Search

Background: SARS-CoV-2 infection is a respiratory infectious disease similar to influenza virus infection. Numerous studies have reported similarities and differences in the clinical manifestations, laboratory tests, and mortality between these two infections. However, the genetic effects of coronavirus and influenza viruses on the host that lead to these characteristics have rarely been reported., Methods: COVID-19 (GSE157103) and influenza (GSE111368, GSE101702) datasets were downloaded from the Gene Expression Ominbus (GEO) database. Differential gene, gene set enrichment, protein-protein interaction (PPI) network, gene regulatory network, and immune cell infiltration analyses were performed to identify the critical impact of COVID-19 and influenza viruses on the regulation of host gene expression., Results: The number of differentially expressed genes in the COVID-19 patients was significantly higher than in the influenza patients. 22 common differentially expressed genes (DEGs) were identified between the COVID-19 and influenza datasets. The effects of the viruses on the regulation of host gene expression were determined using gene set enrichment and PPI network analyses. Five HUB genes were finally identified: IFI27, OASL, RSAD2, IFI6, and IFI44L., Conclusion: We identified five HUB genes between COVID-19 and influenza virus infection, which might be helpful in the diagnosis and treatment of COVID-19 and influenza. This knowledge may also guide future mechanistic studies that aim to identify pathogen-specific interventions., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Sun, Ke, Zhao, Qu, Hu, Gao and Peng.)

The purpose of this research is to develop a predictive model based on necroptosis-related genes to predict the prognosis and survival of lower grade gliomas (LGGs) efficiently. To achieve this goal, we searched for differentially expressed necrotizing apoptosis-related genes using the TCGA and CGGA databases. To construct a prognostic model, LASSO Cox and COX regression analyses were conducted on the differentially expressed genes. In this study, three genes were used to develop a prognostic model of necrotizing apoptosis, and all samples were split into high- and low-risk groups. We observed that patients with a high-risk score had a worse overall survival rate (OS) than those with a low-risk score. In the TCGA and CGGA cohorts, the nomogram plot showed a high capacity to predict overall survival of LGG patients. GSEA analysis revealed that the high-risk group was enriched for inflammatory responses, tumor-related pathways, and pathological processes. Additionally, the high-risk score was associated with invading immune cell expression. In conclusion, our predictive model based on necroptosis-related genes in LGG was shown to be effective in the diagnosis and could predict the prognosis of LGG. In addition, we identified possible targets related to necroptosis-related genes for glioma therapy in this study., Competing Interests: None., (AJCR Copyright © 2023.)

This study aimed to construct an immune-related prognostic model and a nomogram to predict the 1-, 3-, and 5-year overall survival (OS) of breast cancer patients. We applied single-sample gene set enrichment analysis to classify 1,053 breast cancer samples from The Cancer Genome Atlas (TCGA) database into high and low immune cell infiltration clusters. In cluster construction and validation, the R packages "GSVA," "hclust," "ESTIMATE," and "CIBERSORT" and GSEA software were utilized. ImmPort, univariate Cox regression analysis, and Venn analysis were then used to identify 42 prognostic immune-related genes. Eventually, the genes TAPBPL , RAC2 , IL27RA , ULBP2 , PSMB8 , SOCS3 , NFKBIE , IGLV6-57 , CXCL1 , IGHD , AIMP1 , and CXCL13 were chosen for model construction utilizing least absolute shrinkage and selection operator regression analysis. The Kaplan-Meier curves of both the training and validation sets indicated that the overall survival of patients in the low-risk group was superior to that of patients in the high-risk group ( p < .05). The areas under curves (AUCs) of the model at 1, 3, and 5 years were, respectively, .697, .710, and .675 for the training set and .930, .688, and .712 for the validation set. Regarding clinicopathologic characteristics, breast cancer-related genes, and tumor mutational burden, effective differentiation was achieved between high-risk and low-risk groups. A nomogram integrating the risk model and clinicopathologic factors was constructed using the "rms" R software package. The nomogram's 1-, 3-, and 5-year AUCs were .828, .783, and .751, respectively. Overall, our study developed an immune-related model and a nomogram that could reliably predict OS for breast cancer patients, and offered insights into tumor immune and pathological mechanisms., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Li, Li, Liu, Li and Sun.)

Background: The role of inflammation in the formation of idiopathic pulmonary fibrosis (IPF) has gained a lot of attention recently. However, the involvement of genes related to inflammation and immune exchange environment status in the prognosis of IPF remains to be further clarified. The objective of this research is to establish a new model for the prediction of the overall survival (OS) rate of inflammation-related IPF., Methods: Gene Expression Omnibus (GEO) was employed to obtain the three expression microarrays of IPF, including two from alveolar lavage fluid cells and one from peripheral blood mononuclear cells. To construct the risk assessment model of inflammation-linked genes, least absolute shrinkage and selection operator (lasso), univariate cox and multivariate stepwise regression, and random forest method were used. The proportion of immune cell infiltration was evaluated by single sample Gene Set Enrichment Analysis (ssGSEA) algorithm., Results: The value of genes linked with inflammation in the prognosis of IPF was analyzed, and a four-genes risk model was constructed, including tpbg, Myc, ffar2, and CCL2. It was highlighted by Kaplan Meier (K-M) survival analysis that patients with high-risk scores had worse overall survival time in all training and validation sets, and univariate and multivariate analysis highlighted that it has the potential to act as an independent risk indicator for poor prognosis. ROC analysis showed that the prediction efficiency of 1-, 3-, and 5-year OS time in the training set reached 0.784, 0.835, and 0.921, respectively. Immune infiltration analysis showed that Myeloid-Derived Suppressor Cells (MDSC), macrophages, regulatory T cells, cd4+ t cells, neutrophils, and dendritic cells were more infiltrated in the high-risk group than in the low-risk group., Conclusion: Inflammation-related genes can be well used to evaluate the IPF prognosis and impart a new idea for the treatment and follow-up management of IPF patients., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Yin, Peng, Situ, Xie, Tan, Wei, Jiang, Zhang and Liu.)

Osteoarthritis (OA) is a major cause of pain, disability, and social burden in the elderly throughout the world. Although many studies focused on the molecular mechanism of OA, its etiology remains unclear. Therefore, more biomarkers need to be explored to help early diagnosis, clinical outcome measurement, and new therapeutic target development. Our study aimed to retrieve the potential hub genes of osteoarthritis (OA) by weighted gene co-expression network analysis (WGCNA) and assess their clinical utility for predicting OA. Here, we integrated WGCNA to identify novel OA susceptibility modules and hub genes. In this study, we first selected 477 and 834 DEGs in the GSE1919 and the GSE55235 databases, respectively, from the Gene Expression Omnibus (GEO) website. Genes with p -value<0.05 and | log 2 FC | > 1 were included in our analysis. Then, WGCNA was conducted to build a gene co-expression network, which filtered out the most relevant modules and screened out 23 overlapping WGCNA-derived hub genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses elucidated that these hub genes were associated with cell adhesion molecules pathway, leukocyte activation, and inflammatory response. In addition, we conducted the protein-protein interaction (PPI) network in 23 hub genes, and the top four upregulated hub genes were sorted out (CD4, SELL, ITGB2, and CD52). Moreover, our nomogram model showed good performance in predicting the risk of OA (C-index = 0.76), and this model proved to be efficient in diagnosis by ROC curves (AUC = 0.789). After that, a single-sample gene set enrichment (ssGSEA) analysis was performed to discover immune cell infiltration in OA. Finally, human primary synoviocytes and immunohistochemistry study of synovial tissues confirmed that those candidate genes were significantly upregulated in the OA groups compared with normal groups. We successfully constructed a co-expression network based on WGCNA and found out that OA-associated susceptibility modules and hub genes, which may provide further insight into the development of pre-symptomatic diagnosis, may contribute to understanding the molecular mechanism study of OA risk genes., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Wang, Zhou, Chen, He, Qin, Wang, Liu, Zhou, Su and Zhang.)

Immune genes play an important role in the development and progression of acute myeloid leukemia (AML). However, the role of immune genes in the prognosis and microenvironment of AML remains unclear. In this study, we analyzed 151 AML patients in the TCGA database for relevant immune cell infiltration. AML patients were divided into high and low immune cell infiltration clusters based on ssGSEA results. Immune-related pathways, AML pathways and glucose metabolism pathways were enriched in the high immune cell infiltration cluster. Then we screened the differential immune genes between the two immune cell infiltration clusters. Nine prognostic immune genes were finally identified in the train set by LASSO-Cox regression. We constructed a model in the train set based on the nine prognostic immune genes and validated the predictive capability in the test set. The areas under the ROC curve of the train set and the test set for ROC at 1, 3, 5 years were 0.807, 0.813, 0.815, and 0.731, 0.745, 0.830, respectively. The areas under ROC curve of external validation set in 1, 3, and 5 years were 0.564, 0.619, and 0.614, respectively. People with high risk scores accompanied by high TMB had been detected with the worst prognosis. Single-cell sequencing analysis revealed the expression of prognostic genes in AML cell subsets and pseudo-time analysis described the differentiation trajectory of cell subsets. In conclusion, our results reveal the characteristics of immune microenvironment and cell subsets of AML, while it still needs to be confirmed in larger samples studies. The prognosis model constructed with nine key immune genes can provide a new method to assess the prognosis of AML patients., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Lu, Zheng, Dong, Chang, Cao, Liu, Shi, Wang, Yang and Jia.)

Aberrant deposits of neurofibrillary tangles (NFT), the main characteristic of Alzheimer's disease (AD), are highly related to cognitive impairment. However, the pathological mechanism of NFT formation is still unclear. This study explored differences in gene expression patterns in multiple brain regions [entorhinal, temporal, and frontal cortex (EC, TC, FC)] with distinct Braak stages (0- VI), and identified the hub genes via weighted gene co-expression network analysis (WGCNA) and machine learning. For WGCNA, consensus modules were detected and correlated with the single sample gene set enrichment analysis (ssGSEA) scores. Overlapping the differentially expressed genes (DEGs, Braak stages 0 vs. I-VI) with that in the interest module, metascape analysis, and Random Forest were conducted to explore the function of overlapping genes and obtain the most significant genes. We found that the three brain regions have high similarities in the gene expression pattern and that oxidative damage plays a vital role in NFT formation via machine learning. Through further filtering of genes from interested modules by Random Forest, we screened out key genes, such as LYN, LAPTM5, and IFI30. These key genes, including LYN, LAPTM5, and ARHGDIB, may play an important role in the development of AD through the inflammatory response pathway mediated by microglia., (Copyright © 2022 Chai, Zhang, Chen, Gu, Tang, Cao, Wang, Ye, Wan, Liang and Shen.)

There are many experimental methods for characterizing immune profiles of tumors, such as flow and mass cytometry. However, these approaches are time and resource intensive. Thus, several "digital cytometry" methods have been developed to extract cell frequencies from RNA-seq data. Here, we introduce TumorDecon, named for its potential to deconvolve the distribution of cells from the gene expression levels of a bulk of cells, such as a tumor. The Python package provides an accessible way of applying these methods. It includes four deconvolution methods as well as several gene sets, signature matrices, and functions for generating custom signature matrices., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Background: Keloid disorder is a recurrent fibroproliferative cutaneous tumor. Due to the lack of early identification of keloid patients before the formation of keloids, it is impossible to carry out pre-traumatic intervention and prevention for these patients. This led us to identify and determine signatures with diagnostic significance for keloids. Methods: Public series of matrix files were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were calculated from expression profiling data, and the diagnostic signature was identified by constructing a protein-protein interaction (PPI) network. The diagnostic efficacy of the screened signature was assessed by employing receiver operating characteristic (ROC) curves. Furthermore, we calculated the proportion of different immune cells in the gene expression matrix microenvironment by the "ssGSEA" algorithm, and assessed the difference in immune cell abundance between keloids and control groups and the relationship between the signature and immune cell infiltration. Clinical keloid and normal skin tissues were collected, and the expression of the screened diagnostic signature was validated by RT-qPCR and immunohistochemical assay. Results: By screening the key genes in PPI, TGM2 was recognized and validated as a diagnostic signature and the infiltrating abundance of 10 immune cells was significantly correlated with TGM2 expression. Gene ontology enrichment analysis demonstrated that TGM2 and molecules interacting with it were mainly enriched in processes involving wound healing and collagen fiber organization. TGM2 correlated positively with HIF-1A (R = 0.82, p -value = 1.4e-05), IL6 (R = 0.62, p -value = 0.0053), and FN1 (R = 0.66, p -value = 0.0019). Besides, TGM2 was significantly upregulated in clinical keloid samples compared to normal skin tissues. Conclusion: TGM2 may serve as an auxiliary diagnostic indicator for keloids. However, the role of TGM2 in keloids has not been adequately reported in the current literature, which may provide a new direction for molecular studies of keloids., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Xia, Wang, Xiao, Shan, Hao and Zhang.)

Papillary thyroid carcinoma (PTC) is one of the common clinical tumors, where LncRNA plays an important role in tumorigenesis and its development. The purpose of this study was to explore the role of DIO3OS in PTC. Firstly, this study verified the expression of DIO3OS in PTC through the public database. Then, the differences in DIO3OS expression between the PTC group and paracancerous tissues were verified using the qRT-PCR. A series of in vitro experiments were conducted to verify the function of DIO3OS in PTC, while its involvement in possible pathways was analyzed by the GSEA. The ssGSEA algorithm estimated the immune status using the queue transcriptome graph derived from the TCGA database. Further, the correlation analysis was used to confirm the relationship between DIO3OS and the immune genes. The results showed that the expression of DIO3OS was low in PTC. The same results were also confirmed by qRT-PCR analysis (P= 0.0077). In vitro, DIO3OS was localized within the cytoplasm and exosomes. Overexpression of DIO3OS hindered the proliferation, invasion, and migration of PTC cells. According to the degree of immune cell infiltration, the tumor group was divided into high immune cell infiltration group, medium immune cell infiltration group, and low immune cell infiltration group. The results showed that the DIO3OS was highly expressed in the high immune cell infiltration group (P < 0.001), which was positively correlated with the immune cell infiltration and also correlated with multiple immune genes. In summary, this study illustrated the expression pattern of DIO3OS in PTC, which may be involved in the immune-inflammatory pathway. Hence, our results may provide new diagnostic biomarkers and therapeutic targets for PTC. [ABSTRACT FROM AUTHOR]

Background: The role of DnaJ heat shock protein family (Hsp40) member C10 (DNAJC10) in cancers has been reported but its function in glioma is not clear. We reveal the prognostic role and underlying functions of DNAJC10 in glioma in the present study., Methods: Reverse Transcription and Quantitative Polymerase Chain Reaction (RT-qPCR) was used to quantify the relative DNAJC10 messenger RNA (mRNA) expression of clinical samples. Protein expressions of clinical samples were tested by Western blot. The overall survival (OS) of glioma patients with different DNAJC10 expression was compared by Kaplan-Meier method (two-sided log-rank test). Single-sample gene set enrichment analysis (ssGSEA) was used to estimate the immune cell infiltrations and immune-related function levels. The independent prognostic role of DNAJC10 was determined by univariate and multivariate Cox regression analyses. The DNAJC10-based nomogram model was established using multivariate Cox regression by R package 'rms'., Results: Higher DNAJC10 is observed in gliomas and it is up-regulated in higher grade, isocitrate dehydrogenase (IDH)-wild, 1p/19q non-codeletion, O(6)-methylguanine-DNA methyltransferase (MGMT) unmethylated gliomas. Gliomas with higher DNAJC10 expression present poorer prognosis compared with low-DNAJC10 gliomas. The predictive accuracy of 1/3/5-OS of DNAJC10 is found to be stable and robust using time-dependent ROC model. Enrichment analysis recognized that T-cell activation and T-cell receptor signaling were enriched in higher DNAJC10 gliomas. Immune/stromal cell infiltrations, tumor mutation burden (TMB), copy number alteration (CNA) burden and immune checkpoint genes (ICPGs) were also positively correlated with DNAJC10 expression in gliomas. DNAJ10-based nomogram model was established and showed strong prognosis-predictive ability., Conclusion: Higher DNAJC10 expression correlates with poor prognosis of glioma and it was a potential prognostic biomarker for glioma., (© 2022 The Author(s).)

Introduction: Breast cancer (BC) is the most common cancer in women worldwide and has a high mortality rate. The fact that the tumor microenvironment affects clinical outcomes of all types of cancers underlines the involvement of various immune-related genes (IRGs). Therefore, this study aimed to establish an IRGs-based signature for the prognosis of BC patients., Material and Methods: In this study, 12 immune cell infiltrating degrees in 1,102 BC cases from The Cancer Genome Atlas (TCGA) database were assessed, and RNA-sequencing (RNA-seq) data of these samples were analyzed by single-sample gene set enrichment analysis (ssGSEA). Based on the results, high, low, and middle immune infiltrating clusters were constructed. A total of 138 overlapped differentially expressed genes (DEGs) were identified in the high and low infiltrating clusters, as well as in normal and BC samples. Univariate Cox regression and LASSO analyses were also performed. Furthermore, GSEA suggested some highly enriched pathways in the different immune infiltrating clusters, leading to a better understanding of potential mechanisms of immune infiltration in BC., Results: Finally, 19 immune-related genes were identified that could be utilized as a potential prognostic biomarker for BC. Kaplan-Meier plot and ROC curve, univariate as well as multivariate Cox analyses were carried out, which suggested that the 19-IRG-based signature is a significant prognosis factor independent of clinical features. Based on the analysis of protein-protein interactions (PPI), the three hub genes were identified., Conclusions: These results provide a new method to predict the prognosis and survival of BC based on the three genes' features., Competing Interests: The authors declare no conflict of interest., (Copyright © 2022 Termedia.)

• Osteosarcoma (OS) could be divided into two subtypes based on immune-related gene sets. • High immune cell infiltration subtype of OS predicted better prognosis. • Significant differences in immune cell infiltration were shown between two OS subtypes. • Verifying a new immune-related gene prognostic signature based on differentially expressed genes between two OS subtypes. Osteosarcoma (OS) is a highly aggressive cancer with poor prognosis, which mainly occurs in teenagers. Recent studies have shown that tumor-infiltrating immune cells play an important role in the progression of OS. In the present study, we identified two immune subtypes of OS (referred to as high and low immune cell infiltration subtypes, respectively) based on immune-related gene sets using TARGET and GEO cohort datasets. Elevated immune scores, increased stromal scores, decreased tumor purities, and higher infiltration of CD8 + T cells and M1 macrophages were observed for the high immune cell infiltration subtype. Moreover, the high immune cell infiltration subtype was characterized by high expression of immune checkpoint molecules. Gene set enrichment analysis showed that "B cell receptor signaling pathway" and "T cell receptor signaling pathway" gene sets were enriched in the high immune cell infiltration subtype. In addition, patients in the high immune cell infiltration subtype had better prognosis than patients in the low immune cell infiltration subtype. Furthermore, differentially expressed genes were screened according to the two OS subtypes and a risk model was generated by multivariate Cox regression analysis to predict the prognosis of OS patients. These results in this study showed that OS patients could be divided into two immune subtypes and offered a novel two-gene risk signature to predict the prognosis of patients with OS. [ABSTRACT FROM AUTHOR]

Background: Muscle-invasive bladder cancer (MIBC) is a heterogeneous disease with varying clinical courses and responses to treatment. To improve the prognosis of patients, it is necessary to understand such heterogeneity., Methods: We used single-sample gene set enrichment analysis to classify 35 MIBC cases into immunity-high and immunity-low groups. Bioinformatics analyses were conducted to compare the differences between these groups. Eventually, single-cell mass cytometry (CyTOF) was used to compare the characteristics of the immune microenvironment between the patients in the two groups., Results: Compared with patients in the immunity-low group, patients in the immunity-high group had a higher number of tumor-infiltrating immune cells and greater enrichment of gene sets associated with antitumor immune activity. Furthermore, positive immune response-related pathways were more enriched in the immunity-high group. We identified 26 immune cell subsets, including cytotoxic T cells (Tcs), helper T cells (Ths), regulatory T cells (Tregs), B cells, macrophages, natural killer (NK) cells, and dendritic cells (DCs) using CyTOF. Furthermore, there was a higher proportion of CD45+ lymphocytes and enrichment of one Tc subset in the immunity-high group. Additionally, M2 macrophages were highly enriched in the immunity-low group. Finally, there was higher expression of PD-1 and Tim-3 on Tregs as well as a higher proportion of PD-1+ Tregs in the immunity-low group than in the immunity-high group., Conclusion: In summary, the immune microenvironments of the immunity-high and immunity-low groups of patients with MIBC are heterogeneous. Specifically, immune suppression was observed in the immune microenvironment of the patients in the immunity-low group., (© 2021 The Authors. Journal of Clinical Laboratory Analysis published by Wiley Periodicals LLC.)

Bone-related malignancies, such as osteosarcoma, Ewing's sarcoma, multiple myeloma, and cancer bone metastases have similar histological context, but they are distinct in origin and biological behavior. We hypothesize that a distinct immune infiltrative microenvironment exists in these four most common malignant bone-associated tumors and can be used for tumor diagnosis and patient prognosis. After sample cleaning, data integration, and batch effect removal, we used 22 publicly available datasets to draw out the tumor immune microenvironment using the ssGSEA algorithm. The diagnostic model was developed using the random forest. Further statistical analysis of the immune microenvironment and clinical data of patients with osteosarcoma and Ewing's sarcoma was carried out. The results suggested significant differences in the microenvironment of bone-related tumors, and the diagnostic accuracy of the model was higher than 97%. Also, high infiltration of multiple immune cells in Ewing's sarcoma was suggestive of poor patient prognosis. Meanwhile, increased infiltration of macrophages and B cells suggested a better prognosis for patients with osteosarcoma, and effector memory CD8 T cells and type 2 T helper cells correlated with patients' chemotherapy responsiveness and tumor metastasis. Our study revealed that the random forest diagnostic model based on immune infiltration can accurately perform the differential diagnosis of bone-related malignancies. The immune microenvironment of osteosarcoma and Ewing's sarcoma has an important impact on patient prognosis. Suppressing the highly inflammatory environment of Ewing's sarcoma and promoting macrophage and B cell infiltration may have good potential to be a novel adjuvant treatment option for osteosarcoma and Ewing's sarcoma., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Li, Wang, Zhou, Jin, Wang, Albahde, Wu, Li, Zhang, Li and Ye.)

Hepatocellular carcinoma (HCC) is a high mortality malignancy and the second leading cause of cancer-related deaths. Because the immune system plays a dual role by assisting the host barrier and tumor progression, there are complex interactions with considerable prognostic significance. Herein, we performed single-sample gene set enrichment (ssGSEA) to explore the tumor microenvironment (TME) and quantify the tumor-infiltrating immune cell (TIIC) subgroups of immune responses based on the HCC cohort of The Cancer Genome Atlas (TCGA) database. We evaluate molecular subpopulations, survival, function, and expression differential associations, as well as reveal potential targets, and biomarkers for immunotherapy. We combined the TME score and the 29 immune cell types in the low, medium, and high immunity groups. The stromal score, immune score, and ESTIMATE score were positively correlated with immune activity but negatively correlated with the tumor purity. There were 23 human leukocyte antigen (HLA)-related genes that were significantly different. However, KIAA1429 was not significant among the different immunity groups. Besides, programmed death-ligand 1 (PD-L1) and cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) expression increased with the increase of immune activity. This may provide valuable information for HCC immunotherapy. We also found that there was no significant difference in naïve B cells, macrophages M1, activated mast cells, resting natural killer (NK) cells, and T cells gamma delta among the different immunity groups. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that the differential proteins were mainly enriched in alpha-linolenic acid (ALA) metabolism, cytokine-cytokine receptor interaction, glycosaminoglycan biosynthesis-heparan sulfate/heparin, glycosphingolipid biosynthesis-ganglio series and proteasome. Our findings provide a deeper understanding of the immune scene, uncovering remarkable immune infiltration patterns of various subtypes of HCC using ssGSEA. This study advances the understanding of immune response and provides a basis for research to enhance immunotherapy.

Current studies indicate that long non-coding RNAs (lncRNAs) are frequently aberrantly expressed in cancers and implicated with prognosis in gastric cancer (GC). We intended to generate a multi-lncRNA signature to improve prognostic prediction of GC. By analyzing ten paired GC and adjacent normal mucosa tissues, 339 differentially expressed lncRNAs were identified as the candidate prognostic biomarkers in GC. Then we used LASSO Cox regression method to build a 12-lncRNA signature and validated it in another independent GEO dataset. An innovative 12-lncRNA signature was established, and it was significantly associated with the disease free survival (DFS) in the training dataset. By applying the 12-lncRNA signature, the training cohort patients could be categorized into high-risk or low-risk subgroup with significantly different DFS (HR = 4.52, 95%CI= 2.49-8.20, P < 0.0001). Similar results were obtained in another independent GEO dataset (HR=1.58, 95%CI=1.05 - 2.38, P=0.0270). Further analysis showed that the prognostic value of this 12-lncRNA signature was independent of AJCC stage and postoperative chemotherapy. Receiver operating characteristic (ROC) analysis showed that the area under receiver operating characteristic curve (AUC) of combined model reached 0.869. Additionally, a well-performed nomogram was constructed for clinicians. Moreover, single-sample gene-set enrichment analysis (ssGSEA) showed that a group of pathways related to drug resistance and cancer metastasis significantly enriched in the high risk patients. A useful innovative 12-lncRNA signature was established for prognostic evaluation of GC. It might complement clinicopathological features and facilitate personalized management of GC., Competing Interests: Competing Interests: The authors have declared that no competing interest exists.