Immunogenomic Identification for Predicting the Prognosis of Cervical Cancer Patients

Apolipoprotein D is crucial for promoting perineural invasion in salivary adenoid cystic carcinoma

BACKGROUND

Perineural invasion (PNI) is a prevalent phenomenon in salivary adenoid cystic carcinoma (SACC). Nevertheless, the regulatory mechanism of PNI is largely elusive.

METHODS

We detected Apolipoprotein D (ApoD) expression and further determined its role in SACC progression. Subsequently, the contributions of SACC-derived ApoD on neurite outgrowth of dorsal root ganglions (DRGs) cells were explored. Moreover, a series of in vivo assays were conducted to elucidate the role of ApoD in the SACC PNI process.

RESULTS

We observed a dramatic up-regulation of ApoD in the SACC associated with an enhancement of PNI in patient biopsies. We found that SACC-derived ApoD elevated cancer cell migration and invasion. In addition, ApoD could facilitate the neurite outgrowth of cultured DRG cells in a CXCR4-dependent manner in vitro, as well as innervation, angiogenesis, and invasion along peripheral nerves of SACC in vivo. More importantly, by advanced bioinformatic analysis, we unexpectedly revealed a novel phenomenon 'tumour cell to neuron-like cell transition' in the ApoD-rich microenvironment in vivo, contributing to the neurogenesis in the SACC tumour.

CONCLUSION

we discovered a novel role of cancer-derived ApoD in the pathogenesis of PNI, which may represent an effective therapeutic target for SACC in clinics.

In silico research of coagulation- and fibrinolysis-related genes for predicting prognosis of clear cell renal cell carcinoma

Background

Coagulation- and fibrinolysis-related genes (CFRGs) are involved in tumor progression. However, their regulatory mechanisms in clear cell renal cell carcinoma (ccRCC) remain unclear. The aim of this study was to search for genes related to coagulation and fibrinolytic systems in ccRCC and to investigate their potential role in tumor pathogenesis and progression.

Methods

Differentially expressed genes (DEGs) between ccRCC and control samples, as well as key module genes associated with ccRCC, were extracted from The Cancer Genome Atlas-Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) dataset. Differentially expressed CFRGs (DE-CFRGs) were identified by intersecting these DEGs with CFRGs. Prognostic genes were identified through univariate Cox, least absolute shrinkage and selection operator (LASSO), and multivariate Cox analyses of DE-CFRGs. Additional independent prognostic and enrichment analyses were conducted, and potential therapeutic drugs were predicted. In addition, quantitative real-time polymerase chain reaction (RT-qPCR) was performed to validate the expression of prognostic genes.

Results

Sixteen DE-CFRGs were identified by intersecting 3,311 DEGs, 1,719 key module genes, and CFRGs. Four prognostic genes-TIMP1, RUNX1, BMP6, and PROS1-were found to be involved in complement and coagulation cascades and other functional pathways. The prognostic model demonstrated strong predictive power for ccRCC, with stage, risk score, and grade all correlating with prognosis. Additionally, 14 potential drugs, such as tamoxifen citrate and cytarabine, were predicted for therapeutic targeting of the identified prognostic genes. RT-qPCR confirmed that the expression levels of TIMP1, and RUNX1 were significantly upregulated in ccRCC samples, consistent with bioinformatics analysis.

Conclusions

A prognostic model incorporating TIMP1, RUNX1, BMP6, and PROS1 was constructed, offering new insights for prognostic evaluation and therapeutic strategies in ccRCC.

Targeting Smurf1 to block PDK1-Akt signaling in KRAS-mutated colorectal cancer

The phosphoinositide 3-kinase (PI3K)-Akt axis is one of the most frequently activated pathways and is demonstrated as a therapeutic target in Kirsten rat sarcoma viral oncogene homolog (KRAS)-mutated colorectal cancer (CRC). Targeting the PI3K-Akt pathway has been a challenging undertaking through the decades. Here we unveiled an essential role of E3 ligase SMAD ubiquitylation regulatory factor 1 (Smurf1)-mediated phosphoinositide-dependent protein kinase 1 (PDK1) neddylation in PI3K-Akt signaling and tumorigenesis. Upon growth factor stimulation, Smurf1 immediately triggers PDK1 neddylation and the poly-neural precursor cell expressed developmentally downregulated protein 8 (poly-Nedd8) chains recruit methyltransferase SET domain bifurcated histone lysine methyltransferase 1 (SETDB1). The cytoplasmic complex of PDK1 assembled with Smurf1 and SETDB1 (cCOMPASS) consisting of PDK1, Smurf1 and SETDB1 directs Akt membrane attachment and T308 phosphorylation. Smurf1 deficiency dramatically reduces CRC tumorigenesis in a genetic mouse model. Furthermore, we developed a highly selective Smurf1 degrader, Smurf1-antagonizing repressor of tumor 1, which exhibits efficient PDK1-Akt blockade and potent tumor suppression alone or combined with PDK1 inhibitor in KRAS-mutated CRC. The findings presented here unveil previously unrecognized roles of PDK1 neddylation and offer a potential strategy for targeting the PI3K-Akt pathway and KRAS mutant cancer therapy.

Research advances in signaling pathways related to the malignant progression of HSIL to invasive cervical cancer: A review

The progression of high-grade squamous intraepithelial lesion (HSIL) to invasive cervical cancer (ICC) is a complex process involving persistent human papillomavirus (HPV) infection and changes in signal transduction regulation, energy and material metabolism, cell proliferation, autoimmune, and other biological process in vaginal microenvironment and immune microenviroment. Signaling pathways are a series of interacting molecules in cells that regulate various physiological functions of cells, such as growth, differentiation, metabolism, and death. In the progression of HSIL to ICC, abnormal activation or inhibition in signaling pathways plays an essensial role. This review presented some signaling pathways related to the malignant progression of HSIL to ICC, including p53, Rb, PI3K/AKT/mTOR, Wnt/β-catenin, Notch, NF-κB, MAPK, TGF-β, JAK-STAT, Hippo, and Hedgehog. The molecular mechanisms involved in the biological process of pathway regulation were also analyzed, in order to illustrate the molecular pathway of HSIL progression to ICC and provide references for the development of more effective prevention and treatment methods.

Identification of 10 differentially expressed genes involved in the tumorigenesis of cervical cancer via next-generation sequencing

Background

The incidence and mortality of cervical cancer remain high in female malignant tumors worldwide. There is still a lack of diagnostic and prognostic markers for cervical carcinoma. This study aimed to screen differentially expressed genes (DEGs) between normal and cervical cancer tissues to identify candidate genes for further research.

Methods

Uterine cervical specimens were resected from our clinical patients after radical hysterectomy. Three patients' transcriptomic datasets were built by the next generation sequencing (NGS) results. DEGs were selected through the edgeR and DESeq2 packages in the R environment. Functional enrichment analysis, including GO/DisGeNET/KEGG/Reactome enrichment analysis, was performed. Normal and cervical cancer tissue data from the public databases TCGA and GTEx were collected to compare the expression levels of 10 selected DEGs in tumor and normal tissues. ROC curve and survival analysis were performed to compare the diagnostic and prognostic values of each gene. The expression levels of candidate genes were verified in 15 paired clinical specimens via quantitative real-time polymerase chain reaction.

Results

There were 875 up-regulated and 1,482 down-regulated genes in cervical cancer samples compared with the paired adjacent normal cervical tissues according to the NGS analysis. The top 10 DEGs included APOD, MASP1, ACKR1, C1QTNF7, SFRP4, HSPB6, GSTM5, IGFBP6, F10 and DCN. GO, DisGeNET and Reactome analyses revealed that the DEGs were related to extracellular matrix and angiogenesis which might influence tumorigenesis. KEGG enrichment showed that PI3K-Akt signaling pathway might be involved in cervical cancer tumorigenesis and progression. The expression levels of selected genes were decreased in tumors in both the public database and our experimental clinical specimens. All the candidate genes showed excellent diagnostic value, and the AUC values exceeded 0.90. Additionally, APOD, ACKR1 and SFRP4 expression levels could help predict the prognosis of patients with cervical cancer.

Conclusions

In this study, we selected the top 10 DEGs which were down-regulated in cervical cancer tissues. All of them had dramatically diagnostic value. APOD, ACKR1 and SFRP4 were associated with the survivals of cervical cancer. C1QTNF7, HSPB6, GSTM5, IGFBP6 and F10 were first reported to be candidate genes of cervical carcinoma.

Nodal staging score for adequacy of nodal staging in cervical cancer

Background

Cervical cancer remains the fourth most common female malignancy with increasing newly cases around the world. It is of clinical value to precisely evaluate whether false negative nodal existed and develop a nodal staging model in cervical cancer.

Materials and methods

Clinical data of cervical cancer patients was retrieved from the Surveillance, Epidemiology, and End Results database. Probability of missing nodal disease and nodal staging score (NSS) was computed to assess the nodal status of each individual.Prognostic value of NSS was assessed.

Results

A total of 9056 individuals were in this study, with 5115 squamous cell carcinoma, 2791 adenocarcinoma, 512 adenosquamous carcinoma, and 638 other type individuals. A beta-binomial model was used to compute the probability of nodal disease in four histological types, respectively. False negative probability drastically decreased as more nodes examined. To reach 0.05 of false negative probability, it required at least 17 lymph nodes in squamous cell carcinoma patients,18 in adenocarcinoma, 12 in adenosquamous carcinoma patients and 14 in other types. To reach 0.95 of NSS, it took 10 lymph nodes in squamous cell carcinoma, 6 in adenocarcinoma, 10 in adenosquamous carcinoma and 7 in other types. Significant prognostic values of NSS quartiles subsets were found in all four histological sets.

Conclusion

NSS tool enables adequate nodal staging of cervical cancer with significant prognostic value. Exact number of lymph nodes required for surgery in cervical cancer is specified based on histologic type.

BMP6 inhibits gastric cancer growth and predicts good prognosis

BACKGROUND

Gastric cancer (GC) is a common tumors in the digestive tract, and effective treatment methods are still lacking. Bone morphogenetic protein 6 (BMP6) is closely related to the occurrence and development of various tumors, but its relevance to GC is still unclear. The aim of the study was to explore the relationship between BMP6 and the occurrence and development of GC.

METHODS

In this study, we investigated the relationship between BMP6 and the prognosis of GC patients using bioinformatics technology and clinical tissue samples. We also explored the connection between BMP6 and the biological behavior of GC cells through molecular biology experiments and relevant in vivo animal experiments. Finally, we examined the mechanisms by which BMP6 inhibits the onset and progression of GC.

RESULTS

Through analysis of The Cancer Genomics Atlas (TCGA) database, we observed that BMP6 is expressed at low levels in GC, and its low expression is associated with a poor prognosis in GC patients. Cell experiments demonstrated that BMP6 expression can influence the proliferation of GC cells both in vitro and in vivo. Furthermore, we discovered that BMP6 is linked to the nuclear factor-κB (NF-κB) pathway, and subsequent experiments confirmed that BMP6 can inhibit the biological activity of GC cells by activating the NF-κB pathway.

CONCLUSIONS

Our findings suggest that BMP6 is a potential prognostic biomarker in GC and can regulate the biological activity of GC cells through the NF-κB pathway. BMP6 may serve as a promising therapeutic target for GC, and our study introduces novel ideas for the prevention and treatment of this disease.

Identification and validation of a ferroptosis-related signature for prediction of the prognosis and tumor microenvironment in patients with chromophobe renal cell carcinoma

BACKGROUND

Ferroptosis is a novel form of regulated cell death that is different from other forms, which has an important role in tumor growth inhibition. The purpose of this study was to construct and validate a prognostic signature related to ferroptosis in chromophobe renal cell carcinoma (ChRCC) and to explore its role in immune cell infiltration and systemic therapy.

METHODS

The gene expression profiles of ChRCC patients obtained from The Cancer Genome Atlas (TCGA) database were used to identify differentially expressed prognostic ferroptosis-related genes (FRGs) by univariate Cox proportional hazards analyses. Ferroptosis molecular subtypes were obtained by consensus clustering analysis. The FRG-based signature in the training set was established by least absolute shrinkage and selection operator analysis and verified in the testing set. The association between molecular subtypes and the prognostic signature and immune microenvironment was explored to predict responses to immunotherapy. Immunohistochemistry was used to verify expression of the FRG-based signature externally.

RESULTS

ChRCC patients were divided into two FRG subtypes. Two FRGs (TFRC and SLC7A11) were identified to construct the prognostic signature. The high-risk group and cluster 2 had worse overall survival than the low-risk group and cluster 1, respectively. The low-risk group and cluster 1 had higher levels of immune cell infiltration and expression of MHC and immune checkpoint molecules than the high-risk group and cluster 2. The risk score was a predictor of overall survival and had a good predictive ability, which was verified in the testing set and evaluated by ROC and calibration curves. The high-risk group had a higher tumor mutation burden. The different sensitivities of targeted drugs in patients with different risks were evaluated. External immunohistochemical analysis showed that TFRC and SLC7A11 were highly expressed in tumor tissues compared with para-cancer normal tissues, and the expression level was significantly associated with a more advanced stage and worse cancer-specific survival.

CONCLUSIONS

An FRG signature was identified and validated to predict the clinicopathological features and prognosis of ChRCC. A significant association between the signature and immune cell infiltration, immune checkpoint expression, and drug response is helpful to guide comprehensive treatment of ChRCC.

New mechanisms and biomarkers of lymph node metastasis in cervical cancer: reflections from plasma proteomics

OBJECTIVE

Lymph node metastasis (LNM) and lymphatic vasculature space infiltration (LVSI) in cervical cancer patients indicate a poor prognosis, but satisfactory methods for diagnosing these phenotypes are lacking. This study aimed to find new effective plasma biomarkers of LNM and LVSI as well as possible mechanisms underlying LNM and LVSI through data-independent acquisition (DIA) proteome sequencing.

METHODS

A total of 20 cervical cancer plasma samples, including 7 LNM-/LVSI-(NC), 4 LNM-/LVSI + (LVSI) and 9 LNM + /LVSI + (LNM) samples from a cohort, were subjected to DIA to identify differentially expressed proteins (DEPs) for LVSI and LNM. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed for DEP functional annotation. Protein-protein interaction (PPI) and weighted gene coexpression network analysis (WGCNA) were used to detect new effective plasma biomarkers and possible mechanisms.

RESULTS

A total of 79 DEPs were identified in the cohort. GO and KEGG analyses showed that DEPs were mainly enriched in the complement and coagulation pathway, lipid and atherosclerosis pathway, HIF-1 signal transduction pathway and phagosome and autophagy. WGCNA showed that the enrichment of the green module differed greatly between groups. Six interesting core DEPs (SPARC, HPX, VCAM1, TFRC, ERN1 and APMAP) were confirmed to be potential plasma diagnostic markers for LVSI and LNM in cervical cancer patients.

CONCLUSION

Proteomic signatures developed in this study reflected the potential plasma diagnostic markers and new possible pathogenesis mechanisms in the LVSI and LNM of cervical cancer.

Exploration of the Mechanism of Shengxian Decoction Against Chronic Obstructive Pulmonary Disease Based on Network Pharmacology and Experimental Verification

Shengxian decoction (SXT) is clinically used in chronic obstructive pulmonary disease (COPD) treatment. This study aimed to explore the mechanism and target genes of SXT acting on COPD. Differentially expressed genes (DEGs) between COPD and controls were identified and then performed enrichment analysis. The effective active compounds and corresponding target genes were obtained from the traditional Chinese medicine systems pharmacology database. We also compiled COPD related genes from the GeneCards database. Through the protein-protein interaction (PPI) network and least absolute shrinkage and selection operator (LASSO) regression was performed to identify key genes. Molecular docking was used for docking of key genes and compounds. The expression of key genes was detected by quantitative real-time PCR in COPD patients and bronchial epithelial cells stimulated with cigarette stroke extract (CSE). We identified 1,458 intersected DEGs from GSE47460 and GSE57148 datasets. Compared with intersected DEGs, we obtained 33 SXT target COPD-related genes. PI3K-Akt signaling pathway, MAPK signaling pathway, and focal adhesion were enriched by these 33 genes, as well as intersected DEGs. According to LASSO regression, there were 12 genes considered as signature genes. Then we constructed active compounds and corresponding six target genes. Finally, HIF1A and IL1B were selected as key genes by combining PPI network. HIF1A and IL1B were all upregulated expression in COPD and CSE stimulated cells and recovered in SXT treated CSE stimulated cells. This study provides a scientific basis for the identification of active compounds and target genes of SXT in the treatment of COPD.

Knockdown of RBM15 inhibits tumor progression and the JAK-STAT signaling pathway in cervical cancer

BACKGROUND

RNA binding motif protein 15 (RBM15), a writer of N6-methyladenosine (m6A) methylation, contributes significantly to the development of various tumors. However, the function of RBM15 in cervical cancer (CC) has not been determined.

METHODS

Based on the GSE9750, GSE63514, and m6A datasets, m6A-related differentially expressed genes (DEGs) were screened out. The hub genes were identified by generating a Protein-Protein Interaction (PPI) network. RT-qPCR was conducted to assess the mRNA expression of hub genes. CCK8, scratch wound healing, and transwell assays were utilized to examine the influence of RBM15 on HeLa and SiHa cells. Tumor xenograft models were used to assess the effects of RBM15 on tumorigenesis. A mechanistic analysis of RBM15 in CC tumors was conducted using the GeneCards and Coxpresdb databases, followed by a Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, and the pathway-related genes were subsequently validated using Western blotting.

RESULTS

Five DEGs were screened, including WTAP, RBM15, CBLL1, and YTHDC2. Among them, WTAP, RBM15, CBLL1, and YTHDC2 were hub genes and can be used as biomarkers for CC. RBM15 expression was considerably increased, while WTAP, CBLL1, and YTHDC2 were significantly downregulated. Knockdown of RBM15 significantly suppressed the proliferation, invasion, and migration of CC cells and tumorigenesis. Moreover, knockdown of RBM15 significantly reduced the expression levels of proteins related to the JAK-STAT pathway.

CONCLUSIONS

Knockdown of RBM15 inhibited the progression of CC cells, which probably by inhibiting the JAK-STAT pathway pathway.

Analysis on tumor immune microenvironment and construction of a prognosis model for immune-related skin cutaneous melanoma

OBJECTIVES

Malignant melanoma is a highly malignant and heterogeneous skin cancer. Although immunotherapy has improved survival rates, the inhibitory effect of tumor microenvironment has weakened its efficacy. To improve survival and treatment strategies, we need to develop immune-related prognostic models. Based on the analysis of the Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and Sequence Read Archive (SRA) database, this study aims to establish an immune-related prognosis prediction model, and to evaluate the tumor immune microenvironment by risk score to guide immunotherapy.

METHODS

Skin cutaneous melanoma (SKCM) transcriptome sequencing data and corresponding clinical information were obtained from the TCGA database, differentially expressed genes were analyzed, and prognostic models were developed using univariate Cox regression, the LASSO method, and stepwise regression. Differentially expressed genes in prognostic models confirmed by real-time reverse transcription PCR (real-time RT-PCR) and Western blotting. Survival analysis was performed by using the Kaplan-Meier method, and the effect of the model was evaluated by time-dependent receiver operating characteristic curve as well as multivariate Cox regression, and the prognostic model was validated by 2 GEO melanoma datasets. Furthermore, correlations between risk score and immune cell infiltration, Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) score, immune checkpoint mRNA expression levels, tumor immune cycle, or tumor immune micro-environmental pathways were analyzed. Finally, we performed association analysis for risk score and the efficacy of immunotherapy.

RESULTS

We identified 4 genes that were differentially expressed in TCGA-SKCM datasets, which were mainly associated with the tumor immune microenvironment. A prognostic model was also established based on 4 genes. Among 4 genes, the mRNA and protein levels of killer cell lectin like receptor D1 (KLRD1), leukemia inhibitory factor (LIF), and cellular retinoic acid binding protein 2 (CRABP2) genes in melanoma tissues differed significantly from those in normal skin (all P<0.01). The prognostic model was a good predictor of prognosis for patients with SKCM. The patients with high-risk scores had significantly shorter overall survival than those with low-risk scores, and consistent results were achieved in the training cohort and multiple validation cohorts (P<0.001). The risk score was strongly associated with immune cell infiltration, ESTIMATE score, immune checkpoint mRNA expression levels, tumor immune cycle, and tumor immune microenvironmental pathways (P<0.001). The correlation analysis showed that patients with the high-risk scores were in an inhibitory immune microenvironment based on the prognostic model (P<0.01).

CONCLUSIONS

The immune-related SKCM prognostic model constructed in this study can effectively predict the prognosis of SKCM patients. Considering its close correlation to the tumor immune microenvironment, the model has some reference value for clinical immunotherapy of SKCM.

The pathological risk score: A new deep learning-based signature for predicting survival in cervical cancer

PURPOSE

To develop and validate a deep learning-based pathological risk score (RS) with an aim of predicting patients' prognosis to investigate the potential association between the information within the whole slide image (WSI) and cervical cancer prognosis.

METHODS

A total of 251 patients with the International Federation of Gynecology and Obstetrics (FIGO) Stage IA1-IIA2 cervical cancer who underwent surgery without any preoperative treatment were enrolled in this study. Both the clinical characteristics and WSI of each patient were collected. To construct a prognosis-associate RS, high-dimensional pathological features were extracted using a convolutional neural network with an autoencoder. With the score threshold selected by X-tile, Kaplan-Meier survival analysis was applied to verify the prediction performance of RS in overall survival (OS) and disease-free survival (DFS) in both the training and testing datasets, as well as different clinical subgroups.

RESULTS

For the OS and DFS prediction in the testing cohort, RS showed a Harrell's concordance index of higher than 0.700, while the areas under the curve (AUC) achieved up to 0.800 in the same cohort. Furthermore, Kaplan-Meier survival analysis demonstrated that RS was a potential prognostic factor, even in different datasets or subgroups. It could further distinguish the survival differences after clinicopathological risk stratification.

CONCLUSION

In the present study, we developed an effective signature in cervical cancer for prognosis prediction and patients' stratification in OS and DFS.

Recent knowledge of NFATc4 in oncogenesis and cancer prognosis

Nuclear factor of activated T-cells, cytoplasmic 4 (NFATc4), a transcription factor of NFAT family, which is activated by Ca²⁺/calcineurin signaling. Recently, it is reported that aberrantly activated NFATc4 participated and modulated in the initiation, proliferation, invasion, and metastasis of various cancers (including cancers of the lung, breast, ovary, cervix, skin, liver, pancreas, as well as glioma, primary myelofibrosis and acute myelocytic leukemia). In this review, we cover the latest knowledge on NFATc4 expression pattern, post-translational modification, epigenetic regulation, transcriptional activity regulation and its downstream targets. Furthermore, we perform database analysis to reveal the prognostic value of NFATc4 in various cancers and discuss the current unexplored areas of NFATc4 research. All in all, the result from these studies strongly suggest that NFATc4 has the potential as a molecular therapeutic target in multiple human cancer types.

Development and verification of a hypoxia- and immune-associated prognosis signature for esophageal squamous cell carcinoma

BACKGROUND

Esophageal cancer is one of the most common gastrointestinal malignancies worldwide, with high morbidity and mortality in China. The clinical importance of the interaction between hypoxia and immune status in the tumor microenvironment has been established in esophageal squamous cell carcinoma (ESCC). This study aims to develop a new hypoxia- and immune-based gene signature to predict the survival of ESCC patients.

METHODS

The RNA-sequencing and clinical data of 173 cases of ESCC and 271 normal tissues were obtained from The Cancer Genome Atlas (TCGA) data portal and the Genotype-Tissue Expression (GTEx) database. Hypoxia-related genes (HRGs) and immune-related genes (IRGs) were retrieved from publicly shared data. Differentially expressed gene (DEG) analyses were carried out by the DESeq2 method using the edgeR package in R. Based on the intersection of the DEGs and HRGs/IRGs, differentially expressed HRGs (DEHRGs) and differentially expressed IRGs (DEIRGs) were obtained. DEHRGs and DEIRGs associated with prognosis were evaluated using univariate Cox proportional hazards analysis. A prognostic risk score model was constructed according to the genes acquired through Cox regression. Univariate analysis and Cox proportional hazards analysis were used to determine the independent prognostic factors related to prognosis. A nomogram was developed to predict the 1-, 2-, and 3-year overall survival (OS) probability.

RESULTS

A total of 73 intersecting genes were obtained as DEHRGs and a total of 548 intersecting genes were obtained as DEIRGs. The risk score was established using 8 genes (FABP7, TLR1, SYTL1, APLN, OSM, EGFR, IL17RD, MYH9) acquired from univariate Cox analysis. Based on this 8-gene-based risk score, a risk prognosis classifier was constructed to classify the samples into high- and low-risk groups according to the median risk score. The nomogram model was constructed to predict the OS of ESCC patients.

CONCLUSIONS

The hypoxia- and immune-based gene signature might serve as a prognostic classifier for clinical decision-making regarding individualized management, follow-up plans, and treatment strategies for ESCC patients.

Regulation of stability and inhibitory activity of the tumor suppressor SEF through casein-kinase II-mediated phosphorylation

Inflammation and cancer are intimately linked. A key mediator of inflammation is the transcription-factor NF-κB/RelA:p50. SEF (also known as IL-17RD) is a feedback antagonist of NF-κB/RelA:p50 that is emerging as an important link between inflammation and cancer. SEF acts as a buffer to prevent excessive NF-κB activity by sequestering NF-κB/RelA:p50 in the cytoplasm of unstimulated cells, and consequently attenuating the NF-κB response upon pro-inflammatory cytokine stimulation. SEF contributes to cancer progression also via modulating other signaling pathways, including those triggered by growth-factors. Despite its important role in human physiology and pathology, mechanisms that regulate SEF biochemical properties and inhibitory activity are unknown. Here we show that human SEF is an intrinsically labile protein that is stabilized via CK2-mediated phosphorylation, and identified the residues whom phosphorylation by CK2 stabilizes hSEF. Unlike endogenous SEF, ectopic SEF was rapidly degraded when overexpressed but was stabilized in the presence of excess CK2, suggesting a mechanism for limiting SEF levels depending upon CK2 processivity. Additionally, phosphorylation by CK2 potentiated hSef interaction with NF-κB in cell-free binding assays. Most importantly, we identified a CK2 phosphorylation site that was indispensable for SEF inhibition of pro-inflammatory cytokine signaling but was not required for SEF inhibition of growth-factor signaling. To our knowledge, this is the first demonstration of post-translational modifications that regulate SEF at multiple levels to optimize its inhibitory activity in a specific signaling context. These findings may facilitate the design of SEF variants for treating cytokine-dependent pathologies, including cancer and chronic inflammation.

Metabolism-Relevant Molecular Classification Identifies Tumor Immune Microenvironment Characterization and Immunotherapeutic Effect in Cervical Cancer

Cervical cancer (CESC) is a gynecologic malignant tumor associated with high incidence and mortality rates because of its distinctive management complexity. Herein, we characterized the molecular features of CESC based on the metabolic gene expression profile by establishing a novel classification system and a scoring system termed as METAscore. Integrative analysis was performed on human CESC samples from TCGA dataset. Unsupervised clustering of RNA sequencing data on 2,752 formerly described metabolic genes identified three METAclusters. These METAclusters for overall survival time, immune characteristics, metabolic features, transcriptome features, and immunotherapeutic effectiveness existed distinct differences. Then we analyzed 207 DEGs among the three METAclusters and as well identified three geneclusters. Correspondingly, these three geneclusters also differently expressed among the aforementioned features, supporting the reliability of the metabolism-relevant molecular classification. Finally METAscore was constructed which emerged as an independent prognostic biomarker, related to CESC transcriptome features, metabolic features, immune characteristics, and linked to the sensitivity of immunotherapy for individual patient. These findings depicted a new classification and a scoring system in CESC based on the metabolic pattern, thereby furthering the understanding of CESC genetic signatures and aiding in the prediction of the effectiveness to anticancer immunotherapies.