Development of a five-protein signature for predicting the prognosis of head and neck squamous cell carcinoma

Epithelial‑derived head and neck squamous tumourigenesis (Review)

Head and neck squamous cell carcinomas (HNSCCs), a heterogeneous group of cancers that arise from the mucosal epithelia cells in the head and neck areas, present great challenges in diagnosis, treatment and prognosis due to their complex aetiology and various clinical manifestations. Several factors, including smoking, alcohol consumption, oncogenic genes, growth factors, Epstein‑Barr virus and human papillomavirus infections can contribute to HNSCC development. The unpredictable tumour microenvironment adds to the complexity of managing HNSCC. Despite significant advances in therapies, the prediction of outcome after treatment for patients with HNSCC remains poor, and the 5‑year overall survival rate is low due to late diagnosis. Early detection greatly increases the chances of successful treatment. The present review aimed to bring together the latest findings related to the molecular mechanisms of HNSCC carcinogenesis and progression. Comprehensive genomic, transcriptomic, metabolomic, microbiome and proteomic analyses allow researchers to identify important biological markers such as genetic alterations, gene expression signatures and protein markers that drive HNSCC tumours. These biomarkers associated with the stages of initiation, progression and metastasis of cancer are useful in the management of patients with cancer in order to improve their life expectancy and quality of life.

Cuproptosis-Related lncRNA Predict Prognosis and Immune Response of LUAD

Background

Lung cancer is the leading cause of cancer deaths worldwide, primarily due to lung adenocarcinoma (LUAD). However, the heterogeneity of programmed cell death results in varied prognostic and predictive outcomes. This study aimed to develop an LUAD evaluation marker based on cuproptosis-related lncRNAs.

Methods

First, transcriptome data and clinical data related to LUAD were downloaded from the Cancer Genome Atlas (TCGA), and cuproptosis-related genes were analyzed to identify cuproptosis-related lncRNAs. Univariate, LASSO, and multivariate Cox regression analyses were conducted to construct cuproptosis-associated lncRNA models. LUAD patients were categorized into high-risk and low-risk groups using prognostic risk values. Kaplan-Meier analysis, PCA, GSEA, and nomograms were employed to evaluate and validate the results.

Results

7 cuproptosis-related lncRNAs were identified, and a risk model was created. High-risk tumors exhibited cuproptosis-related gene alterations in 95.54% of cases, while low-risk tumors showed alterations in 85.65% of cases, mainly involving TP53. The risk value outperformed other clinical variables and tumor mutation burden as a predictor of 1-, 3-, and 5-year overall survival. The cuproptosis-related lncRNA-based risk model demonstrated high validity for LUAD evaluation, potentially influencing individualized treatment approaches. Expression analysis of four candidate cuproptosis-related lncRNAs (AL606834.1, AL161431.1, AC007613.1, and LINC02835) in LUAD tissues and adjacent normal tissues revealed significantly higher expression levels of AL606834.1 and AL161431.1 in LUAD tissues, positively correlating with tumor stage, lymph node metastasis, and histopathological grade. Conversely, AC007613.1 and LINC02835 exhibited lower expression levels, negatively correlating with these factors. High expression of AL606834.1 and AL161431.1 indicated poor prognosis, while low expression of AC007613.1 and LINC02835 was associated with unfavorable outcomes. Univariate and multivariate analyses confirmed these lncRNAs as independent risk factors for LUAD prognosis.

Conclusion

The 4 cuproptosis-related (lncRNAsAL606834.1, AL161431.1, AC007613.1, and LINC02835) can accurately predict the prognosis of patients with LUAD and may provide new insights into clinical applications and immunotherapy.

Identification of cellular senescence-associated genes as new biomarkers for predicting the prognosis and immunotherapy response of non-small cell lung cancer and construction of a prognostic model

Background

Globally, lung carcinoma remains the leading cause of death, with its associated morbidity and mortality rates remaining elevated. Despite the slow advancement of treatment, the outlook remains bleak. Cellular senescence represents a halt in the cell cycle, encompassing a range of physiological and pathological activities, along with diverse phenotypic alterations, including variations in secretory phenotype, macromolecular harm, and metabolic disturbances. Research has revealed its vital function in the formation and growth of tumors. This study aimed to examine cellular senescence-related mRNAs linked to the outlook of non-small cell lung cancer (NSCLC) and to formulate a predictive risk framework for NSCLC.

Methods

We acquired the NSCLC expression data from The Cancer Genome Atlas (TCGA) to examine mRNAs linked to cellular senescence. Both single-variable and multiple-variable cox proportion risk assessments were utilized to determine the traits of cellular senescence-related mRNAs linked to NSCLC prognosis. Subsequently, the prognostic model for cellular senescence-related mRNAs was integrated with clinical-pathological characteristics to create a prognostic nomogram. Furthermore, the study delved into the risk-oriented predictive model, examining immune infiltration and responses to immunotherapy among both high and low-risk categories.

Results

Utilizing both univariate and multivariate Cox proportion risk assessments, a risk model comprising 12 mRNAs associated with cellular aging was ultimately developed: IGFBP1, TLR3, WT1, ID1, PTTG1, ERRFI1, HEPACAM, MAP2K3, RAD21, NANOG, PRKCD, SOX5. Univariate analysis and multivariate analysis illustrated that the risk score served as a standalone indicator for prognosis, and the hazard ratio (HR) of the risk score were 1.182 (1.139-1.226) (p < 0.001) and 1.162 (1.119 - 1.206) (p < 0.001), respectively. Individual prognoses were forecasted using nomogram, c-index, and principal component analysis (PCA). Furthermore, the risk-oriented model revealed notable statistical variances in immune infiltration and response to immunotherapy among the high and low risk categories.

Conclusions

This study shows that mRNAs related to cell senescence associated with prognosis are reliable predictors of NSCLC immunotherapy reaction and prognosis.

HPV-negative head and neck cancers with adverse pathological features carry specific molecular changes that are associated with survival

BACKGROUND

Adverse pathological features following surgery in head and neck squamous cell carcinoma (HNSCC) are strongly associated with survival and guide adjuvant therapy. We investigated molecular changes associated with these features.

METHODS

We downloaded data from the Cancer Genome Atlas and Cancer Proteome Atlas HNSCC cohorts. We compared tumors positive versus negative for perineural invasion (PNI), lymphovascular invasion (LVI), extracapsular spread (ECS), and positive margins (PSM), with multivariable analysis.

RESULTS

All pathological features were associated with poor survival, as were the following molecular changes: low cyclin E1 (HR = 1.7) and high PKC-alpha (HR = 1.8) in tumors with PNI; six of 13 protein abundance changes with LVI; greater tumor hypoxia and high Raptor (HR = 2.0) and Rictor (HR = 1.6) with ECS; and low p38 (HR = 2.3), high fibronectin (HR = 1.6), low annexin A1 (HR = 3.1), and high caspase-9 (HR = 1.6) abundances with PSM.

CONCLUSIONS

Pathological features in HNSCC carry specific molecular changes that may explain their poor prognostic associations.

Identification of a novel m6A-related lncRNAs signature and immunotherapeutic drug sensitivity in pancreatic adenocarcinoma

BACKGROUND

Pancreatic adenocarcinoma (PDAC) ranks as the fourth leading cause for cancer-related deaths worldwide. N6-methyladenosine (m6A) and long non-coding RNAs (lncRNAs) are closely related with poor prognosis and immunotherapeutic effect in PDAC. The aim of this study is to construct and validate a m6A-related lncRNAs signature and assess immunotherapeutic drug sensitivity in PDAC.

METHODS

RNA-seq data for 178 cases of PDAC patients and 167 cases of normal pancreatic tissue were obtained from TCGA and GTEx databases, respectively. A set of 21 m6A-related genes were downloaded based on the previous report. Co-expression network was conducted to identify m6A-related lncRNAs in PDAC. Cox analyses and least absolute shrinkage and selection operator (Lasso) regression model were used to construct a risk prognosis model. The relationship between signature genes and immune function was explored by single-sample GSEA (ssGSEA). The tumor immune dysfunction and exclusion (TIDE) score and tumor mutation burden (TMB) were utilized to evaluate the response to immunotherapy. Furthermore, the expression levels of 4 m6A-related lncRNAs on PDAC cell lines were measured by the quantitative real-time PCR (qPCR). The drug sensitivity between the high- and low-risk groups was validated using PDAC cell lines by Cell-Counting Kit 8 (CCK8).

RESULTS

The risk prognosis model was successfully constructed based on 4 m6A-related lncRNAs, and PDAC patients were divided into the high- and low-risk groups. The overall survival (OS) of the high-risk groups was more unfavorable compared with the low-risk groups. Receiver operating characteristic (ROC) curves demonstrated that the risk prognosis model reasonably predicted the 2-, 3- and 5-year OS of PDAC patients. qPCR analysis confirmed the decreased expression levels of 4 m6A-related lncRNAs in PDAC cells compared to the normal pancreatic cells. Furthermore, CCK8 assay revealed that Phenformin exhibited higher sensitivity in the high-risk groups, while Pyrimethamine exhibited higher sensitivity in the low-risk groups.

CONCLUSION

The prognosis of patients with PDAC were well predicted in the risk prognosis model based on m6A-related lncRNAs, and selected immunotherapy drugs have potential values for the treatment of pancreatic cancer.

Identification of N6-methylandenosine related lncRNA signatures for predicting the prognosis and therapy response in colorectal cancer patients

Despite recent advances in surgical and multimodal therapies, the overall survival (OS) of advanced colorectal cancer (CRC) patients remains low. Thus, discerning sensitive prognostic biomarkers to give the optimistic treatment for CRC patients is extremely critical. N6-methyladenosine (m6A) and long noncoding RNAs (lncRNAs) play an important role in CRC progression. Nonetheless, few studies have focused on the impact of m6A-related lncRNAs on the prognosis, tumor microenvironment (TME) and treatment of CRC. In this study, 1707 m6A-related lncRNAs were identified through Pearson correlation analysis and Weighted co-expression network analysis (WGCNA) using The Cancer Genome Atlas (TCGA) cohort. Then, 28 m6A-related prognostic lncRNAs were screened by univariate Cox regression analysis, followed by identifying two clusters by consensus clustering analysis. A prognostic model consisted of 8 lncRNA signatures was constructed by the least absolute shrinkage and selection operator (LASSO). Kaplan–Meier curve analysis and a nomogram were performed to investigate the prognostic ability of this model. The risk score of prognostic model act as an independent risk factor for OS rate. Functional enrichment analysis indicated that lncRNA signatures related tumor immunity. The low-risk group characterized by increased microsatellite instability-high (MSI-H), mutation burden, and immunity activation, indicated favorable odds of OS. Moreover, the lncRNA signatures were significantly associated with the cancer stem cell (CSC) index and drug sensitivity. In addition, 3 common immune genes shared by the lncRNA signatures were screened out. We found that these immune genes were widely distributed in 2 cell types of TME. Finally, a ceRNA network was constructed to identify ZEB1-AS1 regulatory axis in CRC. We found that ZEB1-AS1 was significantly overexpressed in tumor tissues, and was related to the metastasis of EMT and the chemoresistance of 5-Fu in CRC. Therefore, our study demonstrated the important role of m6A-related lncRNAs in TME remodeling. Moreover, these results illustrated the levels of ZEB1-AS1 might be valuable for predicting the progression and prognosis of CRC, and further provided a new target for the diagnosis and treatment of CRC patients.

COVID-19–associated lncRNAs as predictors of survival in uterine corpus endometrial carcinoma: A prognostic model

Background: Patients with uterine corpus endometrial carcinoma (UCEC) may be susceptible to the coronavirus disease-2019 (COVID-19). Long non–coding RNAs take on a critical significance in UCEC occurrence, development, and prognosis. Accordingly, this study aimed to develop a novel model related to COVID-19–related lncRNAs for optimizing the prognosis of endometrial carcinoma.

Methods: The samples of endometrial carcinoma patients and the relevant clinical data were acquired in the Carcinoma Genome Atlas (TCGA) database. COVID-19–related lncRNAs were analyzed and obtained by coexpression. Univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses were performed to establish a COVID-19–related lncRNA risk model. Kaplan–Meier analysis, principal component analysis (PCA), and functional enrichment annotation were used to analyze the risk model. Finally, the potential immunotherapeutic signatures and drug sensitivity prediction targeting this model were also discussed.

Results: The risk model comprising 10 COVID-19–associated lncRNAs was identified as a predictive ability for overall survival (OS) in UCEC patients. PCA analysis confirmed a reliable clustering ability of the risk model. By regrouping the patients with this model, different clinic-pathological characteristics, immunotherapeutic response, and chemotherapeutics sensitivity were also observed in different groups.

Conclusion: This risk model was developed based on COVID-19–associated lncRNAs which would be conducive to the precise treatment of patients with UCEC.

N6-Methyladenosine (m6A)-Related lncRNAs Are Potential Signatures for Predicting Prognosis and Immune Response in Lung Squamous Cell Carcinoma

Background

Despite increasing understanding of m⁶A-related lncRNAs in lung cancer, the role of m⁶A-related lncRNAs in the prognosis and treatment of lung squamous cell carcinoma is poorly understood to date. Thus, the current study aims to elucidate its role and build a model to predict the prognosis of LUSC patients.

Materials and Methods

The data of the current study were accessed from the TCGA database. Pearson correlation analysis was performed to identify lncRNAs correlated to m⁶A. Next, an m⁶A-related lncRNAs risk model was built using a single factor, least absolute association, selection operator, and multivariate Cox regression analysis.

Results

The relevance between 23 m⁶A genes and 14,056 lncRNAs is shown by Pearson correlation analysis by Sankey diagram. Multivariate Cox regression analysis determined that 11 m⁶A-lncRNAs show predictive potential in prognosis, which is confirmed by the consistency index, Kaplan–Meier analysis, principal component analysis, and ROC curve. Additionally, the immune analysis showed that the enrichment of immune cells, major histocompatibility complex molecules, and immune checkpoints in the high and low-risk subgroups were markedly disparate, with the high-risk group showing a stronger immune escape ability and a worse response to immunotherapy.

Conclusion

In conclusion, the risk model based on m⁶A-related lncRNAs showed great promise in predicting the prognosis and the efficacy of immunotherapy.

The Prediction of a 3-Protein-Based Model on the Prognosis of Head and Neck Squamous Cell Carcinoma

Background

Head and neck squamous cell carcinoma (HNSCC) is one of the commonest malignant tumors. Using high-throughput genomic methods, RNA-based diagnostic and prognostic models for HNSCC with potential clinical value have been developed. However, the clinical utility and reproducibility of these models are uncertain. Because the complex regulatory processes occurring after mRNA is transcribed, the abundance of proteins in a cell can never be fully predicted or explained by their corresponding mRNA expression. We aimed to assume and verify a novel protein signature for checking the HNSCC patients' prognosis.

Methods

The functional proteomic data of 332 HNSCC cases were collected from The Cancer Proteome Atlas (TCPA), and the related follow-up and clinical data were acquired from The Cancer Genome Atlas (TCGA). This study adopted multivariate and univariate Cox regression analysis, Akaike Information Criterion, receiver operating characteristic (ROC) analysis, and Kaplan-Meier method.

Results

Patients' clinical features in both sets were comparable (all, P > 0.05). The area under the ROC curve (AUC) for the 3-protein signature (X4EBP1_pT37T46, HER3_pY1289, and NF2) in the test set was 0.655 and in the combined cohort (all 332 patients combined) was 0.699. In addition, the 3-protein signature exhibited better predictive value for the survival of HNSCC patients as in comparison with conventional clinical factors like age, gender, tumor stage, and smoking history (TNM stage).

Conclusion

The 3-protein signature developed in this study exhibits good performance in predicting the overall survival of with HNSCC patients. The 3-protein signature exhibited better predictive value for survival than conventional clinical factors just like gender, TNM stage, smoking history, and age.

Liver Endothelium Promotes HER3-mediated Cell Survival in Colorectal Cancer with Wild-type and Mutant KRAS

We previously identified that human epidermal growth factor receptor 3 (HER3, also known as ERBB3) is a key mediator in liver endothelial cell (EC) promoting colorectal cancer (CRC) growth and chemoresistance, and suggested HER3-targeted therapy as a strategy for treating patients with metastatic CRC (mCRC) in the liver. Meanwhile, KRAS mutations occur in 40-50% of mCRC and render CRC resistant to therapies targeting the other HER family protein epidermal growth factor receptor (EGFR). It is necessary to elucidate the roles of KRAS mutation status in HER3-mediated cell survival and CRC response to HER3 inhibition. In the present study, we used primary ECs isolated from non-neoplastic liver tissues to recapitulate the liver EC microenvironment. We demonstrated that liver EC-secreted factors activated CRC-associated HER3, and increased CRC cell survival in vitro and promoted CRC patient-derived xenograft tumor growth in vivo. Moreover, we determined that blocking HER3, either by siRNA knockdown or the humanized antibody seribantumab, blocked EC-induced CRC survival in vitro in both KRAS wild-type and mutant CRC cells, and the HER3 antibody seribantumab significantly decreased CRC tumor growth and sensitized tumors to chemotherapy in an orthotopic xenograft model with CRC tumors developed in the liver. In summary, our findings demonstrated that blocking HER3 had significant effects on attenuating liver EC-induced CRC cell survival independent of the KRAS mutation status. Implications: This body of work highlighted a potential strategy of using HER3 antibodies in combination with standard chemotherapy agents for treating patients with either KRAS wild-type or KRAS mutant mCRC.

m6A-related lncRNAs are potential biomarkers for predicting prognoses and immune responses in patients with LUAD

Lung adenocarcinoma (LUAD) is the most frequent subtype of lung cancer worldwide. However, the survival rate of LUAD patients remains low. N6-methyladenosine (m⁶A) and long noncoding RNAs (lncRNAs) play vital roles in the prognostic value and the immunotherapeutic response of LUAD. Thus, discerning lncRNAs associated with m⁶A in LUAD patients is critical. In this study, m⁶A-related lncRNAs were analyzed and obtained by coexpression. Univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses were conducted to construct an m⁶A-related lncRNA model. Kaplan-Meier analysis, principal-component analysis (PCA), functional enrichment annotation, and nomogram were used to analyze the risk model. Finally, the potential immunotherapeutic signatures and drug sensitivity prediction targeting this model were also discussed. The risk model comprising 12 m⁶A-related lncRNAs was identified as an independent predictor of prognoses. By regrouping the patients with this model, we can distinguish between them more effectively in terms of the immunotherapeutic response. Finally, candidate compounds aimed at LUAD subtype differentiation were identified. This risk model based on the m⁶A-based lncRNAs may be promising for the clinical prediction of prognoses and immunotherapeutic responses in LUAD patients.