Neuroendocrine prostate cancer (NEPC) can arise de novo, but much more commonly occurs as a consequence of a selective pressure from androgen deprivation therapy or androgen receptor antagonists used for prostate cancer (PCa) treatment. The process is known as neuroendocrine transdifferentiation. There is little molecular characterization of NEPCs and consequently there is no standard treatment for this kind of tumors, characterized by highly metastases rates and poor survival. For this purpose, we profiled 54 PCa samples with more than 10-years follow-up for gene and miRNA expression. We divided samples into two groups (NE-like vs. AdenoPCa), according to their clinical and molecular features. NE-like tumors were characterized by a neuroendocrine fingerprint made of known neuroendocrine markers and novel molecules, including long non-coding RNAs and components of the estrogen receptor signaling. A gene expression signature able to predict NEPC was built and tested on independently published datasets. This study identified molecular features (protein-coding, long non-coding, and microRNAs), at the time of surgery, that may anticipate the NE transformation process of prostate adenocarcinoma. Our results may contribute to improving the diagnosis and treatment of this subgroup of tumors for which traditional therapy regimens do not show beneficial effects.

To date, the 5-year overall survival rate of 60% for early-stage non-small cell lung cancer (NSCLC) is still unsatisfactory. Therefore, reliable prognostic factors are needed. Growing evidence shows that cancer progression may depend on an interconnection between cancer cells and the surrounding tumor microenvironment; hence, circulating molecules may represent promising markers of cancer recurrence. In order to identify a prognostic score, we performed in-depth high-throughput analyses of plasma circulating markers, including exosomal microRNAs (Exo-miR) and peptides, in 67 radically resected NSCLCs. The miRnome profile selected the Exo-miR-130a-3p as the most overexpressed in relapsed patients. Peptidome analysis identified four progressively more degraded forms of fibrinopeptide A (FpA), which were depleted in progressing patients. Notably, stepwise Cox regression analysis selected Exo-miR-130a-3p and the greatest FpA (2-16) to build a score predictive of recurrence, where high-risk patients had 18 months of median disease-free survival. Moreover, in vitro transfections showed that higher levels of miR-130a-3p lead to a deregulation of pathways involved in metastasis and angiogenesis, including the coagulation process and metalloprotease increase which might be linked to FpA reduction. In conclusion, by integrating circulating markers, the identified risk score may help clinicians predict early-stage NSCLC patients who are more likely to relapse after primary surgery.

Amyotrophic lateral sclerosis (ALS) is a complex disease characterized by the interplay of genetic and environmental factors for which, despite decades of intense research, diagnosis remains rather delayed, and most therapeutic options fail. Therefore, unravelling other potential pathogenetic mechanisms and searching for reliable markers are high priorities. In the present study, we employ the SOMAscan assay, an aptamer-based proteomic technology, to determine the circulating proteomic profile of ALS patients. The expression levels of ~1300 proteins were assessed in plasma, and 42 proteins with statistically significant differential expression between ALS patients and healthy controls were identified. Among these, four were upregulated proteins, Thymus- and activation-regulated chemokine, metalloproteinase inhibitor 3 and nidogen 1 and 2 were selected and validated by enzyme-linked immunosorbent assays in an overlapping cohort of patients. Following statistical analyses, different expression patterns of these proteins were observed in the familial and sporadic ALS patients. The proteins identified in this study might provide insight into ALS pathogenesis and represent potential candidates to develop novel targeted therapies.

Background: In the era of risk-based prostate cancer (PCa) screening, overcoming the limitations of prostate-specific antigen (PSA) testing and stratifying men by individual risk is crucial. Our study aims to integrate anamnestic and lifestyle data with circulating biomarkers to minimize unnecessary second-level investigations (SLIs) for patients with suspected PCa, while improving the detection of clinically significant PCa (ISUP > 1). Methods: We collected plasma samples, recent clinical history, family cancer history, PSA levels, and lifestyle information from 904 men: 421 undergoing PSA testing, 421 with suspected and 62 with confirmed PCa. Univariable logistic regression was applied to identify ananmestic and lifestyle variables mostly associated with PCa. Penalized logistic regression models predictive of PCa or ISUP > 1 PCa were built both using the 814 subjects with complete information for such variables, applying a 10-fold cross validation approach, and dividing the dataset into a training (n = 445: 132 PCa, 313 non-PCa) and a test (n = 369: 147 PCa, 222 non-PCa) set. The concentration of 50 sphingolipids was analysed on the latter set of 369 subjects by mass-spectrometry, and multivariable penalized regression with 10-fold cross-validation was applied to integrate anamnestic, lifestyle, sphingolipid data. ROC-AUCs on the test sets were compared with PSA ROC-AUCs. Results: Age, cardiovascular disease (CVD), number of medications, and sedentariness were significantly associated with PCa detection and their combination with PSA improved its performance (ROC-AUC from 0.85 to 0.89). In the SLI subgroup (n = 437), adding age improved PSA predictive power (ROC-AUC from 0.60 to 0.70), but performance was still poor. Penalized regression with 10-fold cross-validation on the sphingolipid dataset identified hypertension, CVD, PSA, age, and five sphingolipids (HexCer-20, Cer-20, HexCer-24.1, GM3-24.1, DHCer-24) as key variables for accurate PCa classification (average ROC-AUC: 0.92). Cer-20 and CVD were consistently selected by models predicting ISUP > 1 PCa. In the SLI subgroup, PSA, age, CVD, SM-16, HexCer-20, HexCer-24.1, DHS1P, and DHCer-24 were selected in all 10 models (average ROC-AUC: 0.83). Conclusions: Circulating sphingolipids are promising biomarkers that, when combined with PSA, anamnestic, and lifestyle data, may enhance PCa screening precision and reduce the need for invasive, costly examinations.

Background: In the era of risk-based prostate cancer (PCa) screening, overcoming the limitations of prostate-specific antigen (PSA) testing and stratifying men by individual risk is crucial. Our study aims to integrate anamnestic and lifestyle data with circulating biomarkers to minimize unnecessary second-level investigations (SLIs) for patients with suspected PCa, while improving the detection of clinically significant PCa (ISUP>1). Methods: We collected plasma samples, recent clinical history, family cancer history, PSA levels, and lifestyle information from 904 men: 421 undergoing PSA testing, 421 with suspected and 62 with confirmed PCa. Univariable logistic regression was applied to identify ananmestic and lifestyle variables mostly associated with PCa. Penalized logistic regression models predictive of PCa or ISUP>1 PCa were built both using the 814 subjects with complete information for such variables, applying a 10-fold cross validation approach, and dividing the dataset into a training (n=445: 132 PCa, 313 non-PCa) and a test (n=369: 147 PCa, 222 non-PCa) set. The concentration of 50 sphingolipids was analysed on the latter set of 369 subjects by mass-spectrometry, and multivariable penalized regression with 10-fold cross-validation was applied to integrate anamnestic, lifestyle, sphingolipid data. ROC-AUCs on the test sets were compared with PSA ROC-AUCs. Results: Age, cardiovascular disease (CVD), number of medications, and sedentariness were significantly associated with PCa detection and their combination with PSA improved its performance (ROC-AUC from 0.85 to 0.89). In the SLI subgroup (n=437), adding age improved PSA predictive power (ROC-AUC from 0.60 to 0.70), but performance was still poor. Penalized regression with 10-fold cross-validation on the sphingolipid dataset identified hypertension, CVD, PSA, age, and five sphingolipids (HexCer-20, Cer-20, HexCer-24.1, GM3-24.1, DHCer-24) as key variables for accurate PCa classification (average ROC-AUC: 0.92). Cer-20 and CVD were consistently selected by models predicting ISUP>1 PCa. In the SLI subgroup, PSA, age, CVD, SM-16, HexCer-20, HexCer-24.1, DHS1P, and DHCer-24 were selected in all 10 models (average ROC-AUC: 0.83). Conclusions: Circulating sphingolipids are promising biomarkers that, when combined with PSA, anamnestic, and lifestyle data, may enhance PCa screening precision and reduce the need for invasive, costly examinations.