RESEARCH ARTICLE

Correlation between biochemical, antioxidant and anticancer activity of mushroom polymers

Kanti Nage¹, Dhananjay Tandon¹*, Madhavi Tiwari¹, Rupal Purena¹, Khushbu Verma¹, Prashanta Kumar Mitra²

¹Department of Applied Science, Shri Rawatpura Sarkar University, Raipur-492016, C.G., India.

²Department of Life Science, Shri Rawatpura Sarkar University, Raipur-492016, C.G., India.

*Corresponding Author Email- dhananjay.25t@gmail.com

Graphical Abstract

Abbreviations

MTT: 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium Bromide. DPPH: 1,1-diphenyl-2-picrylhydrazyl. DNS: 3,5-Dinitrosalicyclic acid. BSA: Bovine Serum Albumin. PDA: Potato dextrose agar. PDB: Potato dextrose broth. PBS: Phosphate buffered saline. GSEXP: Ganoderma sp. exopolysaccharide. GSENP: Ganoderma sp. endopolysaccharide. HSEXP: Hexagonia sp. exopolysaccharide. HSENP: Hexagonia sp. endopolysaccharide. PEEXP: Pleurotus eous Exopolymer. PEENP- Pleurotus eous Endopolymer. PFEXP: Pleurotus florida Exopolymer. PFENP: Pleurotus florida Endopolymer. SSEXP: Schizophyllum sp. Exopolymer. SSENP: Schizophyllum sp. Endopolymer. IPS: Intracellular polysaccharide. EPS: Extracellular polysaccharide.

1. Introduction

According to the WHO GLOBOCAN (2022) data, the breast cancer total global incidence is 2,296,840 cases, with an Age-Standardized Rate (ASR) of 46.8%, and scored second rank. In terms of mortality, it ranked fourth, with 666,103 deaths (ASR 12.7%). In Asia, breast cancer is the leading cause of both occurrence and mortality among females. In 2022, GLOBOCAN reported 985,817 new cases (42.9% of global cases) and 315,309 deaths (47.3% of global deaths) in Asia alone. Specifically, in India, the numbers was recorded at 192,020 cases with an ASR of 13.6% and 2.9% cumulative risk (Bray et al., 2024). The mortality rate in India was 98,337, with an ASR of 10.7% and a cumulative risk of 1.6%. In India, it occupied first rank in both incidence and mortality (Kumar et al., 2024).

The breast cancer has 5 stages from 0-4. In stage 0, abnormal cells are present but not able to spread in neighbour cells. Cancer cells spread to nearby cells in stage 1. It develops into 20-50 mm in diameter with lymph node and 50 mm without lymph node in second stage. The tumour size increases more than 50 mm in stage third with lymph nodes. In last fourth stage, the cancer cells spread into another organ. Based on different parts, the breast cancer has been divided into lobular cancer, ductal cancer, and paget cancer (Kaushal, 2020).

Chemotherapy is one of the most commonly used treatments for various types of cancer. Initially, chemotherapeutic drugs were believed to be highly selective, targeting only tumor cells. Having said it, everyone now knows that these drugs can also damage healthy cells, leading to numerous adverse side effects and, in some circumstances, even death (Aslam et al., 2014). Common repercussion include hair loss, headache, dimness, nausea,  fatigue, diarrhea, abdominal cramps, vomiting, mouth sores, xerostomia, amnesia, numbness, hepatotoxicity, nephrotoxicity, ototoxicity, etc. (Kayl and Meyers, 2006; Carr et al., 2008; Partridge et al., 2001;  Altun and Sonkaya, 2018). The search for new and more effective cancer treatments remains a major focus in oncology, particularly because resistance to conventional chemotherapy drugs can develop rapidly. Furthermore, the high toxicity and frequent side effects associated with many chemotherapeutic agents highlight the alternate need for novel anti-cancer drugs. Natural product drugs are more valuable in terms of safety of organelles (Zhang et al., 2018; Talib et al., 2021). These new treatments should ideally be effective against difficult-to-treat cancers while minimizing side effects and improving therapeutic outcomes (Demain & Vaishnav, 2011).

The term "natural medicine" refers to substances that are produced by living organisms, which possess pharmacological properties (Naeem et al., 2022). Medicinal mushrooms have long been valued as sources of bioactive compounds with anticancer properties. Some species act via antioxidant, immunomodulatory, apoptosis induction, inhibition of angiogenesis, and cancer metastasis. Some of them are Lentinula edodes, Ganoderma lucidum, Grifola frondosa, Trametes versicolor, Agaricus bisporus, and Pleurotus corucopiae (Park, 2022; Nandi et al., 2024; Shafiq et al., 2025).

Recent research has focused on understanding within cancer microenvironments. Because of the odd number of electrons in the atoms' outermost orbit, free radicals are highly reactive and unstable. It include reactive oxygen and nitrogen species, which play a role in the development and spread of tumour cells and increase their capacity for metastasis. Actually, they are being identified as a cancer trait (Ríos-Arrabal et al., 2013). Antioxidants are chemicals that stabilize free radical molecules by exchanging one of their own electrons with them (Sanchez, 2017). Members of the reactive oxygen species include superoxide anion (O₂⁻), singlet oxygen, hydrogen peroxide (H₂O₂), peroxyl radical (ROO), and the very reactive hydroxyl radical (OH).

Antioxidant components such as polymers, carotenoids, ergosterol, and ascorbic acid are present in the fruit bodies, mycelium, and culture of mushrooms (Sanchez, 2016). The main naturally occurring antioxidant in the methanolic extract of medicinal mushrooms is phenol (Mau et al., 2002).

In nearly all the studies the biochemical test and functions have been carried out, but the correlation in different parameter sets is missing. Hence, in this present study, an effort was made to screen the anti-cancer activity using the triple negative MDA-MB-231 breast cancer cell line and antioxidant activity by DPPH scavenging assay of two edible mushrooms (Pleurotus euos and P. florida), two wild mushrooms (Schizophyllum sp. and Hexagonia sp.), and one medicinal mushroom (Ganoderma sp.), and the correlation between biochemical tests and antioxidant vs. anticancer activity and biochemical tests vs. bioactivities has been carried out for deep understanding of the effect of different parameters on anticancer activity. As well as evidence of anti-breast cancer activity of Hexagonia sp., they are also not available. So, the first time, we are trying to understand the activity of this. There are some medicinal properties of these mushrooms.  

Proteoglycans, polypeptides, and polymers from oyster mushrooms strengthen the immune system to reduce the negative effects of popular cancer treatments (Mishra et al., 2021). The antitumor bioactive compounds Concanavalin A, Cibacron blue, and affinity-purified protein in Pleurotus spp. have been reported by several researchers (Maiti et al., 2011).

Schizophyllum spp. belongs to basidiomycetes. It grows on dead and decaying wood logs. It produces many pharmacologically significant compounds with immunomodulatory, antineoplastic, anti-cancer, antibacterial, antiparasitic, and anti-inflammatory properties, anti-diabetic (Chandrawanshi et al., 2019). Schizophyllan, a polysaccharide, is the main constituent of this mushroom (Kumar et al., 2022).

Ganoderma lucidum, known as the Reishi or Lingzhi mushroom, is a medicinal mushroom from the ancient history of Asia. It contains polysaccharides, phenolics, terpenoids, fatty acids, peptides and proteins, vitamins, minerals, and sterols. This mushroom has several benefits, including antioxidant, immunomodulatory, anti-inflammatory, anti-cancer, anti-diabetic, cardiovascular disease, anti-viral, and liver protection activities (Plosca et al., 2025). While Hexagonia sp. has inflammatory activity (Thang et al., 2015).

2. Materials and Methods

2.1. Materials

Ethanol was procured from Changshu Hongheng Fine Chemical Co. Ltd, China. DMSO, phosphate buffered saline, the MTT kit, DPPH, and DNS were purchased from Sigma-Aldrich.  Hydrochloric acid (HCl), sulfuric acid, phenol, and 85% (w/v) phosphoric acid were bought from Loba Chemicals, India. Commassie Brilliant Blue G-250, BSA, and Doxorubicin were procured from HiMedia, India. The MDA-MB-231 cell line was acquired from the American Type Culture Collection (accession no. CRM-HTB-26).

2.2. Sample Collection

The fruiting bodies of five distinct kinds of mushrooms were gathered for this investigation from various locations in Raipur, Chhattisgarh, India. Fruiting bodies of Pleurotus florida and Pleurotus eous (P. djamor) were obtained from Indira Gandhi Krishi Vishwavidyalaya, Raipur. Fruiting bodies of Ganoderma and Schizophyllum species were collected from the Shri Rawatpura Sarkar University campus, Raipur. Hexagonia species were sampled from Central Park, New Raipur. The city of Raipur is situated in Chhattisgarh state, India. The naturally harvested fruiting bodies were identified by microbiologists at MycoAsia. All mushroom specimens were stored in airtight polythene bags at 20°C until further processing.

2.3. Isolation of mushroom mycelium

The small pieces of fruiting bodies were surface sterilized with 0.1% HgCl₂ and washed twice with sterile distilled water. Explants were prepared and inoculated on PDA plates supplemented with 2% malt extract and incubated at 27°C for seven days under 95% humidity.

2.4. Production and extraction of exo-polymer.

Seven-day-old fungal mycelia were inoculated in PDB supplemented with 2% malt extract and incubated at 27°C for 15 days under rotatory conditions at 120 rpm. After incubation, the broths were filtered by Whatman No. 1 paper. The filtrates were treated with chilled ethanol in a 1:3 ratio, stirred thoroughly, and kept at 4°C for 24 hours to precipitate the polymer. The precipitated material was collected by centrifugation for 15 minutes at 7,000 rpm at 4°C. The resulting polymer pellets were dried and stored at 4°C for further analysis (Ogidi et al., 2020).

2.5. Extraction of endo polymer from mycelium

A mortar and pestle were used to grind the dried mycelium into a fine powder. The 20 g powders were boiled with 100 ml distilled water at 80°C for 3 hours and centrifuged at 3000 rpm to remove the cell debris (Ren et al., 2015). The extraction procedure was repeated according to section 2.4. 

2.6. Removal of impurities

Soluble phenolics were removed with 70% alcohol, left for 24 hours at 4°C, then centrifuged at 6000 rpm for 20 minutes at 4°C (Ogidi et al., 2020). Castoffed the supernatant and dried the precipitate at 40°C.

2.7. Total carbohydrates test

 20 µl of crude polymer solution were mixed with 2.6 ml of concentrated sulfuric acid and 0.5 ml of 5% phenol, and then incubated at 100°C for 10 minutes. After cooling to room temperature, absorbance was measured at 490 nm. D-glucose was used as a positive control for calculating the total carbohydrate content. The experiment was repeated thrice (Nielsen, 2024).

2.8. Determination of Reducing Sugar

0.1 ml of 1% DNS was added to each 0.1 ml sample and adjusted up to 5 ml with distilled water. The reaction mixtures were treated in boiling water for 5 minutes and cooled to room temperature, and then 2.5 ml of distilled water was added. The absorbance were measured at 540 nm. Standard D-glucose was employed at a concentration of 20-100 µg/ml (Miller, 1959). Total polysaccharide was calculated by deducting total reducing sugar from total carbs.

2.9. Total Protein Assay

Total protein was assessed by Bradford assay. The reagent was prepared by dissolving 100 mg of Coomassie brilliant blue G-250 in 50 ml of 95% ethanol. Then 100 ml of 85% (w/v) phosphoric acid was added.  Distilled water was added until the amount reached one liter. A dark glass container was used to hold the reagent at room temperature. (Bradford, 1976). The BSA was used as a positive control at 20-100 µg/ml concentration in PBS at 20 µg/ml intervals. Briefly, the 0.1 ml extracts were added to 5 ml reagent and incubated for 10 minutes at RT. The absorbance was read at 595 nm against a blank. The experiment was performed thrice, and results were expressed in mean±SE.

2.10. Anticancer activity by MTT assay

Five exopolymers were tested for their cytotoxic effects using the MTT assay and five endopolymers on the MDA-MB-231 breast cancer cell line at seven different concentrations (Ukaegbu et al., 2019). The MDA-MB-231 cells were harvested, and cell viability was counted using Trypan blue exclusion. The cells were then prepared at a concentration of 1 x 10⁵ cells/ml, and 96 wells were seeded with 100 µl of the cell suspension each in microtiter plate. The extracts were prepared at 6.25 μg/ml, 12.5 μg/ml, 25 μg/ml, 50 μg/ml, 100 μg/ml, 200 μg/ml, and 400 μg/ml concentrations in 40% DMSO. 100 μl of each concentration of extracts was loaded to wells with negative controls (media only) and positive controls (Doxorubin). The plates were incubated for 48 hours at 37°C in a 5% CO₂ environment.

For the MTT assay procedure, after the incubation period, 10 µl of MTT solution (5 mg/ml) was applied in each well after the medium was taken out, achieving a final concentration of 0.45 mg/ml. The plates were incubated for an additional 3-4 hours at 37°C. After this incubation, the MTT solution was carefully removed from each well, and 100 µl of isopropanol was added to dissolve the formazan crystals. The contents were mixed gently by pipetting up and down to ensure complete solubilisation. Absorbance was then measured at 540 nm using a plate reader within one hour of adding the solubilisation solution.

The percentage viability and percentage inhibition were calculated using the formulas:

Percentage Viability = (Absorbance of treated wells/Absorbance of control wells)×100

Percentage Inhibition = (1- Absorbance of control wells/ Absorbance of treated wells)×100

2.11. DPPH radical scavenging activity

Different concentrations of ascorbic acid and crude extracts (40, 50, 60, 70, and 80 µg/ml) were added to 3.0 ml DPPH (0.2 mM in methanol) and nursed at RT in the absence of light for 15 minutes, then the absorbance was read at 517.4 nm (Brand-Williams, 1995). The percentage of scavenging effect was determined following the formula:

DPPH scavenging (%) = [(A_control - A_sample)/ A_control] x 100 

Where,

A_control = Absorbance of DPPH without extract.

A_sample = Absorbance of DPPH with extract.

2.12. IC₅₀ value calculation.

IC₅₀ values were calculated for both antioxidant and anticancer activities. The four-parameter logistic regression model was applied to fit the dose-response data, and IC₅₀’S were determined (Motulsky and Christopoulos, 2004; Sebaugh, 2011). The following formula was used:

 Where,

x = the independent variable (need to calculate)

y = the dependent variable (50)

a = the obtained minimum value

b = Hill’s slope of the curve

c = the inflection point

d = the maximum value of observed data. 

The rearranged formula is

2.13. Data analysis

The data were examined in several complementary steps, combining exploratory visualization, hypothesis testing, correlation analysis, and multivariate modelling. Distributions of data were visualized using boxplots, and comprehensive differences among groups were assessed using one‑way ANOVA, p‑values were displayed directly on the plots to integrate inferential results with the visual summaries. To explore inter-dependent effects, line plots were constructed. To characterize relationships among biochemical and bioactivity variables, a correlation matrix was computed, and significance of pairwise correlations was evaluated at a 95% confidence level. The resulting matrix was visualized using correlation matrix plot with a color‑coded heatmap, suppression of the diagonal, and significance symbols at thresholds of p < 0.05, 0.01, and 0.001, enabling identification of strongly positive or negative correlations and their statistical support. Multivariate structure was studied with principal component analysis (PCA) performed on scaled biochemical and activity variables. Variable contributions and directions were visualized, while PCA biplots were generated to display clustering patterns and associations between sample groups and key biochemical or activity.

3. Results and Discussion

3.1. Fruiting body characteristics

The fruiting bodies of mushrooms are a good source for morphological identification. Figure 1 depicts the fruiting bodies. The feathers of the collected fruiting body were characterized under different parameters, which have been given in table 1.

Figure 1. Fruiting bodies of mushroom selected for study.

3.2. Production of exo- and endopolymers

The white mycelium was well grown on the media, and a thick layer of matt was observed (figure 2). The filtrate was processed, and the dried extract was weighed.

Among the samples, Hexagonia sp. produced the highest amount of exo-polymer (1.350±0.56 g), followed by Schizophyllum sp. (0.650±0.20 g) and Ganoderma sp. (0.206±0.11 g), as displayed in table 2. The lowest exo-polymer yield was observed in Pleurotus florida (0.176±0.14 g) and Pleurotus eous (0.218±0.19 g). This suggests that Hexagonia sp. may be a particularly rich source of extracellular polymer.

The highest yield of endo-polymer was obtained from Ganoderma sp. (1.94±0.58 g), followed by Schizophyllum sp. (1.31±0.46 g). These species seem to offer a great deal of promise for therapeutic uses based on their high intracellular polymer levels. Hexagonia sp. produced a very low amount (0.026±0.29 g), despite having the highest exopolymer yield. Yielded varying amounts of exo-endopolymer, indicating significant species-specific differences in polymer production.

The yield of polysaccharide may differ according to the species, production media, and growth parameters. For example, 8.26% polysaccharide was reported in the S. communae fruiting body extracted by hot water (Chen et al., 2020). 4.6 g/l EPS was reported in G. resinaceum (Kim et al., 2006) and 1.32 g/l in G. lucidum (Wan et al., 2016). While 846.39 g/l of polysaccharide was obtained from G. formosanum after 6 days of incubation (Kuo et al., 2021). In P. eous, 4.5 g/l EPS was produced in PDA media after 6 days of incubation (Thulasi et al., 2010).  

3.3 Total Carbohydrate

Total carbohydrate content reflects the overall sugar composition, including both simple and complex forms. PEENP (830±2.58 mg/g) and HSENP (825±0.27 mg/g) were the richest sources. GSENP (487±1.26 mg/g) and GSEXP (384.8±1.33 mg/g) also showed substantial levels (table 3). SSENP had the least amount of carbohydrates (127±1.22 mg/g), suggesting limited sugar reserves. Another report revealed a different amount of total carbohydrates. 57.71 g/kg of total sugar was reported in S. communiae (Li et al., 2025). While 6.63 mg/ml was recorded in IPS and 7.24 mg/ml in EPS of the same species in another report (Cheung et al., 2023). 48.67 µg/ml and 258.3 µg/ml carbohydrates were reported in IPS and EPS of G. lucidum (Vadnerker et al., 2022).  

3.4 Total Reducing Sugar

Reducing sugars play a role in energy metabolism. Among the tested mushroom extracts, PEEXP exhibited the highest reducing sugar content (66.72±0.08 mg/g), as shown in table 3, indicating a rich presence of free sugars. HSEXP had the lowest level (2.72±0.01 mg/g), followed closely by PFEXP (4.33±0.10 mg/g) and SSENP (4.70±0.06 mg/g). Endopolymer forms generally showed moderate levels, with GSENP (8±0.36 mg/g) and PEENP (11.40±0.63 mg/g) being notable. There is some evidence available for the total reducing sugar of different species of these mushrooms. 0.27 mg/ml and 0.34 mg/ml IPS and EPS were recorded in S. commune (Cheng et al., 2023).

3.5 Total Polysaccharides

The analysis revealed that endopolymers generally contained higher total polymer levels than exopolymers. Pleurotus eous (818.6±2.22 mg/g) and Hexagonia sp. (809.8±2.99 mg/g) endopolymer showed the highest concentrations (table 3). Ganoderma sp. also had a high polymer content in both endo- (479.00±1.34 mg/g) and exo- (378.00±2.01 mg/g) extracts. In contrast, Schizophyllum sp. endopolymer had the lowest polymer content (122.30±1.59 mg/g). These data imply that some mushrooms particularly Pleurotus eous, Hexagonia sp., and Ganoderma sp., are promising sources of bioactive polymers. Cheng et al., (2023) examined 6.36 mg/ml IPS, and 6.91 mg/ml in EPS in S. commune.

3.6 Total Protein

SSENP contained more protein (96.84±0.27 mg/g), followed by GSENP (44.81±1.08 mg/g), as presented in table 3. This is notable, as proteins or glycoproteins in mushroom extracts can enhance immunomodulatory and anticancer properties. In contrast, PFEXP had the lowest protein content (03.95±0.53 mg/g). These findings emphasize the diversity in biochemical composition among mushroom species and extract types. Pleurotus eous, Ganoderma sp., and Hexagonia sp. demonstrate strong potential due to their high polymer yields, while Schizophyllum sp. endopolymers stand out for their high protein content, possibly offering synergistic bioactivity. In another report, IPS contained 14.23 µg/ml, and EPS contained 10.27 µg/ml of protein in S. commune (Cheng et al., 2023).

3.7 Correlation between biochemical tests.

Correlation matrix heatmap (Fig. 3a) compares six variables (Total Protein, Antioxidant, Anticancer, Total Polysaccharide, Total Carbohydrate, and Total Reducing Sugar), among them strong positive associations found between, Total Polysaccharide and Total Carbohydrate show a very strong positive correlation (dark blue, marked ***, r = 0.988, p = 0.00000276), indicating that samples rich in polysaccharides also have high total carbohydrate content. Total Reducing Sugar is positively correlated (blue) with both Total Polysaccharide (r = 0.585, p = 0.0399) and Total Carbohydrate (r = 0.640, p = 0.0255), implying that sugar‑rich samples tend to be high in these carbohydrate fractions as well. Total Protein appears negatively or weakly correlated (red or pale) with carbohydrate measures (Total Polysaccharide, r = -0.104, p = 0.3888 and Total Carbohydrate, r = -0.049, p = 0.4487), suggesting that lower protein content is generally found in samples with higher carbohydrate content. Antioxidant and Anticancer activities show correlations with other variables, indicating bioactivity. Significant correlations found between Total Polysaccharide and Total Carbohydrate, and between these and Total Reducing Sugar.

Hierarchical clustering heat map (Fig. 3b) shows dendrogram, groups variables based on similarity of their correlation profiles, revealing that Total Polysaccharide, Total Carbohydrate, and Total Reducing Sugar cluster closely, forming a carbohydrate‑dominated group. Total Protein, Antioxidant, and Anticancer form a separate branch, reflecting their relatively distinct behaviour from the carbohydrate group and from each other.

The hypothesis for the direct proportional correlation between total polysaccharide and total carbohydrates is that, since the total carbohydrate includes monosaccharides, oligosaccharides, and polysaccharide in samples. The amount of polysaccharide can be obtained by subtracting the reducing sugar to total carbohydrates. Most of the reducing sugars are monosaccharide or disaccharide (Lehninger, 2013). Since in this study, the amount of reducing sugars was less than polysaccharides, so polysaccharide was high in extracts.   

One-way ANOVA showed that the type of biochemical test was responsible for most of the differences (59.67%, p < 0.0001), and the identity of the extract was responsible for 18.95% (p < 0.0001). A substantial interaction effect (21.11%, p < 0.0001) demonstrated that the correlation between biochemical compositions differed among extracts. Figure 4 illustrates the statistical analysis of biochemical test in different mushroom extracts.

Total carbohydrate, reducing sugar, polysaccharide, and protein contents vary widely among the five mushroom species, with some species (e.g., Hexagonia sp. and Pleurotus eous) showing higher central values and broader ranges than others. However, the ANOVA p‑values for carbohydrates (p = 0.62), reducing sugar (p = 0.42), polysaccharides (p = 0.69), and protein (p = 0.56) are all greater than 0.05, indicating no statistically significant differences in mean levels of these components among the mushroom species under the tested conditions (Fig. 4 a-d).

When grouped by polymer type (endopolymer vs exopolymer), endopolymers tend to exhibit higher medians and broader ranges for total carbohydrate and total polysaccharide content than exopolymers, whereas reducing sugar and protein contents appear more similar between the two polymer types. Nevertheless, ANOVA p‑values for carbohydrate (p = 0.19), reducing sugar (p = 0.47), polysaccharide (p = 0.17), and protein (p = 0.33) all exceed 0.05, suggesting that the observed numerical differences between endopolymers and exopolymers are not statistically significant at the chosen significance level (Fig. 4 e-h).

Overall, the biochemical profiles of the investigated mushroom species and their associated polymer types show substantial within‑group variability and overlapping distributions, which likely contribute to the non‑significant ANOVA results despite visible differences in medians and ranges.

3.8 Anticancer activity against the MDA-MB-231 breast cancer cell line

The MTT assay was conducted to evaluate the cytotoxic effects of 10 mushroom extracts on breast cancer cell lines at 7 different concentrations (0.625-400 µg/ml). The results were presented as percentage viability and percentage inhibition.

The % viability of MDA-MB-231 was also determined to assess the actual effectiveness of polymers. Since the % inhibition shows functional effect, % viability confirms the biological consequences. It validates the treatment of any drug (Mirzayans et al., 2018). The dose-responses are shown in figure 5(c). At the lowest quantity (6.25 μg/ml), all extracts exhibited good viability (85–96%), suggesting no cytotoxicity. As the concentration rose, a gradual decrease in viability was noted, exhibiting significant variations among extracts. At a concentration of 400 μg/ml, PFENP and GSENP exhibited the most potent cytotoxic effects, diminishing cell viability to 35.5% and 42.1%, respectively, closely aligning with the positive control. PEENP, HSENP, and HSEXP also showed a lot of activity, lowering viability to about 51% and 50.9%, respectively. SSENP and SSEXP had moderate impacts, with viability around 56–57%. PFEXP and PEEXP were less effective, keeping vitality above 63%. GSEXP was the weakest, yet even at the highest concentration, it still had a high viability rate (72.4%). The positive control repeatedly lowered viability to between 5% and 35%, which indicated that the experiment was working. In general, PFENP and GSENP were the most promising extracts since they were quite cytotoxic in a dose-dependent way. On the other hand, GSEXP and PFEXP had only somewhat anticancer potential.

All ten mushroom extracts exhibited a pronounced dose-dependent anticancer activity, with inhibition escalating consistently from the lowest tested concentration (6.25 μg/ml) to the highest (400 μg/ml), as shown in figure 5 (d). At 6.25 μg/ml, inhibition was generally mild, between 3 and 15%. At 400 μg/ml, on the other hand, the extracts had moderate to strong activity, with values ranging from 27 to 64%. PFENP and GSENP were the strongest of the studied compounds. At the highest dose, they inhibited 64.48% and 57.92% of the cell line growth, respectively, which was very close to the positive control. PEENP and HSEXP likewise had quite substantial effects, stopping almost 48–49% of the growth. On the other hand, SSENP and SSEXP only got stronger at greater doses. PFEXP and PEEXP showed less inhibition, leveling off at about 35%. GSEXP was the least effective, with inhibition remaining below 28% even at the highest dose. The positive control regularly exhibited substantial inhibition (65–95%), hence confirming the assay's validity.

The boxplot for viability (Fig. 5a) reports an ANOVA p‑value of 0.35, indicating no significant difference in mean viability among the ten drugs. The inhibition boxplot (Fig. 5b) shows an ANOVA p‑value of 0.49, likewise indicating no significant difference in mean inhibition between drugs. Panel a shows viability percentages clustered mostly between ~50–90% across drugs, with overlapping interquartile ranges and similar medians. Panel b shows inhibition values generally between ~10–50%, again with substantial overlap of distributions across drugs, explaining the lack of statistical separation in ANOVA.

The % viability of MDA-MB-231 was also determined to assess the actual effectiveness of polymers. Since the % inhibition shows functional effect, % viability confirms the biological consequences. It validates the treatment of any drug (Mirzayans et al., 2018). The dose-responses are shown in figure 5(c). At the lowest quantity (6.25 μg/ml), all extracts exhibited good viability (85–96%), suggesting no cytotoxicity. As the concentration rose, a gradual decrease in viability was noted, exhibiting significant variations among extracts. At a concentration of 400 μg/ml, PFENP and GSENP exhibited the most potent cytotoxic effects, diminishing cell viability to 35.5% and 42.1%, respectively, closely aligning with the positive control. PEENP, HSENP, and HSEXP also showed a lot of activity, lowering viability to about 51% and 50.9%, respectively. SSENP and SSEXP had moderate impacts, with viability around 56–57%. PFEXP and PEEXP were less effective, keeping vitality above 63%. GSEXP was the weakest, yet even at the highest concentration, it still had a high viability rate (72.4%). The positive control repeatedly lowered viability to between 5% and 35%, which indicated that the experiment was working. In general, PFENP and GSENP were the most promising extracts since they were quite cytotoxic in a dose-dependent way. On the other hand, GSEXP and PFEXP had only somewhat anticancer potential.

Table 1.  Morphological Characterization of harvested fruiting bodies.

Figure 2—Schematic of Ganoderma sp. mycelium growth in PDB supplemented with 2% malt extract for biopolymer production. The upper layer in the conical flask indicates the growth of mycelium, while the lower part is liquid media turned semitransparent and cloudy. It indicates the production and secretion of biomolecules. The mycelium was white in color and denser compared to that of the microfungi.

The IC₅₀ values against MDA-MB-231 cells, on the other hand, showed big disparities in how hazardous they were. PEEXP (20.04 μg/ml), GSENP (34.5 μg/ml), and HSENP (36.85 μg/ml) were the most effective at low dosages, showing substantial anticancer activity (Table 4). HSEXP (72.25 μg/ml), SSENP (67.38 μg/ml), and SSEXP (80.96 μg/ml) had considerable action, whereas PFENP (163.09 μg/ml) and PFEXP (120.08 μg/ml) were not as strong. PEENP (1874.28 μg/ml) and GSEXP (6487.38 μg/ml) had very high IC₅₀ values, which means they were not very harmful to MDA-MB-231 cells.

Ganoderma species, particularly G. lucidum, are of interest to many researchers, industries, etc., for their therapeutic applications. It has several benefits, as mentioned in the introduction section. Several pieces of evidence are available that it is a promising source of drugs against breast cancer cells. The fungi contain different types of polysaccharides and are classified as per their branching pattern, structure, composition, and molecular weight. They are available in homo- and heteroglucan form. The backbone of homoglucans consists of α/β-glucose, such as (1→6) α-glucans and (1→6) β-glucans (Huang and Nie, 2015). It is also found in the (1→6) β-glucan form and may be branched with (1→6) β-glucans (Chen and Seviour, 2007; Golisch et al., 2021). While the heteropolysaccharide contains arabinose, mannose, xylose, glucuronic acid, galactose, and ribose as the major constituents in mixed patterns (De Silva et al., 2012). In some cases selenium-enriched polysaccharides have the potential to kill the breast cancer cells. SeGLP-2B-1 was found to be effective against the MCF-7 cell line. It inhibited >50% of cells (Shang et al., 2011).

Heteropolysaccharide extracted from G. applanatum inhibited the growth of MCF-7 cells by 50.2% at 500 µg/ml in 48 h. These carbohydrates also induced autophagy through the MAPK signalling pathway in this cell (Hanyu et al., 2020). Uddin et al. (2022) reported an IC₅₀ value of 202.4 µg/ml of endopolysaccharide of G. lucidum against MCF-7 cells. 

3.9 Correlation between % inhibition and % viability.

Spearman correlation analysis between % viability and % inhibition in the MTT assay revealed an exceptionally strong association (r = –0.9947), with the 95% confidence interval ranging from –0.9967 to –0.9913. This highly significant correlation (p < 0.0001) confirms that as cell viability decreases, inhibition increases almost perfectly in a complementary manner. The strength of this relationship underscores the internal consistency of the assay and validates the reliability of the experimental measurements.

Biologically, this near-perfect inverse correlation reflects the fundamental principle of cytotoxicity assays, where inhibition is essentially the reciprocal of viability. The robustness of the correlation across multiple extracts and concentrations indicates that the assay was well controlled and that the observed cytotoxic effects are reproducible. Importantly, such a strong correlation supports the use of either parameter (% (viability or % inhibition) for reporting cytotoxicity, while also reinforcing the suitability of the dataset for further analyses such as dose–response curve fitting and IC₅₀ determination. These results show that mushroom extracts consistently affect breast cancer cell survival, laying the groundwork for mechanistic research and bioactive chemical discovery.

3.10 DPPH scavenging activity

The DPPH radical scavenging activity of various mushroom extracts was assessed to evaluate their antioxidant potential. The results revealed significant differences in the radical scavenging activity of the different extracts at a concentration of 40-80 µg/ml. Among the extracts tested, GSENP exhibited the highest scavenging activity, with an inhibition of 80.96±0.12%, followed by GSEXP (66.43±0.13%) and HSENP (59.73±0.45%), as shown in figure 7.

PEEXP and PFENP demonstrated lower scavenging activities at 14.83±0.19% and 14.78±0.14%, respectively. This suggests that the GSENP contained the most potent antioxidant components capable of reducing DPPH radicals effectively. The high scavenging activity observed for Hexagonia species also supports the potential of these mushroom extracts as significant antioxidant sources. These extracts can scavenge free radicals, suggesting they might operate as primary antioxidants to prevent lipid oxidation and oxidative stress, which contribute to many illnesses, including cancer.

The scavenging activities were compared at a concentration of 70 µg/ml, since until this point the inhibition was in an almost exponential phase. GSENP had the best antioxidant inhibition at 70 µg/ml (71.54%), followed by HSENP (59.21%), ASC (57.5%), and GSEXP (57.56%). HSEXP and SSENP had significant action (51.81% and 46.52%, respectively), although PEENP and the other extracts had modest inhibition (<36%). These results show that GSENP is the strongest extract in the settings that were evaluated.

The IC₅₀ values for DPPH inhibition were between 48.88 and 65.27 μg/ml, suggests that the antioxidant activity of the extracts was modest (Table 4). The response as seen at 70 μg/ml, the highest inhibition was seen in GSENP and HSENP, and the lowest inhibition was seen in SSEXP, PEEXP, PFENP, and PFEXP. The calculated IC₅₀ values using the 4-parameter logistic regression model exposed that GSENP scored 52.94±0.88 µg/ml and HSENP scored 48.88±1.05 µg/ml as per their inhibition pattern.

Two-way ANOVA showed that column factors accounted for 67.14% of the variation (p < 0.0001), while row factors accounted for 21.46% (p < 0.0001). An important interaction effect (11.38%, p < 0.0001) showed that the link between extract identity and parameter type changed across the dataset. These findings indicated that extract is the primary determinant of differences, but both extract identity and extract-specific interactions significantly influence antioxidant property outcomes. In another report, a 202.4 µg/ml IC₅₀ value was recorded in G. lucidum (Uddin et al., 2022). In another study, Tirkey et al., (2025) examined the antioxidant and nutrition property of Calocybe mushrooms. C. indica has highest protein, phenolic, ascorbic acid, and anthocyanin content. Antioxidant activity is also higher in this mushroom as observed by FRAP and DPPH assays.

The data show that PEEXP, GSENP, and HSENP are attractive candidates with both antioxidant and anticancer properties. Other extracts, on the other hand, showed selective activity profiles that could be beneficial for focused applications.

Figure 3: Spearman's r-value matrix of correlation between different biochemical tests. The figure summarizes relationships among biochemical composition and bioactivities using a correlation matrix (panel a) and hierarchical clustering heatmap (panel b).

Table 2. Yield of exo-endopolymer from different species of mushrooms.

All the experiments were performed in triplicate and result has been shown in mean±SE.

Table 3 – The quantity of total carbohydrate, total reducing sugar, total polysaccharide, and total protein extracted from media and mycelium.

Table 4. IC₅₀ values of mushroom extracts against DPPH radical and the MDA-MB-231 breast cancer cell line.

Figure 4: The figure compares biochemical composition across different mushroom species (panels a–d) and between endopolymers and exopolymers (panels e–h), using boxplots and one‑way ANOVA p‑values.

Figure 5: The figure summarizes viability and inhibition responses for ten plant extracts (Drug 1-Drug 10) and how inhibition changes with concentration for each extract. Drug 1- HSENP, Drug 2- PFEXP, Drug 3- PEENP, Drug 4- SSENP, Drug 5-PFENP, Drug 6- SSEXP, Drug 7-HSEXP, Drug 8- GSENP, Drug 9- PEEXP, Drug 10- GSEXP.

Figure 6- Spearman regression correlation analysis between % inhibition and % viability of MDA-MB-231 cells. The negative value of R indicates a positive relationship. The correlation is statistically significant at p<0.0001.

Figure 7. Dose-response curve of DPPH scavenging event at different concentrations of mushroom extracts. The highest inhibition was recorded by GSENP.

Figure 8: Spearman correlation analysis of _IC50 values for antioxidant and anticancer activities. The p > 0.05 denotes that there are no signs.

3.11 Correlation between antioxidant and anticancer activities

The Spearman correlation analysis of IC₅₀ values for antioxidant and anticancer activity indicated a modest positive connection (r = 0.5394); Nevertheless, as seen in figure 8, it failed to achieve statistical significance (p = 0.0569, α = 0.05). Overall, substantial biological variability but no statistically significant differences in DPPH‑based antioxidant activity or MDA‑MB anticancer activity among mushroom species, between polymer types, or between assays within species at the chosen significance threshold.

The one way ANOVA analysis has been depicted in figure 9. For DPPH values, all five mushroom species show overlapping distributions with medians around the mid‑50s to low‑60s; the ANOVA p = 0.80 indicates no statistically significant difference in antioxidant activity among species. For MDA‑MB values, Ganoderma sp. and Pleurotus eous display higher central values and wider variability than the other species, yet the ANOVA p = 0.56 shows that mean anticancer activity against MDA‑MB cells does not differ significantly among the mushrooms under the tested conditions (Fig. 9a).

When grouped by polymer (endopolymer vs exopolymer), DPPH values remain broadly overlapping and the ANOVA p = 0.93 confirms no significant difference in antioxidant activity between polymer types. For the MDA‑MB assay, endopolymers appear to have slightly higher values than exopolymers, but the ANOVA p = 0.51 again suggests that this difference is not statistically significant at the conventional 0.05 level (Fig. 9b).

Within each mushroom species, comparison of DPPH and MDA‑MB values shows that anticancer responses tend to vary more widely than antioxidant responses, especially for Ganoderma sp. and Pleurotus eous, where MDA‑MB values span a broad range while DPPH values are relatively tight. ANOVA p‑values for within‑mushroom comparisons (Ganoderma sp. p = 0.43, Hexagonia sp. p = 0.98, Pleurotus eous p = 0.44, Pleurotus florida p = 0.068, Schizophyllum sp. p = 0.24) are all above 0.05, indicating that the two assays do not show statistically different mean responses within any single species, although Pleurotus florida shows a trend toward difference (p ≈ 0.07) (Fig. 9c).

Metabolic reactions generate the free radicals, hypertension, smoking, alcohol consumption, aging, pollution, etc. It damages different biomolecules such as lipids, proteins, nucleotides, and polysaccharides. Free radicals are also linked with the development of cancers (Wang et al., 2021). The disruption of the structure of these biomolecules may alter or diminish their biological function, leading to disease. The antioxidants are molecules that scavenge the radicals and neutralize them (Gulcin, 2020). In vitro experiments use several radical markers such as DPPH, hydroxyl radical, peroxyl radicals, ferric ions, ammonium molybdate, etc. But in in vivo experiments, several targets are focused on to enhance the production of antioxidant enzymes. Liu et al. (2020) attempted to link these two exopolysaccharide characteristics of Floccularia luteovirens. They found that ALF1 increases SOD, CAT, and GSH-Px while inhibiting the growth of tumors. Additionally, it reduced MDA synthesis, which shielded PC12 cells from oxidative damage brought on by H₂O₂. ALF1 stabilized the potential of the mitochondrial membrane and reduced the generation of ROS.

The cancer development is an event of genetic mutations. Prevention of ROS development protects the genetic materials and mutations that lead to cancer. In this study we found the moderate level of correlation. The correlation also depends on the structure-activity relationship.

3.12 Correlation between biochemical and biological activities

In this section we will discuss the effect of tested biomolecules on antioxidant and anticancer activities. The two-way ANOVA study indicated a substantial association between biochemical parameters and biological activities, supported by strong statistical evidence (p < 0.0001), as shown in figure 9. The column factor, which shows the type of parameter assessed (total carbohydrate, reducing sugar, polysaccharide, protein, IC₅₀ anticancer, and IC₅₀ DPPH), is responsible for most of the variation (67.44%). This statistic shows that the main causes of variability in the dataset were the differences between biochemical composition and biological activity. The row component, which is the same as extract identity, accounted for 10.21% of the variation, which was likewise significant (p < 0.0001). This analysis showed that different mushroom extracts have varied biochemical and biological profiles, but the difference was not as big as the overall biochemical–biological difference.

The interaction term accounted for 22.14% of the variation (p < 0.0001), indicating that the correlation between biochemical makeup and biological activity varies among extracts. To put it another way, the effect of carbohydrates, polysaccharides, or proteins on antioxidant and anticancer activities varies depending on the extract being researched. Polysaccharide content and bioactivity may significantly connect in some extracts, while protein or decreasing sugar concentrations may have a lower impact on others.

The model explains almost all of the observed variability, as evidenced by the relatively low residual variance, demonstrating the strength of the analysis. These data indicate that biochemical ingredients are crucial determinants of biological activity, though their effects are extract-specific. This underscores the need to profile individual extracts to determine the biological components that most significantly enhance their antioxidant and anticancer properties.

The purpose of the principal component analysis (PCA) was to examine the relationship between biochemical tests and biological processes. The analysis have been depicted in figure 11. PCA of variables presented in figure 11(a). Dim1 explains 42.3% of the variance and is driven mainly by Total Polysaccharide, Total Carbohydrate and Total Reducing Sugar, whose arrows point strongly in the positive Dim1 direction. Dim2 explains 27.1% of the variance and is mainly loaded by Antioxidant and Anticancer activities (positive Dim2) contrasted with Total Protein (negative Dim2), indicating a trade‑off between protein content and bioactivity.

Endopolymer samples (red) lie mostly on the right side of Dim1, close to the polysaccharide and total carbohydrate vectors, suggesting higher levels of these carbohydrate fractions. Exopolymer samples (blue) are shifted towards the lower part of the plot, nearer to Total Reducing Sugar and away from Antioxidant/Anticancer, indicating relatively higher reducing sugars but lower functional activities (Fig. 11b).

Species‑level separation has been shown in figure 11(c). Ganoderma sp. (red) clusters in the upper‑right quadrant, aligned with Anticancer and moderately with antioxidant vectors, suggesting strong functional activities with moderate carbohydrate content.  Pleurotus florida (cyan) extends along the positive Dim1 axis and towards Total Reducing Sugar, indicating carbohydrate‑rich, sugar‑rich profiles but less association with antioxidant capacity. Hexagonia sp. (olive) lies close to the origin with modest loading on Dim1 and Dim2, implying intermediate composition and activity without strong specialization. Pleurotus eous (blue) and Schizophyllum sp. (magenta) occupy opposite sides along Dim2, with Schizophyllum associated more with Total Protein (negative Dim2) and Pleurotus eous positioned nearer antioxidant vectors, reflecting a contrast between protein‑rich and antioxidant‑rich profiles.

Together, the PCA results indicate two main axes of variability: a carbohydrate–sugar axis (Dim1) and a bioactivity–protein axis (Dim2), which jointly explain about 69% of the total variance. These axes effectively differentiate both polymer type and mushroom species, highlighting that carbohydrate richness, sugar profile, and the balance between protein and antioxidant/anticancer activities are key drivers of sample differentiation

The polysaccharide is the main concern of discussion since the amount is more than proteins, and several data are available for activities. The carbohydrate contains several hydroxyl groups in the carbon backbone. The hydroxyl ions have the electrophile property, which can accept the electrons (Klein, 2020). Polysaccharides have several properties, like antiviral, anticancer, anti-inflammatory, antioxidant, and immunomodulatory effects. Polysaccharides act as regulators of the Nrf2/ARE pathway for the expression of downstream antioxidant enzymes. These antioxidant enzymes thus neutralize the produced radicals (Mu et al., 2021; Zhao et al., 2023). However, it happens in in-vivo condition. But, when the experiments are carried out in-vitro using the different radical markers, this mechanism may not work. In in-vitro conditions the polysaccharide may transform into several forms, like it may degrade and be converted into oligosaccharides, disaccharides, and monosaccharides. It may also convert into a carbonyl group (-C=O), a carboxylic group (-COOH), aldehydes, and ketones. May the radical cross-link with polysaccharide and form a complex structure (Chen et al., 2021). Since the protein was also found in the extract, the protein may also deform and lose its function or it may have no biological activities. The modified polysaccharides, such as sulfated, phosphorylated, selenium-containing, and acetylated, are much better than simple polysaccharides at scavenging the radicals (Wang et al., 2016).

Joseph et al. (2011) extracted polysaccharide from G. lucidum against Ehrlich’s ascites carcinoma cell line. The polysaccharides inhibited 80.8% and 77.6% in tumour volume and tumour mass at 100 mg/kg body mass. Sun et al. (2015) studied the in vitro anticancer properties of GLPS. This study examined how GLPS affects cytokine levels in mononuclear cells. Mouse splenic mononuclear lymphocytes were activated by phytohemagglutinin after incubation with GLPS and B16F10 cell culture supernatant. GLPS treatment reduced cytokine production at both mRNA and protein levels, suggesting a potential involvement in cancer reduction.

Several reports are available on the detailed mechanism of polysaccharide against breast cancer cells. The macromolecule acts via inhibiting the tumour growth through suppression of cell proliferation and by apoptotic pathways via PI3K/AKT/mTOR, ERK, ERα-caspase, NF-κB p53-dependent and MAPK/ERK signaling pathways, and caspase-7-mediated mitochondrial pathways. It also induces autophagy through LC3 conversion (Corso et al., 2021; Arroyo-Cruz et al., 2024; Jiao et al., 2025; Sipping et al., 2025). The polysaccharide of G. lucidum enhances the immunity by stimulating natural killer cell activity, CTL activity, and TNF and IL production. It also inhibits angiogenesis via inhibiting the capillary morphogenesis and VEGF expression and increases the intracellular antioxidant enzyme production (Kao et al., 2013). Several mechanisms of Ganoderma-derived polysaccharides have been reviewed by Gao and Homayoonfal (2023), and Pleurotus-derived saccharides have been reviewed by Mishra et al. (2021) and Sharma et al., (2021).

Figure 9: The figure compares antioxidant activity (DPPH assay) and anticancer activity (MDA‑MB assay) across mushroom species, polymer types, and assays, using boxplots with one‑way ANOVA p‑values.

Figure 10—Effect of biochemical tests on anticancer and antioxidant activity tested by two-way ANOVA analysis. Two-way ANOVA revealed that biochemical parameters accounted for the majority of the variations in biological activity (67.44%, p < 0.0001).

Figure 11: The PCA figure describes how biochemical traits (polysaccharides, carbohydrates, reducing sugars, protein) and bioactivities (antioxidant, anticancer) co‑vary and how they discriminate between polymers and mushroom species.