) in esophageal squamous cell carcinoma (ESCC) remain inconclusive. In this study, univariate Cox regression analysis was carried out in the TCGA cohort, and 9 lncRNAs were shown to be significantly associated with prognosis. Least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate Cox regression analysis were then used in the GEO cohort. 6 LRLs were identified as independent prognostic factors for ESCC patients used to construct a prognostic risk-related signature subsequently. Two groups were formed based on the middle value of risk scores: a low-risk group and a high-risk group. Following that, we conducted Kaplan-Meier survival analysis, which revealed that the high-risk group had a lower survival probability than the low-risk group in both GEO and TCGA cohorts. On multivariate Cox regression analysis, the prognostic signature was shown to be independent prognostic factor, and it was found to be a better predictor of the prognosis of ESCC patients than the currently widely used grading and staging approaches. The established nomogram can be conveniently applied in the clinic to predict the 1-, 3-, and 5- year survival rates of patients. There was a significant link found between the 6 LRLs-based prognostic signature and immune-cell infiltration, tumor microenvironment (TME), tumor somatic mutational status, and chemotherapeutic treatment sensitivity in the study population. Finally, we used GTEx RNA-seq data and qRT-PCR experiments to verify the expression levels of 6 LRLs. In conclusion, we constructed a prognostic signature which could predict the prognosis and immunotherapy response of ESCC patients.

A Novel Immune-Prognosis Index Predicts the Benefit of Lung Adenocarcinoma Patients.

Background: Constructed an immune-prognosis index (IPI) and divided lung adenocarcinoma (LUAD) patients into different subgroups according to IPI score, describe the molecular and immune characteristics of patients between different IPI subgroups, and explore their response to immune checkpoint blockade (ICB) treatment. Methods: Based on the transcriptome profile of LUAD patients in TCGA and immune gene sets from ImmPort and InnateDB, 15 hub immune genes were identified through correlation and Bayesian causal network analysis. Then, IPI was constructed with 5 immune genes by using COX regression analysis and verified with external datasets (GSE30219, GSE37745, GSE68465, GSE126044 and GSE135222). Finally, the characteristics and the response to ICB treatment of LUAD patients between two different IPI subgroups were analyzed. Results: IPI was constructed based on the expression of 5 genes, including A2M, ADRB1, ADRB2, VIPR1 and PTH1R. IPI-high LUAD patients have a better overall survival than IPI-low LUAD patients, consistent with the results in the GEO cohorts. The comprehensive results showed that patients in the IPI-high subgroup were exhibited characters as metabolism-related signaling pathways activation, lower TP53 and TTN mutation rate, more infiltrations of CD8 T cells, dendritic cells and macrophages M1, especially earned more benefit from ICB treatment. In contrast, patients in the IPI-low subgroup were exhibited characters as p53 signaling pathways activation, higher TP53 and TTN mutation rate, more infiltrations of resting memory CD4 T cells, macrophages M2, immune-suppressive response and less benefit from ICB treatment. Conclusion: IPI is a potentially valuable prognostic evaluation method for LUAD, which works well in the benefit predicting of LUAD patients within ICB treatment.

Development of a Novel Immune-Related Gene Prognostic Index for Breast Cancer.

ObjectiveTo construct an immune-related gene prognostic index (IRGPI) for breast cancer (BC) and investigate its prognostic specificity and the molecular and immune characteristics.MethodsBC hub genes were identified from The Cancer Genome Atlas and immune-related databases using weighted gene co-expression network analysis (WGCNA). IRGPI was constructed using univariate, LASSO, and multivariate regression analyses, and was validated with GSE58812 and GSE97342 in the Gene Expression Omnibus database (GEO). At the same time, we evaluated the predictive ability of IRGPI for different BC subtypes. Subsequently, the molecular and immune characteristics, clinical relevance, and benefits of immune checkpoint inhibitor treatment were analyzed for different IRGPI subgroups.ResultsIRGPI consisted of six genes: SOCS3, TCF7L2, TSLP NPR3, ANO6, and HMGB3. The IRGPI 1-, 5-, and 10-years area under curve (AUC) values were 0.635, 0.752, and 0.753, respectively, indicating that IRGPI has good potential in predicting the long-term survival of BC patients, consistent with the results in the GEO cohort. IRGPI showed good predictive power in four different breast cancer subtypes: ER positive, PR positive, HER2 positive and triple-negative (P<0.01). Compared with the low-IRGPI group, the high-IRGPI group had a worse prognosis and a lower degree of immune infiltrating cells (p < 0.05). IRGPI showed specificity in distinguishing age, TNM stage, ER, and HER2 statuses, and our study found that the high-IRGPI group had low tumor immune dysfunction and exclusion (TIDE), microsatellite instability (MSI), and T cell dysfunction scores (p < 0.05). In addition, compared with the TIDE and TIS models, showed that the AUCs of IRGPI were better during the 5-year follow-up.ConclusionIRGPI can be used as an independent prognostic indicator of breast cancer, providing a method for monitoring the long-term treatment of BC.

Systematic Construction and Validation of an Immune-Related Gene-Based Model to Predict Prognosis for Ovarian Cancer.

Ovarian cancer (OC) is a malignancy with poor prognosis, stubborn resistance, and frequent recurrence. Recently, it has been widely recognized that immune-related genes (IRGs) have demonstrated their indispensable importance in the occurrence and progression of OC. Given this, this study aimed to identify IRGs with predictive value and build a prognostic model for a more accurate assessment. First, we obtained transcriptome and clinical information of ovarian samples from both TCGA and GTEx databases. After integration, we figured out 10 genes as immune-related prognostic genes (IRPGs) by performing the univariate Cox regression analysis. Subsequently, we established a TF-associated network to investigate its internal mechanism. The prognosis model consisting of 5 IRPGs was constructed later by lasso regression analysis. The comparison of the score with the clinical factors validated its independence and superiority in OC's prognosis. Moreover, the association between the signature and immune cell infiltration demonstrated its ability to image the immune situation of the tumor microenvironment. Finally, the reliability of the risk model was confirmed by the GEO cohort. Together, our study has constructed an independent prognostic model for OC, which may deepen the understanding of the immune microenvironment and help present novel biomarkers or ideas for targeted therapy.

Improving ovarian cancer treatment decision using a novel risk predictive tool.

Background: As a major component of the tumor tissue, the tumor microenvironment (TME) has been proven to associate with tumor progression and immunotherapy. Ovarian cancer accounts for the highest mortality rate among gynecologic malignancies. Its clinical treatment decision is highly correlated with the prognosis, underscoring the need to evaluate the prognosis and choose the proper clinical treatment through TME information.Method: This study constructs a score with TME information obtained by the CIBERSORT algorithm, which classifies the patients into high and low TMEscore groups with quantified TME infiltration patterns through the PCA algorithm. TMEscore was constructed by TCGA cohort and validated in GEO cohort. Univariate and multivariate Cox proportional hazards model analyses were used to demonstrate prognostic value of TMEscore in overall and stratified analysis.Result: TMEscore is highly correlated with survival and high TMEscore group has a better prognosis. In order to improve treatment decision, the expression of immune checkpoints, immunophenoscore (IPS) and ESTIMATE score showed a high TMEscore have a better immune microenvironment and respond better to immune checkpoint inhibitors (ICIs). Meanwhile, the mutation landscape between TMEscore groups was profiled, and 13 genes were found mutated differently between the two groups. Among them, BRCA1 has more mutations in the high TMEscore group and speculated that high TMEscore patients might be a beneficiary population of PARP inhibitors combined with immunotherapy.Conclusion: TMEscore based on TME with prognostic value and clinical value is proposed for the identification of targets treatment and immunotherapy strategies for ovarian cancer.

Identification and Validation of Chromobox Family Members as Potential Prognostic Biomarkers and Therapeutic Targets for Human Esophageal Cancer.

Background: Chromobox family proteins (CBXs) are vital components of epigenetic regulation complexes and transcriptionally inhibit target genes by modifying the chromatin. Accumulating evidence indicates that CBXs are involved in the initiation and progression of multiple malignancies. However, the expression, function, and clinical relevance such as the prognostic and diagnostic values of different CBXs in esophageal carcinoma (ESCA) are still unclear. Methods: We applied Oncomine, TCGA, GEO, GEPIA, UALCAN, Kaplan–Meier plotter, cBioPortal, Metascape, and TIMER to investigate the roles of CBX family members in ESCA. Additionally, quantitative real-time PCR (RT-PCR), western blot, and immunofluorescence were used to verify the expression of CBX family members in ESCA clinical samples. Results: Compared with normal tissues, the mRNA expression levels of CBX1/3/8 were significantly increased in ESCA, whereas CBX7 mRNA expression was reduced in both the TCGA cohort and GEO cohort. In the TCGA cohort, ROC curves suggested that CBX1/2/3/4/8 had great diagnostic value in ESCA, and the AUCs were above 0.9. Furthermore, upregulation of CBX1/3/8 and downregulation of CBX7 were closely related to the clinicopathological parameters in ESCA patients, such as tumor grades, tumor nodal metastasis status, and TP53 mutation status. The survival analysis indicated that higher CBX1/3/8 mRNA expressions and lower CBX7 expression suggested an unfavorable prognosis in ESCA. High genetic change rate (52%) of CBXs was found in ESCA patients. Functions and pathways of mutations in CBXs and their 50 frequently altered neighbor genes in ESCA patients were investigated; the results showed that DNA repair and DNA replication were correlated to CBX alterations. Moreover, we found a significant correlation between the expression level of CBX family members and the infiltration of immune cells in ESCA. Finally, we verified the expression of CBX family members in clinical samples and found the results were consistent with the databases. Conclusion: Our study implied that CBX1/3/7/8 are potential targets of precision therapy for ESCA patients and new biomarkers for the prognosis.

Prognostic Implications and Immune Infiltration Characteristics of Chromosomal Instability-Related Dysregulated CeRNA in Lung Adenocarcinoma.

An accumulating body of research indicates that long-noncoding RNAs (lncRNAs) regulate the target genes and act as competitive endogenous RNAs (ceRNAs) playing an indispensable role in lung adenocarcinoma (LUAD). LUAD is frequently accompanied by the feature of chromosomal instability (CIN); however, CIN-related ceRNAs have not been investigated yet. We systematically analyzed and integrated CIN-related dysregulated ceRNAs characteristics in LUAD samples for the first time. In TCGA LUAD cohort, CIN in tumor samples was significantly higher than that in those of adjacent, and patients with high CIN risk tended to have worse clinical outcomes. We constructed a double-weighted CIN-related dysregulated ceRNA network, in which edge weight and node weight represented the disorder extent of ceRNA and the correlation of RNA expression level and prognosis, respectively. After module mining and analysis, a potential prognostic biomarker composed of 12 RNAs (8 mRNAs and 4 lncRNAs) named CIN-related dysregulated ceRNAs (CRDC) was obtained. The CRDC risk score had a positive relation with clinical stage and CIN, and patients with high CRDC risk scores exhibited poor prognosis. Moreover, CRDC tended to be an independent risk factor with high robustness to overcome the effect of multicollinearity among other explanatory variables for disease-specific survival (DSS) in TCGA and two GEO cohorts. The result of functional analysis indicated that CRDC was involved in multiple cancer progresses, especially immune-related pathways. The patients with lower CRDC risk had higher B cell, T cell CD4+, T cell CD8+, neutrophil, macrophage, and myeloid dendritic cell infiltration than the patients with higher CRDC risk. Meanwhile, patients with lower CRDC risk could get more benefits from immunological therapy. The results suggested that the CRDC could be a potential prognostic biomarker and an immunotherapy predictor for lung adenocarcinoma.

Endoplasmic Reticulum Stress-Related Four-Biomarker Risk Classifier for Survival Evaluation in Esophageal Cancer.

Purpose Esophageal cancer (EC) is a lethal digestive tumor worldwide with a dismal clinical outcome. Endoplasmic reticulum (ER) stress poses essential implications for a variety of tumor malignant behaviors. Here, we set up an ER stress-based risk classifier for assessing patient outcome and exploiting robust targets for medical decision-making of EC cases. Methods 340 EC cases with transcriptome and survival data from two independent public datasets (TCGA and GEO) were recruited for this project. Cox regression analyses were employed to create a risk classifier based on ER stress-related genes (ERGs) which were strongly linked to EC cases' outcomes. Then, we detected and confirmed the predictive ability of our proposed classifier via a host of statistical methods, including survival analysis and ROC method. In addition, immune-associated algorithm was implemented to analyze the immune activity of EC samples. Results Four EGRs (BCAP31, HSPD1, PDHA1, and UBE2D1) were selected to build an EGR-related classifier (ERC). This classifier could distinguish the patients into different risky subgroups. The remarkable differences in patient outcome between the two groups were observed, and similar results were also confirmed in GEO cohort. In terms of the immune analysis, the ERC could forecast the infiltration level of immunocytes, such as Tregs and NK cells. Conclusion We created a four-ERG risk classifier which displays the powerful capability of survival evaluation for EC cases.

Development and Validation of a 7-Gene Inflammatory Signature Forecasts Prognosis and Diverse Immune Landscape in Lung Adenocarcinoma.

Background: Inflammatory responses are strongly linked with tumorigenesis and cancer development. This research aimed to construct and validate a novel inflammation response–related risk predictive signature for forecasting the prognosis of patients with LUAD. Methods: Differential expression analysis, univariate Cox, LASSO, and multivariate Cox regression analyses of 200 inflammatory response–related genes (IRRG) were performed to establish a risk predictive model in the TCGA training cohort. The performance of the IRRG model was verified in eight GEO datasets. GSEA analysis, ESTIMATE algorithms, and ssGSEA analysis were applied to elucidate the possible mechanisms. Furthermore, the relationship analysis between risk score, model genes, and chemosensitivity was performed. Last, we verified the protein expression of seven model genes by immunohistochemical staining or Western blotting. Results: We constructed a novel inflammatory response–related 7-gene signature (MMP14, BTG2, LAMP3, CCL20, TLR2, IL7R, and PCDH7). Patients in the high-risk group presented markedly decreased survival time in the TCGA cohort and eight GEO cohorts than the low-risk group. Interestingly, multiple pathways related to immune response were suppressed in high-risk groups. The low infiltration levels of B cell, dendritic cell, natural killer cell, and eosinophil can significantly affect the unsatisfactory prognosis of the high-risk group in LUAD. Moreover, the tumor cells’ sensitivity to anticancer drugs was markedly related to risk scores and model genes. The protein expression of seven model genes was consistent with the mRNA expression. Conclusion: Our IRRG prognostic model can effectively forecast LUAD prognosis and is tightly related to immune infiltration.

Multidimension Analysis of the Prognostic Value, Immune Regulatory Function, and ceRNA Network of LY6E in Individuals with Colorectal Cancer.

Background Lymphocyte antigen 6 complex, locus E (LY6E) is abnormally expressed in several cancers and is associated with poor outcomes. However, the biological role of LY6E in colorectal cancer (CRC) remains largely unknown. Hence, we aimed to evaluate the expression levels, prognostic value, biological functions, and immune effects of LY6E via pan-cancer and CRC analyses using multiple databases. Methods We analyzed the expression pattern of LY6E in various cancers. The prognostic value of LY6E expression was identified using the Kaplan–Meier analysis and the Cox regression models. We used gene set enrichment analysis (GSEA) to identify the potential functions of LY6E. Correlations between the LY6E expression and various factors, including LY6E methylation level, copy number variation (CNV), microsatellite instability (MSI), and immune checkpoint genes, were also analyzed. The levels of LY6E expression and immune infiltration were analyzed using CIBERSORT. We constructed a regulatory network that was in compliance with the competing endogenous RNA (ceRNA) hypothesis. A ceRNA expression-based nomogram was established. Real-time PCR (qRT-PCR) was applied to validate the expression of LY6E-related ceRNA in CRC cell lines. Results LY6E is overexpressed in several tumor types, including CRC, and patients with high expression levels of LY6E have a poor prognosis. The Kaplan–Meier analysis and Cox regression analysis showed that LY6E could be considered a favorable prognostic factor in TCGA and GEO cohort. The results of GSEA showed that high LY6E expression levels were associated with immune-related pathways, such as those involved in antigen processing and presentation and the intestinal immune network for IgA production. Six methylation sites of LY6E that were associated with prognostic survival were screened. Moreover, the high levels of LY6E expression were correlated with copy number gain, microsatellite instability high, and immunotherapy response. The results of CIBERSORT analysis demonstrated that the LY6E expression levels were correlated with the infiltration of multiple immune cells, especially T cells. Then, we constructed a ceRNA network (LINC00963/miR-92a-3p/LY6E) and validated it using qRT-PCR. A predictive ceRNA-based nomogram was established and validated. Conclusion The oncogenic LY6E may serve as a promising marker for the diagnosis and treatment of CRC.

The Expression Pattern of Ferroptosis-Related Genes in Colon Adenocarcinoma: Highly Correlated to Tumor Microenvironment Characteristics.

In the latest literatures, ferroptosis caused by T cells in cancerous cells provided new insights of improving curative effect of the PD-1/PD-L1 antibody. The microenvironment on which tumor cells develop and survive was also emphasized as its crucial role in tumor occurrence, development, metastasis and immune escape. Thus, the interaction of ferroptosis related genes and tumor microenvironment (TME) was urgently be detected in a comprehensive perspective. We comprehensively evaluated the transcriptional feature of ferroptosis related genes in colon adenocarcinoma (COAD), and systematically associated these ferroptosis subtypes with DNA damage repair (DDR) and TME characteristics. We found two unique patterns of ferroptosis characterized by distinct biological pathways activation. We also demonstrated that FRG score constructed based on ferroptosis subtypes has a significant correlation with prognosis of colon cancer and could act as an independent prognostic biomarker for predicting patients’ survival. The higher immune infiltrating level, immune functional pathways activation was observed in the high FRG score group. Furthermore, these results were verified by an independent external GEO cohort. This work revealed ferroptosis was highly associated with TME complexity and diversity. A novel ferroptosis subtypes related gene scoring system can be used for prognostic prediction in COAD. Targeting ferroptosis may be a therapeutic alternative for COAD.

Comprehensive Analysis of TRP Channel-Related Genes for Estimating the Immune Microenvironment, Prognosis, and Therapeutic Effect in Patients With Esophageal Squamous Cell Carcinoma.

The Nobel Prize in Physiology or Medicine for the year 2021 was awarded to Ardem Patapoutian and David Julius for their discoveries of temperature-sensitive receptors (TRP channels) and tactile receptors (Piezo channels), both of which were previously unknown. TRP channels are at the heart of the human ability to detect temperature, and they also play crucial regulatory functions in the occurrence and progression of cancer. Despite this, there have been no research conducted on the prognostic significance of TRP channels in individuals with esophageal squamous cell carcinoma (ESCC). In GEO and TCGA cohorts, unsupervised clustering was first conducted based on 18 TRP channel-associated differentially expressed genes (DEGs) extracted from MSigDB database and KEGG database. Two TRP subtypes were identified and patients in subtype B had the best prognosis among the two subtypes. Significant differences in staging and grading existed among the different subtypes. In GEO cohort, univariate Cox analysis were performed to screen prognosis related genes. A TRP channel-related prognostic signature, which included 7 signature-related genes, was constructed by the least absolute shrinkage and selection operator (LASSO) Cox regression. Patients were divided into a high-risk group and low-risk group by the median risk score. In GEO and TCGA cohorts, Receiver operating characteristic (ROC) curves, principal component analysis (PCA), and univariate and multivariate Cox regression were performed to confirm the validity of signature. Following a more in-depth study of the TME based on the risk signature, it was discovered that the high-risk group had higher immune cell infiltration and lower tumor purity, indicating a bad prognosis. Patients with high risk scores also had increased immune checkpoint expression, indicating that these patients may be more likely to benefit from immunotherapy than other patients. We also found that paclitaxel, cisplatin, and 5-fluorouracil displayed a better response in treating the low-risk score ESCC patients. This study also adopted GTEx and qRT-PCR to perform experimental verification processes. In summary, we identified a TRP channel-associated prognostic signature. This signature can predict prognosis and immune microenvironment in ESCC.