Development and clinical validation of a novel 9-gene prognostic model based on multi-omics in pancreatic adenocarcinoma

Lactylation stabilizes DCBLD1 activating the pentose phosphate pathway to promote cervical cancer progression

BACKGROUND

Discoidin, CUB, and LCCL domain-containing type I (DCBLD1) is identified as an oncogene involved in multiple regulation of tumor progression, but specific mechanisms remain unclear in cervical cancer. Lactate-mediated lactylation modulates protein function. Whether DCBLD1 can be modified by lactylation and the function of DCBLD1 lactylation are unknown. Therefore, this study aims to investigate the lactylation of DCBLD1 and identify its specific lactylation sites. Herein, we elucidated the mechanism by which lactylation modification stabilizes the DCBLD1 protein. Furthermore, we investigated DCBLD1 overexpression activating pentose phosphate pathway (PPP) to promote the progression of cervical cancer.

METHODS

DCBLD1 expression was examined in human cervical cancer cells and adjacent non-tumorous tissues using quantitative reverse transcription-polymerase chain reaction, western blotting, and immunohistochemistry. In vitro and in vivo studies were conducted to investigate the impact of DCBLD1 on the progression of cervical cancer. Untargeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) metabolomics studies were used to characterize DCBLD1-induced metabolite alterations. Western blot, immunofuorescence and transmission electron microscopy were performed to detect DCBLD1 degradation of G6PD by activating autophagy. Chromatin immunoprecipitation, dual luciferase reporter assay for detecting the mechanism by which lactate increases DCBLD1 transcription. LC-MS/MS was employed to verify specific modification sites within the DCBLD1 protein.

RESULTS

We found that lactate increased DCBLD1 expression, activating the PPP to facilitate the proliferation and metastasis of cervical cancer cells. DCBLD1 primarily stimulated PPP by upregulating glucose-6-phosphate dehydrogenase (G6PD) expression and enzyme activity. The mechanism involved the increased enrichment of HIF-1α in the DCBLD1 promoter region, enhancing the DCBLD1 mRNA expression. Additionally, lactate-induced DCBLD1 lactylation stabilized DCBLD1 expression. We identified DCBLD1 as a lactylation substrate, with a predominant lactylation site at K172. DCBLD1 overexpression inhibited G6PD autophagic degradation, activating PPP to promote cervical cancer progression. In vivo, 6-An mediated inhibition of G6PD enzyme activity, inhibiting tumor proliferation.

CONCLUSIONS

Our findings revealed a novel post-translational modification type of DCBDL1, emphasizing the significance of lactylation-driven DCBDL1-mediated PPP in promoting the progression of cervical cancer.

Copy Number Variations in Pancreatic Cancer: From Biological Significance to Clinical Utility

Pancreatic ductal adenocarcinoma (PDAC) is the most common type of pancreatic cancer, characterized by high tumor heterogeneity and a poor prognosis. Inter- and intra-tumoral heterogeneity in PDAC is a major obstacle to effective PDAC treatment; therefore, it is highly desirable to explore the tumor heterogeneity and underlying mechanisms for the improvement of PDAC prognosis. Gene copy number variations (CNVs) are increasingly recognized as a common and heritable source of inter-individual variation in genomic sequence. In this review, we outline the origin, main characteristics, and pathological aspects of CNVs. We then describe the occurrence of CNVs in PDAC, including those that have been clearly shown to have a pathogenic role, and further highlight some key examples of their involvement in tumor development and progression. The ability to efficiently identify and analyze CNVs in tumor samples is important to support translational research and foster precision oncology, as copy number variants can be utilized to guide clinical decisions. We provide insights into understanding the CNV landscapes and the role of both somatic and germline CNVs in PDAC, which could lead to significant advances in diagnosis, prognosis, and treatment. Although there has been significant progress in this field, understanding the full contribution of CNVs to the genetic basis of PDAC will require further research, with more accurate CNV assays such as single-cell techniques and larger cohorts than have been performed to date.

Comprehensive analysis of necroptotic patterns and associated immune landscapes in individualized treatment of skin cutaneous melanoma

Skin cutaneous melanoma (SKCM) constitutes a malignant cutaneous neoplasm characterized by an exceedingly unfavorable prognosis. Over the past years, necroptosis, a manifestation of inflammatory programmed cell demise, has gained substantial traction in its application. However, a conclusive correlation between the expression of necroptosis-related genes (NRGs) and SKCM patient's prognosis remains elusive. In this endeavor, we have undertaken an integrative analysis of genomic data, aiming to provide an exhaustive evaluation of the intricate interplay between melanoma necroptosis and immune-infiltration nuances within the tumor microenvironment. Through meticulous scrutiny, we have endeavored to discern the prognostic potency harbored by individual necroptosis-associated genes. Our efforts culminated in the establishment of a risk stratification framework, allowing for the appraisal of necroptosis irregularities within each afflicted cutaneous melanoma patient. Notably, those SKCM patients classified within the low-risk cohort exhibited a markedly elevated survival quotient, in stark contrast to their high-risk counterparts (p < 0.001). Remarkably, the low-risk cohort not only displayed a more favorable survival rate but also exhibited an enhanced responsiveness to immunotherapeutic interventions, relative to their high-risk counterparts. The outcomes of this investigation proffer insights into a conceivable mechanistic underpinning linking necroptosis-related attributes to the intricacies of the tumor microenvironment. This prompts a conjecture regarding the plausible association between necroptosis characteristics and the broader tumor microenvironmental milieu. However, it is imperative to emphasize that the pursuit of discerning whether the expression profiles of NRG genes can indeed be regarded as viable therapeutic targets necessitates further comprehensive exploration and scrutiny. In conclusion, our study sheds light on the intricate interrelationship between necroptosis-related factors and the tumor microenvironment, potentially opening avenues for therapeutic interventions. However, the prospect of translating these findings into clinical applications mandates rigorous investigation.

Pancreatic Cancer Health Disparity: Pharmacologic Anthropology

Pancreatic cancer (PCa) remains a formidable global health challenge, with high mortality rates and limited treatment options. While advancements in pharmacology have led to improved outcomes for various cancers, PCa continues to exhibit significant health disparities, disproportionately affecting certain populations. This paper explores the intersection of pharmacology and anthropology in understanding the health disparities associated with PCa. By considering the socio-cultural, economic, and behavioral factors that influence the development, diagnosis, treatment, and outcomes of PCa, pharmacologic anthropology provides a comprehensive framework to address these disparities and improve patient care.

Exosome and lipid metabolism-related genes in pancreatic adenocarcinoma: a prognosis analysis

OBJECTIVE

The purpose of the study was to investigate the role of exosome and lipid metabolism-related genes (EALMRGs) mRNA levels in the diagnosis and prognosis of Pancreatic Adenocarcinoma (PAAD).

METHODS

The mRNA expression pattern of PAAD and pan-cancers with prognostic data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. EALMRGs were acquired from GeneCards and MSigDB database after merging and deduplication. Prognostic EALMRGs were screened through univariate COX regression analysis, and a prognostic model was constructed based on these genes by least absolute shrinkage and selection operator (LASSO) regression. The prognostic value of EALMRGs was then validated in pan-cancer data. The time characteristics ROC curve analysis was performed to evaluate the effectiveness of the prognostic genes.

RESULTS

We identified 5 hub genes (ABCB1, CAP1, EGFR, PPARG, SNCA) according to high and low-risk groups of prognoses. The risk formula was verified in three other cohort of pancreatic cancer patients and was explored in pan-cancer data. Additionally, T cell and dendritic cell infiltration was significantly increased in low-risk group. The expression of the 5 hub genes was also identified in single-cell sequencing data of pancreatic cancer with pivotal pathways. Additionally, functional enrichment analysis based on pancreatic cancer data in pancreatic cancer showed that protein serine/threonine kinase activity, focal adhesion, actin binding, cell-substrate junction, organic acid transport, and regulation of transporter activity were significant related to the expression of genes in EALMRGs.

CONCLUSIONS

Our risk formula shows potential prognostic value in multiple cancers and manifest pivotal alterations in immune infiltration and biological pathway in pancreatic cancer.

HtrA3: a promising prognostic biomarker and therapeutic target for head and neck squamous cell carcinoma

Objective

The dysregulation of the human high-temperature requirement A (HtrA) family of serine proteases is associated with many malignancies. However, there are few reports on HtrAs in head and neck squamous cell carcinoma (HNSCC). The aim of this study was to investigate the expression, prognostic value, and biological functions of HtrAs in HNSCC.

Methods

The RNA-sequencing data and clinical data of HNSCC were downloaded from The Cancer Genome Atlas (TCGA) database. The GSE30784 and GSE31056 datasets from the Gene Expression Omnibus (GEO) database were used for further verification. This study explored the differential expression of HtrAs and assessed their potential impact on the prognosis of HNSCC patients using a survival module. Correlations between clinical characteristics and HtrA expression levels were then explored using a Wilcoxon rank sum test. A Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) were performed using "clusterProfile" in the R software. A Pearson/Spearman correlation test was applied to analyze the relationship between HtrAs and immune infiltration level/checkpoint genes. Validation of HtrA expression levels were carried out by RT-PCR and western blot in human squamous carcinoma cell lines (Fadu and Cal-27) and human non-tumorigenic bronchial epithelium cells (BEAS-2B). Finally, through cell transfection, CCK-8, Ki-67 immunofluorescence, and flow cytometry assays, the effect of HtrA3 knockdown on the malignant biological behavior of HNSCC cells was explored.

Results

The gene expression levels of HtrAs were significantly upregulated and associated with patient age, TNM stage, clinical stage, and TP53 mutation status in the TCGA-HNSCC cohort. High expressions of HtrA1/3 were associated with shorter overall survival, shorter progress-free interval, and lower disease-specific survival in HNSCC. A nomogram for HtrAs was constructed and validated. HtrA-related genes were significantly enriched in the immune response and cell apoptosis pathway. In addition, the expression of HtrAs showed significant correlations with B cells, M cells, DC cell infiltration, and immune infiltration checkpoint (CD276, TNFRSF14). Validation of HtrA expression was carried out by RT-PCR and western blot. Results of in vitro experiments indicated that HtrA3 gene knockdown inhibits the proliferation of FaDu and Cal-27 cells while concurrently promoting apoptosis.

Conclusions

HtrA3 shows significant potential as both a prognostic marker and a promising therapeutic target for HNSCC, highlighting its relevance and importance in future research and potential clinical applications.

The Illustration of Altered Glucose Dependency in Drug-Resistant Cancer Cells

A chemotherapeutic approach is crucial in malignancy management, which is often challenging due to the development of chemoresistance. Over time, chemo-resistant cancer cells rapidly repopulate and metastasize, increasing the recurrence rate in cancer patients. Targeting these destined cancer cells is more troublesome for clinicians, as they share biology and molecular cross-talks with normal cells. However, the recent insights into the metabolic profiles of chemo-resistant cancer cells surprisingly illustrated the activation of distinct pathways compared with chemo-sensitive or primary cancer cells. These distinct metabolic dynamics are vital and contribute to the shift from chemo-sensitivity to chemo-resistance in cancer. This review will discuss the important metabolic alterations in cancer cells that lead to drug resistance.

M2 macrophage marker CHI3L2 could serve as a potential prognostic and immunological biomarker in glioma by integrated single-cell and bulk RNA-Seq analysis

BACKGROUND

CHI3L2 plays a crucial role in multiple cancers, but its importance in glioma remains unclear. Hence, we comprehensively integrated bulk RNA-sequencing (RNA-seq), proteomics and single-cell RNA-seq (scRNA-seq) to determine the roles of CHI3L2 in gliomas.

METHODS

Bulk RNA-seq, proteomics and scRNA-seq data of CHI3L2 in glioma were obtained from online databases. The quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry (IHC) were conducted to verify the CHI3L2 expression. Then, univariate and multivariate Cox regression analyses, Norman charts and gene set enrichment analysis (GSEA) were performed. Finally, the associations between CHI3L2 and tumor immunity were explored.

RESULTS

The expression of CHI3L2 was markedly higher in glioma cancers compared with normal tissues from analysis of the data of the Cancer Genome Atlas and Chinese Glioma Genome Atlas datasets and as verified by GSE4290, GSE50161, qRT-PCR and IHC results (p < 0.05). High expression of CHI3L2 suggested poor overall survival (OS) prognosis in gliomas (p < 0.05). CHI3L2 might also serve as an independent predictor of OS for gliomas (p < 0.05) and we also constructed a Norman chart to predict these patients' survival prognosis with good performance. GSEA analysis showed that CHI3L2 might be involved with eight pathways in gliomas. Regarding tumor immunity, CHI3L2 was found to be significantly involved with immune cell infiltration levels of low-grade glioma, the tumor immune microenvironment, immune checkpoints and immune cells in both low-grade glioma and glioblastoma (p < 0.05). Additionally, scRNA-seq data for CHI3L2 in glioma from the TISCH2 website showed that CHI3L2 is mainly expressed in astrocytes, endothelial cells, CD8+ T cells, mono/macrophage cells, etc. CONCLUSIONS: CHI3L2 presents prognostic and immunological values in glioma, providing novel therapeutic targets for glioma patients.

Integrating the characteristic genes of macrophage pseudotime analysis in single-cell RNA-seq to construct a prediction model of atherosclerosis

BACKGROUND

Macrophages play an important role in the occurrence and development of atherosclerosis. However, few existing studies have deliberately analyzed the changes in characteristic genes in the process of macrophage phenotype transformation.

METHOD

Carotid atherosclerotic plaque single-cell RNA (scRNA) sequencing data were analyzed to define the cells involved and determine their transcriptomic characteristics. KEGG enrichment analysis, CIBERSORT, ESTIMATE, support vector machine (SVM), random forest (RF), and weighted correlation network analysis (WGCNA) were applied to bulk sequencing data. All data were downloaded from Gene Expression Omnibus (GEO).

RESULT

Nine cell clusters were identified. M1 macrophages, M2 macrophages, and M2/M1 macrophages were identified as three clusters within the macrophages. According to pseudotime analysis, M2/M1 macrophages and M2 macrophages can be transformed into M1 macrophages. The ROC curve values of the six genes in the test group were statistically significant (AUC (IL1RN): 0.899, 95% CI: 0.764-0.990; AUC (NRP1): 0.817, 95% CI: 0.620-0.971; AUC (TAGLN): 0.846, 95% CI: 0.678-0.971; AUC (SPARCL1): 0.825, 95% CI: 0.620-0.988; AUC (EMP2): 0.808, 95% CI: 0.630-0.947; AUC (ACTA2): 0.784, 95% CI: 0.591-0.938). The atherosclerosis prediction model showed significant statistical significance in both the train group (AUC: 0.909, 95% CI: 0.842-0.967) and the test group (AUC: 0.812, 95% CI: 0.630-0.966).

CONCLUSIONS

IL1RNHigh M1, NRP1High M2, ACTA2High M2/M1, EMP2High M1/M1, SPACL1High M2/M1 and TAGLNHigh M2/M1 macrophages play key roles in the occurrence and development of arterial atherosclerosis. These marker genes of macrophage phenotypic transformation can also be used to establish a model to predict the occurrence of atherosclerosis.

The coagulation-related genes for prognosis and tumor microenvironment in pancreatic ductal adenocarcinoma

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is a malignancy characterized by challenging early diagnosis and poor prognosis. It is believed that coagulation has an impact on the tumor microenvironment of PDAC. The aim of this study is to further distinguish coagulation-related genes and investigate immune infiltration in PDAC.

METHODS

We gathered two subtypes of coagulation-related genes from the KEGG database, and acquired transcriptome sequencing data and clinical information on PDAC from The Cancer Genome Atlas (TCGA) database. Using an unsupervised clustering method, we categorized patients into distinct clusters. We investigated the mutation frequency to explore genomic features and performed enrichment analysis, utilizing Gene Ontology (GO) and Kyoto Encyclopedia of Genes (KEGG) to explore pathways. CIBERSORT was used to analyze the relationship between tumor immune infiltration and the two clusters. A prognostic model was created for risk stratification, and a nomogram was established to assist in determining the risk score. The response to immunotherapy was assessed using the IMvigor210 cohort. Finally, PDAC patients were recruited, and experimental samples were collected to validate the infiltration of neutrophils using immunohistochemistry. In addition, and identify the ITGA2 expression and function were identified by analyzing single cell sequencing data.

RESULTS

Two coagulation-related clusters were established based on the coagulation pathways present in PDAC patients. Functional enrichment analysis revealed different pathways in the two clusters. Approximately 49.4% of PDAC patients experienced DNA mutation in coagulation-related genes. Patients in the two clusters displayed significant differences in terms of immune cell infiltration, immune checkpoint, tumor microenvironment and TMB. We developed a 4-gene prognostic stratified model through LASSO analysis. Based on the risk score, the nomogram can accurately predict the prognosis in PDAC patients. We identified ITGA2 as a hub gene, which linked to poor overall survival (OS) and short disease-free survival (DFS). Single-cell sequencing analysis demonstrated that ITGA2 was expressed by ductal cells in PDAC.

CONCLUSIONS

Our study demonstrated the correlation between coagulation-related genes and the tumor immune microenvironment. The stratified model can predict the prognosis and calculate the benefits of drug therapy, thus providing the recommendations for clinical personalized treatment.

Analysis of immunogenic cell death in atherosclerosis based on scRNA-seq and bulk RNA-seq data

BACKGROUND

Regulated cell death plays a very important role in atherosclerosis (AS). Despite a large number of studies, there is a lack of literature on immunogenic cell death (ICD) in AS.

METHOD

Carotid atherosclerotic plaque single-cell RNA (scRNA) sequencing data were analyzed to define involved cells and determine their transcriptomic characteristics. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, CIBERSORT, ESTIMATE and ssGSEA (Gene Set Enrichment Analysis), consensus clustering analysis, random forest (RF), Decision Curve Analysis (DCA), and the Drug-Gene Interaction and DrugBank databases were applied for bulk sequencing data. All data were downloaded from Gene Expression Omnibus (GEO).

RESULT

mDCs and CTLs correlated obviously with AS occurrence and development (k2(mDCs) = 48.333, P < 0.001; k2(CTL) = 130.56, P < 0.001). In total, 21 differentially expressed genes were obtained for the bulk transcriptome; KEGG enrichment analysis results were similar to those for differentially expressed genes in endothelial cells. Eleven genes with a gene importance score > 1.5 were obtained in the training set and validated in the test set, resulting in 8 differentially expressed genes for ICD. A model to predict occurrence of AS and 56 drugs that may be used to treat AS were obtained with these 8 genes.

CONCLUSION

Immunogenic cell death occurs mainly in endothelial cells in AS. ICD maintains chronic inflammation in AS and plays a crucial role in its occurrence and development. ICD related genes may become drug-targeted genes for AS treatment.

Identification of DLL3-related genes affecting the prognosis of patients with colon adenocarcinoma

Background: Delta-like ligand 3 (DLL3) is one of the NOTCH family of ligands, which plays a pro- or anti-carcinogenic role in some cancers. But the role of DLL3 in colon adenocarcinoma (COAD) has not been studied in depth. Materials and methods: First, we used Kaplan-Meier (K-M) curve to evaluate the effect of DLL3 on the prognosis of COAD in The Cancer Genome Atlas (TCGA), which was further validated in clinical samples for immunohistochemistry. Then we screened for differentially expressed genes (DEGs) of DLL3 by analyzing datasets of COAD samples from Gene Expression Omnibus (GEO) and TCGA. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, and Gene Set Enrichment Analysis (GSEA) were conducted to explore the underlying mechanisms of DLL3-related in the development and prognosis of COAD. On the basis of DLL3-related signature genes, a prognostic model and a nomogram were constructed. Finally, CIBERSORT was applied to assess the proportion of immune cell types in COAD sample. Results: Survival analysis showed a significant difference in overall survival between high- and low-expression group (p = 0.0092), with COAD patients in the high-group having poorer 5-year survival rate. Gene functional enrichment analysis revealed that DLL3-related DEGs were mainly enriched in tumor- and immunity-related signaling pathways, containing AMPK pathway and mitophagy-animal. The comparison of COAD tumor and normal, DLL3 high- and low-expression groups by GSEA found that AMPK signaling pathway and mitophagy-animal were inhibited. Nomogram constructed from DLL3-related signature genes had a good predictive effect on the prognosis of COAD. We found the highest correlation between DLL3 and interstitial dendritic cell (iDC), natural killer (NK) cell and Interstitial dendritic cell (Tem). DLL3 was also revealed to be diagnostic for COAD. In clinical sample, we identified higher DLL3 expression in colon cancer tissue than in adjacent control (p < 0.0001) and in metastasis than in primary lesion (p = 0.0056). DLL3 expression was associated with stage and high DLL3 expression was observed to predict poorer overall survival (p = 0.004). Conclusion: It suggested that DLL3 may offer prognostic value and therapeutic potential for individualized treatment of COAD, and that it may has a diagnostic role in COAD.

Machine learning algorithms assisted identification of post-stroke depression associated biological features

Objectives

Post-stroke depression (PSD) is a common and serious psychiatric complication which hinders functional recovery and social participation of stroke patients. Stroke is characterized by dynamic changes in metabolism and hemodynamics, however, there is still a lack of metabolism-associated effective and reliable diagnostic markers and therapeutic targets for PSD. Our study was dedicated to the discovery of metabolism related diagnostic and therapeutic biomarkers for PSD.

Methods

Expression profiles of GSE140275, GSE122709, and GSE180470 were obtained from GEO database. Differentially expressed genes (DEGs) were detected in GSE140275 and GSE122709. Functional enrichment analysis was performed for DEGs in GSE140275. Weighted gene co-expression network analysis (WGCNA) was constructed in GSE122709 to identify key module genes. Moreover, correlation analysis was performed to obtain metabolism related genes. Interaction analysis of key module genes, metabolism related genes, and DEGs in GSE122709 was performed to obtain candidate hub genes. Two machine learning algorithms, least absolute shrinkage and selection operator (LASSO) and random forest, were used to identify signature genes. Expression of signature genes was validated in GSE140275, GSE122709, and GSE180470. Gene set enrichment analysis (GSEA) was applied on signature genes. Based on signature genes, a nomogram model was constructed in our PSD cohort (27 PSD patients vs. 54 controls). ROC curves were performed for the estimation of its diagnostic value. Finally, correlation analysis between expression of signature genes and several clinical traits was performed.

Results

Functional enrichment analysis indicated that DEGs in GSE140275 enriched in metabolism pathway. A total of 8,188 metabolism associated genes were identified by correlation analysis. WGCNA analysis was constructed to obtain 3,471 key module genes. A total of 557 candidate hub genes were identified by interaction analysis. Furthermore, two signature genes (SDHD and FERMT3) were selected using LASSO and random forest analysis. GSEA analysis found that two signature genes had major roles in depression. Subsequently, PSD cohort was collected for constructing a PSD diagnosis. Nomogram model showed good reliability and validity. AUC values of receiver operating characteristic (ROC) curve of SDHD and FERMT3 were 0.896 and 0.964. ROC curves showed that two signature genes played a significant role in diagnosis of PSD. Correlation analysis found that SDHD (r = 0.653, P &lt; 0.001) and FERM3 (r = 0.728, P &lt; 0.001) were positively related to the Hamilton Depression Rating Scale 17-item (HAMD) score.

Conclusion

A total of 557 metabolism associated candidate hub genes were obtained by interaction with DEGs in GSE122709, key modules genes, and metabolism related genes. Based on machine learning algorithms, two signature genes (SDHD and FERMT3) were identified, they were proved to be valuable therapeutic and diagnostic biomarkers for PSD. Early diagnosis and prevention of PSD were made possible by our findings.

High MICAL1 expression correlates with cancer progression and immune infiltration in renal clear cell carcinoma

BACKGROUND

Molecule interacting with CasL 1 (MICAL1), a multidomain flavoprotein monooxygenase, is strongly involved in the biological processes related to cancer cell proliferation and metastasis. However, there were few reports on the clinical significance of MICAL1 in renal clear cell carcinoma.

METHODS

The expression and prognostic value of MICAL1 in renal clear cell carcinoma were explored using immunohistochemical assays, public TCGA-KIRC databases and multiple analysis methods, including survival analysis, univariate and multivariate analyses, KEGG and GSEA. Wound healing and Transwell assays were performed to check the 786-O cell and Caki-1 cell migration abilities after knockdown of MICAL1. Western blotting was used to assess the regulatory effect of MICAL1 on the Rac1 activation. Additionally, the function of MICAL1 and the correlations between MICAL1 and immune infiltration levels in KIRC were investigated using TIMER and TISIDB.

RESULTS

MICAL1 expression was significantly higher in carcinoma tissue compared with non-cancerous tissue. A survival analysis revealed that patients with high MICAL1 expression had shorter overall survival (OS) and disease-specific survival (DSS) compared with patients with low MICAL1 expression. ROC analysis also confirmed that MICAL1 has a high diagnostic value in KIRC. Importantly, the univariate and multivariate Cox analysis further confirmed that high MICAL1 expression was an independent risk factor for OS in patients with KIRC. In accordance with this, knockdown of MICAL1 expression decreased Rac1 activation and cell migration. KEGG and GSEA analysis revealed that the immune infiltration and Ras signaling pathways were significantly upregulated in the high MICAL1 expression group. In terms of immune infiltrating levels, MICAL1 expression was positively associated with CD8+/Treg cell infiltration levels. Specifically, bioinformatic analysis showed that MICAL1 expression had strong relationships with various T cell exhaustion markers.

CONCLUSIONS

MICAL1 expression may act as a prognostic biomarker for determining the prognosis in renal clear cell carcinoma and plays an important role in regulating tumor immune microenvironment and cell migratory capacity.

Kelch-like proteins in the gastrointestinal tumors

Gastrointestinal tumors have become a worldwide health problem with high morbidity and poor clinical outcomes. Chemotherapy and surgery, the main treatment methods, are still far from meeting the treatment needs of patients, and targeted therapy is in urgent need of development. Recently, emerging evidence suggests that kelch-like (KLHL) proteins play essential roles in maintaining proteostasis and are involved in the progression of various cancers, functioning as adaptors in the E3 ligase complex and promoting the specific degradation of substrates. Therefore, KLHL proteins should be taken into consideration for targeted therapy strategy discovery. This review summarizes the current knowledge of KLHL proteins in gastrointestinal tumors and discusses the potential of KLHL proteins as potential drug targets and prognostic biomarkers.

Identification and validation of a novel cellular senescence-related lncRNA prognostic signature for predicting immunotherapy response in stomach adenocarcinoma

Background: Cellular senescence is a novel hallmark of cancer associated with patient outcomes and tumor immunotherapy. However, the value of cellular senescence-related long non-coding RNAs (lncRNAs) in predicting prognosis and immunotherapy response for stomach adenocarcinoma (STAD) patients needs further investigation.

Methods: The transcriptome and corresponding clinical information of STAD and cellular senescence-related genes were, respectively, downloaded from the Cancer Genome Atlas (TCGA) and CellAge databases. Differential expression analysis and coexpression analysis were performed to obtain cellular senescence-related lncRNAs. Univariate regression analysis and least absolute shrinkage and selection operator (LASSO) Cox analysis were conducted to establish the cellular senescence-related lncRNA prognostic signature (CSLPS). Next, the survival curve, ROC curve, and nomogram were developed to assess the capacity of predictive models. Moreover, principal component analysis (PCA), gene set enrichment analysis (GSEA), tumor microenvironment (TME), tumor mutation burden (TMB), microsatellite instability (MSI), and tumor immune dysfunction and exclusion (TIDE) score analysis were performed between high- and low-risk groups.

Results: A novel CSLPS involving fifteen lncRNAs (REPIN1-AS1, AL355574.1, AC104695.3, AL033527.2, AC083902.1, TYMSOS, LINC00460, AC005165.1, AL136115.1, AC007405.2, AL391152.1, SCAT1, AC129507.1, AL121748.1, and ADAMTS9-AS1) was developed. According to the nomogram, the risk model based on the CSLPS was an independent prognostic factor and could predict 1-, 3-, and 5-year overall survival for STAD patients. GSEA suggested that the high-risk group was mainly associated with Toll-like receptor, JAK/STAT, NOD-like receptor, and chemokine signaling pathways. Further analysis revealed that STAD patients in the low-risk group with better clinical outcomes had a higher TMB, higher proportion of high microsatellite instability (MSI-H), better immune infiltration, and lower TIDE scores.

Conclusion: A fifteen-CSlncRNA prognostic signature could predict survival outcomes, and patients in the low-risk group may be more sensitive to immunotherapy.

Construction of a Pyroptosis-Related Genes Signature to Improve the Prognostic Prediction and Therapeutic Drugs Selection in Patients with Pancreatic Cancer

Background

Effective prognostic assessment and appropriate drug selection are important for the clinical management of pancreatic cancer (PaC). Here, we aimed to establish a pyroptosis-associated genes (PRGs) signature to predict the prognostic outcomes of PaC and guide clinical drug therapy.

Methods

We identified the differentially expressed PRGs between pancreatic adenocarcinoma (n = 178) and control pancreas samples (n = 171) obtained from different databases, and performed Lasso and Cox regression analysis to create a prognosis signature. Kaplan–Meier (K-M) survival curves and time-dependent receiver operating characteristics were further constructed to assess the utility of the risk model. The International Cancer Genome Consortium (ICGC) PACA-AU cohort (n = 95) was used as a validation dataset to examine the validity of this prognostic model. The correlations of risk score (RS) with clinical features, immune cell infiltration, tumor mutation burden and half-maximal inhibitory concentrations (IC50) of chemotherapeutic drugs were analyzed, and the expression levels of PRGs in cell lines were detected.

Results

A prognostic signature was constructed, which consisted of 4 PRGs (AIM2, IL18, GSMDC and PLCG1). K-M analysis demonstrated a remarkable difference in overall survival (OS) time between low-risk (LR) and high-risk (HR) groups (P < 0.001). The RS contributed to the progression of PaC, and could be a significant independent factor for prognostic prediction. The validation of the ICGC cohort confirmed the effectiveness of the proposed signature. The patients with a HR score in the TCGA cohort had higher tumor mutation burden and more sensitivity to paclitaxel, gemcitabine, 5-fluorouracil and cisplatin than those with a LR score. The differential expression levels of signature genes were verified in vitro.

Conclusion

The PRGs signature can be applied for predicting the prognosis of PaC, and may provide useful information for selection of therapeutic drugs.

Oxidation and reduction of actin: Origin, impact in vitro and functional consequences in vivo

Actin is among the most abundant proteins in eukaryotic cells and assembles into dynamic filamentous networks regulated by many actin binding proteins. The actin cytoskeleton must be finely tuned, both in space and time, to fulfill key cellular functions such as cell division, cell shape changes, phagocytosis and cell migration. While actin oxidation by reactive oxygen species (ROS) at non-physiological levels are known for long to impact on actin polymerization and on the cellular actin cytoskeleton, growing evidence shows that direct and reversible oxidation/reduction of specific actin amino acids plays an important and physiological role in regulating the actin cytoskeleton. In this review, we describe which actin amino acid residues can be selectively oxidized and reduced in many different ways (e.g. disulfide bond formation, glutathionylation, carbonylation, nitration, nitrosylation and other oxidations), the cellular enzymes at the origin of these post-translational modifications, and the impact of actin redox modifications both in vitro and in vivo. We show that the regulated balance of oxidation and reduction of key actin amino acid residues contributes to the control of actin filament polymerization and disassembly at the subcellular scale and highlight how improper redox modifications of actin can lead to pathological conditions.

Identification of a Solute Carrier Family-Based Signature for Predicting Overall Survival in Osteosarcoma

Given the important role of SLC family in essential physiological processes including nutrient uptake, ion transport, and waste removal, and that their dysregulation was found in distinct forms of cancer, here we identified a novel gene signature of SLC family for patient risk stratification in osteosarcoma. Gene expression data and relevant clinical materials of osteosarcoma samples were retrieved from The Cancer Genome Atlas (TCGA) database. Prognosis-related SLC genes were identified by performing univariate Cox regression analysis and were utilized to construct a four-SLC gene signature in osteosarcoma. It allowed patients to be classified into high- and low-risk groups, and Kaplan-Meier survival analysis in the training, testing, entire, and external GSE21257 cohorts suggested that the overall survival of patients in high-risk group was consistently worse than that in low-risk group, suggesting the promising accuracy and generalizability of the SLC-based signature in predicting the prognosis of patients with osteosarcoma. Moreover, univariate and multivariate Cox regression analyses indicated that the derived risk score was the only independent prognostic factor for osteosarcoma patients in TCGA and GSE21257 cohorts. Besides, a prognostic nomogram comprising the derived risk score and clinical features including gender and age was developed for clinical decision-making. Functional enrichment analyses of the differentially expressed genes between high- and low-risk group revealed that immune-related biological processes and pathways were significantly enriched. Estimation of tumor immune microenvironment using ESTIMATE algorithm revealed that patients with lower risk score had higher stromal, immune, and ESTIMATE score, and lower tumor purity. ssGSEA analyses indicated that the scores of various immune subpopulations including CD8+ T cells, DCs, and TIL were lower in high-risk group than these in low-risk group in both cohorts. As for the related immune functions, the scores of APC co-inhibition, CCR, check-point, T cell co-stimulation, and Type II IFN response were lower in high-risk group than these in low-risk group in both cohorts. In all, we identified a novel prognostic signature based on four SLC family genes that accurately predicted overall survival in osteosarcoma patients. Furthermore, the signature is linked to differences in immunological status and immune cell infiltrations in the tumor microenvironment.

Pan-Cancer Analysis Shows That ALKBH5 Is a Potential Prognostic and Immunotherapeutic Biomarker for Multiple Cancer Types Including Gliomas

Background

AlkB homolog 5 (ALKBH5) is a N⁶-methyladenosine (m⁶A) demethylase associated with the development, growth, and progression of multiple cancer types. However, the biological role of ALKBH5 has not been investigated in pan-cancer datasets. Therefore, in this study, comprehensive bioinformatics analysis of pan-cancer datasets was performed to determine the mechanisms through which ALKBH5 regulates tumorigenesis.

Methods

Online websites and databases such as NCBI, UCSC, CCLE, HPA, TIMER2, GEPIA2, cBioPortal, UALCAN, STRING, SangerBox, ImmuCellAl, xCell, and GenePattern were used to extract data of ALKBH5 in multiple cancers. The pan-cancer patient datasets were analyzed to determine the relationship between ALKBH5 expression, genetic alterations, methylation status, and tumor immunity. Targetscan, miRWalk, miRDB, miRabel, LncBase databases and Cytoscape tool were used to identify microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) that regulate expression of ALKBH5 and construct the lncRNA-miRNA-ALKBH5 network. In vitro CCK-8, wound healing, Transwell and M2 macrophage infiltration assays as well as in vivo xenograft animal experiments were performed to determine the biological functions of ALKBH5 in glioma cells.

Results

The pan-cancer analysis showed that ALKBH5 was upregulated in several solid tumors. ALKBH5 expression significantly correlated with the prognosis of cancer patients. Genetic alterations including duplications and deep mutations of the ALKBH5 gene were identified in several cancer types. Alterations in the ALKBH5 gene correlated with tumor prognosis. GO and KEGG enrichment analyses showed that ALKBH5-related genes were enriched in the inflammatory, metabolic, and immune signaling pathways in glioma. ALKBH5 expression correlated with the expression of immune checkpoint (ICP) genes, and influenced sensitivity to immunotherapy. We constructed a lncRNA-miRNA network that regulates ALKBH5 expression in tumor development and progression. In vitro and in vivo experiments showed that ALKBH5 promoted proliferation, migration, and invasion of glioma cells and recruited the M2 macrophage to glioma cells.

Conclusions

ALKBH5 was overexpressed in multiple cancer types and promoted the development and progression of cancers through several mechanisms including regulation of the tumor-infiltration of immune cells. Our study shows that ALKBH5 is a promising prognostic and immunotherapeutic biomarker in some malignant tumors.

Multimetric feature selection for analyzing multicategory outcomes of colorectal cancer: random forest and multinomial logistic regression models

Colorectal cancer (CRC) is one of the most common cancers worldwide, and a leading cause of cancer deaths. Better classifying multicategory outcomes of CRC with clinical and omic data may help adjust treatment regimens based on individual's risk. Here, we selected the features that were useful for classifying four-category survival outcome of CRC using the clinical and transcriptomic data, or clinical, transcriptomic, microsatellite instability and selected oncogenic-driver data (all data) of TCGA. We also optimized multimetric feature selection to develop the best multinomial logistic regression (MLR) and random forest (RF) models that had the highest accuracy, precision, recall and F1 score, respectively. We identified 2073 differentially expressed genes of the TCGA RNASeq dataset. MLR overall outperformed RF in the multimetric feature selection. In both RF and MLR models, precision, recall and F1 score increased as the feature number increased and peaked at the feature number of 600-1000, while the models' accuracy remained stable. The best model was the MLR one with 825 features based on sum of squared coefficients using all data, and attained the best accuracy of 0.855, F1 of 0.738 and precision of 0.832, which were higher than those using clinical and transcriptomic data. The top-ranked features in the MLR model of the best performance using clinical and transcriptomic data were different from those using all data. However, pathologic staging, HBS1L, TSPYL4, and TP53TG3B were the overlapping top-20 ranked features in the best models using clinical and transcriptomic, or all data. Thus, we developed a multimetric feature-selection based MLR model that outperformed RF models in classifying four-category outcome of CRC patients. Interestingly, adding microsatellite instability and oncogenic-driver data to clinical and transcriptomic data improved models' performances. Precision and recall of tuned algorithms may change significantly as the feature number changes, but accuracy appears not sensitive to these changes.

Establishment of a 4-miRNA Prognostic Model for Risk Stratification of Patients With Pancreatic Adenocarcinoma

Pancreatic adenocarcinomas (PAADs) often remain undiagnosed until later stages, limiting treatment options and leading to poor survival. The lack of robust biomarkers complicates PAAD prognosis, and patient risk stratification remains a major challenge. To address this issue, we established a panel constructed by four miRNAs (miR-4444-2, miR-934, miR-1301 and miR-3655) based on The Cancer Genome Atlas (TCGA) and Human Cancer Metastasis Database (HCMDB) to predicted the prognosis of PAAD patients. Then, a risk prediction model of these four miRNAs was constructed by using Cox regression analysis with the least absolute shrinkage and selection operator (LASSO) regression analysis. This model stratified TCGA PAAD cohort into the low-risk and high-risk groups based on the panel-based risk score, which was significantly associated with 1-, 2-, 3-year OS (AUC=0.836, AUC=0.844, AUC=0.952, respectively). The nomogram was then established with a robust performance signature for predicting prognosis compared to clinical characteristics of pancreatic cancer (PC) patients, including age, gender and clinical stage. Moreover, two GSE data were validated the expressions of 4 miRNAs with prognosis/survival outcome in PC. In the external clinical sample validation, the high-risk group with the upregulated expressions of miR-934/miR-4444-2 and downregulated expressions of miR-1301/miR-3655 were indicated a poor prognosis. Furthermore, the cell counting kit-8 (CCK-8) assay, clone formation, transwell and wound healing assay also confirmed the promoting effect of miR-934/miR-4444-2 and the inhibiting effect of miR-1301/miR-3655 in PC cell proliferation and migration. Taken together, we identified a new 4-miRNA risk stratification model could be used in predicting prognosis in PAAD.

Alterations in the Ca2+ toolkit in oesophageal adenocarcinoma

Aim:

To investigate alterations in transcription of genes, encoding Ca²⁺ toolkit proteins, in oesophageal adenocarcinoma (OAC) and to assess associations between gene expression, tumor grade, nodal-metastatic stage, and patient survival.

Methods:

The expression of 275 transcripts, encoding components of the Ca²⁺ toolkit, was analyzed in two OAC datasets: the Cancer Genome Atlas [via the University of Alabama Cancer (UALCAN) portal] and the oesophageal-cancer, clinical, and molecular stratification [Oesophageal Cancer Clinical and Molecular Stratification (OCCAMS)] dataset. Effects of differential expression of these genes on patient survival were determined using Kaplan-Meier log-rank tests. OAC grade- and metastatic-stage status was investigated for a subset of genes. Adjustment for the multiplicity of testing was made throughout.

Results:

Of the 275 Ca²⁺-toolkit genes analyzed, 75 displayed consistent changes in expression between OAC and normal tissue in both datasets. The channel-encoding genes, N-methyl-D-aspartate receptor 2D (GRIN2D), transient receptor potential (TRP) ion channel classical or canonical 4 (TRPC4), and TRP ion channel melastatin 2 (TRPM2) demonstrated the greatest increase in expression in OAC in both datasets. Nine genes were consistently upregulated in both datasets and were also associated with improved survival outcomes. The 6 top-ranking genes for the weighted significance of altered expression and survival outcomes were selected for further analysis: voltage-gated Ca²⁺ channel subunit α 1D (CACNA1D), voltage-gated Ca²⁺ channel auxiliary subunit α2 δ4 (CACNA2D4), junctophilin 1 (JPH1), acid-sensing ion channel 4 (ACCN4), TRPM5, and secretory pathway Ca²⁺ ATPase 2 (ATP2C2). CACNA1D, JPH1, and ATP2C2 were also upregulated in advanced OAC tumor grades and nodal-metastatic stages in both datasets.

Conclusions:

This study has unveiled alterations of the Ca²⁺ toolkit in OAC, compared to normal tissue. Such Ca²⁺ signalling findings are consistent with those from studies on other cancers. Genes that were consistently upregulated in both datasets might represent useful markers for patient diagnosis. Genes that were consistently upregulated, and which were associated with improved survival, might be useful markers for patient outcome. These survival-associated genes may also represent targets for the development of novel chemotherapeutic agents.

A Prognostic Model of Pancreatic Cancer Based on Ferroptosis-Related Genes to Determine Its Immune Landscape and Underlying Mechanisms

Pancreatic cancer is one of the malignant tumors with the worst prognosis in the world. As a new way of programmed cell death, ferroptosis has been proven to have potential in tumor therapy. In this study, we used the TCGA-PAAD cohort combined with the previously reported 60 ferroptosis-related genes to construct and validate the prognosis model and in-depth analysis of the differences in the function and immune characteristics of different RiskTypes. The results showed that the six-gene signature prognostic model that we constructed has good stability and effectiveness. Further analysis showed that the upregulated genes in the high-risk group were mainly enriched in extracellular matrix receptor-related pathways and other tumor-related pathways and the infiltration of immune cells, such as B, T, and NK cells, was suppressed. In short, our model shows good stability and effectiveness. Further studies have found that the prognostic differences between different RiskTypes may be due to the changes in the ECM-receptor pathway and activation of the immune system. Additionally, ICI drugs can treat pancreatic cancer in high-risk groups.

A Novel Ferroptosis-Related Gene Signature Predicts Recurrence in Patients With Pancreatic Ductal Adenocarcinoma

Background: Recurrence after surgery is largely responsible for the extremely poor outcomes for patients with pancreatic ductal adenocarcinoma (PDAC). Ferroptosis is implicated in chemotherapy sensitivity and tumor recurrence, we aimed to find out survival-associated ferroptosis-related genes and use them to build a practical risk model with the purpose to predict PDAC recurrence. Methods: Univariate Cox regression analysis was conducted to obtain prognostic ferroptosis-related genes in The Cancer Genome Atlas (TCGA, N = 140) cohort. Multivariate Cox regression analysis was employed to construct a reliable and credible gene signature. The prognostic performance was verified in a MTAB-6134 (N = 286) validation cohort and a PACA-CA (N = 181) validation cohort. The stability of the signature was tested in TCGA and MTAB-6134 cohorts by ROC analyses. Pathway enrichment analysis was adopted to preliminary illuminate the biological relevance of the gene signature. Results: Univariate and multivariate Cox regression analyses identified a 5-gene signature that contained CAV1, DDIT4, SLC40A1, SRXN1 and TFAP2C. The signature could efficaciously stratify PDAC patients with different recurrence-free survival (RFS), both in the training and validation cohorts. Results of subgroup receiver operating characteristic curve (ROC) analyses confirmed the stability and the independence of this signature. Our signature outperformed clinical indicators and previous reported models in predicting RFS. Moreover, the signature was found to be closely associated with several cancer-related and drug response pathways. Conclusion: This study developed a precise and concise prognostic model with the clinical implication in predicting PDAC recurrence. These findings may facilitate individual management of postoperative recurrence in patients with PDAC.

Signature based on metabolic-related gene pairs can predict overall survival of osteosarcoma patients

BACKGROUND

Osteosarcoma is a tumour of malignant origin in children and adolescents. Recent progression indicates that it is necessary to develop new therapies to improve the patient's prognosis rather than strengthen anti-tumour chemotherapy. Researchers recently realised that cancer is a kind of disease with a metabolic disorder, and metabolic reprogramming is becoming a new cancer hallmark. Hence, our study's primary purpose is to explore the value of genes related to osteosarcoma metabolism.

METHODS

From public databases, three osteosarcoma datasets with adequate clinical information were obtained. Besides, the IMvigor dataset through the 'IMvigor' package as a supplement was downloaded, the metabolic-related genes were identified, and these genes were used to construct the metabolic-related gene pairs (MRGP). Based on the prognosis-related MRGP, two molecular subtypes were identified. There are significant differences in the metabolic characteristics between the two molecular subtypes. Subsequently, the MRGP signature is constructed using the least absolute shrinkage and selection operator regression method. Finally, use SubMap analysis to evaluate the response of patients in the MRPG signature group to immunotherapy.

RESULTS

The MRGP signature can reliably predict overall survival in patients with osteosarcoma. The MRGP signature is also associated with osteosarcoma patients' metastatic status and can be used for subsequent risk classification of metastatic patients. The immunotherapy is more likely to benefit the patients in the MRGP low-risk group.

CONCLUSION

Metabolic-related gene pairs signature can assess the prognosis of patients with osteosarcoma.

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

Background: Previous prognostic signatures of pancreatic ductal adenocarcinoma (PDAC) are mainly constructed to predict the overall survival (OS), and their predictive accuracy needs to be improved. Gene signatures that efficaciously predict both OS and disease-free survival (DFS) are of great clinical significance but are rarely reported. Methods: Univariate Cox regression analysis was adopted to screen common genes that were significantly associated with both OS and DFS in three independent cohorts. Multivariate Cox regression analysis was subsequently performed on the identified genes to determine an optimal gene signature in the MTAB-6134 training cohort. The Kaplan-Meier (K-M), calibration, and receiver operating characteristic (ROC) curves were employed to assess the predictive accuracy. Biological process and pathway enrichment analyses were conducted to elucidate the biological role of this signature. Results: Multivariate Cox regression analysis determined a 7-gene signature that contained ASPH, DDX10, NR0B2, BLOC1S3, FAM83A, SLAMF6, and PPM1H. The signature had the ability to stratify PDAC patients with different OS and DFS, both in the training and validation cohorts. ROC curves confirmed the moderate predictive accuracy of this signature. Mechanically, the signature was related to multiple cancer-related pathways. Conclusion: A novel OS and DFS prediction model was constructed in PDAC with multi-cohort and cross-platform compatibility. This signature might foster individualized therapy and appropriate management of PDAC patients.

Three Immune-Related Prognostic mRNAs as Therapeutic Targets for Pancreatic Cancer

Objective: Pancreatic cancer is a highly lethal malignancy globally. This study aimed to probe and validate immune-related prognostic mRNAs as therapeutic targets for pancreatic cancer.

Methods: Gene transcriptome data of pancreatic cancer and normal pancreas were retrieved from TCGA-GTEx projects. Two thousand four hundred and ninety-eight immune-related genes were obtained from the IMMUPORT database. Abnormally expressed immune-related genes were then identified. Under univariate and multivariate cox models, a gene signature was constructed. Its predictive efficacy was assessed via ROCs. The interactions between the 21 genes were analyzed by Spearson analysis and PPI network. Using the GEPIA and The Human Protein Atlas databases, their expression and prognostic value were evaluated. The TIMER database was utilized to determine the relationships between MET, OAS1, and OASL mRNAs and immune infiltrates. Finally, their mRNA expression was externally verified in the GSE15471 and GSE62452 datasets.

Results: An immune-related 21-gene signature was developed for predicting patients' prognosis. Following verification, this signature exhibited the well predictive performance. There were physical and functional interactions between them. MET, OAS1, and OASL mRNAs were all up-regulated in pancreatic cancer and associated with unfavorable prognosis. They showed strong correlations with tumor progression. Furthermore, the three mRNAs were distinctly associated with immune infiltrates. Their up-regulation was confirmed in the two external datasets.

Conclusion: These findings identified three immune-related prognostic mRNAs MET, OAS1, and OASL, which may assist clinicians to choose targets for immunotherapy and make personalized treatment strategy for pancreatic cancer patients.

Identification of Prognostic Stromal-Immune Score-Based Genes in Hepatocellular Carcinoma Microenvironment

A growing amount of evidence has suggested the clinical importance of stromal and immune cells in the liver cancer microenvironment. However, reliable prognostic signatures based on assessments of stromal and immune components have not been well-established. This study aimed to identify stromal-immune score–based potential prognostic biomarkers for hepatocellular carcinoma. Stromal and immune scores were estimated from transcriptomic profiles of a liver cancer cohort from The Cancer Genome Atlas using the ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumors using Expression data) algorithm. Least absolute shrinkage and selection operator (LASSO) algorithm was applied to select prognostic genes. Favorable overall survivals and progression-free interval were found in patients with high stromal score and immune score, and 828 differentially expressed genes were identified. Functional enrichment analysis and protein–protein interaction networks further showed that these genes mainly participated in immune response, extracellular matrix, and cell adhesion. MMP9 (matrix metallopeptidase 9) was identified as a prognostic tumor microenvironment–associated gene by using LASSO and TIMER (Tumor IMmune Estimation Resource) algorithms and was found to be positively correlated with immunosuppressive molecules and drug response.