The promising feature of the fungi from the marine environment as a source for anticancer agents belongs to the fungal ability to produce several compounds and enzymes which contribute effectively against the cancer cells growth. L-asparaginase acts by degrading the asparagine which is the main substance of cancer cells. Moreover, the compounds produced during the secondary metabolic process acts by changing the cell morphology and DNA fragmentation leading to apoptosis of the cancer cells. The current review has analyed the available information on the anticancer activity of the fungi based on the data extracted from the Scopus database. The systematic and bibliometric analysis revealed many of the properties available for the fungi to be the best candidate as a source of anticancer drugs. Doxorubicin, actinomycin, and flavonoids are among the primary chemical drug used for cancer treatment. In comparison, the most anticancer compounds producing fungi are Aspergillus niger, A. fumigatus A. oryzae, A. flavus, A. versicolor, A. terreus, Penicillium citrinum, P. chrysogenum, and P. polonicum and have been used for investigating the anticancer activity against the uterine cervix, pancreatic cancer, ovary, breast, colon, and colorectal cancer.

The present study aimed to optimize the production of L-asparaginase from Aspergillus arenarioides EAN603 in submerged fermentation using a radial basis function neural network with a specific genetic algorithm (RBFNN-GA) and response surface methodology (RSM). Independent factors used included temperature (x₁), pH (x₂), incubation time (x₃), and soybean concentration (x₄). The coefficient of the predicted model using the Box–Behnken design (BBD) was R² = 0.9079 (p < 0.05); however, the lack of fit was significant indicating that independent factors are not fitted with the quadratic model. These results were confirmed during the optimization process, which revealed that the standard error (SE) of the predicted model was 11.65 while the coefficient was 0.9799, at which 145.35 and 124.54 IU mL⁻¹ of the actual and predicted enzyme production was recorded at 34 °C, pH 8.5, after 7 days and with 10 g L⁻¹ of organic soybean powder concentrations. Compared to the RBFNN-GA, the results revealed that the investigated factors had benefits and effects on L-asparaginase, with a correlation coefficient of R = 0.935484, and can classify 91.666667% of the test data samples with a better degree of precision; the actual values are higher than the predicted values for the L-asparaginase data.

Fungi have unique properties and are used in many areas of agriculture and industry because they can produce different enzymes. This study aims to study the fungal diversity in peat soil from Pontian in Johor, Malaysia. The fungal isolates were described on different culture media and on a new culture medium called EVA medium and were identified using the phenotypical characteristics and molecular properties of the D1/D2 domain of the 28S large subunit ribosomal RNA (28S rRNA) and ITS (ITS1-ITS4) rDNA regions. The results revealed that 14 fungal species (15 isolates) were identified, among them, 6 were categorized as newly isolated strains and recorded in Malaysia; these include Aspergillus arenarioides EAN603, A. iizukae EAN605, Paraconiothyrium brasiliense EAN202, Parengyodontium album EAN602, Penicillium pedernalense EAN604, and Purpureocillium lilacinum EAN601. The cultural, morphological, microstructure, and molecular characteristics of these new strains have been described in this study. It was noted that the EVA medium exhibited a moderate support for fungal growth and sporulation compared to other culture media. Furthermore, the efficiency of the new medium as an enrichment medium to isolate fungi from peat soils with high ligninolytic content was discussed.