Development of a KRAS-Associated Metabolic Risk Model for Prognostic Prediction in Pancreatic Cancer

Biomarkers Identification in the Microenvironment of Oral Squamous Cell Carcinoma: A Systematic Review of Proteomic Studies

An important determinant for oral squamous cell carcinoma (OSCC) onset and outcome is the composition of the tumor microenvironment (TME). Thus, the study of the interactions occurring among cancer cells, immune cells, and cancer-associated fibroblasts within the TME could facilitate the understanding of the mechanisms underlying OSCC development and progression, as well as of its sensitivity or resistance to the therapy. In this context, it must be highlighted that the characterization of TME proteins is enabled by proteomic methodologies, particularly mass spectrometry (MS). Aiming to identify TME protein markers employable for diagnosing and prognosticating OSCC, we have retrieved a total of 119 articles spanning 2001 to 2023, of which 17 have passed the selection process, satisfying all its criteria. We have found a total of 570 proteins detected by MS-based proteomics in the TME of OSCC; among them, 542 are identified by a single study, while 28 are cited by two or more studies. These 28 proteins participate in extracellular matrix remodeling and/or energy metabolism. Here, we propose them as markers that could be used to characterize the TME of OSCC for diagnostic/prognostic purposes. Noteworthy, most of the 28 individuated proteins share one feature: being modulated by the hypoxia that is present in the proliferating OSCC mass.

Prognostic utility of TME-associated genes in pancreatic cancer

Background: Pancreatic cancer (PC) is a deadly disease. The tumor microenvironment (TME) participates in PC oncogenesis. This study focuses on the assessment of the prognostic and treatment utility of TME-associated genes in PC. Methods: After obtaining the differentially expressed TME-related genes, univariate and multivariate Cox analyses and least absolute shrinkage and selection operator (LASSO) were performed to identify genes related to prognosis, and a risk model was established to evaluate risk scores, based on The Cancer Genome Atlas (TCGA) data set, and it was validated by external data sets from the Gene Expression Omnibus (GEO) and Clinical Proteomic Tumor Analysis Consortium (CPTAC). Multiomics analyses were adopted to explore the potential mechanisms, discover novel treatment targets, and assess the sensitivities of immunotherapy and chemotherapy. Results: Five TME-associated genes, namely, FERMT1, CARD9, IL20RB, MET, and MMP3, were identified and a risk score formula constructed. Next, their mRNA expressions were verified in cancer and normal pancreatic cells. Multiple algorithms confirmed that the risk model displayed a reliable ability of prognosis prediction and was an independent prognostic factor, indicating that high-risk patients had poor outcomes. Immunocyte infiltration, gene set enrichment analysis (GSEA), and single-cell analysis all showed a strong relationship between immune mechanism and low-risk samples. The risk score could predict the sensitivity of immunotherapy and some chemotherapy regimens, which included oxaliplatin and irinotecan. Various latent treatment targets (LAG3, TIGIT, and ARID1A) were addressed by mutation landscape based on the risk model. Conclusion: The risk model based on TME-related genes can reflect the prognosis of PC patients and functions as a novel set of biomarkers for PC therapy.

Mitophagy-Related Gene Signature for Prediction Prognosis, Immune Scenery, Mutation, and Chemotherapy Response in Pancreatic Cancer

Mitophagy is a conserved cellular process that plays a vital role in maintaining cellular homeostasis by selectively removing dysfunctional mitochondria. Notwithstanding that growing evidence suggests that mitophagy is implicated in pancreatic tumorigenesis, the effect of mitophagy-related genes on pancreatic cancer (PC) prognosis and therapeutic response remains largely unknown. In this study, we sought to construct a mitophagy-related gene signature and assessed its ability to predict the survival, immune activity, mutation status, and chemotherapy response of PC patients. During the screening process, we identified three mitophagy-related genes (PRKN, SRC, VDAC1) from The Cancer Genome Atlas (TCGA) cohort and a 3-gene signature was established. The prognostic model was validated using an International Cancer Genome Consortium (ICGC) cohort and two Gene Expression Omnibus (GEO) cohorts. According to the median risk score, PC patients were divided into high and low-risk groups, and the high-risk group correlated with worse survival in the four cohorts. The risk score was then identified as an independent prognostic predictor, and a predictive nomogram was constructed to guide clinical decision-making. Remarkably, enhanced immunosuppressive levels and higher mutation rates were observed in patients from the high-risk group, which may account for their poor survival. Furthermore, we found that high-risk patients were more sensitive to paclitaxel and erlotinib. In conclusion, a mitophagy-related gene signature is a novel prognostic model that can be used as a predictive indicator and allows prognostic stratification of PC patients.

Risk factors and prognostic index model for pancreatic cancer

BACKGROUND

Pancreatic cancer is a highly malignant tumor with poor prognosis. Chronic inflammation contributes to the progression of pancreatic cancer. However, few studies have examined the prognostic role of inflammatory markers in this cancer. Our study sought to analyze the prognostic risk factors of and construct a prognostic index (PI) model using inflammatory markers for pancreatic cancer.

METHODS

Forty-eight patients diagnosed with pancreatic cancer at our hospital were selected for this retrospective analysis. Data on the general clinical characteristics, tumor-related features, blood index factors, and treatment methods were collected. The Cox proportional-hazards model was used to analyze the factors affecting the prognosis, and the Kaplan-Meier analysis was used to draw the survival curve.

RESULTS

The median overall survival time was 14.5 months, and the 1-, 2-, and 3-year survival rates were 20.83% (10/48), 6.25% (3/48), and 4.17% (2/48), respectively. The univariate analysis showed that tumor grade, vascular invasion, adjacent tissue invasion, lymph node metastasis, tumor-node-metastasis (TNM) stage, the neutrophil-lymphocyte ratio (NLR), the platelet-lymphocyte ratio (PLR), and the lymphocyte-monocyte ratio (LMR) were significantly correlated with the median survival of pancreatic cancer patients (P<0.05). The Cox regression equation showed that tumor grade III-IV (X1), vascular invasion (X2), TNM stage III-IV (X3), a NLR >3.8 (X4), and a PLR >182.1 (X5) were independent risk factors affecting the prognosis of patients with pancreatic cancer (all P<0.05). The prognostic model for pancreatic cancer can be expressed as: PI =3.521X1+4.157X2+1.282X3+2.441X4+6.015X5. Patients with tumor grade I-II, non-vascular invasion, TNM stage I-II, a NLR ≤3.8, and a PLR ≤182.1 exhibited a higher 1-year survival rate. The areas under the receiver operating characteristic (ROC) curves for the NLR >3.8 and the PLR >182.1 were 0.778 and 0.713, respectively.

CONCLUSIONS

Tumor grade, vascular invasion, TNM staging, the NLR, and the PLR are independent risk factors affecting the prognosis of pancreatic cancer patients. The NLR and PLR have good clinical value in predicting the survival outcomes of pancreatic cancer patients.