Mining TCGA Data for Key Biomarkers Related to Immune Microenvironment in Endometrial cancer by Immune Score and Weighted Correlation Network Analysis

Expression and function of VISTA on myeloid cells

V-domain containing Ig Suppressor of T cell Activation (VISTA) is predominantly expressed on myeloid cells and functions as a ligand/receptor/soluble molecule. In inflammatory responses and immune responses, VISTA regulates multiple functions of myeloid cells, such as chemotaxis, phagocytosis, T cell activation. Since inflammation and immune responses are critical in many diseases, VISTA is a promising therapeutic target. In this review, we will describe the expression and function of VISTA on different myeloid cells, including neutrophils, monocytes, macrophages, dendritic cells (DCs), myeloid-derived suppressor cells (MDSCs). In addition, we will discuss whether the functions of VISTA on these cells impact the disease processing.

GIMAP7 inhibits epithelial-mesenchymal transition and glycolysis in lung adenocarcinoma cells via regulating the Smo/AMPK signaling pathway

BACKGROUND

GTPase immunity-associated protein 7 (GIMAP7) has been previously recognized as a prognostic marker in pan-cancer. Our objective was to explore the function of GIMAP7 in the progression of lung adenocarcinoma (LUAD).

METHODS

GIMAP7 was overexpressed by transfection with GIMAP7 plasmid, and knocked down using siRNAs. The biological functions of GIMAP7 were examined by employing CCK-8, EdU, colony formation, flow cytometry, wound healing, and transwell assays. The effects of GIMAP7 on the extracellular acidification rate (ECAR), oxygen consumption rate (OCR), lactate production, and glucose uptake were evaluated. In addition, the related mRNA and protein expression was detected employing immunohistochemical, western blot, and qRT-PCR. A xenograft tumor model was established in nude mice to evaluate the effects of GIMAP7 on tumor growth.

RESULTS

GIMAP7 was lowly expressed in LUAD tissues and cells. GIMAP7 inhibited the proliferation, mobility, EMT, glycolysis, but promoted apoptosis in LUAD cells. Moreover, we also confirmed that GIMAP7 suppressed Smo/AMPK signaling in LUAD cells. By adding the Smo agonist SAG and AMPK agonist GSK621, the results of rescue experiments further verified that GIMAP7 played a role in LUAD inhibition through inhibition of the Smo/AMPK signaling pathway. Furthermore, the role of GIMAP7 in inhibiting tumor growth was verified in vivo.

CONCLUSIONS

These results demonstrate that GIMAP7 could inhibit cell proliferation, mobility and glycolysis, but accelerate apoptosis via repressing the Smo/AMPK signaling pathway in LUAD.

Integrated analysis of single-cell RNA-seq and bulk RNA-seq unravels the heterogeneity of cancer-associated fibroblasts in TNBC

BACKGROUND

The treatment of triple-negative breast cancer (TNBC) is one of the main focuses and key difficulties because of its heterogeneity, and the source of this heterogeneity is unclear.

METHODS

Single-cell RNA (scRNA) and transcriptomics data of TNBC and normal breast samples were retrieved from Gene Expression Omnibus (GEO) database and TCGA-BRCA database. These cells were clustered using the t-SNE and UMAP method, and the marker genes for each cluster were found. We annotated the clusters using the published literature, CellMarker database and "SingleR" R package.

RESULTS

A total of 1535 cells and 21785 genes from 6 TNBC patients and 2068 cells and 15868 genes from 3 normal breast tissues were used for downstream analyses. The scRNA data were divided into 14 clusters labeled into 8 cell types, including epithelial cells, immunocytes, CAFs/fibroblasts and etc. In the TNBC samples, CAFs were divided into three clusters and labelled as prCAFs, myCAFs and emCAFs, and the marker genes were DCN, FAP and RGS5, respectively. The prCAF subgroup is functionally characterized by promoting proliferation and multi drug resistance; myCAF subgroup is involved in constituting the extracellular matrix and collagen production, matrix composition and collagen production, and the emCAF functionally characterized by energy metabolism.

CONCLUSIONS

TNBC has inter- and intra-tumor heterogeneity, and CAF is one of the sources of this heterogeneity. CD74, SASH3, CD2, TAGAP and CCR7 served as significant marker genes with prognostic and therapeutic value.

Tumor Immune Microenvironment in Gynecologic Cancers

Gynecologic cancers have varying response rates to immunotherapy due to the heterogeneity of each cancer's molecular biology and features of the tumor immune microenvironment (TIME). This article reviews key features of the TIME and its role in the pathophysiology and treatment of ovarian, endometrial, cervical, vulvar, and vaginal cancer. Knowledge of the role of the TIME in gynecologic cancers has been rapidly developing with a large body of preclinical studies demonstrating an intricate yet dichotomous role that the immune system plays in either supporting the growth of cancer or opposing it and facilitating effective treatment. Many targets and therapeutics have been identified including cytokines, antibodies, small molecules, vaccines, adoptive cell therapy, and bacterial-based therapies but most efforts in gynecologic cancers to utilize them have not been effective. However, with the development of immune checkpoint inhibitors, we have started to see the rapid and successful employment of therapeutics in cervical and endometrial cancer. There remain many challenges in utilizing the TIME, particularly in ovarian cancer, and further studies are needed to identify and validate efficacious therapeutics.

Integrative analysis of circadian clock with prognostic and immunological biomarker identification in ovarian cancer

Objective: To identify circadian clock (CC)-related key genes with clinical significance, providing potential biomarkers and novel insights into the CC of ovarian cancer (OC). Methods: Based on the RNA-seq profiles of OC patients in The Cancer Genome Atlas (TCGA), we explored the dysregulation and prognostic power of 12 reported CC-related genes (CCGs), which were used to generate a circadian clock index (CCI). Weighted gene co-expression network analysis (WGCNA) and protein-protein interaction (PPI) network were used to identify potential hub genes. Downstream analyses including differential and survival validations were comprehensively investigated. Results: Most CCGs are abnormally expressed and significantly associated with the overall survival (OS) of OC. OC patients with a high CCI had lower OS rates. While CCI was positively related to core CCGs such as ARNTL, it also showed significant associations with immune biomarkers including CD8⁺ T cell infiltration, the expression of PDL1 and CTLA4, and the expression of interleukins (IL-16, NLRP3, IL-1β, and IL-33) and steroid hormones-related genes. WGCNA screened the green gene module to be mostly correlated with CCI and CCI group, which was utilized to construct a PPI network to pick out 15 hub genes (RNF169, EDC4, CHCHD1, MRPL51, UQCC2, USP34, POM121, RPL37, SNRPC, LAMTOR5, MRPL52, LAMTOR4, NDUFB1, NDUFC1, POLR3K) related to CC. Most of them can exert prognostic values for OS of OC, and all of them were significantly associated with immune cell infiltration. Additionally, upstream regulators including transcription factors and miRNAs of key genes were predicted. Conclusion: Collectively, 15 crucial CC genes showing indicative values for prognosis and immune microenvironment of OC were comprehensively identified. These findings provided insight into the further exploration of the molecular mechanisms of OC.

Discovery of CLEC2B as a diagnostic biomarker and screening of celastrol as a candidate drug for psoriatic arthritis through bioinformatics analysis

BACKGROUND

Psoriatic arthritis (PSA) is a chronic, immune-mediated inflammatory joint disease that is liked to mortality due to cardiovascular disease. Diagnostic markers and effective therapeutic options for PSA remain limited due to the lack of understanding of the pathogenesis. We aimed to identify potential diagnostic markers and screen the therapeutic compounds for PSA based on bioinformatics analysis.

METHODS

Differentially expressed genes (DEGs) of PSA were identified from the GSE61281 dataset. WGCNA was used to identify PSA-related modules and prognostic biomarkers. Clinical samples were collected to validate the expression of the diagnostic gene. These DEGs were subjected to the CMap database for the identification of therapeutic candidates for PSA. Potential pathways and targets for drug candidates to treat PSA were predicted using Network Pharmacology. Molecular docking techniques were used to validate key targets.

RESULTS

CLEC2B was identified as a diagnostic marker for PSA patients (AUC > 0.8) and was significantly upregulated in blood samples. In addition, celastrol was identified as a candidate drug for PSA. Subsequently, the network pharmacology approach identified four core targets (IL6, TNF, GAPDH, and AKT1) of celastrol and revealed that celastrol could treat PSA by modulating inflammatory-related pathways. Finally, molecular docking demonstrated stable binding of celastrol to four core targets in the treatment of PSA. Animal experiments indicated celastrol alleviated inflammatory response in the mannan-induced PSA.

CONCLUSION

CLEC2B was a diagnostic marker for PSA patients. Celastrol was identified as a potential therapeutic drug for PSA via regulating immunity and inflammation.

Low Expression of CLEC2B Indicates Poor Prognosis in Melanoma

Background

Melanoma is a highly malignant skin tumor with a poor prognosis. Identification of novel biomarkers might potentially reveal the underlying mechanisms of melanoma progression.

Methods

We demonstrated the relationship between pan-cancer CLEC2B expression and melanoma samples in The Cancer Genome Atlas (TCGA) database. Next, the Kaplan-Meier plot and Cox regression analysis determined the prognostic value of CLEC2B in melanoma. Biological pathway enrichment was screened by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA), enabling the correlation analysis between the immune infiltration level and CLEC2B expression in melanoma. Our final claim was validated using qPCR, immunohistochemistry, Western blot, cell colony formation assays, ethynyldeoxyuridine (Edu) analysis, and cell Invasion assays.

Results

Our study revealed that the high CLEC2B expression correlates with poor overall survival of melanoma patients. Moreover, a low expression of CLEC2B was found in the A375 cell line. In addition, CLEC2B has significant prognostic value in melanoma diagnosis, with an AUC of 0.896. Prognostic analysis showed the low expression of CLEC2B to be independently associated with melanoma patients. Moreover, the expression of CLEC2B was significantly correlated with B cells, eosinophils, macrophages, neutrophils, NK cells, T helper cells, Tregs, Th1 cells, Th17 cells, and Th2 cells. PCR and immunohistochemistry indicated CLEC2B to be significantly downregulated in melanoma. The cell colony formation assay showed CLEC2B knockout increased the proliferation of A375 cells.

Conclusion

Our study established low levels of CLEC2B to be poor prognostic markers, enabling immunosuppressive cell infiltration in melanoma.

METTL3-mediated N6-methyladenosine modification and HDAC5/YY1 promote IFFO1 downregulation in tumor development and chemo-resistance

Ovarian cancer (OC) is a malignant tumor that seriously threatens women's health. Due to the difficulty of early diagnosis, most patients exhibit advanced disease or peritoneal metastasis at diagnosis. We discovered that IFFO1 is a novel tumor suppressor, but its role in tumorigenesis, development and chemoresistance is unknown. In this study, IFFO1 levels were downregulated across cancers, leading to the acceleration of tumor development, metastasis and/or cisplatin resistance. Overexpression of IFFO1 inhibited the translocation of β-catenin to the nucleus and decreased tumor metastasis and cisplatin resistance. Furthermore, we demonstrated that IFFO1 was regulated at both the transcriptional and posttranscriptional levels. At the transcriptional level, the recruitment of HDAC5 inhibited IFFO1 expression, which is mediated by the transcription factor YY1, and the METTL3/YTHDF2 axis regulated the mRNA stability of IFFO1 in an m6A-dependent manner. Mice injected with IFFO1-overexpressing cells had lower ascites volumes and tumor weights throughout the peritoneal cavity than those injected with parental cells expressing the vector control. In conclusion, we demonstrated that IFFO1 is a novel tumor suppressor that inhibits tumor metastasis and reverses drug resistance in ovarian cancer. IFFO1 was downregulated at both the transcriptional level and posttranscriptional level by histone deacetylase and RNA methylation, respectively, and the IFFO1 signaling pathway was identified as a potential therapeutic target for cancer.

GIMAP7 induces oxidative stress and apoptosis of ovarian granulosa cells in polycystic ovary syndrome by inhibiting sonic hedgehog signalling pathway

Polycystic ovary syndrome (PCOS) is a gynaecological endocrine disease. The objective of the present study was to investigate the role of GTPase immunity-associated protein (GIMAP) 7 in PCOS. A PCOS rat model was established using dehydroepiandrosterone injection. The data showed that GIMAP7 was mainly located in granulosa cells and was abundantly expressed in the ovarian granulosa cells of PCOS rats. GIMAP7 silencing decreased blood glucose levels, HOMA-IR scores, and number of cystic follicles. In addition, GIMAP7 silencing corrected erratic oestrous cycles, inhibited apoptosis and reduced c-caspase-3 protein expression in the ovarian tissues of PCOS rats. GIMAP7 silencing reduced malondialdehyde (MDA) but increased glutathione (GSH) and superoxide dismutase (SOD) levels in the serum and ovarian tissues of PCOS rats. The effects of GIMAP7 were further investigated in human ovarian granulosa KGN cells. GIMAP7 silencing increased the viability, promoted proliferation, and increased the percentage of S-phase KGN cells. The apoptosis rate was significantly decreased by GIMAP7 silencing. GIMAP7 also inhibited oxidative stress in KGN cells, resulting in decreased levels of reactive oxygen species (ROS) and MDA and increased levels of GSH and SOD. Notably, GIMAP7 inhibited the sonic hedgehog (SHH) signalling pathway, and GIMAP7 silencing increased the expression of the SHH signalling pathway downstream genes SHH, SMO, and Gli1. Inhibition of the SHH signalling pathway using cyclopamine reduced the effect of GIMAP7 silencing on KGN cells. This study proved that GIMAP7 promotes oxidative stress and apoptosis in ovarian granulosa cells in PCOS by inhibiting the SHH signalling pathway.

Characterization of cellular senescence patterns predicts the prognosis and therapeutic response of hepatocellular carcinoma

Background: Hepatocellular carcinoma (HCC) is a prevalent malignancy with a high mortality rate. Cellular senescence, an irreversible state of cell cycle arrest, plays a paradoxical role in cancer progression. Here, we aimed to identify Hepatocellular carcinoma subtypes by cellular senescence-related genes (CSGs) and to construct a cellular senescence-related gene subtype predictor as well as a novel prognostic scoring system, which was expected to predict clinical outcomes and therapeutic response of Hepatocellular carcinoma. Methods: RNA-seq data and clinical information of Hepatocellular carcinoma patients were derived from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC). The "multi-split" selection was used to screen the robust prognostic cellular senescence-related genes. Unsupervised clustering was performed to identify CSGs-related subtypes and a discriminant model was obtained through multiple statistical approaches. A CSGs-based prognostic model-CSGscore, was constructed by LASSO-Cox regression and stepwise regression. Immunophenoscore (IPS) and Tumor Immune Dysfunction and Exclusion (TIDE) were utilized to evaluate the immunotherapy response. Tumor stemness indices mRNAsi and mDNAsi were used to analyze the relationship between CSGscore and stemness. Results: 238 robust prognostic differentially expressed cellular senescence-related genes (DECSGs) were used to categorize all 336 hepatocellular carcinoma patients of the TCGA-LIHC cohort into two groups with different survival. Two hub genes, TOP2A and KIF11 were confirmed as key indicators and were used to form a precise and concise cellular senescence-related gene subtype predictor. Five genes (PSRC1, SOCS2, TMEM45A, CCT5, and STC2) were selected from the TCGA training dataset to construct the prognostic CSGscore signature, which could precisely predict the prognosis of hepatocellular carcinoma patients both in the training and validation datasets. Multivariate analysis verified it as an independent prognostic factor. Besides, CSGscore was also a valuable predictor of therapeutic responses in hepatocellular carcinoma. More downstream analysis revealed the signature genes were significantly associated with stemness and tumor progression. Conclusion: Two subtypes with divergent outcomes were identified by prognostic cellular senescence-related genes and based on that, a subtype indicator was established. Moreover, a prognostic CSGscore system was constructed to predict the survival outcomes and sensitivity of therapeutic responses in hepatocellular carcinoma, providing novel insight into hepatocellular carcinoma biomarkers investigation and design of tailored treatments depending on the molecular characteristics of individual patients.

Tumor immune microenvironment in endometrial cancer of different molecular subtypes: evidence from a retrospective observational study

Objective

Tumor immune microenvironmental features may predict survival and guide treatment. This study aimed to comprehensively decipher the immunological features of different molecular subtypes of endometrial cancer.

Methods

In this retrospective study, 26 patients with primary endometrial cancer and four with recurrent disease treated in our center from December 2018 to November 2021 were included. Next-generation sequencing was performed on tumor samples. Patients were classified into four subtypes, including POLE mutant, microsatellite instability high (MSI-H), no specific molecular profile (NSMP) and TP53 mutant subtypes. Tumor-infiltrating immune cells were quantified using multiplex immunofluorescence assays.

Results

Of the 26 primary endometrial cancer cases, three were POLE mutant, six were MSI-H, eight were NSMP and nine were TP53 mutant. Of the four recurrent cases, two belonged to the NSMP subtype and two belonged to the TP53 mutant subtype. The tumor mutation burden (TMB) levels of POLE mutant and MSI-H cases were significantly higher than that of the other two subtypes (p< 0.001). We combined POLE mutant and MSI-H subtypes into the TMB high (TMB-H) subtype. The TMB-H subtype showed a high degree of infiltration of CD8⁺ T cells. In the NSMP subtype, the overall degree of intra-tumoral infiltrating immune cells was low. In the TP53 mutant subtype, the densities of both PD-L1⁺ macrophages (p = 0.047) and PD-1⁺ T cells (p = 0.034) in tumor parenchyma were the highest among the four subtypes.

Conclusion

Endometrial cancer of TMB-H, NSMP and TP53 mutant subtypes displayed phenotypes of normal immune response, absence of immune infiltration, and suppressed immune response, respectively. These features may provide mechanistic explanations for the differences in patients' prognosis and efficacy of immune checkpoint blockade therapies among different endometrial cancer subtypes.

Hallmark-guided subtypes of hepatocellular carcinoma for the identification of immune-related gene classifiers in the prediction of prognosis, treatment efficacy, and drug candidates

Hepatocellular carcinoma (HCC), accounting for ~90% of all primary liver cancer, is a prevalent malignancy worldwide. The intratumor heterogeneity of its causative etiology, histology, molecular landscape, and immune phenotype makes it difficult to precisely recognize individuals with high mortality risk or tumor-intrinsic treatment resistance, especially immunotherapy. Herein, we comprehensively evaluated the activities of cancer hallmark gene sets and their correlations with the prognosis of HCC patients using gene set variation analysis (GSVA) and identified two HCC subtypes with distinct prognostic outcomes. Based on these subtypes, seven immune-related genes (TMPRSS6, SPP1, S100A9, EPO, BIRC5, PLXNA1, and CDK4) were used to construct a novel prognostic gene signature [hallmark-guided subtypes-based immunologic signature (HGSIS)] via multiple statistical approaches. The HGSIS-integrated nomogram suggested an enhanced predictive performance. Interestingly, oncogenic hallmark pathways were significantly enriched in the high-risk group and positively associated with the risk score. Distinct mutational landscapes and immune profiles were observed between different risk groups. Moreover, immunophenoscore (IPS) and tumor immune dysfunction and exclusion (TIDE) analysis showed different sensitivities of HGSIS risk groups for immune therapy efficacy, and the pRRophetic algorithm indicated distinguishable responses for targeted/chemotherapies in different groups. KIF2C was picked out as the key target concerning HGSIS, and the top 10 small molecules were predicted to bind to the active site of KIF2C via molecular docking, which might be further used for candidate drug discovery of HCC. Taken together, our study offers novel insights for clinically significant subtype recognition, and the proposed signature may be a helpful guide for clinicians to improve the treatment regimens.

A novel tumor mutational burden-based risk model predicts prognosis and correlates with immune infiltration in ovarian cancer

Tumor mutational burden (TMB) has been reported to determine the response to immunotherapy, thus affecting the patient’s prognosis in many cancers. However, it is unclear whether TMB or TMB-related signature could be used as prognostic indicators for ovarian cancer (OC), as its potential association with immune infiltration remains poorly understood. Therefore, this study aimed to develop a novel TMB-related risk model (TMBrisk) to predict the prognosis of OC patients on the basis of exploring TMB-related genes, and to explore the potential association between TMB/TMBrisk and immune infiltration. The mutational landscape, TMB scores, and correlations between TMB and clinical characteristics and immune infiltration were investigated in The Cancer Genome Atlas (TCGA)-OV cohort. Differentially expressed gene (DEG) analyses and weighted gene co-expression network analysis (WGCNA) were performed to derive TMB-related genes. TMBrisk was constructed by Cox regression and further validated in Gene Expression Omnibus (GEO) datasets. The mRNA and protein expression levels and biological functions of TMBrisk hub genes were verified through Gene Expression Profiling Interactive Analysis (GEPIA), GSCA Lite, the Human Protein Atlas (HPA) database, and RT-qPCR. TMBrisk-related biological phenotypes were analyzed in function enrichment and tumor immune infiltration signature. Potential therapeutic regimens were inferred utilizing the Genomics of Drug Sensitivity in Cancer (GDSC) database and connectivity map (CMap). According to our results, higher TMB was associated with better survival and higher CD8+ T cell, regulatory T cell, and NK cell infiltration. TMBrisk was developed based on CBWD1, ST7L, RFX5-AS1, C3orf38, LRFN1, LEMD1, and HMGB1. High TMBrisk was identified as a poor factor for prognosis in TCGA and GEO datasets; the high-TMBrisk group comprised more higher-grade (G2 and G3) and advanced clinical stage (stage III/IV) tumors. Meanwhile, higher TMBrisk was associated with an immunosuppressive phenotype, with less infiltration of a majority of immunocytes and less expression of several genes of the human leukocyte antigen (HLA) family. Moreover, a nomogram containing TMBrisk showed a strong predictive ability demonstrated by time-dependent ROC analysis. Overall, this novel TMB-related risk model (TMBrisk) could predict prognosis, evaluate immune infiltration, and discover new therapeutic regimens in OC, which is very promising in clinical promotion.

Identification and Validation of Immune-Related Long Non-Coding RNA Signature for Predicting Immunotherapeutic Response and Prognosis in NSCLC Patients Treated With Immunotherapy

Background

Numerous studies have reported that long non-coding RNAs (lncRNAs) play important roles in immune-related pathways in cancer. However, immune-related lncRNAs and their roles in predicting immunotherapeutic response and prognosis of non-small cell lung cancer (NSCLC) patients treated with immunotherapy remain largely unexplored.

Methods

Transcriptomic data from NSCLC patients were used to identify novel lncRNAs by a custom pipeline. ImmuCellAI was utilized to calculate the infiltration score of immune cells. The marker genes of immunotherapeutic response-related (ITR)-immune cells were used to identify immune-related (IR)-lncRNAs. A co-expression network was constructed to determine their functions. LASSO and multivariate Cox analyses were performed on the training set to construct an immunotherapeutic response and immune-related (ITIR)-lncRNA signature for predicting the immunotherapeutic response and prognosis of NSCLC. Four independent datasets involving NSCLC and melanoma patients were used to validate the ITIR-lncRNA signature.

Results

In total, 7,693 novel lncRNAs were identified for NSCLC. By comparing responders with non-responders, 154 ITR-lncRNAs were identified. Based on the correlation between the marker genes of ITR-immune cells and lncRNAs, 39 ITIR-lncRNAs were identified. A co-expression network was constructed and the potential functions of 38 ITIR-lncRNAs were annotated, most of which were related to immune/inflammatory-related pathways. Single-cell RNA-seq analysis was performed to confirm the functional prediction results of an ITIR-lncRNA, LINC01272. Four-ITIR-lncRNA signature was identified and verified for predicting the immunotherapeutic response and prognosis of NSCLC. Compared with non-responders, responders had a lower risk score in both NSCLC datasets (P<0.05). NSCLC patients in the high-risk group had significantly shorter PFS/OS time than those in the low-risk group in the training and testing sets (P<0.05). The AUC value was 1 of responsiveness in the training set. In melanoma validation datasets, patients in the high-risk group also had significantly shorter OS/PFS time than those in the low-risk group (P<0.05). The ITIR-lncRNA signature was an independent prognostic factor (P<0.001).

Conclusion

Thousands of novel lncRNAs in NSCLC were identified and characterized. In total, 39 ITIR-lncRNAs were identified, 38 of which were functionally annotated. Four ITIR-lncRNAs were identified as a novel ITIR-lncRNA signature for predicting the immunotherapeutic response and prognosis in NSCLC patients treated with immunotherapy.

Bioinformatics Analysis Highlights Five Differentially Expressed Genes as Prognostic Biomarkers of Cervical Cancer and Novel Option for Anticancer Treatment

Cervical cancer is one of the most common gynecological malignancies and is related to human papillomavirus (HPV) infection, especially high-risk type HPV16 and HPV18. Aberrantly expressed genes are involved in the development of cervical cancer, which set a genetic basis for patient prognosis. In this study, we identified a set of aberrantly expressed key genes from The Cancer Genome Atlas (TCGA) database, which could be used to accurately predict the survival rate of patients with cervical squamous cell carcinoma (CESC). A total of 3,570 genes that are differentially expressed between normal and cancerous samples were analyzed by the algorithm of weighted gene co-expression network analysis (WGCNA): 1,606 differentially expressed genes (DEGs) were upregulated, while 1,964 DEGs were downregulated. Analysis of these DEGs divided them into 7 modules including 76 hub genes. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analysis revealed a significant increase of genes related to cell cycle, DNA replication, p53 signaling pathway, cGMP-PKG signaling pathway, and Fanconi anemia (FA) pathway in CESC. These biological activities are previously reported to associate with cervical cancer or/and HPV infection. Finally, we highlighted 5 key genes (EMEMP2, GIMAP4, DYNC2I2, FGF13-AS1, and GIMAP1) as robust prognostic markers to predict patient’s survival rate (p = 3.706e-05) through univariate and multivariate regression analyses. Thus, our study provides a novel option to set up several biomarkers for cervical cancer prognosis and anticancer drug targets.

Gene Expression of the D-Series Resolvin Pathway Predicts Activation of Anti-Tumor Immunity and Clinical Outcomes in Head and Neck Cancer

In human medicine, the progression from early neoplasia development to either complete resolution of tumorigenesis and associated inflammation or chronicity and fatal outcomes remain difficult to predict. Resolution of inflammation is an active process that stimulates the termination of the inflammatory response and promotes return to homeostasis, while failure in resolution contributes to the development of a number of diseases. To understand how resolution pathways contribute to tumorigenesis, we defined and employed a cumulative score based on the expression level of genes involved in synthesis, signaling, and metabolism of the D-series resolvin (RvD). This score was used for comparative analyses of clinical, cellular, and molecular features of tumors, based on RNA-sequencing (RNA-seq) datasets collected within The Cancer Genome Atlas (TCGA) program. Our results indicate that higher RvD scores are associated with better clinical outcome of patients with head and neck squamous cell carcinoma (HNSC), and with molecular and cellular signatures indicative of enhanced anti-tumor immunity and better response to immune-checkpoint inhibitors (ICI), also in human papilloma virus (HPV) negative HNSC subtypes. Thus, higher activity of the RvD pathway identifies patients with improved resolution and a more efficient immune reaction against cancer.

Identification of a Tumor Immunological Phenotype-Related Gene Signature for Predicting Prognosis, Immunotherapy Efficacy, and Drug Candidates in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the predominant subtype of primary liver cancer and represents a highly heterogeneous disease, making it hard to predict the prognosis and therapy efficacy. Here, we established a novel tumor immunological phenotype-related gene index (TIPRGPI) consisting of 11 genes by Univariate Cox regression and the least absolute shrinkage and selection operator (LASSO) algorithm to predict HCC prognosis and immunotherapy response. TIPRGPI was validated in multiple datasets and exhibited outstanding performance in predicting the overall survival of HCC. Multivariate analysis verified it as an independent predictor and a TIPRGPI-integrated nomogram was constructed to provide a quantitative tool for clinical practice. Distinct mutation profiles, hallmark pathways, and infiltration of immune cells in tumor microenvironment were shown between the TIPRGPI high and low-risk groups. Notably, significant differences in tumor immunogenicity and tumor immune dysfunction and exclusion (TIDE) were observed between the two risk groups, suggesting a better response to immune checkpoint blockade (ICB) therapy of the low-risk group. Besides, six potential drugs binding to the core target of the TIPRGPI signature were predicted via molecular docking. Taken together, our study shows that the proposed TIPRGPI was a reliable signature to predict the risk classification, immunotherapy response, and drugs candidate with potential application in the clinical decision and treatment of HCC. The novel “TIP genes”-guided strategy for predicting the survival and immunotherapy efficacy, we reported here, might be also applied to more cancers other than HCC.

Protein-based prognostic signature for predicting the survival and immunotherapeutic efficiency of endometrial carcinoma

Background

Endometrial cancer (EC) is the most frequent malignancy of the female genital tract worldwide. Our study aimed to construct an effective protein prognostic signature to predict prognosis and immunotherapy responsiveness in patients with endometrial carcinoma.

Methods

Protein expression data, RNA expression profile data and mutation data were obtained from The Cancer Proteome Atlas (TCPA) and The Cancer Genome Atlas (TCGA). Prognosis-related proteins in EC patients were screened by univariate Cox regression analysis. Least absolute shrinkage and selection operator (LASSO) analysis and multivariate Cox regression analysis were performed to establish the protein-based prognostic signature. The CIBERSORT algorithm was used to quantify the proportions of immune cells in a mixed cell population. The Immune Cell Abundance Identifier (ImmuCellAI) and The Cancer Immunome Atlas (TCIA) web tools were used to predict the response to immunochemotherapy. The pRRophetic algorithm was used to estimate the sensitivity of chemotherapeutic and targeted agents.

Results

We constructed a prognostic signature based on 9 prognostic proteins, which could divide patients into high-risk and low-risk groups with distinct prognoses. A novel prognostic nomogram was established based on the prognostic signature and clinicopathological parameters to predict 1, 3 and 5-year overall survival for EC patients. The results obtained with Clinical Proteomic Tumor Analysis Consortium (CPTAC), Human Protein Atlas (HPA) and immunohistochemical (IHC) staining data from EC samples in our hospital supported the predictive ability of these proteins in EC tumors. Next, the CIBERSORT algorithm was used to estimate the proportions of 22 immune cell types. The proportions of CD8 T cells, T follicular helper cells and regulatory T cells were higher in the low-risk group. Moreover, we found that the prognostic signature was positively associated with high tumor mutation burden (TMB) and high microsatellite instability (MSI-H) status in EC patients. Finally, ImmuCellAI and TCIA analyses showed that patients in the low-risk group were more inclined to respond to immunotherapy than patients in the high-risk group. In addition, drug sensitivity analysis indicated that our signature had potential predictive value for chemotherapeutics and targeted therapy.

Conclusion

Our study constructed a novel prognostic protein signature with robust predictive ability for survival and efficiency in predicting the response to immunotherapy, chemotherapy and targeted therapy. This protein signature represents a promising predictor of prognosis and response to cancer treatment in EC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09402-w.