Identification of an autophagy-related gene signature for survival prediction in patients with cervical cancer

Potential role of transmembrane 9 superfamily member 1 as a biomarker in urothelial cancer

Bladder cancer is a urological tumor with high rates of recurrence despite recent advances in novel therapies. Many proteins involved in the molecular mechanisms are currently an enigma, especially the transmembrane 9 superfamily member 1 which has an unclear function. Wei et al published the function and mechanism of this protein, and showed that it could participate in the proliferation, migration and invasion of tumor cells in bladder cancer, therefore treatments directed against this protein may be beneficial in avoiding this condition.

TM9SF1 promotes bladder cancer cell growth and infiltration

BACKGROUND

Bladder cancer (BC) is the most common urological tumor. It has a high recurrence rate, displays tutor heterogeneity, and resists chemotherapy. Furthermore, the long-term survival rate of BC patients has remained unchanged for decades, which seriously affects the quality of patient survival. To improve the survival rate and prognosis of BC patients, it is necessary to explore the molecular mechanisms of BC development and progression and identify targets for treatment and intervention. Transmembrane 9 superfamily member 1 (TM9SF1), also known as MP70 and HMP70, is a member of a family of nine transmembrane superfamily proteins, which was first identified in 1997. TM9SF1 can be expressed in BC, but its biological function and mechanism in BC are not clear.

AIM

To investigate the biological function and mechanism of TM9SF1 in BC.

METHODS

Cells at 60%-80% confluence were transfected with lentiviral vectors for 48-72 h to achieve stable TM9SF1 overexpression or silencing in three BC cell lines (5637, T24, and UM-UC-3). The effect of TM9SF1 on the biological behavior of BC cells was then investigated through CCK8, wound-healing assay, transwell assay, and flow cytometry.

RESULTS

Overexpression of TM9SF1 increased the in vitro proliferation, migration, and invasion of BC cells by promoting the entry of BC cells into the G2/M phase. Silencing of TM9SF1 inhibited in vitro proliferation, migration, and invasion of BC cells and blocked BC cells in the G1 phase.

CONCLUSION

TM9SF1 may be an oncogene in BC.

signifinder enables the identification of tumor cell states and cancer expression signatures in bulk, single-cell and spatial transcriptomic data

Over the last decade, many studies and some clinical trials have proposed gene expression signatures as a valuable tool for understanding cancer mechanisms, defining subtypes, monitoring patient prognosis, and therapy efficacy. However, technical and biological concerns about reproducibility have been raised. Technical reproducibility is a major concern: we currently lack a computational implementation of the proposed signatures, which would provide detailed signature definition and assure reproducibility, dissemination, and usability of the classifier. Another concern regards intratumor heterogeneity, which has never been addressed when studying these types of biomarkers using bulk transcriptomics. With the aim of providing a tool able to improve the reproducibility and usability of gene expression signatures, we propose signifinder, an R package that provides the infrastructure to collect, implement, and compare expression-based signatures from cancer literature. The included signatures cover a wide range of biological processes from metabolism and programmed cell death, to morphological changes, such as quantification of epithelial or mesenchymal-like status. Collected signatures can score tumor cell characteristics, such as the predicted response to therapy or the survival association, and can quantify microenvironmental information, including hypoxia and immune response activity. signifinder has been used to characterize tumor samples and to investigate intra-tumor heterogeneity, extending its application to single-cell and spatial transcriptomic data. Through these higher-resolution technologies, it has become increasingly apparent that the single-sample score assessment obtained by transcriptional signatures is conditioned by the phenotypic and genetic intratumor heterogeneity of tumor masses. Since the characteristics of the most abundant cell type or clone might not necessarily predict the properties of mixed populations, signature prediction efficacy is lowered, thus impeding effective clinical diagnostics. Through signifinder, we offer general principles for interpreting and comparing transcriptional signatures, as well as suggestions for additional signatures that would allow for more complete and robust data inferences. We consider signifinder a useful tool to pave the way for reproducibility and comparison of transcriptional signatures in oncology.

Immune infiltration and a necroptosis-related gene signature for predicting the prognosis of patients with cervical cancer

Background: Cervical cancer (CC), the fourth most common cancer among women worldwide, has high morbidity and mortality. Necroptosis is a newly discovered form of cell death that plays an important role in cancer development, progression, and metastasis. However, the expression of necroptosis-related genes (NRGs) in CC and their relationship with CC prognosis remain unclear. Therefore, we screened the signature NRGs in CC and constructed a risk prognostic model. Methods: We downloaded gene data and clinical information of patients with cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) from The Cancer Genome Atlas (TCGA) database. We performed functional enrichment analysis on the differentially expressed NRGs (DENRGs). We constructed prognostic models and evaluated them by Cox and LASSO regressions for DENRGs, and validated them using the International Cancer Genome Consortium (ICGC) dataset. We used the obtained risk score to classify patients into high- and low-risk groups. We employed the ESTIMATE and single sample gene set enrichment analysis (ssGSEA) algorithms to explore the relationship between the risk score and the clinical phenotype and the tumor immune microenvironment. Results: With LASSO regression, we established a prognostic model of CC including 16 signature DENRGs (TMP3, CHMP4C, EEF1A1, FASN, TNF, S100A10, IL1A, H1.2, SLC25A5, GLTP, IFNG, H2AC13, TUBB4B, AKNA, TYK2, and H1.5). The risk score was associated with poor prognosis in CC. Survival was lower in the high-risk group than the low-risk group. The nomogram based on the risk score, T stage, and N stage showed good prognostic predictive power. We found significant differences in immune scores, immune infiltration analysis, and immune checkpoints between the high- and low-risk groups (p < 0.05). Conclusion: We screened for DENRGs based on the TCGA database by using bioinformatics methods, and constructed prognostic models based on the signature DENRGs, which we confirmed as possibly having important biological functions in CC. Our study provides a new perspective on CC prognosis and immunity, and offers a series of new targets for future treatment.

Identification and Validation a Necroptosis-Related Prognostic Signature in Cervical Cancer

Necroptosis is a promising novel target for cervical cancer therapy. Nevertheless, differentially expressed necroptosis-related genes (NRGs) in cervical cancer and their associations with prognosis are far from fully clarified. In this study, differentially expressed NRGs (DE-NRGs) were screened out and their bio-function was elucidated. Subsequently, a prognostic scoring model based on the regression coefficients of the screened out NRGs and their corresponding mRNA expressions were constructed and validated. Finally, the survival probability of cervical cancer patients based on the constructed prognostic scoring model in 3 and 5 years was predicted and assessed. We found 17 DE-NRGs in cervical cancer tissues which were closely related to cancer progression, and most of them were significantly highly expressed. Furthermore, 3 NRG were confirmed as the prognostic signature genes from 17 DE-NRGs by regression analysis. Overall survival predicted through our prognostic scoring model was lower in the high-risk group than in the low-risk group (p < 0.05) in both the TCGA cohort and the external GEO44001 validation cohort. What's more, the prediction performance of our prognostic scoring models well verified by the ROC curve, and the risk score calculated could act as an independent prognostic factor for cervical cancer patients. The calibration curve and C-index (0.776) of the nomogram analysis suggested that the predictive performance of the nomogram was satisfactory. Our study identified and validated a necroptosis-related prognostic signature in cervical cancer, which could well predict the prognosis for cervical cancer patients.

Assessment of alterations in histone modification function and guidance for death risk prediction in cervical cancer patients

Background: Cervical cancer is the second most lethal malignancy among women, and histone modification plays a fundamental role in most biological processes, but the prognostic value of histone modification in cervical cancer has not been evaluated.

Methods: A total of 594 cervical cancer patients from TCGA-CESC, GSE44001, and GSE52903 cohorts were enrolled in the current study, along with the corresponding clinicopathological features. Patients with a follow-up time less than one month were removed. A total of 122 histone modification-associated signaling pathways were obtained from the MSigDB. The activation scores of these pathways were evaluated using the “GSVA” package, differentially expressed genes were identified by the “limma” package, and pathway enrichment was conducted using the “clusterProfiler 4.0” package. The subsequent least absolute shrinkage and selection operator (LASSO) regression analysis was performed using the “glmnet” package, and a prognostic nomogram was established using the “regplot” package. For the prediction of potential therapeutic drugs, we used the data from GDSC2016 and visualized them via “MOVICS”.

Results: Nine of 23 histone modification-associated prognostic genes were identified to construct the prognostic signature by LASSO analysis, named the histone modification-associated gene (HMAG) signature. Cervical patients with HMAG-H in TCGA-CESC cohort showed a 2.68-fold change of death risk, with the 95% CI from 1.533 to 4.671 (p < 0.001), as well as the increased death risk of HMAG-H in the GSE44001 cohort (HR: 2.83, 95% CI: 1.370–5.849, p = 0.005) and GSE44001 cohort (HR: 4.59, 95% CI: 1.658–12.697, p = 0.003). We observed the preferable AUC values of the HMAG signature in TCGA-CESC cohort (1-year: 0.719, 3-year: 0.741, and 5-year: 0.731) and GSE44001 cohort (1-year: 0.850, 3-year: 0.781, and 5-year: 0.755). The C-index of the nomogram showed a prognostic value as high as 0.890, while the C-index for age was only 0.562, and that for grade was only 0.542. Patients with high HMAG scores were more suitable for the treatment of CHIR-99021, embelin, FTI-277, JNK-9L, JQ12, midostaurin, PF-562271, pyrimethamine, and thapsigargin, and patients with low HMAG scores were more suitable for the treatment of BMS-536924, CP466722, crizotinib, PHA-665752, rapamycin, and TAE684.

Conclusion: We comprehensively evaluated the histone modification status in cervical cancer patients and revealed histone modification-associated prognostic genes to construct the HMAG signature, aiming to provide a new insight into prognosis prediction and precise clinical treatment.

The association between several autophagy-related genes and their prognostic values in hepatocellular carcinoma: a study on the foundation of TCGA, GEPIA and HPA databases

BACKGROUND

The purpose of this study was to investigate the relationship between the expression of autophagy-related genes and prognosis in hepatocellular carcinoma (HCC).

METHODS AND RESULTS

We selected three autophagy-related genes (ATG3, ATG7, and ATG9A) from gene expression data of liver cancer patients in The Cancer Genome Atlas (TCGA) database by Kaplan-Meier survival analysis, univariate and multivariate Cox regression analysis, and Gene Set Enrichment Analysis (GSEA). Human Protein Atlas (HPA) and Gene Expression Profiling Interactive Analysis (GEPIA) databases were applied to testify the credibility of our results. The expression levels of ATG3, ATG7, and ATG9A were verified by real-time quantitative PCR (RT-qPCR) in normal liver cells (L02) and three HCC cell lines (HepG2, Hep3b, and Li-7). Data analysis results from TCGA showed high ATG3, ATG7, ATG9A expression in HCC tumor tissues. Kaplan-Meier survival analysis showed that the survival rate of the high expression group of ATG3, ATG7, and ATG9A was all significantly lower than the low expression group. GSEA analysis showed that many signaling pathways (such as the regulation of autophagy, glycine serine and threonine metabolism, pathways in cancer, mitogen-activated protein kinase (MAPK) signaling pathway, mammalian target of rapamycin (mTOR) signaling pathway, as well as P53 signaling pathway) were differentially enriched in HCCs with ATG3, ATG7, and ATG9A expression. GEPIA and RT-qPCR also identified that the mRNA expression level of ATG3, ATG7, and ATG9A in normal liver cells were significantly lower than in HCC cells. High protein expression of ATG3, ATG7, and ATG9A was displayed in HCCs from the HPA database.

CONCLUSIONS

The ATG3, ATG7, ATG9A might be utilized as prognostic biomarkers for liver cancer.

A Novel Prognostic Risk Model for Cervical Cancer Based on Immune Checkpoint HLA-G-Driven Differentially Expressed Genes

Human leukocyte antigen G (HLA-G) is a potential checkpoint molecule that plays a key role in cervical carcinogenesis. The purpose of this study was to construct and validate a prognostic risk model to predict the overall survival (OS) of cervical cancer patients, providing a reference for individualized clinical treatment that may lead to better clinical outcomes. HLA-G-driven differentially expressed genes (DEGs) were obtained from two cervical carcinoma cell lines, namely, SiHa and HeLa, with stable overexpression of HLA-G by RNA sequencing (RNA-seq). The biological functions of these HLA-G-driven DEGs were analysed by GO enrichment and KEGG pathway using the “clusterProfiler” package. The protein-protein interactions (PPIs) were assessed using the STRING database. The prognostic relevance of each DEG was evaluated by univariate Cox regression using the TCGA-CESC dataset. After the TCGA-CESC cohort was randomly divided into training set and testing set, and a prognostic risk model was constructed by LASSO and stepwise multivariate Cox regression analysis in training set and validated in testing set or in different types of cervical cancer set. The predictive ability of the prognostic risk model or nomogram was evaluated by a series of bioinformatics methods. A total of 1108 candidate HLA-G-driven DEGs, including 391 upregulated and 717 downregulated genes, were obtained and were enriched mostly in the ErbB pathway, steroid biosynthesis, and MAPK pathway. Then, an HLA-G-driven DEG signature consisting of the eight most important prognostic genes CD46, LGALS9, PGM1, SPRY4, CACNB3, PLIN2, MSMO1, and DAGLB was identified as a key predictor of cervical cancer. Multivariate Cox regression analysis showed that this signature is an independent risk factor for the overall survival of CESC patients. Kaplan-Meier survival analysis showed that the 5-year overall survival rate is 23.0% and 84.6% for the high-risk and low-risk patients, respectively (P<0.001). The receiver operating characteristic (ROC) curve of this prognostic model with an area under the curve (AUC) was 0.896 for 5 years, which was better than that of other clinical traits. This prognostic risk model was also successfully validated in different subtypes of cervical cancer, including the keratinizing squamous cell carcinoma, non-keratinizing squamous cell carcinoma, squamous cell neoplasms, non-squamous cell neoplasms set. Single-sample gene set enrichment (ssGSEA) algorithm and Tumor Immune Dysfunction and Exclusion (TIDE) analysis confirmed that this signature influence tumour microenvironment and immune checkpoint blockade. A nomogram that integrated risk score, age, clinical stage, histological grade, and pathological type was then built to predict the overall survival of CESC patients and evaluated by calibration curves, AUC, concordance index (C-index) and decision curve analysis (DCA). To summarize, we developed and validated a novel prognostic risk model for cervical cancer based on HLA-G-driven DEGs, and the prognostic signature showed great ability in predicting the overall survival of patients with cervical cancer.

High Expression of TMEM33 Predicts Poor Prognosis and Promotes Cell Proliferation in Cervical Cancer

Background: The prognosis of patients with advanced cervical cancer remains unsatisfactory. A study indicated that transmembrane protein 33 (TMEM33) was implicated in tumor recurrence, while its role in cervical cancer has not been elucidated.

Methods: TMEM33 expression in cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) was primarily screened in The Cancer Genome Atlas (TCGA), and further validated in Gene Expression Omnibus (GEO) database. The Kaplan–Meier plotter analysis and Cox regression were constructed to evaluate the prognostic value of TMEM33 in CESC. Functional enrichment analysis was performed with GO, KEGG and GSEA tools. CCK-8 assay and colony formation assay were performed to investigate the carcinogenesis role of TMEM33 in cervical cancer cell proliferation.

Results: TMEM33 expression was significantly elevated in CESC compared with normal tissues. High expression of TMEM33 was associated with poor prognostic clinical characteristics in CESC patients. KM-plotter analysis revealed that patients with increased TMEM33 had shorter overall survival (OS), progress free interval (PFI), and disease specific survival (DSS). Moreover, Multivariate Cox analysis confirmed that high TMEM33 expression was an independent risk factor for OS in patients with CESC. TMEM33 was associated with immune infiltrates, and its expression was correlated with tumorigenesis-related genes RNF4, OCIAD1, TMED5, DHX15, MED28 and LETM1. More importantly, knockdown of TMEM33 in cervical cancer cells decreased the expression of those genes and inhibited cell proliferation.

Conclusion: Increased TMEM33 in cervical cancer can serve as an independent prognostic marker and might play a role in tumorigenesis by promoting cell proliferation.

Construction of a Six-Gene Prognostic Risk Model Related to Hypoxia and Angiogenesis for Cervical Cancer

Background: The prognosis of cervical cancer (CC) is poor and not accurately reflected by the primary tumor node metastasis staging system. Our study aimed to develop a novel survival-prediction model.

Methods: Hallmarks of CC were quantified using single-sample gene set enrichment analysis and univariate Cox proportional hazards analysis. We linked gene expression, hypoxia, and angiogenesis using weighted gene co-expression network analysis (WGCNA). Univariate and multivariate Cox regression was combined with the random forest algorithm to construct a prognostic model. We further evaluated the survival predictive power of the gene signature using Kaplan-Meier analysis and receiver operating characteristic (ROC) curves.

Results: Hypoxia and angiogenesis were the leading risk factors contributing to poor overall survival (OS) of patients with CC. We identified 109 candidate genes using WGCNA and univariate Cox regression. Our established prognostic model contained six genes (MOCSI, PPP1R14A, ESM1, DES, ITGA5, and SERPINF1). Kaplan-Meier analysis indicated that high-risk patients had worse OS (hazard ratio = 4.63, p < 0.001). Our model had high predictive power according to the ROC curve. The C-index indicated that the risk score was a better predictor of survival than other clinicopathological variables. Additionally, univariate and multivariate Cox regressions indicated that the risk score was the only independent risk factor for poor OS. The risk score was also an independent predictor in the validation set (GSE52903). Bivariate survival prediction suggested that patients exhibited poor prognosis if they had high z-scores for hypoxia or angiogenesis and high risk scores.

Conclusions: We established a six-gene survival prediction model associated with hypoxia and angiogenesis. This novel model accurately predicts survival and also provides potential therapeutic targets.

Long non-coding RNA HOXA11-AS knockout inhibits proliferation and overcomes drug resistance in ovarian cancer

In ovarian carcinogenesis and progression, long non-coding RNAs (lncRNAs) have been shown to have a role, although the underlying processes remain a mystery. By modulating the degree of autophagy in ovarian cancer cells, we sought to learn more about the function lncRNA HOXA11-AS plays in the development of ovarian cancer. The expression of HOXA11-AS in ovarian normal cells and ovarian cancer cell lines was measured using R package and qRT-PCR. Ovarian cancer cells expressed HOXA11-AS substantially higher than normal cells, while cisplatin-resistant cells expressed HOXA11-AS significantly higher than ovarian cancer cells. Next, we studied the prognostic data of HOXA11-AS in ovarian cancer in the Tissue Cancer Genome Atlas (TCGA). In the next step, lentiviral transfection of ovarian cancer cells A2780, OVCAR3, and A2780/DDP (cisplatin-resistant) were performed, and HOXA11-AS knockdown was found to significantly inhibit cell viability, migration, and invasion of A2780 and OVCAR3 cells, and promote apoptosis by CCK-8 assay, transwell assay, cell cycle, and apoptosis assay, and promoted the sensitivity of A2780/DDP cells to cisplatin. It has been shown by the western blot test that HOXA11-AS knockdown increases the amount of cellular autophagy in cells. In contrast, adding the autophagy inhibitor 3-methyladenine (3-MA) to HOXA11-AS cells knocked down in vivo reduced its anti-tumor properties. As a whole, this study found that HOXA11-AS knockdown increased the expression of autophagy-related proteins and improved cisplatin sensitivity, decreased ovarian cancer cell proliferation, and promoted cell apoptosis. This study provides new insights into the role of HOXA11-AS in ovarian cancer regulation.

Integrated clinical characteristics and omics analysis identifies a ferroptosis and iron-metabolism-related lncRNA signature for predicting prognosis and therapeutic responses in ovarian cancer

Background

Ferroptosis and iron-metabolism are regulated by Long non-coding RNAs (lncRNAs) in ovarian cancer (OC). Therefore, a comprehensive analysis of ferroptosis and iron-metabolism related lncRNAs (FIRLs) in OC is crucial for proposing therapeutic strategies and survival prediction.

Methods

In multi-omics data from OC patients, FIRLs were identified by calculating Pearson correlation coefficients with ferroptosis and iron-metabolism related genes (FIRGs). Cox-Lasso regression analysis was performed on the FIRLs to screen further the lncRNAs participating in FIRLs signature. In addition, all patients were divided into two robust risk subtypes using the FIRLs signature. Receiver operator characteristic (ROC) curve, Kaplan–Meier analysis, decision curve analysis (DCA), Cox regression analysis and calibration curve were used to confirm the clinical benefits of FIRLs signature. Meanwhile, two nomograms were constructed to facilitate clinical application. Moreover, the potential biological functions of the signature were investigated by genes function annotation. Finally, immune microenvironment, chemotherapeutic sensitivity, and the response of PARP inhibitors were compared in different risk groups using diversiform bioinformatics algorithms.

Results

The raw data were randomized into a training set (n = 264) and a testing set (n = 110). According to Pearson coefficients between FIRGs and lncRNAs, 1075 FIRLs were screened for univariate Cox regression analysis, and then LASSO regression analysis was used to construct 8-FIRLs signature. It is worth mentioning that a variety of analytical methods indicated excellent predictive performance for overall survival (OS) of FIRLs signature (p < 0.05). The multivariate Cox regression analysis showed that FIRLs signature was an independent prognostic factor for OS (p < 0.05). Moreover, significant differences in the abundance of immune cells, immune-related pathways, and drug response were excavated in different risk subtypes (p < 0.05).

Conclusion

The FIRLs signature can independently predict overall survival and therapeutic effect in OC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13048-022-00944-y.

Understanding the molecular mechanism of the effect of ginkgo folium on the treatment of IgA nephropathy using network pharmacology and molecular docking

This work focused on identifying the molecular target of Ginkgo Folium (GF) for treating IgA nephropathy and underlying mechanism through network pharmacology (NP). The active components and targets of GF and targets associated with IgAN were obtained by TCMSP database, DrugBank etc. The key targets of GF against IgAN were searched by network topology. The drug-disease intersection targets were performed GO functional annotation as well as KEGG pathway analysis, and molecular docking (MD) was conducted to verify the degree of combination of the target and ligand. Three core compounds and seven key targets were found by topological analysis. GO and KEGG results suggested that GF effect on IgAN was strongly associated with OS cellular response and AGE-RAGE pathways. Molecular docking of the three core components with AKT1 indicated that they had good binding activity. Ginkgo biloba had multicomponent, multitarget, and multi-pathway effects on IgAN.

Increased ATG5 Expression Predicts Poor Prognosis and Promotes EMT in Cervical Carcinoma

Cervical cancer has the second-highest incidence and mortality of female malignancy. The major causes of mortality in patients with cervical cancer are invasion and metastasis. The epithelial–mesenchymal transition (EMT) process plays a major role in the acquisition of metastatic potential and motility. Autophagy-related genes (ARGs) are implicated in the EMT process, and autophagy exerts a dual function in EMT management at different phases of tumor progression. However, the role of specific ARGs during the EMT process has not yet been reported in cervical cancer. Based on the data from the Cancer Genome Atlas (TCGA) cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) sequencing database, we performed the prognosis analysis for those ARGs obtained from the Human Autophagy database. ATG5 was identified as the only important harmful marker influencing survival of cervical cancer patients by univariate Cox regression (HR 1.7; 95% CI: 1.0–2.8, p = 0.047), and the 5-years survival rate for the high- and low-ATG5 expression groups was 0.486 (0.375–0.631) and 0.782 (0.708–0.863), respectively. TCGA CESC methylation data showed that eight methylation sites of ATG5 could also be significantly associated with the overall survival (OS) of cervical cancer patients. Single-sample gene-set enrichment and gene functional enrichment results showed that ATG5 was correlated with some cancer-related pathways, such as phagocytosis-related genes, endocytosis-related genes, immune-related genes, EMT score, and some EMT signature-related genes. Next, cell migration and invasion assay and Western blot were applied to detect the function of ATG5 in EMT of cervical cancer. In cervical cancer cells, ATG5 knockdown resulted in attenuation of migration and invasion. The functional study showed that knockdown of ATG5 could reverse EMT process by P-ERK, P-NFκBp65, P-mTOR pathways, and so on. In conclusion, the present study implies that ATG5 was a major contributor to EMT regulation and poor prognosis in cervical cancer.

Codon Usage Bias in Autophagy-Related Gene 13 in Eukaryotes: Uncovering the Genetic Divergence by the Interplay Between Nucleotides and Codon Usages

Synonymous codon usage bias is a universal characteristic of genomes across various organisms. Autophagy-related gene 13 (atg13) is one essential gene for autophagy initiation, yet the evolutionary trends of the atg13 gene at the usages of nucleotide and synonymous codon remains unexplored. According to phylogenetic analyses for the atg13 gene of 226 eukaryotic organisms at the nucleotide and amino acid levels, it is clear that their nucleotide usages exhibit more genetic information than their amino acid usages. Specifically, the overall nucleotide usage bias quantified by information entropy reflected that the usage biases at the first and second codon positions were stronger than those at the third position of the atg13 genes. Furthermore, the bias level of nucleotide ‘G’ usage is highest, while that of nucleotide ‘C’ usage is lowest in the atg13 genes. On top of that, genetic features represented by synonymous codon usage exhibits a species-specific pattern on the evolution of the atg13 genes to some extent. Interestingly, the codon usages of atg13 genes in the ancestor animals (Latimeria chalumnae, Petromyzon marinus, and Rhinatrema bivittatum) are strongly influenced by mutation pressure from nucleotide composition constraint. However, the distributions of nucleotide composition at different codon positions in the atg13 gene display that natural selection still dominates atg13 codon usages during organisms’ evolution.

Cancer-Derived Exosomal miR-651 as a Diagnostic Marker Restrains Cisplatin Resistance and Directly Targets ATG3 for Cervical Cancer

Objective

Cancer-derived exosomes can facilitate drug resistance in cervical cancer. However, the mechanisms remain elusive. Herein, we observed the roles of exosomal miR-651 in cisplatin resistance of cervical cancer.

Methods

Circulating miR-651 was detected in cervical cancer and healthy individuals. The diagnostic efficacy was determined. When transfected with miR-651 mimics, cisplatin resistance, apoptosis, and proliferation were assessed. The cancer-derived exosomes were separated and identified. We observed the uptake of PKH67-labeled exosomes by HeLa/S cells. After coculture with exosomes secreted by HeLa/S or HeLa/DDP cells, malignant behaviors were examined in HeLa/S cells. The interactions between ATG3 and miR-651 were validated by dual luciferase report. Biological behaviors were investigated for HeLa/S cells cocultured with exosomes secreted by miR-651 mimic-transfected HeLa/DDP cells.

Results

Downregulated circulating miR-651 was found in cancer subjects than healthy individuals. It possessed high sensitivity and accuracy in diagnosing cervical cancer (AUC = 0.9050). Lower miR-651 expression was confirmed in HeLa/DDP than HeLa/S cells. Forced miR-651 lessened cisplatin resistance and proliferation and elevated apoptosis in HeLa cells. ATG3 was a direct target of miR-651. The exosomes isolated from HeLa cells were rich in CD63, CD9, and CD81 proteins, thereby identifying the isolated exosomes. Exosomes secreted by HeLa/DDP cells can be absorbed by HeLa/S cells. When being cocultured with exosomes secreted by HeLa/DDP cells, malignant behaviors of HeLa/S cells were enhanced, which were ameliorated by miR-651 mimic exosomes.

Conclusion

Our findings showed that cancer-derived exosomal miR-651 restrained cisplatin resistance and directly targeted ATG3, indicating that exosomal miR-651 could be a therapeutic agent.

Identification of autophagy-related risk signatures for the prognosis, diagnosis, and targeted therapy in cervical cancer

Background

To rummage autophagy-related prognostic, diagnostic, and therapeutic biomarkers in cervical cancer (CC).

Methods

The RNA-sequence and clinical information were from the TCGA and GTEx databases. We operated Cox regression to determine signatures related to overall survival (OS) and recurrence-free survival (RFS) respectively. The diagnostic and therapeutic effectiveness of prognostic biomarkers were further explored.

Results

We identified nine (VAMP7, MTMR14, ATG4D, KLHL24, TP73, NAMPT, CD46, HGS, ATG4C) and three risk signatures (SERPINA1, HSPB8, SUPT20H) with prognostic values for OS and RFS respectively. Six risk signatures (ATG4C, ATG4D, CD46, TP73, SERPINA1, HSPB8) were selected for qPCR. We screened five prognostic signatures(ATG4C, CD46, HSPB8, MTMR14, NAMPT) with diagnostic function through the GEO database. Correlation between our models and treatment targets certificated the prognostic score provided a reference for precision medicine.

Conclusions

We constructed OS and RFS prognostic models in CC. Autophagy-related risk signatures might serve as diagnostic and therapeutic biomarkers.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02073-w.

Immunogenomic Identification for Predicting the Prognosis of Cervical Cancer Patients

Cervical cancer is primarily caused by the infection of high-risk human papillomavirus (hrHPV). Moreover, tumor immune microenvironment plays a significant role in the tumorigenesis of cervical cancer. Therefore, it is necessary to comprehensively identify predictive biomarkers from immunogenomics associated with cervical cancer prognosis. The Cancer Genome Atlas (TCGA) public database has stored abundant sequencing or microarray data, and clinical data, offering a feasible and reliable approach for this study. In the present study, gene profile and clinical data were downloaded from TCGA, and the Immunology Database and Analysis Portal (ImmPort) database. Wilcoxon-test was used to compare the difference in gene expression. Univariate analysis was adopted to identify immune-related genes (IRGs) and transcription factors (TFs) correlated with survival. A prognostic prediction model was established by multivariate cox analysis. The regulatory network was constructed and visualized by correlation analysis and Cytoscape, respectively. Gene functional enrichment analysis was performed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). A total of 204 differentially expressed IRGs were identified, and 22 of them were significantly associated with the survival of cervical cancer. These 22 IRGs were actively involved in the JAK-STAT pathway. A prognostic model based on 10 IRGs (APOD, TFRC, GRN, CSK, HDAC1, NFATC4, BMP6, IL17RD, IL3RA, and LEPR) performed moderately and steadily in squamous cell carcinoma (SCC) patients with FIGO stage I, regardless of the age and grade. Taken together, a risk score model consisting of 10 novel genes capable of predicting survival in SCC patients was identified. Moreover, the regulatory network of IRGs associated with survival (SIRGs) and their TFs provided potential molecular targets.

Identification of a Six-Gene Signature for Predicting the Overall Survival of Cervical Cancer Patients

Background

Although the incidence of cervical cancer has decreased in recent decades with the development of human papillomavirus vaccines and cancer screening, cervical cancer remains one of the leading causes of cancer-related death worldwide. Identifying potential biomarkers for cervical cancer treatment and prognosis prediction is necessary.

Methods

Samples with mRNA sequencing, copy number variant, single nucleotide polymorphism and clinical follow-up data were downloaded from The Cancer Genome Atlas database and randomly divided into a training dataset (N=146) and a test dataset (N=147). We selected and identified a prognostic gene set and mutated gene set and then integrated the two gene sets with the random survival forest algorithm and constructed a prognostic signature. External validation and immunohistochemical staining were also performed.

Results

We obtained 1416 differentially expressed prognosis-related genes, 624 genes with copy number amplification, 1038 genes with copy number deletion, and 163 significantly mutated genes. A total of 75 candidate genes were obtained after overlapping the differentially expressed genes and the genes with genomic variations. Subsequently, we obtained six characteristic genes through the random survival forest algorithm. The results showed that high expression of SLC19A3, FURIN, SLC22A3, and DPAGT1 and low expression of CCL17 and DES were associated with a poor prognosis in cervical cancer patients. We constructed a six-gene signature that can separate cervical cancer patients according to their different overall survival rates, and it showed robust performance for predicting survival (training set: p ˂ 0.001, AUC = 0.82; testing set: p ˂ 0.01, AUC = 0.59).

Conclusion

Our study identified a novel six-gene signature and nomogram for predicting the overall survival of cervical cancer patients, which may be beneficial for clinical decision-making for individualized treatment.